No abstract

No abstract

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, finetuning 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, H 2 O/C entering the syngas section, and CO 2 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.

No abstract

In this study, a multiobjective optimization problem (MOOP) with two objective functions (maximization of di-methyl-ether (DME) production rate and minimization of carbon dioxide release) was applied to the direct synthesis of DME from a natural gas-derived synthesis gas (syngas). Twelve degrees of freedom were considered. The MOOP results suggest that the process with a maximum DME production rate of 1686 kmol/hr releases 4788 kmol/hr CO 2 while the CO 2 release of the process with DME production rate of 1282 kmol/hr is 1761 kmol/hr. The higher the DME production rate, the lower net CO 2 emission to the air, natural gas consumption, and energy consumption per kg of the DME produced. In addition, the process with a higher DME production rate has a higher carbon efficiency and power production from the produced steam. The annual profit criteria of the overall process were used as a posterior preference index to select the best point from the resultant multiobjective optimization Pareto front. It was shown that the plant with the topmost DME production rate has the utmost annual profit compared to other Pareto optimum points. Lastly, the effect of degrees of freedom on the maximum DME production rate is discussed. K E Y W O R D Sannual profit, CO 2 utilization, di-methyl-ether synthesis, multiobjective optimization, natural gas-derived syngas Abbreviations: CE, carbon efficiency; C OL , cost of operating labor (million USD/year); C RM , cost of raw materials (million USD/year); C UT , cost of utilities (million USD/year); C i , concentration of component i À mol * ð x * Þ, vector of objective functions; x *