Machine Learning-Based Modeling of Boiler Efficiency: Impact Analysis of Operational Variables Using Random Forest, XGBoost, and ANNMuhamad Iqbal Syachjaya1,5, Heri Sutanto2, Marcelinus Christwardana1,3,4*, Subhan Hasisi6, Napolin Niuhardson Siregar5*Email corresponding author: marcelinus@lecturer.undip.ac.id1)Master Program of Energy, School of Postgraduate Studies, Diponegoro University, Indonesia 502412)Department of Physics, Faculty of Science and Mathematics, Diponegoro University, Indonesia 502753)Department of Chemistry, Faculty of Science and Mathematics, Diponegoro University, Indonesia 502754)Research Collaboration Center for Electrochemistry, BRIN – Diponegoro University, Indonesia 502755)Department of Operation, PT SKS Listrik Kalimantan, Indonesia 745606)Department of Maintenance, PT SKS Listrik Kalimantan, Indonesia 74560Article history: Received: 6 May 2026 | Revised: 15 June 2026 | Accepted: 17 June 2026Abstract. Enhancing Circulating Fluidized Bed (CFB) boiler efficiency is a critical objective in industrial energy management, yet existing literature often lacks comparative clarity on how diverse machine learning architectures interpret operational parameter coupling. This study addresses this gap by benchmarking three architectures,Random Forest (RF), XGBoost, and Artificial Neural Network (ANN),to develop a robust predictive model for boiler thermal efficiency using historical industrial telemetry. The analysis utilizes six key operational variables, including Air-Fuel Ratio (AFR) and Bed Temperature, for model training and cross-validation. Empirical results demonstrate that XGBoost serves as the most effective predictive framework, achieving a Coefficient of Determination (R²) of 0.8515 and a Root Mean Square Error (RMSE) of 0.0191, thereby outperforming RF and ANN in capturing industrial data noise through its sequential optimization and regularization mechanisms. A primary finding identifies AFR as the most influential factor, exhibiting a strong positive correlation (0.84) and consistent top-tier feature importance rankings across all paradigms. This research provides a validated data-driven methodology for real-time boiler optimization, emphasizing stoichiometric synchronization as the paramount strategy for improving thermal performance and minimizing fuel-related operational expenditures.Keywords - Boiler efficiency; machine learning; XGBoost; air-fuel ratio; operational optimization.Introduction Industrial boilers serve as the cornerstone of thermal energy generation, facilitating the critical conversion of water into steam for diverse manufacturing processes. In a broader context, the efficiency of boiler units directly influences national CO2 emission trajectories, particularly in Indonesia, which has committed to achieving Net Zero Emissions by 2060. As the industrial sector is a significant driver of the energy-related carbon output, precision-tuning of boiler performance has transitioned from a localized cost-saving measure to a strategic necessity for fulfilling Indonesia's decarbonization mandates and supporting global resource conservation ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1bYS9KMr","properties":{"unsorted":false,"formattedCitation":"[1]","plainCitation":"[1]","noteIndex":0},"citationItems":[{"id":13,"uris":["http://zotero.org/users/17768309/items/8DEM3H6K"],"itemData":{"id":13,"type":"article-journal","abstract":"Indonesia has set a target to achieve net-zero emissions (NZE) by 2060. To support this goal, a co-firing approach has been implemented in coal-fired power plants (CFPPs) to reduce the use of coal as the primary energy source. This study assessed the performance and impact of fuel modifications on objective functions (exergy efficiency, generated cost, and CO2 emissions) of a coal-fired steam power plant. The objective of evaluating the performance of CFPPs is to improve exergy efficiency, lower the cost of electricity generation, and reduce CO2 emissions. Previous studies lacked discussion of coal-fired power facilities that use biomass co-firing. The objective function was optimized using a Multi-Objective Genetic algorithm (MOGA). A parametric analysis was performed by varying the power plant load and coal usage to observe how they affected the changes in the objective function. As the load value increased, the exergy efficiency also increased, decreasing the generated cost (GC) required to produce 1 kWh (Kilowatt hours). This phenomenon occurs because of the auxiliary power usage of the Paiton CFPP. The optimization results indicate that the ideal point for the Paiton CFPP is an exergy efficiency of 32.05 %, a GC of 471.49 IDR/kWh, and CO2 emissions of 27,291.77 tonnes. This result was obtained using a MOGA-based ANN method with nine hidden neurons. In the MOGA RSM analysis, the most favorable Pareto front yields the following respective values for exergy efficiency, generation cost, and CO2 emissions: 33.09 %, 473.33 IDR/kWh, and 24,349.01 tonnes.","container-title":"Thermal Science and Engineering Progress","DOI":"10.1016/j.tsep.2025.103716","ISSN":"24519049","journalAbbreviation":"Thermal Science and Engineering Progress","language":"en","page":"103716","source":"DOI.org (Crossref)","title":"Performance analysis and multi-objective optimization of biomass co-firing power plants using multi-objective genetic algorithm","volume":"63","author":[{"family":"Gultom","given":"Enrico"},{"literal":"Nasruddin"},{"family":"Fakhri Muzhoffar","given":"Dimas Angga"},{"literal":"Sholahudin"}],"issued":{"date-parts":[["2025",7]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [1].Among the various technologies utilized, Circulating Fluidized Bed (CFB) boilers have gained widespread adoption due to their high combustion efficiency and fuel flexibility, allowing for the utilization of low-grade coals and biomass. However, maintaining peak efficiency is fundamentally hindered by the combustion environment's complexity. Thermal processes inside a furnace are characterized by highly non-linear dynamics, strong parameter coupling, and substantial thermal inertia. While traditional thermodynamic and mechanistic models are theoretically robust, high-fidelity implementations often require solving intricate mass and energy conservation equations that are computationally intensive. Although reduced-order models (ROMs) and hybrid physical-data approaches have been developed for real-time applications, they often rely on idealized assumptions that struggle to account for the stochastic nature of industrial variables, such as fluctuating fuel quality and transient load shifts ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"lRpn2jxL","properties":{"unsorted":false,"formattedCitation":"[2], [3]","plainCitation":"[2], [3]","noteIndex":0},"citationItems":[{"id":642,"uris":["http://zotero.org/users/17768309/items/8M4LRVJW"],"itemData":{"id":642,"type":"article-journal","abstract":"Bed temperature control, being of great significance for the safety, durability and NOx reduction of circulating fluidized bed (CFB) boiler, is nowadays becoming increasingly challenging due to the participation of CFB boiler in load regulation and the existence of various uncertainties such as the feed fuel quality variation and other unknown disturbances. To this end, this paper develops an engineering-friendly multi-model predictive sliding mode control (MM-PSMC) strategy for bed temperature regulation to attain a better adaption to wide load variation. A second-order plus time-delay (SOPDT) model based predictive sliding mode controller (PSMC) is designed to deal with the time-delay, model parameter uncertainty and external disturbances, and taken as local controller in MM-PSMC. An improved hysteresis switching logic is introduced to select a controller that is most suitable for the current operating condition and avoid system chattering caused by fast and unnecessary switching of controllers among local models. Simulation results on a 220t/h CFB boiler demonstrate the merits of the proposed MM-PSMC strategy over a wide operating range, depicting a promising prospect in industrial applications.","container-title":"Control Engineering Practice","DOI":"10.1016/j.conengprac.2020.104484","ISSN":"09670661","journalAbbreviation":"Control Engineering Practice","language":"en","page":"104484","source":"DOI.org (Crossref)","title":"Multi-model based predictive sliding mode control for bed temperature regulation in circulating fluidized bed boiler","volume":"101","author":[{"family":"Zhu","given":"Hongxia"},{"family":"Shen","given":"Jiong"},{"family":"Lee","given":"Kwang Y."},{"family":"Sun","given":"Li"}],"issued":{"date-parts":[["2020",8]]}}},{"id":248,"uris":["http://zotero.org/users/17768309/items/5R54HPTW"],"itemData":{"id":248,"type":"article-journal","abstract":"Circulating fluidized bed (CFB) units play an important role in thermal power generation system in China. Because of advantages of wide fuel flexibility and low pollutant emissions, the proportion of CFB units is increasing constantly. For an accurate bed temperature changing trend prediction in advance, sequence prediction is needed, and accurate bed temperature change interval prediction is also required, a sequence-interval prediction indicates the 2D-interval prediction. This paper presents a bed temperature sequence interval prediction model for typical 300 MW CFB unit using long-short term memory network (LSTM) based on actual operation unit, and the coal feed rate, primary air rate and secondary air rate are selected as input variables using grey relational analysis. Previous bed temperature and automatic generation control instruction are introduced to the prediction models, and the length of input variables sequences are optimized using genetic algorithm. Several model patterns are compared and discussed, and the effect of introducing of automatic generation control instruction is investigated. The results reveal that the model structure could effectively described the characteristic of bed temperature of CFB unit and the model could achieve an accurate 2D-interval trend prediction of bed temperature.","container-title":"Energy","DOI":"10.1016/j.energy.2019.116733","ISSN":"03605442","journalAbbreviation":"Energy","language":"en","page":"116733","source":"DOI.org (Crossref)","title":"Modeling for the bed temperature 2D-interval prediction of CFB boilers based on long-short term memory network","volume":"194","author":[{"family":"Hong","given":"Feng"},{"family":"Long","given":"Dongteng"},{"family":"Chen","given":"Jiyu"},{"family":"Gao","given":"Mingming"}],"issued":{"date-parts":[["2020",3]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [2], [3]. This often results in a "modeling gap" where theoretical predictions fail to capture the high-frequency disturbances typical of field operations.The recent paradigm shift toward data-driven architectures offers a robust alternative to classical modeling by leveraging historical operational data to map complex functional relationships without necessitating an exhaustive a priori physical structure ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"uUtUAabI","properties":{"unsorted":false,"formattedCitation":"[4]","plainCitation":"[4]","noteIndex":0},"citationItems":[{"id":8,"uris":["http://zotero.org/users/17768309/items/8N9K8W72"],"itemData":{"id":8,"type":"article-journal","abstract":"Coal-fired power plants remain important energy source in many countries, but releasing significant nitrogen oxides (NOx) emissions with serious environmental and health impacts. This study focuses on optimizing lignite combustion in a large utility boiler to minimize NOx emission by adjusting fuel distribution over the burner tiers. The proposed methodology integrates an in-house developed numerical code, correlation analysis, Extreme Gradient Boosting (XGBoost) model and Particle Swarm Optimization (PSO) algorithm. A database of computational fluid dynamics (CFD) simulations was generated to develop machine learning (ML) models for predicting NOx emission and furnace exit gas temperature (FEGT). Based on the ML models, PSO was applied to minimize NOx emissions while maintaining FEGT close to reference case. The framework is applied to a real scale utility boiler, demonstrating practical applicability and measurable environmental impact. Its multidisciplinary significance lies in the synergistic combination of CFD, artificial intelligence, and PSO for combustion control in large-scale energy system. Numerical simulation confirmed the accuracy of the PSO-based optimization, showing excellent agreement between predicted and simulated NOx emission and FEGT. The optimized case corresponds to a 30% reduction in NOx emission compared to the reference scenario. The results also indicated risk of localized overheating and slagging at the furnace walls and possible problems with ascending flame, highlighting the importance of adjusting the operating conditions and flame control to ensure stable boiler performance. The presented comprehensive method provides a novel and effective solution to improve the environmental performance of thermal power plants through intelligent optimization of combustion parameters.","container-title":"Engineering Applications of Artificial Intelligence","DOI":"10.1016/j.engappai.2026.114359","ISSN":"09521976","journalAbbreviation":"Engineering Applications of Artificial Intelligence","language":"en","page":"114359","source":"DOI.org (Crossref)","title":"Numerical simulation-driven machine learning and particle swarm optimization of burner fuel distribution for cleaner combustion in a