首页|Incorporating machine learning for thermal engines modeling in industrial waste heat recovery
Incorporating machine learning for thermal engines modeling in industrial waste heat recovery
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
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.
Francisco Javier Lopez-Flores、Eusiel Rubio-Castro、Jose Maria Ponce-Ortega
展开 >
Chemical Engineering Department, Universidad Michoacana de San Nicolas de Hidalgo, Ciudad Universitaria, Francisco J. Mujica S/N, Edificio V1, 58060 Morelia, Michoacan, Mexico
Chemical and Biological Sciences Department, Universidad Autonoma de Sinaloa, Au. de las Americas S/N, Culiacan, Sinaloa 80010, Mexico
NSTL
