A novel optimization method for transcritical-subcritical parallel organic Rankine cycle under variable engine loads and cooling water temperature

Abstract

Organic Rankine cycle (ORC) is an effective technology for engine waste heat recovery. However, when engine loads and cooling water temperature are variable, the optimization of ORC system will be difficult and time-consuming, especially for complex system like transcritical two-stage ORC. In this study, a novel optimization method is proposed for optimizing the transcritical-subcritical parallel ORC (TSPORC) system, and the system operates under variable engine loads and cooling water temperature. The key idea of the optimization method is a combination of artificial neural network (ANN) and non-dominated sorting genetic algorithm (NSGA-II), the ANN is used to establish a fast and accurate prediction model of the system performance to reduce computing time significantly, and the NSGA-II is furtherly used to optimize system performance fast and accurately based on the ANN prediction model. To improve the accuracy of ANN model, three different artificial neural network models are optimized and compared. To satisfy different objective optimization for application, both single-objective and multi-objective optimization are conducted. The results demonstrate that the optimization method can achieve real time optimization for TSPORC system operating under variable engine loads and cooling water temperature fast and accurately.