Ensemble learning-based nonlinear time series prediction and dynamic multi-objective optimization of organic rankine cycle (ORC) under actual driving cycle

Abstract

Complicated road conditions make organic Rankine cycle (ORC) operation characteristics show hysteresis and uncertainty. Under the strong coupling correlation of many operating parameters, how to realize the dynamic optimization of ORC comprehensive performance is the key to obtain practical application potential. Based on ensemble learning mechanism, neural network modeling, ensemble system, unsupervised learning, partial mutual information and optimization algorithm are integrated. This paper presents a nonlinear time series prediction and dynamic multi-objective optimization scheme. The average accuracy increased by at least 59.6%. Taking the thermodynamic performance and environmental impact as optimization objectives, dynamic multi-objective optimization is carried out under road conditions. The optimization scheme can effectively trade off the nonlinear correlation between thermal efficiency and emissions of CO2 equivalent.