thermal power plant","volume":"172","author":[{"family":"Milićević","given":"Aleksandar"},{"family":"Belošević","given":"Srđan"},{"family":"Tomanović","given":"Ivan"},{"family":"Crnomarković","given":"Nenad"},{"family":"Deng","given":"Lei"},{"family":"Che","given":"Defu"}],"issued":{"date-parts":[["2026",5]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [4]. While algorithms such as Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN) have gained prominence for their predictive capabilities, current literature remains heavily skewed toward maximizing accuracy metrics (R², RMSE). A critical research gap exists regarding the comparative interpretability of these models: specifically, how structurally diverse paradigms ranging from bagging ensembles to connectionist architectures, prioritize operational variables like the Air-Fuel Ratio (AFR) versus auxiliary parameters like Bed Pressure or Cyclone Temperature ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"TtnuY7DO","properties":{"unsorted":false,"formattedCitation":"[5], [6], [7]","plainCitation":"[5], [6], [7]","noteIndex":0},"citationItems":[{"id":177,"uris":["http://zotero.org/users/17768309/items/WLCWI4M6"],"itemData":{"id":177,"type":"article-journal","abstract":"Circulating fluidized bed (CFB) combustion is a new clean coal technology with advantages of wide fuel flexibility and low pollutant emissions. The bed temperature of CFB boilers is an important factor that influences operating security and pollutant emission generation. An accurate model to describe the dynamic characteristics of bed temperature is beneficial in reducing temperature fluctuations. This study presents a dynamic model for predicting the bed temperature of a 300 MW CFB boiler based on the least squares support vector machine method with real operational data. Coal feed rate and primary air rate are selected as the independent variables. The current values and previous sequences of the variables are considered as the model inputs to describe the dynamic characteristics of bed temperature. In addition, the past values of bed temperature are taken as feedback and then added to the inputs. The particle swarm optimization technique is used to determine optimal delay orders. Several model patterns are also discussed and compared. Comparison results show that the proposed model structure is reasonable and that the model can achieve the accurate prediction of the bed temperature.","container-title":"Energy","DOI":"10.1016/j.energy.2017.02.031","ISSN":"03605442","journalAbbreviation":"Energy","language":"en","page":"284-294","source":"DOI.org (Crossref)","title":"A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data","volume":"124","author":[{"family":"Lv","given":"You"},{"family":"Hong","given":"Feng"},{"family":"Yang","given":"Tingting"},{"family":"Fang","given":"Fang"},{"family":"Liu","given":"Jizhen"}],"issued":{"date-parts":[["2017",4]]}}},{"id":14,"uris":["http://zotero.org/users/17768309/items/ZPXSY8BD"],"itemData":{"id":14,"type":"article-journal","abstract":"Carbon mitigation policies and emission trading systems have heightened the need to monitor and predict the carbon emission intensity (CEI) of coal-fired power plants. Leveraging big data and machine learning (ML) technologies, this study trains predictive models of CEI using operational parameters, load rate, and coal-quality data from three Chinese power plants. The performance of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Network (ANN) was evaluated, and the challenge of limited data in individual plants was mitigated through instance-based transfer learning (ITL) and graft learning (GL) technologies. The results indicate that while traditional ML models struggle with poor data quality and limited samples, transfer learning between different plants will improve predictive accuracy substantially, and GL delivers the greatest gains. Among the most influential features, air supply temperature and load rate critically impact CEI and therefore should be carefully managed to achieve emission reductions. Nevertheless, the effectiveness of transfer learning depends on source data quality and model choice, and the proposed operational strategies require further validation before practical adoption. Our findings offer a robust framework for enhancing the predictive accuracy of plant CEI under data-scarce conditions and inform effective strategies for promoting a low-carbon transition in the energy sector.","container-title":"Expert Systems with Applications","DOI":"10.1016/j.eswa.2025.129743","ISSN":"09574174","journalAbbreviation":"Expert Systems with Applications","language":"en","page":"129743","source":"DOI.org (Crossref)","title":"An explainable transfer learning approach to predict carbon emission intensity of coal-fired power plants with multi-source monitoring data","volume":"298","author":[{"family":"Jin","given":"Xiaodong"},{"family":"Zhang","given":"Lingzhen"},{"family":"Li","given":"Fangyi"},{"family":"Xie","given":"Wu"},{"family":"Ma","given":"Dawei"},{"family":"Wu","given":"Yan"}],"issued":{"date-parts":[["2026",3]]}}},{"id":12,"uris":["http://zotero.org/users/17768309/items/WTJXULRI"],"itemData":{"id":12,"type":"article-journal","abstract":"Accurate prediction of NOx is essential for improving the efficiency of denitrification systems in CFB boilers, particularly when the boilers are operating under low-load conditions. A prediction model based on feature engineering combined with machine learning was proposed to enhance the accuracy of NOx concentration prediction for CFB boilers. Initially, all the original input parameters were analyzed and selected through feature engineering techniques, including PCA, t-SNE, UMAP, and RF fusion. Additionally, SHAP model explanations were incorporated for the final selected features. Finally,15 parameters with higher correlation were identified for further research. Next, regression prediction of the above parameters was performed using a method that combines various machine learning techniques. K-fold and random search cross-validation were respectively utilized to determine the hyperparameters of the different models. Lastly, the performance of the studied model was assessed using evaluation indices such as R2, MAE, and RMSE. These metrics helped identify the optimal prediction model from all the models mentioned, which was then validated using a new set of data. The results of validity testing indicate that feature engineering and hyperparameters significantly impact the accuracy of the regression model. Specifically, combining feature engineering with Deep Random Forest leads to superior prediction performance and generalization ability.","container-title":"Process Safety and Environmental Protection","DOI":"10.1016/j.psep.2025.01.008","ISSN":"09575820","journalAbbreviation":"Process Safety and Environmental Protection","language":"en","page":"106754","source":"DOI.org (Crossref)","title":"NOx concentration modeling in CFB coal-fired power plants based on feature engineering and deep random forest","volume":"195","author":[{"family":"Yan","given":"Gaocheng"},{"family":"Qiao","given":"Jie"},{"family":"Wu","given":"Yuchao"},{"family":"Zheng","given":"Lixing"},{"family":"Xue","given":"Xiaojun"}],"issued":{"date-parts":[["2025",3]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [5], [6], [7]. For field operators, a model that achieves high accuracy but lacks transparency regarding feature significance is of limited utility for proactive control adjustments.This research addresses this gap by developing a comprehensive comparative framework utilizing RF, XGBoost, and ANN architectures. Beyond benchmarking predictive fidelity, this study conducts a rigorous interrogation of "feature importance" to determine if a cross-algorithmic consensus exists regarding the primary drivers of boiler efficiency. By bridging the gap between advanced machine learning theory and practical energy management, this study aims to identify the most robust operational levers for efficiency optimization, providing actionable insights that are both statistically validated and physically grounded.MethodA. Research WorkflowThe study is structured into a logical sequence of operations, as depicted in the research flowchart (Figure 1). The process begins with data acquisition from industrial telemetry, followed by multi-stage preprocessing to ensure data integrity. The core of the methodology involves the parallel implementation and parameter optimization of the RF, XGBoost, and ANN models. Finally, the outputs are subjected to a rigorous dual-layer evaluation: predictive accuracy metrics and variable impact analysis.Figure 1. Flowchart of analysis method frameworkB. Dataset and PreprocessingThe experimental dataset utilized in this study was derived from historical telemetry logs of an industrial boiler unit, covering a continuous one-year period (365 days) and capturing combustion dynamics across diverse load conditions. This dataset comprises six independent operational variables as input features, with calculated boiler efficiency defined as the target output. To ensure model stability and mitigate biased weight distributions, the raw data underwent a rigorous cleaning phase to remove outliers and erroneous sensor readings arising from signal losses, sensor malfunctions, or unit outages ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"v4pRUvnc","properties":{"unsorted":false,"formattedCitation":"[8]","plainCitation":"[8]","noteIndex":0},"citationItems":[{"id":174,"uris":["http://zotero.org/users/17768309/items/RPREQKIP"],"itemData":{"id":174,"type":"article-journal","abstract":"Statistical regression models have been applied in various biotechnical applications to determine the relationship between response and conditional factors as well as to facilitate prediction. This research focuses on sustainable biodegradation process of crude oil using native and recombinant microbial strains to alleviate the significant environmental threat posed by crude oil contamination. This sustainable process is energy and cost-efficient compared to the traditional measures adopted in the extermination of the contaminated soil due to crude oil. The reaction of the wild and recombinant microbial strains to biodegradation rate was detected in the experi­ mental study. These experimental data were used to develop multiple linear regression (MLR) and polynomial regression models (PR). The coefficient of determination, or R2 value, was used to validate the constructed models. The PR model had a high R2 of 0.863, whereas the MLR model had a low value of 0.495. The PR model provided the best match, and it also explains the impact of conditioning factors on the biodegradation rate. To summarize, the recombinant microbial strain yielded a better biodegradation rate with increasing incubation days at lower crude oil contents.","container-title":"Sustainable Energy Technologies and Assessments","DOI":"10.1016/j.seta.2022.102797","ISSN":"22131388","journalAbbreviation":"Sustainable Energy Technologies and Assessments","language":"en","page":"102797","source":"DOI.org (Crossref)","title":"Application of multiple linear and polynomial regression in the sustainable biodegradation process of crude oil","volume":"54","author":[{"family":"Ajona","given":"M."},{"family":"Vasanthi","given":"P."},{"family":"Vijayan","given":"D.S."}],"issued":{"date-parts":[["2022",12]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [8]. Following this preprocessing, a refined dataset of 329 days of valid operational data was retained for model development. The data was subsequently partitioned into a 75% training subset and a 25% testing subset using block random sampling, ensuring balanced representation and robust model validation. Furthermore, given the sensitivity of the ANN architecture to input magnitudes, all features were scaled to a uniform [0, 1] range using the Min-Max Normalization technique ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"JWtESor8","properties":{"unsorted":false,"formattedCitation":"[9]","plainCitation":"[9]","noteIndex":0},"citationItems":[{"id":653,"uris":["http://zotero.org/users/17768309/items/D9RFVRBR"],"itemData":{"id":653,"type":"article-journal","abstract":"In this study, two machine-learning algorithms based on the artificial neural network (ANN) model are proposed to estimate the ultimate compressive strength of square concrete-filled steel tubular columns. The development of such prognostic models is achievable since an extensive set of experimental tests exist for these members. The models are developed to use the simplest possible network architecture but attain very high accuracy. A total dataset of 1022 specimens with 685 stub columns and 337 slender columns subjected to pure axial compression is collected from the available literature. This is significant for the development of the initial model considering that for this field it falls under the scope of big data analysis. The ANN models are validated by comparison with experimental results. The validation study has shown the superiority of surrogate models over the Eurocode 4 design code. The empirical equation derived from the best-tuned Bayesian regularization algorithm shows a better agreement with the experimental results than those obtained by the Levenberg–Marquardt algorithm, and Eurocode 4 design code. A similar conclusion applies to stub and slender columns independently. The Bayesian regularization-based model is negligibly slower than the one developed on the Levenberg–Marquardt algorithm but gives a better generalization even with simplified ANN. Generally, besides its high accuracy, one of the key benefits of the presented ANN model is its applicability to a broader range of columns than Eurocode 4 and other studies.","container-title":"Journal of Big Data","DOI":"10.1186/s40537-023-00739-y","ISSN":"2196-1115","issue":"1","journalAbbreviation":"J Big Data","language":"en","page":"67","source":"DOI.org (Crossref)","title":"Practical ANN prediction models for the axial capacity of square CFST columns","volume":"10","author":[{"family":"Đorđević","given":"Filip"},{"family":"Kostić","given":"Svetlana M."