Prediction and optimization of emission in an agricultural harvest engine with biodiesel-diesel blends by a method of ANN and CMA-ES

Abstract

To address the current problems of high exhaust emission level of domestic agricultural equipments and serious human and environmental hazards of agricultural machinery engine emissions, in this study, an agricultural harvester diesel engine test platform was firstly built to conduct a biodiesel-diesel combustion emission test and collect data. In addition, an Artificial Neural Network high-precision model was designed and constructed for training and prediction of the outputs such as nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxide (CO), opaque smoke, and post-turbo emission temperatures at different engine operating conditions and various biodiesel-diesel blend ratios, speed and engine load. The type of neural network selected is a feed-forward multi-layer perceptron network because of its advantage of reflecting the correlations between input and output. The model's regression coefficients of NOx, HC, CO, opaque smoke, and post-turbo emission temperatures R2 = 0.9822, 0.9934, 0.9937, 0.9967 0.9970, were close to 1; mean square error (MSE) = 9.79%, 7.73%, 6.48%, 9.58% 2.27%, root mean square error (RMSE) = 18.41 ppm, 3.21 ppm, 16.48 ppm, 0.39 m−1, 10.86 °C. The results show that the established models have high confidence and can be used for high-precision agricultural machinery engine emission prediction and calculation. Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES) was applied to obtain the best matching parameters for the optimum engine operating conditions with optimized emissions. The biodiesel-diesel blend ratio, engine speed, and engine load were optimized with different single-target emissions as the objective function, respectively. In addition, with multi-target emissions as the objective function, the values of the three optimization variables are 5%, 1850 rpm, 25% respectively. The error rate between the experimental data and predicted values is around 3%-10%, and thereby the method is considered practicable through experimental validation. The experimental results show that ANN supported by CMA-ES is a good method for predicting and optimizing emissions of diesel engines burning biodiesel-diesel blends in agricultural machinery.