Hybrid Artificial Neural Network-Genetic Algorithm-Based Technique to Optimize a Steady-State Gas-to-Liquids Plant

The development of process optimization is essential for optimal energy consumption, production cost reduction, and product generation maximization. Modeling and simulation of a large scale gas to liquids (GTL) process involves numerous complex mathematical calculations. Accordingly, fine-tuning and optimizing the key parameters of the GTL process is computationally very demanding and time consuming. To alleviate this problem, this study first develops an artificial neural network (ANN) model of the GTL process. The inputs to this model are tail gas unpurged ratio, recycled tail gas to FT ratio, H2O/C entering the syngas section, and CO2 removal percentage, and the ANN model quickly yet precisely estimates the wax production rate. This surrogate model is then imbedded into an optimization problem where the purpose is to maximize the wax production rate by finding the optimal values for the key parameters of the GTL process. The genetic algorithm (GA) is applied for effectively searching the parameter space and finding the global optimum solution. Simulation results indicate that an ANN with a structure of 4:7:15:1 achieves the best prediction performance (mean squared error less than 0.0006). The relative error of estimating the optimum value by the ANN is approximately 0.057%, which is an acceptable value. In addition, optimal GTL parameters found by the proposed ANN-GA technique improves the wax production rate (+107 kg/h). Last but not least, the optimization elapsed has been significantly reduced from about several days to less than a few seconds.