Introducing machine learning and hybrid algorithm for prediction and optimization of multistage centrifugal pump in an ORC system
Introducing machine learning and hybrid algorithm for prediction and optimization of multistage centrifugal pump in an ORC system
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引入机器学习和混合算法来预测和优化 ORC 系统中的多级离心泵
DOI：
10.1016/j.energy.2021.120007
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发表时间：
2021-05
期刊：
影响因子：
9
通讯作者：
Song Gege
中科院分区：
文献类型：
--
作者：
Ping Xu;Yang Fubin;Zhang Hongguang;Zhang Jian;Zhang Wujie;Song Gege
工质泵的等熵效率对有机朗肯循环（ORC）系统的整体性能有显着影响。基于机器学习，本文提出了一种实验数据驱动的等熵效率预测模型。同时，采用S折交叉验证算法和平滑因子循环筛选技术，提高模型的预测能力。比较优化模型和未优化模型的预测精度。分析了几个操作参数对等熵效率的影响。此外，通过智能算法确定运行参数的边界值。将遗传算法（GA）和粒子群优化（PSO）结合成GA-PSO混合算法。随后，将混合算法与机器学习模型集成，以预测和优化全运行条件下的等熵效率。等熵效率最高可达58.73%。多级离心泵全工况下等熵效率的预测和优化不仅为理论分析中泵效率的假设提供了有益的指导，而且为获得ORC系统的最佳整体运行性能提供了有意义的参考。
The isentropic efficiency of the working fluid pump has a significant impact on the overall performance of the organic Rankine cycle (ORC) system. Based on machine learning, this paper proposes an experimental data-driven isentropic efficiency prediction model. Meanwhile, S-fold cross validation algorithm and smoothing factor circulation screening technology are used to improve the predictive ability of the model. The prediction accuracy of the optimized model and the unoptimized model are compared with each other. The influence of several operating parameters on isentropic efficiency are analyzed. In addition, with intelligent algorithm, the boundary values of operating parameters are determined. The genetic algorithm (GA) and particle swarm optimization (PSO) are combined into a GA-PSO hybrid algorithm. Subsequently, the hybrid algorithm is integrated with the machine learning model to predict and optimize the isentropic efficiency under full operating conditions. The highest isentropic efficiency reaches up to 58.73%. The prediction and optimization of the isentropic efficiency of multistage centrifugal pump under full operating conditions provides not only a useful guidance on assuming pump efficiencies in theoretical analysis, but also a meaningful reference for obtaining the optimum overall operating performance of the ORC system.
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