}],"issued":{"date-parts":[["2023",5,17]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [9], as expressed in Equation (1):xnorm=x-xminxmax-xmin (1)C. Algorithmic ImplementationsThis study implements three distinct machine learning paradigms to provide a comparative analysis of ensemble methods versus neural architectures, both of which are increasingly utilized to solve non-linear optimization problems in modern power plants ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"5E0aMQ7T","properties":{"unsorted":false,"formattedCitation":"[10]","plainCitation":"[10]","noteIndex":0},"citationItems":[{"id":15,"uris":["http://zotero.org/users/17768309/items/7VYSSH67"],"itemData":{"id":15,"type":"article-journal","abstract":"The efficiency of coal-fired power plant depends on various operating parameters such as main steam/ reheat steam pressures and temperatures, turbine extraction pressures, and excess air ratio for a given fuel. However, simultaneous optimization of all these operating parameters to achieve the maximum plant efficiency is a challenging task. This study deals with the coupled ANN and GA based (neuro-genetic) optimization of a high ash coal-fired supercritical power plant in Indian climatic condition to determine the maximum possible plant efficiency. The power plant simulation data obtained from a flow-sheet program, ‘‘Cycle-Tempo’’ is used to train the artificial neural network (ANN) to predict the energy input through fuel (coal). The optimum set of various operating parameters that result in the minimum energy input to the power plant is then determined by coupling the trained ANN model as a fitness function with the genetic algorithm (GA). A unit size of 800 MWe currently under development in India is considered to carry out the thermodynamic analysis based on energy and exergy. Apart from optimizing the design parameters, the developed model can also be used for on-line optimization when quick response is required. Furthermore, the effect of various coals on the thermodynamic performance of the optimized power plant is also determined.","container-title":"Applied Energy","DOI":"10.1016/j.apenergy.2011.06.029","ISSN":"03062619","issue":"12","journalAbbreviation":"Applied Energy","language":"en","page":"4867-4873","source":"DOI.org (Crossref)","title":"ANN-GA based optimization of a high ash coal-fired supercritical power plant","volume":"88","author":[{"family":"Suresh","given":"M.V.J.J."},{"family":"Reddy","given":"K.S."},{"family":"Kolar","given":"Ajit Kumar"}],"issued":{"date-parts":[["2011",12]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [10]. The Random Forest (RF) algorithm is the first methodology employed. As an ensemble learning method, RF constructs a multitude of decision trees during the training phase using Bootstrap Aggregating (bagging) and random subspace algorithms. This approach is highly effective at reducing model variance and enhancing robustness against the parameter coupling and sensor noise commonly observed in industrial boiler telemetry, providing stable outputs without requiring predefined temporal correlations ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"NkZWL7r8","properties":{"unsorted":false,"formattedCitation":"[11]","plainCitation":"[11]","noteIndex":0},"citationItems":[{"id":10,"uris":["http://zotero.org/users/17768309/items/36SQVVAI"],"itemData":{"id":10,"type":"article-journal","abstract":"In a context where thermal power plants are deeply engaged in rapid peak shaving, the high-temperature superheater of a 1000 MW four-corner tangential combustion boiler faces challenges such as abnormal wall temperatures, thermal deviations, and increased risk of tube burst due to frequent load fluctuations. In this study, a significant dynamic correlation is revealed between the rate of change in the load and the standard deviation of the wall temperature. During stable operation, when the load change rate remains within the range 0.7 to 1.5 MW/min, fluctuations in the standard deviation of the wall temperature can be controlled to within 4 ℃, to maintain an acceptable range. However, during phases of rapid peak shaving, when the rate of change surges to between 7 and 7 MW/min, the abrupt fluctuations in load cause rates that far exceed stable conditions. This triggers a sharp rise in the standard deviation of the wall temperature to approximately 12 ℃, which significantly degrades the uniformity of the wall temperature. Based on these findings, we applied an innovative approach in which operational experience of a power plant was combined with a Pearson correlation coefficient analysis to construct a high-dimensional feature dataset. Following multi-model cross-validation, a random forest model was employed for prediction of the wall temperature. The results demonstrated that this model achieved a fitting accuracy of over 96% for the standard deviation in the high-temperature pipeline temperature and wall temperature under both transient and steady-state conditions. The model precisely captured parameter variations in working fluid flow and convective heat transfer during rapid load changes, identified high-risk zones that are prone to overheating and pipeline rupture, and can provide technical support for establishing intelligent early warning systems in power plants.","container-title":"Fuel","DOI":"10.1016/j.fuel.2026.139054","ISSN":"00162361","journalAbbreviation":"Fuel","language":"en","page":"139054","source":"DOI.org (Crossref)","title":"Data-driven intelligent modeling for superheater wall temperature prediction and operational optimization of 1000 MW deep peak shaving coal-fired power plants","volume":"421","author":[{"family":"Ji","given":"Xingliang"},{"family":"Wang","given":"Yanming"},{"family":"Ma","given":"Xiang"},{"family":"Cui","given":"Liming"},{"family":"Wang","given":"Jiawei"},{"family":"Ni","given":"Yongzhong"},{"family":"Li","given":"Hongyuan"},{"family":"Wang","given":"Tao"},{"family":"Zhang","given":"Yongsheng"},{"family":"Xu","given":"Hong"}],"issued":{"date-parts":[["2026",10]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [11]. Complementing this, the study utilizes eXtreme Gradient Boosting (XGBoost), a scalable, end-to-end tree boosting system. XGBoost builds its ensemble sequentially to minimize residual errors via gradient descent-based optimization. By incorporating a regularized objective function that penalizes model complexity to prevent overfitting, XGBoost offers robust predictive performance for the complex thermochemical reactions inherent in boiler furnaces ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"qr5JbNLd","properties":{"unsorted":false,"formattedCitation":"[12]","plainCitation":"[12]","noteIndex":0},"citationItems":[{"id":16,"uris":["http://zotero.org/users/17768309/items/K7Z6VEE5"],"itemData":{"id":16,"type":"article-journal","abstract":"In the context of deep peak shaving and the use of low-quality coal, combustion optimization in coal-fired boilers for safety, efficiency, and low emissions remains crucial. However, due to the lack of real-time monitoring for temperature and chemical species with high resolution, as well as the significant spatial scale differences between optimization objectives, achieving multi-objective combustion optimization that balances safety, efficiency, and low pollution remains a challenge. This study develops a multi-fidelity hybrid modeling framework that couples computational fluid dynamics (CFD) simulations with field measurements to predict hightemperature corrosion and slagging severity, NOx emissions, and exhaust gas temperature. To optimize boiler operating conditions, an enhanced non-dominated sorting genetic algorithm II (NSGA-II) is employed in conjunction with three distinct optimization strategies. The three-dimensional fields of temperature and chemical species are first predicted using proper orthogonal decomposition (POD). Based on these predictions, water wall slagging and corrosion tendencies are predicted with fuzzy comprehensive evaluation (FCE) and staged evaluation. The extreme gradient boosting (XGBoost) models, integrated with k-means clustering, are trained on field data to predict NOx emissions and exhaust gas temperature. Three optimization strategies with an improved NSGA-II and constraint boundary condition are proposed and evaluated for optimization performance and time. High prediction accuracy is achieved for temperature and chemical species via the POD model, and for NOx emissions and exhaust gas temperature using the XGBoost approach. All three optimization strategies effectively reduce NOx emissions and exhaust gas temperature, with Strategy 1 requiring the shortest time, taking 28 s. However, its impact on high-temperature corrosion is limited, with a reduction of 16.1 %. Optimization Strategy 3 provides the best optimization for water wall safety. But it requires predefining a reasonable water wall safety index (WSI) limit range and longer time of 16 min. This approach introduces a novel framework for combustion optimization that simultaneously considers operational safety, thermal efficiency, and pollutant emissions.","container-title":"Energy","DOI":"10.1016/j.energy.2025.138442","ISSN":"03605442","journalAbbreviation":"Energy","language":"en","page":"138442","source":"DOI.org (Crossref)","title":"Three multi-fidelity data hybrid-driven strategies for multi-objective combustion optimization in coal-fired boilers using POD and XGBoost","volume":"336","author":[{"family":"Wang","given":"Tianyi"},{"family":"Zhong","given":"Wenqi"},{"family":"Chen","given":"Xi"},{"family":"Zhou","given":"Guanwen"},{"family":"Guo","given":"Xiaogang"},{"family":"Yu","given":"Li"},{"family":"Yang","given":"Zhengze"}],"issued":{"date-parts":[["2025",11]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [12].In contrast to these ensemble techniques, an Artificial Neural Network (ANN), following a Multi-Layer Perceptron (MLP) architecture, is implemented to simulate biological neural connections through a feedforward mechanism. The architecture consists of an input layer for operational variables, hidden layers for deep feature extraction, and an output layer for efficiency prediction, utilizing the backpropagation algorithm and the Adam optimizer to fine-tune weights and biases ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"ZUCHndNF","properties":{"unsorted":false,"formattedCitation":"[13]","plainCitation":"[13]","noteIndex":0},"citationItems":[{"id":655,"uris":["http://zotero.org/users/17768309/items/F8AGY35F"],"itemData":{"id":655,"type":"article-journal","abstract":"Improving operational performance and reducing fuel consumption is increasingly important for shipping companies. Ship performance degrades over time due to hull and propeller fouling; therefore assessing when fouling effects are significant enough to warrant cleaning is critical. Advancements in onboard data logging systems, combined with machine learning techniques, unlock the potential to predict fouling effects accurately and determine when to clean. This study evaluates five models for shaft power prediction: Multiple Linear Regression, Decision Tree (AdaBoost), K – Nearest Neighbours, Artificial Neural Network and Random Forest. The importance of pre-processing is highlighted, contributing to the creation of a model with lower errors than previous studies. The significance of environmental parameters was explored, with the novel integration of wave statistics to the operational dataset, and simulated power-speed curves created from predictions to identify performance deterioration due to fouling. The Random Forest model was most effective in predicting shaft power, with an error of 1.17%. The addition of ‘Days Since Clean’ and ‘Significant Wave Height’ increased prediction accuracy by 0.07% and 0.12% respectively. Simulated power-speed curves revealed a 5.2% increase in shaft power due to fouling. This study provides operators with a method to determine when to conduct hull and propeller cleaning.","container-title":"Ocean Engineering","DOI":"10.1016/j.oceaneng.2021.108886","ISSN":"00298018","journalAbbreviation":"Ocean Engineering","language":"en","page":"108886","source":"DOI.org (Crossref)","title":"Machine learning for shaft power prediction and analysis of fouling related performance deterioration","volume":"234","author":[{"family":"Laurie","given":"Anastasia"},{"family":"Anderlini","given":"Enrico"},{"family":"Dietz","given":"Jesper"},{"family":"Thomas","given":"Giles"}],"issued":{"date-parts":[["2021",8]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [13]. By applying the Rectified Linear Unit (ReLU) activation function within the hidden layers, the network effectively models the high-dimensional, non-linear boundaries between operational inputs and target efficiency directly from the data. This eliminates the necessity for a predefined physical model structure, allowing the architecture to learn complex patterns autonomously and provide a modern alternative to tree-based methods ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"XgctkRgd","properties":{"unsorted":false,"formattedCitation":"[14]","plainCitation":"[14]","noteIndex":0},"citationItems":[{"id":11,"uris":["http://zotero.org/users/17768309/items/YD9X2GNR"],"itemData":{"id":11,"type":"article-journal","abstract":"Combustion optimization is an effective technique for energy conservation in power plants. An accurate NOX emission estimation model is crucial for combustion optimization. A novel NOX emission estimation model is proposed that integrates an improved random forest (RF) and a wrapper feature selection based on an improved binary flower pollination algorithm (FPA). Three improvements to the basic FPA are proposed, an elite-selection strategy, a mutation operator, and a dynamic switch probability. Moreover, the optimization precision of the proposed FPA is compared to those of five other optimization algorithms. A feature selection method based on the proposed FPA is employed to identify the optimal inputs for the NOX emission model. In addition, a finetuning mechanism to update the population (based on mutual information) is designed and embedded in the feature selection process to speed up convergence. To improve the basic RF, an ensemble pruning algorithm based on a back propagation neural network that can evaluate the importance of each tree and prune “detrimental” trees is developed. With the optimal inputs, the NOX emission model is constructed using the improved RF. The prediction results demonstrate that it is of higher accuracy and better robustness compared to the basic RF and the three other models. The designed feature selection could also decrease the computational complexity and improve the prediction accuracy.","container-title":"Measurement","DOI":"10.1016/j.measurement.2018.04.069","ISSN":"02632241","journalAbbreviation":"Measurement","language":"en","page":"303-312","source":"DOI.org (Crossref)","title":"A hybrid model integrating improved flower pollination algorithm-based feature selection and improved random forest for NO X emission estimation of coal-fired power plants","volume":"125","author":[{"family":"Wang","given":"Fang"},{"family":"Ma","given":"Suxia"},{"family":"Wang","given":"He"},{"family":"Li","given":"Yaodong"},{"family":"Qin","given":"Zhiguo"},{"family":"Zhang","given":"Junjie"}],"issued":{"date-parts":[["2018",9]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [14].C.1. eXtreme Gradient Boosting (XGBoost)The XGBoost regressor was configured to implement a sequential gradient-boosting framework using regression trees. The model was trained using 200 estimators (n_estimators = 200) with a conservative learning rate (shrinkage factor) of 0.04 (learning_rate = 0.04) to ensure a stable and progressive descent along the objective function's gradient. To prevent over-parameterization while capturing localized non-linear interactions, the maximum depth of each decision tree was restricted to 4 (max_depth = 4).Stochastic variance reduction was achieved by introducing both row and column subsampling: at each boosting iteration, the model randomly samples 80% of the operational training records (subsample = 0.8) and 80% of the feature space (colsample_bytree = 0.8) to construct the individual base learners. Furthermore, structural regularization was explicitly enforced by setting an L₂ weight regularization penalty (Ridge) of λ = 5.0 (reg_lambda = 5.0) and an L₁ penalty (Lasso) of α = 0.1 (reg_alpha = 0.1) within the objective function. This dual-regularization framework penalizes leaf complexity and stabilizes prediction gradients against industrial telemetry noise.C.2. Random Forest (RF)The Random Forest regressor was deployed as a parallel bootstrap-aggregated (bagging) ensemble comprising 100 independent decision trees (n_estimators = 100). To limit model complexity and suppress high-frequency process noise, the maximum depth of each tree was heavily constrained to 3 (max_depth = 3). Additionally, to smooth decision boundaries and prevent the model from isolating localized outliers, the minimum number of training samples required to populate a terminal leaf node was set to 8 (min_samples_leaf = 8). By combining deep bagging randomization with these structural limits, the RF model provides a stable, low-variance baseline that relies on the consensus of highly simplified estimators.C.3. Artificial Neural Network (ANN / MLP)The connectionist architecture was implemented as a feedforward Multi-Layer Perceptron (MLP) regressor. The network's topology consists of:Input Layer: Comprising either 6 baseline operational nodes or 10 nodes when chronological lag-one features (Efficiency_Lag1, Bed_Temp_Lag1, Cyclone_Temp_Lag1, Load_Lag1) are enabled to account for process inertia.Hidden Layer: Exactly one hidden layer containing 4 fully-connected neurons (hidden_layer_sizes = (4,)). This compact architecture was chosen deliberately to prevent overparameterization and limit learning capacity on tabular datasets.Output Layer: A single continuous regression node representing the predicted boiler thermal efficiency.The mathematical core of the network utilizes the Rectified Linear Unit (ReLU) activation function max(0, x) within the hidden neurons to project linear inputs into a non-linear space. Network optimization was executed using the Adam (Adaptive Moment Estimation) optimizer with an initial learning rate (η₀) of 0.001. The mini-batch size was automated (batch_size = 'auto'), dynamically allocating min(200, n_samples) samples per backpropagation step.To satisfy reproducibility requirements and prevent overfitting, the network incorporates rigorous regularization and stopping mechanisms:L₂ Regularization (Weight Decay): An exceptionally high weight penalty α = 15.0 (alpha = 15.0) was enforced to aggressively decay large weights and restrict the model's complexity.Dynamic Early Stopping: Enabled (early_stopping = True) using a validation split of 20% (validation_fraction = 0.20). During training, the Adam optimizer set aside 20% of the training subset to monitor validation loss. If the validation loss failed to improve by a threshold of 10⁻⁴ for 10 consecutive epochs, the training process was automatically terminated to prevent overfitting, capping the maximum allowable training epoch limit at 2000 (max_iter = 2000).D. Performance Metrics and Impact AnalysisThe evaluation of the models is conducted using primary statistical indicators, notably the Coefficient of Determination (R²) and the Root Mean Square Error (RMSE), which are widely adopted for validating regression model accuracy ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"3tAw4xUw","properties":{"unsorted":false,"formattedCitation":"[15]","plainCitation":"[15]","noteIndex":0},"citationItems":[{"id":170,"uris":["http://zotero.org/users/17768309/items/AQSX3GIZ"],"itemData":{"id":170,"type":"article-journal","abstract":"Canopy cover is an important biophysical indicator of forested habitats, but quantifying and monitoring with the accurate and cost-efficient method is always difficult in fragmented canopy cover areas. Therefore, this study was designed to explore the comparative performance of linear regression, polynomial regression and generalized additive model (GAM), and assess the important predictors for canopy cover estimation in the dry deciduous forest of West Bengal. We used the Sentinel-2 based individual bands and spectral indices as predictor variables from the different composition of imagery in the growing season (June–September) between 2018 and 2020. Canopy cover of 667 plots were measured using collect earth online (CEO) from high resolution imagery. Uni­ variate and multiple models were fitted and assessed by 10 fold cross-validation. Root mean square error (RMSE) and the coefficient of determination (R2) was used for the evaluation of model performance. The result showed that univariate models using GAM were fitted better followed polynomial regression and linear regression model. SWIR bands and SWIR bands based indices were fitted well than other groups of variables. Individual bands based multiple prediction models performed better followed by NIR, SWIR, atmospheric and red edge indices based multiple prediction models for multiple linear regression, multiple polynomial regression and multiple GAM. Multiple GAM performed better (RMSE = 0.163, R2 = 0.777) than multiple polynomial regression (RMSE = 0.167, R2 = 0.772) and multiple linear regression model (RMSE = 0.182, R2 = 0.721). Although, the difference of performance between multiple polynomial regression model and multiple GAM was negligible. But in respect to prediction range, and overestimation and underestimation multiple GAM was superior to multiple polynomial regression model. The study demonstrates that the potential utility of GAM for canopy cover estimation and GAM can be superior to linear and polynomial regression model using suitable predictor variables.","container-title":"Remote Sensing Applications: Society and Environment","DOI":"10.1016/j.rsase.2021.100502","ISSN":"23529385","journalAbbreviation":"Remote Sensing Applications: Society and Environment","language":"en","page":"100502","source":"DOI.org (Crossref)","title":"Comparative performance of linear regression, polynomial regression and generalized additive model for canopy cover estimation in the dry deciduous forest of West Bengal","volume":"22","author":[{"family":"Bera","given":"Dipankar"},{"family":"Chatterjee","given":"Nilanjana Das"},{"family":"Bera","given":"Sudip"}],"issued":{"date-parts":[["2021",4]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [15]. The R² score is utilized to measure the proportion of variation in the dependent variable that is explained by the independent variables. Meanwhile, the RMSE provides a direct measure of prediction error in the original units, indicating the overall agreement between the datasets of observed and modeled values ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"H9hcWYWN","properties":{"unsorted":false,"formattedCitation":"[16]","plainCitation":"[16]","noteIndex":0},"citationItems":[{"id":165,"uris":["http://zotero.org/users/17768309/items/ETB87Y43"],"itemData":{"id":165,"type":"article-journal","abstract":"The assessment of uncertainty in water quality prediction models is crucial for understanding the reliability of these models. This research focuses on the southern basin area of the Caspian Sea in Iran, where there have been limited studies on uncertainty analysis in water quality models. First, a forest layer was extracted from land cover map and 25 large watersheds were selected. River water quality was then assessed from the perspective of 8 landscape connectivity metrics and 12 graph theory indices. We developed predictive models using stepwise linear regression, power, exponential, and logarithmic models to locate the best model form for each water quality parameter (dependent variable) we examined. The goal of this study was to analyze the uncertainty in both linear and non-linear water quality regression models for this region. The researchers used a Monte Carlo simulation method, which is a common approach to investigate uncertainty. First, they determined the best distribution of the independent variables in the regression models using the Kolmogorov-Smirnov goodness of fit test. Then, they generated 15,000 random data points based on the characteristics of the identified variable distributions. In the third stage, new regression models were created using the data from the previous stages. Probability density functions related to the output of the models were developed, and uncertainty analysis was conducted based on the behavior of the models. The results indicated that the CO3 model had the lowest uncertainty bandwidth and the highest R2 among other models. Additionally, the K models had less bandwidth than the other models developed. The research results showed that more than 70% of the models produced results within an acceptable range of uncertainty. Furthermore, the normalized RMSE (NRMSE) values associated with the models were all in the range of 0 to 30%, which is considered acceptable. These outcomes suggest that landscape indicators and graph theory continuity can be used to model water quality parameters with an acceptable level of uncertainty.","container-title":"Ecological Indicators","DOI":"10.1016/j.ecolind.2024.112979","ISSN":"1470160X","journalAbbreviation":"Ecological Indicators","language":"en","page":"112979","source":"DOI.org (Crossref)","title":"Uncertainty analysis of linear and non-linear regression models in the modeling of water quality in the Caspian Sea basin: Application of Monte-Carlo method","title-short":"Uncertainty analysis of linear and non-linear regression models in the modeling of water quality in the Caspian Sea basin","volume":"170","author":[{"family":"Masteali","given":"Sahar Heidari"},{"family":"Bayat","given":"Mahmoud"},{"family":"Bettinger","given":"Pete"},{"family":"Ghorbanpour","given":"Mansour"}],"issued":{"date-parts":[["2025",1]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [16]. The RMSE is calculated as per Equation (2):RMSE=1ni=1n(yi-yi)2 (2)Furthermore, the study incorporates Pearson correlation matrices to identify linear dependencies among the variables and "Permutation Importance" techniques to rank the impact of each operational variable on the predicted efficiency ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"oe85uOgG","properties":{"unsorted":false,"formattedCitation":"[16]","plainCitation":"[16]","noteIndex":0},"citationItems":[{"id":165,"uris":["http://zotero.org/users/17768309/items/ETB87Y43"],"itemData":{"id":165,"type":"article-journal","abstract":"The