An ANN-PSO-Based Method for Optimizing Agricultural Tractors in Field Operation for Emission Reduction

Abstract

Aiming at the serious problem of agricultural tractor emission pollution, especially the limitation of nitrogen dioxide (NOx) and soot emissions, we took an agricultural diesel engine as the research object, and a diesel engine combustion chamber model was established for both simulated calculations and experimental verification analysis. The in-cylinder pressure and heat release obtained from the combustion chamber model simulation calculations were within 6% error of the experimental data. The overall trend of change was basically consistent. The established model can simulate the working conditions of the experimental engine relatively well. An artificial neural network (ANN) was also established as an agent model based on the indentation rate, tab depth, and combustion chamber depth as the input, and NOx and soot as the output. The decision coefficients of the ANN model were R2 = 0.95 and 0.93, with corresponding Mean Relative Error (MRE) values of 10.13 and 8.18%, respectively, which are within the generally required interval, indicating that the obtained ANN model has good adaptability and accuracy. On the basis of the general particle swarm optimization (PSO) algorithm, an improved PSO algorithm was proposed, in which the inertia factor is continuously adjusted with the help of the skip line function in the optimization process so that the inertia factor adapts to different rates and adjusts the magnitude of the corresponding values in different periods. The improved PSO algorithm was used to optimize the optimal input parameter matching of the agent model to form a new combustion chamber structure, which was imported into CONVERGE CFD software for emission simulation and comparison with the emissions of the original combustion chamber. It was found that the NOx reduction was about 1.21 g·(kW·h)−1, and the soot reduction was about 0.06 g·(kW·h)−1 with the new combustion chamber structure. The ANN + PSO optimization method proved to be effective in reducing the NOx and soot emissions of diesel engine pollutants, and it may also provide a reference and ideas for the design and development of relevant agricultural engine combustion chamber systems.

A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm

“…Aiming at the improvement strategy of particle swarm optimization algorithm, many scholars have made many attempts in different fields. Zheng et al improved the tractor field operation emission reduction problem by optimizing the inertia factor [49]. Chen et al introduced the advantages of combining particle swarm optimization and a genetic algorithm in solving distributed planning problems [50].…”