Incorporating machine learning for thermal engines modeling in industrial waste heat recovery

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This paper proposes a methodology for efficient, accurate, and sustainable waste heat recovery, where the energy needs of an industrial plant allow the installation of thermal engines. In this methodology, pinch analysis, mathematical equations, machine learning models, and an optimization algorithm are combined for the first time. To satisfy the industrial requirements, the selected thermal engines are the steam Rankine cycle, the organic Rankine cycle, and the absorption refrigeration cycle, which are modeled by using multilayer perceptron neural networks. The Non-dominated Sorting Genetic Algorithm-III is used to solve the optimization problem. Moreover, multi-objective trade-offs between economic, environmental, and social aspects are studied. A case study is presented to show the applicability of the proposed methodology. The multilayer perceptron models of the thermal engines were created with high accuracy. Furthermore, the results show that with this methodology it is possible to find the optimal operating conditions of thermal engines and solutions that allow the use of different fuels to fulfill the three objective functions.