assessment of uncertainty in water quality prediction models is crucial for understanding the reliability of these models. This research focuses on the southern basin area of the Caspian Sea in Iran, where there have been limited studies on uncertainty analysis in water quality models. First, a forest layer was extracted from land cover map and 25 large watersheds were selected. River water quality was then assessed from the perspective of 8 landscape connectivity metrics and 12 graph theory indices. We developed predictive models using stepwise linear regression, power, exponential, and logarithmic models to locate the best model form for each water quality parameter (dependent variable) we examined. The goal of this study was to analyze the uncertainty in both linear and non-linear water quality regression models for this region. The researchers used a Monte Carlo simulation method, which is a common approach to investigate uncertainty. First, they determined the best distribution of the independent variables in the regression models using the Kolmogorov-Smirnov goodness of fit test. Then, they generated 15,000 random data points based on the characteristics of the identified variable distributions. In the third stage, new regression models were created using the data from the previous stages. Probability density functions related to the output of the models were developed, and uncertainty analysis was conducted based on the behavior of the models. The results indicated that the CO3 model had the lowest uncertainty bandwidth and the highest R2 among other models. Additionally, the K models had less bandwidth than the other models developed. The research results showed that more than 70% of the models produced results within an acceptable range of uncertainty. Furthermore, the normalized RMSE (NRMSE) values associated with the models were all in the range of 0 to 30%, which is considered acceptable. These outcomes suggest that landscape indicators and graph theory continuity can be used to model water quality parameters with an acceptable level of uncertainty.","container-title":"Ecological Indicators","DOI":"10.1016/j.ecolind.2024.112979","ISSN":"1470160X","journalAbbreviation":"Ecological Indicators","language":"en","page":"112979","source":"DOI.org (Crossref)","title":"Uncertainty analysis of linear and non-linear regression models in the modeling of water quality in the Caspian Sea basin: Application of Monte-Carlo method","title-short":"Uncertainty analysis of linear and non-linear regression models in the modeling of water quality in the Caspian Sea basin","volume":"170","author":[{"family":"Masteali","given":"Sahar Heidari"},{"family":"Bayat","given":"Mahmoud"},{"family":"Bettinger","given":"Pete"},{"family":"Ghorbanpour","given":"Mansour"}],"issued":{"date-parts":[["2025",1]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [16]. This ensures that the results are not only accurate but also physically interpretable for industrial application.Result and DiscussionA. Exploratory Correlation AnalysisFigure 2. Correlation Matrix for Boiler Operational VariableThe Pearson correlation analysis provides a preliminary quantitative map of the linear dependencies within the boiler’s operational ecosystem. As illustrated in the Correlation Matrix (Figure 2), the Air-Fuel Ratio (AFR) emerges as the most dominant linear factor, exhibiting a strong positive correlation of 0.84 with boiler efficiency. From a thermochemical perspective, this high coefficient indicates that the synchronization between combustion air and fuel flow is the primary lever for maximizing heat release ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"aZO5V31t","properties":{"unsorted":false,"formattedCitation":"[17]","plainCitation":"[17]","noteIndex":0},"citationItems":[{"id":657,"uris":["http://zotero.org/users/17768309/items/KMYRGGKF"],"itemData":{"id":657,"type":"article-journal","abstract":"Electric power generation system development is reviewed with special attention to plant efficiency. It is generally understood that efficiency improvement that is consistent with high plant reliability and low cost of electricity is economically beneficial, but its effect upon reduction of all plant emissions without installation of additional environmental equipment, is less well appreciated. As CO2 emission control is gaining increasing acceptance, efficiency improvement, as the only practical tool capable of reducing CO2 emission from fossil fuel plant in the short term, has become a key concept for the choice of technology for new plant and upgrades of existing plant. Efficiency is also important for longer-term solutions of reducing CO2 emission by carbon capture and sequestration (CCS); it is essential for the underlying plants to be highly efficient so as to mitigate the energy penalty of CCS technology application. Power generating options, including coal-fired Rankine cycle steam plants with advanced steam parameters, natural gas-fired gas turbine-steam, and coal gasification combined cycle plants are discussed and compared for their efficiency, cost and operational availability. Special attention is paid to the timeline of the various technologies for their development, demonstration and commercial availability for deployment.","container-title":"Progress in Energy and Combustion Science","DOI":"10.1016/j.pecs.2006.08.002","ISSN":"03601285","issue":"2","journalAbbreviation":"Progress in Energy and Combustion Science","language":"en","license":"https://www.elsevier.com/tdm/userlicense/1.0/","page":"107-134","source":"DOI.org (Crossref)","title":"High efficiency electric power generation: The environmental role","title-short":"High efficiency electric power generation","volume":"33","author":[{"family":"Beér","given":"János M."}],"issued":{"date-parts":[["2007",4]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [17]. However, it is essential to acknowledge that this correlation likely serves as a proxy for a cluster of latent variables, including fuel quality variations, secondary air distribution efficiency, and the homogenization of the air-fuel mixture. While the R = 0.84 value suggests a strong link, it represents an aggregated effect of these underlying combustion dynamics rather than a isolated causal relationship[15].In contrast, Cyclone Temperature shows a significant negative correlation of -0.64. This inverse relationship suggests that elevated temperatures in the particle separation unit are often symptomatic of energy bypass; instead of being absorbed by the water walls in the furnace, high-grade heat is carried into the convection pass, signifying a reduction in primary heat transfer efficiency. Similarly, Load demonstrates a moderate negative correlation of -0.50, reflecting the "high-load penalty" typically observed in Circulating Fluidized Bed (CFB) systems. At increased capacities, higher flue gas velocities reduce the residence time of fuel particles, leading to elevated unburnt carbon losses and a subsequent decline in efficiency[16].Furthermore, the analysis reveals that variables such as Bed Temperature 0.06 and Oxygen Excess -0.07 have negligible linear impacts on efficiency within the observed operational range. These low scores do not imply a lack of importance; rather, they underscore the limitations of Pearson correlation in capturing the highly non-linear and coupled nature of CFB combustion. In such systems, Bed Temperature and Oxygen Excess often interact through complex, high-dimensional boundaries that simple linear regression cannot resolve. This finding confirms that while Pearson correlation is useful for identifying first-order approximations, it is insufficient for a complete diagnostic. This gap directly justifies the necessity of moving toward the advanced machine learning architectures (RF, XGBoost, and ANN) described in the previous section, as they are capable of mapping the subtle, non-linear interactions between these seemingly independent variables across the specific steady-state operational envelope used in this dataset [2].B. Model Performance ComparisonThe predictive capabilities of the three machine learning architectures were rigorously benchmarked using the testing dataset. The quantitative results, summarized in Table 1 and visualized in Figure 3, demonstrate a clear performance hierarchy.Table 1. Comparison of Model Performance on Training and Testing DataModelR² (Training)R² (Testing)RMSE (Training)RMSE (Testing)XGBoost0.99120.85150.00510.0191Random Forest0.98540.82010.00750.0210ANN (MLP)0.55210.49790.03420.0351Figure 3. Comparison of Model Performance on Training and Testing DataAs summarized in Table 1, the three machine learning architectures demonstrate distinct performance profiles when benchmarked across training and testing datasets. XGBoost emerges as the most robust architecture, yielding a testing Coefficient of Determination (R²) of 0.8515 and an RMSE of 0.0191, with corresponding training metrics of 0.9912 and 0.0051. Random Forest follows closely with a testing R² of 0.8201 (RMSE 0.0210) and training R² of 0.9854 (RMSE 0.0075). In the context of stochastic industrial telemetry, an R² exceeding 0.82 for the tree-based models is statistically significant, indicating successful capture of efficiency variance despite unmeasured latent variables such as coal calorific fluctuations, secondary air leakage, and ambient humidity shifts. To ensure generalizability, k-fold cross-validation confirmed stable performance across data folds.The superiority of XGBoost over Random Forest is rooted in its sequential optimization logic. Unlike Random Forest, which constructs trees independently via bagging, XGBoost utilizes a "functional gradient descent" approach to iteratively minimize residual errors, allowing it to converge aggressively on the complex, non-linear boundaries of the combustion process. Furthermore, the inclusion of L₁ and L₂ regularization within its objective function provides an inherent defense against the high-frequency sensor noise typical of boiler telemetry, effectively filtering transient outliers that might otherwise skew the predictive surface . This structural advantage is reflected in the performance gap observed in Table 1, where XGBoost consistently achieves higher predictive accuracy across both training and testing phases ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"317qUiUO","properties":{"unsorted":false,"formattedCitation":"[18]","plainCitation":"[18]","noteIndex":0},"citationItems":[{"id":659,"uris":["http://zotero.org/users/17768309/items/PBWQQYRS"],"itemData":{"id":659,"type":"article-journal","abstract":"Abstract\n Interfacial tension (IFT) is a key physical property that affects various processes in the oil and gas industry, such as enhanced oil recovery, multiphase flow, and emulsion stability. Accurate prediction of IFT is essential for optimizing these processes and increasing their efficiency. This article compares the performance of six machine learning models, namely Support Vector Regression (SVR), Random Forests (RF), Decision Tree (DT), Gradient Boosting (GB), Catboosting (CB), and XGBoosting (XGB), in predicting IFT between oil/gas and oil/water systems. The models are trained and tested on a dataset that contains various input parameters that influence IFT, such as gas-oil ratio, gas formation volume factor, oil density, etc. The results show that SVR and Catboost models achieve the highest accuracy for oil/gas IFT prediction, with an R-squared value of 0.99, while SVR outperforms Catboost for Oil/Water IFT prediction, with an R-squared value of 0.99. The study demonstrates the potential of machine learning models as a reliable and resilient tool for predicting IFT in the oil and gas industry. The findings of this study can help improve the understanding and optimization of IFT forecasting and facilitate the development of more efficient reservoir management strategies.","container-title":"Scientific Reports","DOI":"10.1038/s41598-024-51597-4","ISSN":"2045-2322","issue":"1","journalAbbreviation":"Sci Rep","language":"en","page":"858","source":"DOI.org (Crossref)","title":"Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems: a performance analysis","title-short":"Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems","volume":"14","author":[{"family":"Yousefmarzi","given":"Fatemeh"},{"family":"Haratian","given":"Ali"},{"family":"Mahdavi Kalatehno","given":"Javad"},{"family":"Keihani Kamal","given":"Mostafa"}],"issued":{"date-parts":[["2024",1,9]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [18].In contrast, the ANN exhibited significantly lower predictive fidelity, with a testing R² of 0.4979 and RMSE of 0.0351, trailing well behind the training performance (R² 0.5521, RMSE 0.0342). While this underperformance is partially attributable to the inductive bias of tree-based models favoring tabular data, the results indicate a deeper optimization challenge; the connectionist architecture struggled to navigate the highly noisy and non-linear search space, failing to reach a global minimum despite the use of the Adam optimizer and ReLU activation. Visual confirmation in the prediction plots (Figure 4) corroborates these metrics, demonstrating that while XGBoost maintains high fidelity, the ANN model exhibits excessive dispersion, particularly at lower efficiency points where combustion stability is compromised ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"YlkYfqjq","properties":{"unsorted":false,"formattedCitation":"[19]","plainCitation":"[19]","noteIndex":0},"citationItems":[{"id":661,"uris":["http://zotero.org/users/17768309/items/Z4EA4ZFE"],"itemData":{"id":661,"type":"article-journal","container-title":"Scientific Reports","DOI":"10.1038/s41598-025-02388-y","ISSN":"2045-2322","issue":"1","journalAbbreviation":"Sci Rep","language":"en","page":"36514","source":"DOI.org (Crossref)","title":"Machine learning predictions for enhancing engine performance and emission using aluminum oxide nano additives in castor biodiesel","volume":"15","author":[{"family":"Nachippan","given":"Murugu"},{"family":"Pathmanabhan","given":"P."