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Kinetic Study Of Product Distribution Using Various Data-Driven And Statistical Models For Fischer-Tropsch Synthesis, Yixiao Wang, Jing Hu, Xiyue Zhang, Abubakar Yusuf, Binbin Qi, Huan Jin, Yiyang Liu, Jun He, Yunshan Wang, Gang Yang, Yong Sun
Kinetic Study Of Product Distribution Using Various Data-Driven And Statistical Models For Fischer-Tropsch Synthesis, Yixiao Wang, Jing Hu, Xiyue Zhang, Abubakar Yusuf, Binbin Qi, Huan Jin, Yiyang Liu, Jun He, Yunshan Wang, Gang Yang, Yong Sun
Research outputs 2014 to 2021
Three modeling techniques, namely, a radial basis function neural network (RBFNN), a comprehensive kinetic with genetic algorithm (CKGA), and a response surface methodology (RSM), were used to study the kinetics of Fischer-Tropsch (FT) synthesis. Using a 29 × 37 (4 independent process parameters as inputs and corresponding 36 responses as outputs) matrix with total 1073 data sets for data training through RBFNN, the established model is capable of predicting hydrocarbon product distribution i.e., the paraffin formation rate (C2-C15) and the olefin to paraffin ratio (OPR) within acceptable uncertainties. With additional validation data sets (15 × 36 matrix with total 540 …