Yu, Wenbin; Zhao, Feiyang published the artcile< Prediction of critical properties of biodiesel fuels from FAMEs compositions using intelligent genetic algorithm-based back propagation neural network>, SDS of cas: 112-63-0, the main research area is biodiesel fatty acid methyl ester back propagation neural network.
In this study, an intelligent genetic algorithm (GA)-based back propagation neural network (BPNN) model was proposed to predict the properties of biodiesel fuels according to FAMEs compositions The hybrid BPNN-GA model has five inputs (Me palmitate, Me stearate, Me oleate, Me linoleate and Me linolenate) corresponding to the FAMEs compositions and outputs with estimated fuel properties, with the GA assisting on training to find out local min. deviation and updated weighting configurations. It was found that the intelligent learning-training method proposed hybrid BPNN-GA model enabled to map the non-linear relationships between the FAMEs compositions and key properties of biodiesel fuels with fairly good agreement. The predicted value of fuel properties agrees with measured ones with R-square up to 96%, along with lower value (less than 10%) over Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) than those of other empirical correlations. In addition, a sensitivity anal. was conducted to in-depth investigate the FAMEs compositions on key properties. It was concluded that saturated FAMEs have pos. impacts on CN, KV and the CFPP, while IV is typically dependent on unsaturated FAMEs. Therefore, it is attainable to formulate new types of alternative fuels based on the required properties on diesel engine applications.
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects published new progress about Biodiesel fuel. 112-63-0 belongs to class esters-buliding-blocks, and the molecular formula is C19H34O2, SDS of cas: 112-63-0.
Referemce:
Ester – Wikipedia,
Ester – an overview | ScienceDirect Topics