Multi-objective optimization of operating parameters for a gasoline Wankel rotary engine by hydrogen enrichment

The purpose of current research was to implement an intelligent regression model and multi-objective optimization of performance, combustion and emissions characteristics for a hydrogen-enriched gasoline rotary engine. The brake thermal efficiency (BTE), fuel energy flow rate (E-f), nitrogen oxides (NOX), carbon monoxide (CO) and hydrocarbon (HC) were predicted by intelligent regression model with hydrogen volume fraction (alpha(H2)), excess air ratio (lambda) and ignition timing (IT) as independent variables. The intelligent regression models were based on support vector machine (SVM) and optimized by the genetic algorithm (GA) to obtain the optimal parameters of the regression model. The data for training the SVM model were derived from the experimental results of a hydrogen-enriched rotary engine, in which the speed was kept constant at 4500 r/min, the absolute manifold pressure remained at 35 KPa, the variation of alpha(H2), lambda and IT were 0-6%, 1-1.3 and 24-42 degrees CA before top dead center (bTDC), respectively. After optimized by GA, the coefficient of determination of BTE, E-f, NOX, CO and HC between the SVM model and the corresponding data were greater than 0.98, and the mean absolute percentage error were <1%. The performance, combustion, and emissions characteristics including BTE, E-f, NOX, CO and HC were considered for multi-objective optimization to obtain higher performance and lower emissions, and were solved using the non-dominated sorting genetic algorithm II. For this study, when the Pareto-optimal solutions were obtained, the optimal operating parameters were further obtained by limiting the performance and emissions parameters with the alpha(H2) of 5.06%, lambda of 1.09%, and IT of 34.27 degrees CA bTDC.

Automated summary

The research implemented an intelligent regression model and multi-objective optimization for a hydrogen-enriched gasoline rotary engine to predict and optimize performance, combustion, and emissions characteristics. The models were based on support vector machine (SVM) and optimized by genetic algorithm (GA) to obtain optimal parameters. The results showed that the models had high accuracy and low error rates, and the optimization process using non-dominated sorting genetic algorithm II led to Pareto-optimal solutions for higher performance and lower emissions.