},{"family":"Nagappan","given":"Beemkumar"},{"family":"Upadhye","given":"Vijay J."},{"family":"Kaliappan","given":"Nandagopal"},{"family":"Balaji","given":"V."},{"family":"Kamakshi Priya","given":"K."}],"issued":{"date-parts":[["2025",10,21]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [19].Figure 4. Comparison of Prediction plotC. Statistical Validation and Residual AnalysisAlthough the baseline metrics presented in Table 1 establish a preliminary performance hierarchy, they do not intrinsically confirm the robustness of these models nor validate the statistical significance of their performance differentials. In industrial telemetry, where predictive reliability is paramount, a deeper diagnostic assessment is required to move beyond simple point estimates. Consequently, this section establishes a rigorous statistical validation framework that encompasses bootstrapped 95% confidence interval (CI) estimation, residual distribution analysis, normality testing, and temporal autocorrelation diagnostics. By systematically analyzing the stability and consistency of the XGBoost, Random Forest, and ANN paradigms, this validation ensures that the reported accuracy reflects genuine predictive capability rather than overfitting to stochastic data noise.Table 2. Summary of Testing Performance Metrics (BLOCK_RANDOM SPLIT) ModelR² [95% CI]RMSE [95% CI]MAE [95% CI]Residuals (Mean±Std)Shapiro-Wilk (p)Durbin-WatsonXGBoost0.8515 [0.79, 0.89]0.0191 [0.016, 0.022]0.0152 [0.013, 0.018]0.0059 ± 0.0181Normal (0.725)1.28Random Forest0.8201 [0.75, 0.87]0.0210 [0.018, 0.024]0.0171 [0.014, 0.020]0.0080 ± 0.0194Normal (0.873)1.27ANN (MLP)0.4979 [0.31, 0.62]0.0351 [0.030, 0.040]0.0288 [0.025, 0.033]0.0201 ± 0.0287Normal (0.406)0.89Table 2 presents a comparative statistical summary of the three machine learning models, highlighting their predictive fidelity through bootstrapped 95% confidence intervals (CI), error dispersion, and residual diagnostic tests. The tree-based models, XGBoost and Random Forest, demonstrate superior stability and estimation precision, evidenced by their narrow, non-overlapping R² confidence intervals. Conversely, the ANN model exhibits a significantly broader R² CI of [0.3094, 0.6162], which underscores its high susceptibility to prediction variance when extrapolated to unseen operational data. This performance gap is further illustrated by the Residual Mean and Standard Deviation metrics; while XGBoost and Random Forest maintain tight error control (± 0.0181 and ± 0.0194, respectively), the ANN model shows larger residual dispersion (± 0.0287), indicating that its internal weight distribution struggles to generalize the complex, high-frequency transients inherent in the combustion process ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"fq0uDX6E","properties":{"unsorted":false,"formattedCitation":"[20]","plainCitation":"[20]","noteIndex":0},"citationItems":[{"id":663,"uris":["http://zotero.org/users/17768309/items/U3UIM9Q5"],"itemData":{"id":663,"type":"article-journal","abstract":"Abstract\n \n Accurate prediction of fuel deposition during crude oil pyrolysis is pivotal for sustaining the combustion front and ensuring the effectiveness of in-situ combustion enhanced oil recovery (ISC EOR). Employing 2071 experimental TGA datasets from 13 diverse crude oil samples extracted from the literature, this study sought to precisely model crude oil pyrolysis. A suite of robust machine learning techniques, encompassing three black-box approaches (Categorical Gradient Boosting—CatBoost, Gaussian Process Regression—GPR, Extreme Gradient Boosting—XGBoost), and a white-box approach (Genetic Programming—GP), was employed to estimate crude oil residue at varying temperature intervals during TGA runs. Notably, the XGBoost model emerged as the most accurate, boasting a mean absolute percentage error (MAPE) of 0.7796% and a determination coefficient (R\n 2\n ) of 0.9999. Subsequently, the GPR, CatBoost, and GP models demonstrated commendable performance. The GP model, while displaying slightly higher error in comparison to the black-box models, yielded acceptable results and proved suitable for swift estimation of crude oil residue during pyrolysis. Furthermore, a sensitivity analysis was conducted to reveal the varying influence of input parameters on residual crude oil during pyrolysis. Among the inputs, temperature and asphaltenes were identified as the most influential factors in the crude oil pyrolysis process. Higher temperatures and oil °API gravity were associated with a negative impact, leading to a decrease in fuel deposition. On the other hand, increased values of asphaltenes, resins, and heating rates showed a positive impact, resulting in an increase in fuel deposition. These findings underscore the importance of precise modeling for fuel deposition during crude oil pyrolysis, offering insights that can significantly benefit ISC EOR practices.","container-title":"Scientific Reports","DOI":"10.1038/s41598-023-49349-x","ISSN":"2045-2322","issue":"1","journalAbbreviation":"Sci Rep","language":"en","page":"22649","source":"DOI.org (Crossref)","title":"Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques","volume":"13","author":[{"family":"Hadavimoghaddam","given":"Fahimeh"},{"family":"Rozhenko","given":"Alexei"},{"family":"Mohammadi","given":"Mohammad-Reza"},{"family":"Mostajeran Gortani","given":"Masoud"},{"family":"Pourafshary","given":"Peyman"},{"family":"Hemmati-Sarapardeh","given":"Abdolhossein"}],"issued":{"date-parts":[["2023",12,19]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [20].The robustness of these models is further validated by the diagnostic statistics provided in the latter columns of Table 2. The Shapiro-Wilk test results (p > 0.05) confirm that the prediction errors for all architectures are normally distributed, suggesting that the models have successfully captured the underlying deterministic patterns, leaving only white noise in the residuals. However, the Durbin-Watson statistics reveal significant structural differences in how each model handles temporal dependencies. XGBoost and Random Forest exhibit Durbin-Watson values (1.28 and 1.27) that indicate a manageable level of serial correlation, consistent with the thermal inertia of large-scale boiler systems. In contrast, the ANN model’s markedly lower Durbin-Watson statistic (0.89) points to pronounced residual autocorrelation, further confirming that the connectionist architecture is structurally less effective at resolving the non-linear, time-dependent boundaries of the combustion environment compared to the tree-based ensemble frameworks. Figure 5. Residual Distribution Analysis: (A) Density and (B) Deviation Boxplots Figure 5 provides a visual diagnostic of the error distribution for each predictive model, confirming the performance trends observed in the quantitative metrics. The residual density plots (Figure 5A) demonstrate that both XGBoost and Random Forest exhibit sharp, concentrated peaks centered near zero, indicating that the majority of their prediction errors are minimal and unbiased. This tight alignment suggests that these tree-based architectures effectively captured the deterministic signal within the training data. Conversely, the ANN model displays a significantly broader, flatter distribution, signaling higher prediction variance and a greater susceptibility to larger residual errors when generalizing across the diverse operational conditions of the boiler.Complementing the density analysis, the residual deviation boxplots (Figure 5B) further illustrate the dispersion and reliability of each model. The tree-based ensembles maintain relatively narrow interquartile ranges with minimal outlier influence, reinforcing their robustness in handling the stochastic, high-frequency fluctuations inherent in industrial boiler telemetry. In contrast, the ANN model exhibits a wider boxplot and a higher density of outliers, confirming that the connectionist architecture struggled to resolve the complex, non-linear boundaries of the combustion environment as effectively as the ensemble frameworks. This visual confirmation underscores why the tree-based models, particularly XGBoost, provide a more stable and reliable foundation for real-time operational optimization ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"t9SsmwOh","properties":{"unsorted":false,"formattedCitation":"[21]","plainCitation":"[21]","noteIndex":0},"citationItems":[{"id":665,"uris":["http://zotero.org/users/17768309/items/4T7BKFXY"],"itemData":{"id":665,"type":"article-journal","abstract":"This study evaluates models for predicting volatile fatty acid (VFA) concentrations in sludge processing, ranging from classical statistical methods (Gaussian and Surge) to diverse machine learning algorithms (MLAs) such as Decision Tree, XGBoost, CatBoost, LightGBM, Multiple linear regression (MLR), Support vector regression (SVR), AdaBoost, and GradientBoosting. Anaerobic bio-methane potential tests were carried out using domestic wastewater treatment primary and secondary sludge. The tests were monitored over 40 days for variations in pH and VFA concentrations under different experimental conditions. The data observed was compared to predictions from the Gaussian and Surge models, and the MLAs. Based on correlation analysis using basic statistics and regression, the Gaussian model appears to be a consistent performer, with high R2 values and low RMSE, favoring precision in forecasting VFA concentrations. The Surge model, on the other hand, albeit having a high R2, has high prediction errors, especially in dynamic VFA concentration settings. Among the MLAs, Decision Tree and XGBoost excel at predicting complicated patterns, albeit with overfitting issues. This study provides insights underlining the need for context-specific considerations when selecting models for accurate VFA forecasts. Real-time data monitoring and collaborative data sharing are required to improve the reliability of VFA prediction models in AD processes, opening the way for breakthroughs in environmental sustainability and bioprocessing applications.","container-title":"Environmental Science and Pollution Research","DOI":"10.1007/s11356-024-33281-2","ISSN":"1614-7499","issue":"49","journalAbbreviation":"Environ Sci Pollut Res","language":"en","page":"28239-28252","source":"DOI.org (Crossref)","title":"Evaluation of traditional and machine learning approaches for modeling volatile fatty acid concentrations in anaerobic digestion of sludge: potential and challenges","title-short":"Evaluation of traditional and machine learning approaches for modeling volatile fatty acid concentrations in anaerobic digestion of sludge","volume":"32","author":[{"family":"Abubakar","given":"Umar Alfa"},{"family":"Lemar","given":"Gul Sanga"},{"family":"Bello","given":"Al-Amin Danladi"},{"family":"Ishaq","given":"Aliyu"},{"family":"Dandajeh","given":"Aliyu Adamu"},{"family":"Jagun","given":"Zainab Toyin"},{"family":"Houmsi","given":"Mohamad Rajab"}],"issued":{"date-parts":[["2024",4,23]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [21].Figure 6. Sequence Efficiency Trend: Actual vs Model PredictionFigure 6 illustrates the chronological performance of the predictive models by comparing the actual boiler efficiency against the predictions generated by the XGBoost, Random Forest, and ANN architectures. The visualization reveals that the tree-based ensembles, particularly XGBoost, demonstrate a high degree of fidelity in tracking the complex, time-dependent fluctuations of the actual process data. By effectively capturing the rapid peaks and valleys characteristic of industrial boiler operations, these models exhibit a robust ability to resolve the non-linear temporal dependencies inherent in the combustion cycle. In contrast, the ANN model displays a noticeable lag and a flatter response, struggling to align with the extreme transient values, which corroborates the higher residual dispersion observed in earlier statistical analyses.This temporal tracking capability is critical for practical industrial application, as it confirms that the XGBoost framework is capable of providing reliable, real-time efficiency estimation under shifting operational conditions. The visual proximity between the actual efficiency curve and the XGBoost prediction line suggests that the model successfully integrates stochastic process variables into a coherent optimization signal. This validation reinforces the potential for deploying XGBoost as the core predictive engine in automated control loops, where the ability to anticipate efficiency drops in response to load or air-fuel fluctuations is essential for proactive operational management ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"6H0fMZtw","properties":{"unsorted":false,"formattedCitation":"[19]","plainCitation":"[19]","noteIndex":0},"citationItems":[{"id":661,"uris":["http://zotero.org/users/17768309/items/Z4EA4ZFE"],"itemData":{"id":661,"type":"article-journal","container-title":"Scientific Reports","DOI":"10.1038/s41598-025-02388-y","ISSN":"2045-2322","issue":"1","journalAbbreviation":"Sci Rep","language":"en","page":"36514","source":"DOI.org (Crossref)","title":"Machine learning predictions for enhancing engine performance and emission using aluminum oxide nano additives in castor biodiesel","volume":"15","author":[{"family":"Nachippan","given":"Murugu"},{"family":"Pathmanabhan","given":"P."