After the advances in biogas upgrading techniques, the optimization of power and heat generation engines fueled by biogas with varying compositions has become a necessity. Therefore, the aim of the present study is to investigate the necessity of upgrading biogas, mainly CO2 removal, in order to generate power and to determine the optimal operating parameters of a spark-ignition (SI) engine fueled by upgraded biogas. Thus, we propose to couple the artificial neural network technique, multi-objective optimization concept and decision-making approaches in order to improve the performance of a stationary SI engine and to reduce its nitrogen oxides (NOx) emissions while keeping the level of biogas upgrading as low as possible. The brake power (BP), brake thermal efficiency (BTE), and NOx emissions of a SI engine were predicted by using artificial neural network models. The spark ignition timing (IT), biogas CH4 concentration, and throttle valve opening (TVO) were taken as independent variables. The artificial neural network models were trained by using the genetic algorithm (GA) to achieve the best weights and biases. The data used to train and test the ANN models came from experimental tests carried out at constant engine speed (1500 rpm) and air-fuel ratio (λ = 1.4). In the optimization part, the BP, BTE, NOx emissions, and CH4 concentration were all taken as objective functions in order to achieve better performance while lowering NOx emissions and biogas upgrading level. In addition, the optimum operational parameters were obtained by using decision-making approaches. The final optimal point, for the best compromise between high engine performance and low NOx emissions, shows that there is no need to enhance biogas upgrading (lowering CO2 content) in order to generate power and heat. As a result, a SI engine under full load fueled by cleaned raw biogas is the best option.
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