Liquid fuel production from syngas: Simulation and optimization using artificial neural network

Abstract

This study aimed to increase liquid fuel (C5+) product selectivity from CO and H2 conversion over a Cu/Fe-based catalyst and followed by artificial neural network simulation to enable a profound visualization strategy for Fischer–Tropsch (FT) process. The experiments were performed in a fixed-bed reactor over temperatures (T) of 483–553 K, pressures (P) of 14.5–203 psig, space velocities (SV) of 1000–2900 hr−1, and H₂/CO feed ratios of 1–6.2. Mathematical modeling (MM) and artificial neural network (ANN) were utilized to simulate the nonlinear CO conversion and product selectivity of the FT process. Nine simultaneous reaction pathway was developed to evaluate the reactant and product behaviors under varying FT conditions. In addition, a multilayered parameter processed by a neural network was used to predict the performance of the CO hydrogenation process. A comparison between these two approaches demonstrated that ANN more closely mimicked the nonlinear conversion and selectivity of the FT process than MM, and the correlation coefficients for the predicted mole fractions and CO conversion were 0.991 and 0.986, respectively. A hybrid Genetic Algorithm–ANN procedure was constructed to predict the optimum operating conditions to increase liquid fuel over the catalyst. The optimum C5+ selectivity (75.1 %), which meets the FT process objective, was achieved under operating conditions of T = 483 K, P = 203 psig, SV = 1303 hr−1, and H₂/CO feed ratio = 1.01 for process design optimization.