Abstract
Abstract Analysis of renewable fuels production systems via modeling enable industries to economically investigate the effective factors besides working in optimized conditions. Accordingly, an innovative strategy using hybrid Artificial Neural Network/Response Surface Methodology (RSM) is proposed for data simulation in order to comprehensively analyze the effect of operating conditions including temperature, pressure, H2/CO ratio, space velocity, and time on stream on Fischer–Tropsch product distribution. The quadratic response surface models were then validated using the correlation of determination ( R c o d 2 ) proving the certainty of the proposed strategy with near-to-one values of R c o d 2 which is capable of successfully implementing in industrial applications to explore every complex process. Ultimately, single and multi-objective functions were optimized showing that maximum amount of C2 and minimum amount of other products can be achieved under the following conditions: T = 500.13 K, P = 1.5 MPa, space velocity = 1 NL/gcat/h, and H2/CO Ratio = 1.93.
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