},{"family":"Nagappan","given":"Beemkumar"},{"family":"Upadhye","given":"Vijay J."},{"family":"Kaliappan","given":"Nandagopal"},{"family":"Balaji","given":"V."},{"family":"Kamakshi Priya","given":"K."}],"issued":{"date-parts":[["2025",10,21]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [19].Table 3. Pairwise Statistical Significance Matrix (Wilcoxon Signed-Rank Test - Testing Data)Pairwise Model ComparisonWilcoxon Test Statisticp-valueSignificance Decision (Testing Data)XGBoost vs Random Forest1123.00.0376XGBoost SuperiorXGBoost vs ANN (MLP)259.0<0.0001XGBoost SuperiorRandom Forest vs ANN (MLP)301.0<0.0001Random Forest SuperiorTable 3 presents the results of the Wilcoxon Signed-Rank Test, a non-parametric assessment used to determine if the performance differences between the three machine learning models are statistically significant rather than mere artifacts of random variation. By evaluating the pairwise differences in absolute prediction errors across the testing dataset, the test confirms a decisive performance hierarchy. The low p-values obtained, particularly when comparing the ensemble models (XGBoost and Random Forest) against the ANN, provide strong statistical evidence that the hierarchical superiority of the tree-based architectures is robust and replicable across the operational testing samples.The results definitively classify XGBoost as the most statistically reliable predictive framework among those tested, consistently outperforming both Random Forest (p=0.0376) and the ANN (p<0.0001). While Random Forest also demonstrates significant superiority over the ANN, the marginal improvement shown by XGBoost over Random Forest underscores the efficacy of gradient-based sequential optimization in refining predictive accuracy within complex, noisy telemetry environments. Consequently, the statistical consensus provided by this matrix offers a rigorous justification for prioritizing XGBoost in real-time control applications where both predictive accuracy and error stability are critical operational requirements ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"0LpfE1I1","properties":{"unsorted":false,"formattedCitation":"[22]","plainCitation":"[22]","noteIndex":0},"citationItems":[{"id":667,"uris":["http://zotero.org/users/17768309/items/ZP64W9S8"],"itemData":{"id":667,"type":"article-journal","abstract":"Taiwan has introduced specific policies, such as the 2050 net-zero goal and the 2024 carbon fee, to support global climate action and encourage carbon reduction efforts. Although previous research has developed various models to predict greenhouse gas emissions, few studies have compared statistical models, machine learning, and deep learning techniques when applied to small sample datasets, particularly in the case of Taiwan, which typically has limited sample sizes. Accordingly, this study uses Taiwan’s small dataset to train predictive models, comparing statistical, machine learning, and deep learning approaches. This study significantly improved the models’ learning efficiency and predictive accuracy despite limited data resources by utilizing multiple feature selection techniques to identify highly predictive features and integrating extensive data preprocessing and data augmentation techniques. Results indicate that extreme gradient boosting outperforms other models, particularly under Gaussian noise conditions, achieving higher accuracy in mean squared error and mean absolute percentage error metrics. Although long short-term memory proves suitable for time series, it exhibits greater sensitivity to noise. Conversely, the non-linear Grey Bernoulli model (NGBM (1, N)) exhibits robust resistance to noise, although it demonstrates relatively lower predictive accuracy under standard conditions. This model aids Taiwan in tracking emission trends and informing climate policies, contributing valuable data insights for future environmental strategies. Moreover, this model demonstrates significant potential for application in environmental monitoring and climate policy formulation.","container-title":"Stochastic Environmental Research and Risk Assessment","DOI":"10.1007/s00477-025-03099-6","ISSN":"1436-3240, 1436-3259","issue":"12","journalAbbreviation":"Stoch Environ Res Risk Assess","language":"en","page":"5765-5787","source":"DOI.org (Crossref)","title":"Apply data science and feature selection techniques to predict carbon dioxide emissions in Taiwan","volume":"39","author":[{"family":"Tseng","given":"Shih-Hsien"},{"family":"Feng","given":"Pei Han"},{"family":"Duong","given":"Thi Ha Trang"}],"issued":{"date-parts":[["2025",12]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [22].D. Physical Interpretation of Feature Importance and Process DynamicsTo decode the underlying predictive logic and evaluate feature interdependencies, importance rankings were extracted and visualized in Figure 7. A notable cross-algorithmic consensus was observed regarding the primacy of the Air-Fuel Ratio (AFR), which secured the top ranking across RF (Gini-based), XGBoost (Gain-based), and ANN (Permutation-based) frameworks. This convergence is statistically significant; while each metric possesses distinct mathematical biases, the dominance of AFR across diverse methodologies suggests it is the most robust predictive correlate within the operational space, rather than an artifact of algorithm-specific bias ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"YeE2sAbw","properties":{"unsorted":false,"formattedCitation":"[23]","plainCitation":"[23]","noteIndex":0},"citationItems":[{"id":245,"uris":["http://zotero.org/users/17768309/items/YBDZRPAI"],"itemData":{"id":245,"type":"article-journal","abstract":"The paper reviews the results of theoretical and experimental study on development and application of the method aimed at excess air optimization for utility/industrial steam boilers fired with fossil fuels. A concept of the cost-based excess air optimization is presented. Various application options (experimental/theoretical; firing different fossil fuels in utility/industrial boilers; pursuing distinct goals, etc.) are discussed. Three case studies including utility boilers firing lignite and fuel oil as well as an industrial boiler installed at a refinery are reviewed. Limitations and constraints in method application are discussed as well. As shown in this work, switching the combustion excess air to the optimized or “compromise” values ensures noticeable reduction of the total operational costs, associated with the fuel consumption (“internal” costs) and environmental impact (“external” costs), for the particular boiler.","container-title":"Renewable and Sustainable Energy Reviews","DOI":"10.1016/j.rser.2004.05.006","ISSN":"13640321","issue":"5","journalAbbreviation":"Renewable and Sustainable Energy Reviews","language":"en","license":"https://www.elsevier.com/tdm/userlicense/1.0/","page":"474-498","source":"DOI.org (Crossref)","title":"Applications of a cost-based method of excess air optimization for the improvement of thermal efficiency and environmental performance of steam boilers","volume":"9","author":[{"family":"Kuprianov","given":"Vladimir I."}],"issued":{"date-parts":[["2005",10]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [23].Physically, the significance of the AFR aligns with stoichiometric combustion principles, where efficiency is maximized by navigating a "Goldilocks zone" between incomplete combustion and excessive sensible heat loss. While secondary feature rankings, such as Boiler Load or Cyclone Temperature, reveal structural sensitivities unique to each architecture, the overwhelming cross-model consensus on AFR provides a clear mandate for industrial practitioners. For field operators, these findings imply that high-frequency automatic synchronization of air and fuel mass flows remains the most effective lever for real-time optimization. By prioritizing AFR adjustments over auxiliary parameters, operators can achieve more stable thermal performance and proactively mitigate efficiency drops during transient load shifts, thereby reducing both fuel consumption and carbon intensity ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"Bp0UEaCx","properties":{"unsorted":false,"formattedCitation":"[24], [25]","plainCitation":"[24], [25]","noteIndex":0},"citationItems":[{"id":158,"uris":["http://zotero.org/users/17768309/items/B3A8QZVG"],"itemData":{"id":158,"type":"article-journal","abstract":"Optimization of excess air coefficient is extremely significant for boiler operating performance. Currently theoretical modeling of optimal excess air coefficient is still an intractable technical issue for pulverized coal boiler, limits further efficient utilization of coal. In this work, a mechanistic optimization model considering influence mechanisms was developed to model optimal excess air coefficient under partial boiler loads. To investigate efficiency of analyzed steam boiler, indirect method and individual boiler losses computation was applied. The accuracy of model results was verified at three different partial loads. Subsequently, modeling approach is used for a case study on 660 MW boiler. Influence mechanism between excess air coefficient and boiler operating parameters was graphically analyzed and meticulously discussed. Optimum energy efficiency and exergy efficiency of boiler were determined and given accurately.","container-title":"Energy","DOI":"10.1016/j.energy.2022.125128","ISSN":"03605442","journalAbbreviation":"Energy","language":"en","page":"125128","source":"DOI.org (Crossref)","title":"Mechanism modeling of optimal excess air coefficient for operating in coal fired boiler","volume":"261","author":[{"family":"Wang","given":"Yanhong"},{"family":"Li","given":"Xiaoyu"},{"family":"Mao","given":"Tianqin"},{"family":"Hu","given":"Pengfei"},{"family":"Li","given":"Xingcan"},{"literal":"GuanWang"}],"issued":{"date-parts":[["2022",12]]}}},{"id":168,"uris":["http://zotero.org/users/17768309/items/P52W3F7X"],"itemData":{"id":168,"type":"article-journal","abstract":"The effect of the pressure drop across the furnace on heat transfer inside large scale CFB boilers was investigated with a 1-D CFB combustion model developed at Tsinghua University. The results from the 1-D model agree well with the field test data carried out in a 135 MWe CFB boiler. It was found that, at a certain load and with fixed size distribution of the coal particles fed to the boiler, a decrease in furnace pressure drop yields an increase in furnace temperature and reduced heat transfer coefficient. According to the heat balance, when the pressure drop decreases by 1 kPa, the heat transfer coefficient is expected to decrease approximately by 2%. The increase in temperature with decreasing pressure drop is less steep, when the boiler load increases or smaller sized coal particles are fed to the boiler. In both cases, more fine particles are carried to the upper parts of the furnace which results in a more uniform solid volume fraction distribution along the furnace height. The influence of the pressure drop on heat transfer by changing the solid suspension density weakens. Operation of the bed at relatively low pressure drop by reducing the size of coal particles will ensure adequate heat transfer.","container-title":"Powder Technology","DOI":"10.1016/j.powtec.2015.06.019","ISSN":"00325910","journalAbbreviation":"Powder Technology","language":"en","page":"19-24","source":"DOI.org (Crossref)","title":"Effect of furnace pressure drop on heat transfer in a 135MW CFB boiler","volume":"284","author":[{"family":"Liu","given":"Xuemin"},{"family":"Zhang","given":"Man"},{"family":"Lu","given":"Junfu"},{"family":"Yang","given":"Hairui"}],"issued":{"date-parts":[["2015",11]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [24], [25].Figure 7. Feature importance rankingsD.1. The Dominance of Air-Fuel Ratio (AFR) on Combustion StoichiometryAcross all three modeling paradigms, XGBoost, Random Forest, and the ANN, the Air-Fuel Ratio (AFR) consistently emerged as the primary determinant of boiler thermal efficiency. This statistical convergence aligns with fundamental combustion stoichiometry. In utility-scale boilers, thermal efficiency is governed by the minimization of structural heat losses, particularly through the careful management of sensible heat loss from dry flue gas, heat loss due to incomplete gaseous combustion, and unburnt carbon loss.The AFR directly balances the non-linear trade-off between these efficiency-degrading factors. If the AFR is set too low (fuel-rich conditions), the oxygen concentration in the combustion zone is insufficient to fully oxidize the organic constituents of the coal, leading to increased carbon monoxide formation and higher unburnt carbon content in the ash. Conversely, operating at an excessively high AFR (highly lean conditions) introduces a vast excess of nitrogen and unreacted oxygen into the furnace. Since this excess air does not contribute to combustion, it acts as a thermal sink, absorbing combustion energy and carrying it out through the stack, which significantly increases the sensible heat loss from dry flue gas. The strong positive correlation coefficient (0.84) within the observed