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- W2760763546 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 (Rcod2) proving the certainty of the proposed strategy with near-to-one values of Rcod2 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." @default.
- W2760763546 created "2017-10-06" @default.
- W2760763546 creator A5037919171 @default.
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- W2760763546 date "2017-11-01" @default.
- W2760763546 modified "2023-09-27" @default.
- W2760763546 title "Process conditions effects on Fischer–Tropsch product selectivity: Modeling and optimization through a time and cost-efficient scenario using a limited data size" @default.
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- W2760763546 doi "https://doi.org/10.1016/j.jtice.2017.09.006" @default.
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