A synergistic multi-objective optimization mixed nonlinear dynamic modeling approach for organic Rankine cycle (ORC) under driving cycle

Abstract

Organic Rankine cycle (ORC) synergistic multi-objective optimization is the key to obtain the actual waste heat recovery potential under dynamic driving cycle. The ORC operation shows uncertainty and hysteresis under the disturbance of variable high temperature waste heat source. In this paper, a synergistic multi-objective optimization mixed nonlinear dynamic modeling approach is proposed by deeply integrating data selection, feature dimensionality reduction, integrated system, neural network modeling mechanism, ensemble learning mechanism and synergistic multi-objective optimization. Compared with direct modeling, the nonlinear dynamic modeling approach can reduce the data volume by 27.65 % on average. The decision variables and construction time are reduced at least by 69.1 % and 24.26 % on average, respectively. Generalization ability is improved by at least 28.14 % on average. Taking thermodynamic performance, economic performance, thermoeconomic performance and environmental impact as optimization objectives, a synergistic multi-objective optimization of ORC comprehensive performance under driving cycle is carried out. The optimal emissions of CO2 equivalent (ECE) will suppress the optimal power output of per unit heat transfer area (POPA) to a certain extent. A decrease in optimal ECE is accompanied by an increase in optimal electricity production cost. The approach proposed in this paper can provide new ideas and solutions for ORC dynamic modeling and synergistic multi-objective optimization under road conditions.