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Considering the improved interpretable performance of Kolmogorov–Arnold Networks (KAN) algorithm compared to multi-layer perceptron (MLP) algorithm, a fundamental research question arises on how modifying the loss function of KAN affects its modelling performance for energy systems, particularly industrial-scale thermal power plants. In this regard, first, we modify the loss function of both KAN and MLP algorithms and embed Pearson Correlation Coefficient (PCC). Second, the algorithmic configurations built on PCC, i.e., KAN_PCC and MLP_PCC as well as original architecture of KAN and MLP are deployed for modelling and optimisation analyses for two case studies of energy systems: (i) energy efficiency cooling and energy efficiency heating of buildings, and (ii) power generation operation of 660 MW capacity thermal power plant. The analysis reveals superior modelling performance of KAN and KAN_PCC algorithms than those of MLP and MLP_PCC for the two case studies. KAN models are embedded in the optimisation framework of nonlinear programming and feasible optimal solutions are estimated, maximising thermal efficiency up to 42.17 ± 0.88 % and minimising turbine heat rate to 7487 ± 129 kJ/kWh corresponding to power generation of 500 ± 14 MW for the thermal power plant. It is anticipated that the scientific, research and industrial community may benefit from the fundamental insights presented in this paper for the ML algorithm selection and carrying out model-based optimisation analysis for the performance enhancement of energy systems.

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

Abstract Adsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods.

Abstract Copyrolysis, being an active area of research due to its synergistic impact in utilizing diverse fuel resources, including waste materials, like, peach stone (PS), has been the focal point for this study. PS, produced in vast quantities annually and typically intended for landscaping or insulation purposes, is being studied in combination with low‐grade bituminous coal for energy utilization focusing on thermokinetics and synergistic aspects. Coal‐peach stone (C‐PS) blends were formulated at different ratios and subjected to comprehensive characterization techniques, including ultimate analysis (CHN‐S), gross calorific value (GCV), Fourier transform infrared spectroscopy, and thermogravimetric analyzer (TGA). The ultimate analysis revealed an enhancement in carbon and hydrogen content from 45.38% to 68.08% and from 3.89% to 6.96%, respectively. Additionally, a reduction in sulfur and nitrogen content from 0.54% to 0.11% and from 1.16% to 0.42%, respectively, was observed with an increase in the ratio of PS in the C‐PS blends. The GCV of C‐PS blends ranged from 20.75 to 26.01 MJ kg−1. The pyrolysis conditions simulated in TGA are pivotal for evaluating thermokinetics and synergistic effects. The 60C:40PS blend shows a positive synergy index (SI) value of 0.0203% concerning total mass loss (MLT) indicating a favorable condition for bio‐oil generation. Coats–Redfern model‐fitting method reveals that the activation energy (Ea) of C‐PS blends increases in Section II with the addition of PS, and conversely, it decreases in Section III. The Ea for 100PS and 100C was 106.76 and 45.85 kJ mol−1 through (D3) and (F1), respectively, which was improved through the optimal blend 60C:40PS with an Ea of 94.56 and 27.58 kJ mol−1 through (D3) and (F2), respectively. The values obtained from linear regression prove that the kinetic models are effective while the thermodynamic analysis indicates that the pyrolytic behavior of C‐PS blends is characterized as endothermic, nonspontaneous, and capable of achieving thermodynamic equilibrium more rapidly.

Abstract Accurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning‐based models for predicting the LHV of various fuel blends. Coal of two different ranks and two types of biomass is used in this study. One was the South African imported bituminous coal, and the other was lignite thar coal extracted from the Thar Coal Block‐2 mine by Sind Engro Coal Mining Company, Pakistan. Two types of biomass, that is, sugarcane bagasse and rice husk, were obtained locally from a sugar mill and rice mill located in the vicinity of Sahiwal, Punjab. Bituminous coal mixture with other coal types and both types of biomass are used with 10%, 20%, 30%, 40%, and 50% weight fractions, respectively. The calculation and model development procedure resulted in 91 different AI‐based models. The best is the Ridge Regressor, a high‐level end‐to‐end approach for fitting the model. The model can predict the LHV of the bituminous coal with lignite and biomass under a vast share of fuel blends.

Abstract Clean water and renewable energy sources are becoming increasingly important in the current era, as well as a future challenge, and one of the potential solutions is photocatalysis. In the current study, a simple one‐step hydrothermal technique is employed to fabricate the pure and La‐doped CuO (0%, 1%, 3%, 5%, and 7%) photocatalysts. The influence of varying La concentration on structure, morphology, and optical properties is determined by scanning electron microscope (SEM), X‐ray diffraction (XRD), ultraviolet (UV)–visible spectroscopy, and photoluminescence. SEM showed that synthesized nanostructures are irregularly spherical and transform into needle‐like nanostructures on increasing La concentration. XRD revealed the monoclinic phase with a crystallite size of 15–23 nm. The UV–visible spectrum exhibited a decrease in the band gap of La‐doped CuO needle‐like nanostructures from UV to visible light. The composition and purity of synthesized nanostructures are evaluated via the energy‐dispersive X‐ray spectrum which revealed that needle‐like nanostructures are pure without any impurity traces. The synthesized nanostructures were used as a photocatalyst against methylene blue dye to examine their photocatalytic activity. The synthesized CuO‐3La photocatalyst exhibited excellent photocatalytic performance of dye degradation and hydrogen production 95.3 μmol h−1 g−1 with more than 97% cyclic stability. Therefore, the synthesized La‐doped CuO nanostructures are potential candidates for photocatalytic water splitting and hydrogen evolution.