operational range indicates that the physical system was operating within a sub-stoichiometric or highly transient excess air regime. Consequently, incremental increases in the AFR shifted the combustion state closer to the optimal stoichiometric 'Goldilocks zone,' minimizing incomplete combustion and unburnt carbon losses without triggering excessive dry gas penalties ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"vR6qrLPr","properties":{"unsorted":false,"formattedCitation":"[26]","plainCitation":"[26]","noteIndex":0},"citationItems":[{"id":669,"uris":["http://zotero.org/users/17768309/items/URAHE3Q6"],"itemData":{"id":669,"type":"article-journal","abstract":"Nowadays, the significant efforts are focussed on reduction of gaseous emissions generated by the transport, what is resulting in development of electromobility. • CO2 neutrality in the transport sector cannot be reached by the electromobility alone. A potential solution is a combination of the sustainable fuels and advanced low-temperature combustion technologies. • These two systems, which are presented in the article, were the subject of experimental research and development.","container-title":"MRS Energy & Sustainability","DOI":"10.1557/s43581-023-00079-7","ISSN":"2329-2237","issue":"1","journalAbbreviation":"MRS Energy & Sustainability","language":"en","page":"123-135","source":"DOI.org (Crossref)","title":"Advanced hybrid combustion systems as a part of efforts to achieve carbon neutrality of the vehicles","volume":"11","author":[{"family":"Puškár","given":"Michal"},{"family":"Kopas","given":"Melichar"}],"issued":{"date-parts":[["2024",1,8]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [26].D.2. Non-linear Interaction Between Bed Temperature and Excess OxygenAlthough Pearson correlation coefficients suggested that Bed Temperature (0.06) and Excess Oxygen (-0.07) had minimal linear impacts on thermal efficiency, their high feature importance in tree-based models highlights a complex, non-linear physical interaction. In CFB boilers, Bed Temperature acts as the primary driver for reaction kinetics, while Excess Oxygen governs the mass-transport concentration gradient. At low bed temperatures (T_bed < 750°C), the combustion process is kinetically limited; consequently, elevating excess oxygen provides negligible efficiency gains as the reaction rate remains restricted by temperature. Conversely, at higher bed temperatures (>850°C), char combustion shifts into a diffusion-limited regime where energy release becomes highly sensitive to oxygen availability, making precise air control critical for maintaining optimal combustion efficiency ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"mwdZA1YL","properties":{"unsorted":false,"formattedCitation":"[27], [28]","plainCitation":"[27], [28]","noteIndex":0},"citationItems":[{"id":671,"uris":["http://zotero.org/users/17768309/items/N4NUPEI6"],"itemData":{"id":671,"type":"article-journal","abstract":"The purpose of this work is the kinetic study of biomass char combustion in a low temperature fluidized bed reactor. This char was obtained from fast pyrolysis of beech stick in an annex batch fluidized bed reactor at 923 K and atmospheric pressure. Operating conditions of the combustion were thoroughly characterized so that the reaction takes place in isothermal conditions and a constant oxygen partial pressure. The kinetic study was performed for temperatures up to 643 K, oxygen partial pressures ranging from 5065 to 21273 Pa and cylindrical char particles (D = 4 mm and L = 9 mm). The Volumetric Model was found to be in very good agreement with experimental data. Values of activation energy and reaction order with respect to oxygen are respectively equal to 144 kJ/mol and 0.59. The reaction scheme during char combustion showed that char first reacts with oxygen to form CO which is further oxidized either at the particle surface or in the gas phase to produce CO2. Besides, it was found that the char reactivity in combustion is higher in a fluidized bed reactor compared to TGA. This was explained by diffusional effects of oxygen and CO oxidation within the crucible in the TGA.","container-title":"Chemical Engineering Journal","DOI":"10.1016/j.cej.2017.08.063","ISSN":"13858947","journalAbbreviation":"Chemical Engineering Journal","language":"en","page":"265-277","source":"DOI.org (Crossref)","title":"Kinetic study of biomass char combustion in a low temperature fluidized bed reactor","volume":"331","author":[{"family":"Morin","given":"Mathieu"},{"family":"Pécate","given":"Sébastien"},{"family":"Hémati","given":"Mehrdji"}],"issued":{"date-parts":[["2018",1]]}}},{"id":673,"uris":["http://zotero.org/users/17768309/items/VNR5B8WS"],"itemData":{"id":673,"type":"article-journal","abstract":"Coal remains an important energy source. Nonetheless, pollutant emissions – in particular Oxides of Nitrogen (NOx) – as a result of the combustion process in a boiler, are subject to strict legislation due to their damaging effects on the environment. Optimising combustion parameters to achieve a lower NOx emission often results in combustion inefficiency measured with the proportion of unburned coal content (UBC). Consequently there is a range of solutions that trade-off efficiency for emissions. Generally, an analytical model for NOx emission or UBC is unavailable, and therefore data-driven models are used to optimise this multi-objective problem. We introduce the use of Gaussian process models to capture the uncertainties in NOx and UBC predictions arising from measurement error and data scarcity. A novel evolutionary multi-objective search algorithm is used to discover the probabilistic trade-off front between NOx and UBC, and we describe a new procedure for selecting parameters yielding the desired performance. We discuss the variation of operating parameters along the trade-off front. We give a novel algorithm for discovering the optimal trade-off for all load demands simultaneously. The methods are demonstrated on data collected from a boiler in Jianbi power plant, China, and we show that a wide range of solutions trading-off NOx and efficiency may be efficiently located.","container-title":"Applied Energy","DOI":"10.1016/j.apenergy.2018.07.101","ISSN":"03062619","journalAbbreviation":"Applied Energy","language":"en","page":"446-458","source":"DOI.org (Crossref)","title":"Data-driven multi-objective optimisation of coal-fired boiler combustion systems","volume":"229","author":[{"family":"Rahat","given":"Alma A.M."},{"family":"Wang","given":"Chunlin"},{"family":"Everson","given":"Richard M."},{"family":"Fieldsend","given":"Jonathan E."}],"issued":{"date-parts":[["2018",11]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [27], [28].However, increasing excess oxygen too aggressively at these elevated temperatures risks diluting the bed, which lowers localized temperatures and accelerates thermal NOx emissions. Furthermore, extreme bed temperatures can lead to coal ash softening, causing bed agglomeration and fluidization failure that degrades convective heat transfer. While traditional linear correlations fail to resolve these localized, conditional constraints, the tree-based architectures of XGBoost and Random Forest successfully map these "if-then" thermodynamic regimes. By utilizing hierarchical split thresholds, these models accurately capture how the optimal impact of excess oxygen is strictly contingent upon the prevailing bed temperature boundaries, providing a more reliable foundation for operational control.D.3. Impact of Load Fluctuations on Multiphase Heat TransferBoiler Load was ranked as the second most critical variable by XGBoost, capturing the intricate relationship between thermal demand and multiphase heat transfer dynamics. CFB boiler thermal efficiency exhibits a distinct sensitivity to load fluctuations due to changes in fluidization velocity and particle suspension density. The overall heat transfer coefficient between the fluidized solid-gas suspension and the steam-carrying water walls inside the furnace is highly dynamic and load-dependent, heavily influenced by the solid circulation rate and local suspension density. As boiler load increases, primary air flow is ramped up to sustain fluidization and combustion rates, elevating the fluidization velocity which increases the entrainment of solid bed material.While a higher solid concentration along the water walls enhances the bed-to-wall heat transfer coefficient up to a critical point, excessive fluidization velocities shorten the residence time of coal particles within the combustor. This reduced contact period limits complete char burnout, leading to high-frequency char carry-over into the cyclone and convection pass, which elevates unburnt carbon losses. Furthermore, under high-load conditions, the exit flue gas temperature rises due to the higher mass flow of hot gases bypassing the primary evaporative zones, elevating dry gas sensible heat loss. At low loads, fluidization velocities drop, causing localized dead zones and decreasing the heat transfer coefficient. By capturing these dynamic load changes, the tree-based ensemble models successfully navigate the operational penalties of both low-load thermal degradation and high-load residence-time constraints, providing a highly accurate, load-compensated map of boiler thermal efficiency ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"sVfkmaRB","properties":{"unsorted":false,"formattedCitation":"[29]","plainCitation":"[29]","noteIndex":0},"citationItems":[{"id":675,"uris":["http://zotero.org/users/17768309/items/W9JYI5HJ"],"itemData":{"id":675,"type":"article-journal","abstract":"The global energy demand is still primarily reliant on fossil-fueled thermal power plants. With the growing share of renewables, these plants must frequently adjust their loads. Maintaining, or ideally increasing operational efficiency under these conditions is crucial. Increasing the efficiency of such systems directly reduces associated greenhouse gas emissions, but it requires sophisticated models and monitoring systems. Data-driven models have proven their value here, as they can be used for monitoring, operational state estimation, and prediction. However, they are also sensitive to (1) the training approach, (2) the selected feature set, (3) and the algorithm used. Using operational data, we comprehensively investigate these model parameters for performance prediction in a thermal plant for process steam generation. Specifically, four regression algorithms are evaluated for the prediction of the highly fluctuating live steam flow with two training approaches and three feature subsets of the raw dataset. Furthermore, manual and automatic clustering methods are used to identify different states of operation regarding the fuel amounts used in the combustion chamber. Our results show that the live steam flow is predicted with excellent accuracy for a testing period of one month (R2=0.994 and NMAE=0.55%) when using a dynamic training approach and a comprehensive feature set comprised of 48 features representing the combustion process. It is also seen that the statically trained model predicts various load changes with strong accuracy and that the accuracy of the dynamically trained model can be approached by incorporating the cluster information into the static model. These models reflect the plant’s physical intricacies under varying loads, where deviations from the predicted live steam flow indicate unwanted longterm drifts. They can be directly implemented to help operators detect inefficiencies and optimize plant performance.","container-title":"Carbon Neutrality","DOI":"10.1007/s43979-024-00108-5","ISSN":"2788-8614, 2731-3948","issue":"1","journalAbbreviation":"Carb Neutrality","language":"en","page":"32","source":"DOI.org (Crossref)","title":"A data-driven regression model for predicting thermal plant performance under load fluctuations","volume":"3","author":[{"family":"Prokhorskii","given":"Gleb"},{"family":"Rudra","given":"Souman"},{"family":"Preißinger","given":"Markus"},{"family":"Eder","given":"Elias"}],"issued":{"date-parts":[["2024",12]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [29].ConclusionThis study successfully benchmarked Random Forest, XGBoost, and ANN models for utility-scale CFB boiler efficiency prediction. XGBoost emerged as the superior architecture, achieving an R² of 0.8515 and RMSE of 0.0191, demonstrating robust performance in capturing non-linear industrial telemetry despite high-frequency sensor noise.A key research contribution is the cross-algorithmic consensus identifying the Air-Fuel Ratio (AFR) as the primary determinant of thermal efficiency. This finding provides empirical validation of stoichiometric synchronization, optimizing the balance between air and fuel flows, as the paramount strategy for minimizing energy losses in industrial thermal systems.Practically, these results offer a validated data-driven methodology for real-time boiler optimization and operational control. 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I., et al., Machine Learning-Based Modeling of Boiler Efficiency: Impact Analysis of Operational Variables Using Random Forest, XGBoost, and ANN, R.E.M. (Rekayasa Energi Manufaktur) Jurnal, vol. 11, no. 1, pp. 157-168, 2026. How to cite: -127008890000 PAGE \* MERGEFORMAT 2R.E.M. (Rekayasa Energi Manufaktur) Jurnal | Vol.11 No.1/2026ISSN online (2528-3723)http://doi.org/10.21070/rem.v11i1.1833-127008890000Copyright © 2026 Author [s]. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. 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