Process Optimization Based on Artificial Neural Network of Potassium Hydroxide to Preparing Biodiesel

Abstract

In order to obtain the optimal technological conditions of preparing biodiesel, artificial neural network was used to study the biodiesel processing model on transesterification method based on the single factor experiment and orthogonal experiment. The results of experiment indicated that we used the back propagation BP algorithm of artificial neural network to set the network prediction model based on the orthogonal test data can forecast the biodiesel conversion rate under different reaction conditions more accurately.The optimal conditions were obtained from this network model as follows: Molar ratio of methanol to oil was 6:1, the catalyst was 1.0% (w/w, based on oil), reaction temperature and reaction time was 65°Cand 2.5h respectively. Under the optimal conditions, the conversion rate of prediction was 94.93%, the conversion rate of experiment was 95.42% and the relative error was 0.51% compared with the predicted value. Therefore, the network k model could reflect inherent law of sample.

Research of extraction behavior of heavy metal Cd in tea based on backpropagation neural network

“…And the information science approach as a nonlinear simulation technology, that is an artificial neural network (ANN) (Fan, He, & Lin, ; Hattori & Kito, ; Yang, Yu, & Guan, ) including Hopfield neural network, adaptive resonance technology, fuzzy neural network, and backpropagation (BP) neural network, is structured to study the relationship between a set of factors and the results. According to research of Wang et al (), BP neural network is most widely used in various evaluation fields (Chen, ; Ding & Jia, ; Fan, Piao, & Chen, ; Ju, Tade, & Zhu, ) for its good performance of self‐learning, self‐adapting, self‐organizing, and self‐reasoning (Maier, Jain, & Dandy, ; Miao, Zhao, & Gao, ; Palani, Liong, & Tkalich, ), which was proposed by Rumelhart, Hintont, and Williams (). Yang et al () applied BP neural network to the performance evaluation of environmental catalytic materials and pollutant purification research; Fan et al () applied BP neural network to the corrosion prediction research of petrochemical tower; Ju et al () applied BP neural network to the prediction of hydrogen content in coal resources, etc.…”