Multi-objective optimization of a diesel engine-ORC combined system integrating artificial neural network with genetic algorithm

Organic Rankine cycle (ORC) could compensate for the brake power loss caused by in-engine purification by recovering exhaust energy, thus achieving lower emissions and higher power output for the engine. Aimed at identifying optimum parameters that could maximize the performance of a diesel engine-ORC combined system, this study conducts a multi-objective optimization of dynamic performance, fuel consumption, and NOx emission of the combined system, considering the engine's injection timing, intake, and exhaust phases, ORC's working fluid pump speed, and expander speed as decision variables. Initially, a validated simulation model is used to study the effect of these parameters on the performance of the combined system, indicating the necessity of optimization. Subsequently, initial datasets are obtained, with the data of decision variables obtained using the D-optimum Latin hypercube sampling method and the performance indexes calculated by the simulation model. Following these initial datasets, artificial neural network (ANN) models predicting the performance of the combined system are constructed. And the power output, brake-specific fuel consumption (bsfc), and NOx emission are optimized by coupling ANN with non-dominated sorting genetic algorithm-III. Multi-objective optimization results indicate that overly reducing NOx emissions can degrade the dynamic performance and fuel efficiency of the combined system, with the degradation of 1.85% in power output and 1.89% in bsfc as a consequence of a maximum reduction of 43.79% in Brake-specific NOx (BSNOx). Prioritizing maximum power could substantially reduce fuel consumption but weaken the effectiveness of NOx reduction, with a 4.03% improvement in power output and a reduction of 3.87% and 16.48% in bsfc and BSNOx, respectively. A balanced approach considering both dynamic performance and NOx emission is the most suitable for enhancing performance with emission control in mind. This could ensure a 37.16% reduction in BSNOx, maintaining comparable power and fuel consumption. This study offers valuable insights for future advancements in engine performance.