Optimization of the Temperature Profile of a Temperature Gradient Reactor for DME Synthesis Using a Simple Genetic Algorithm Assisted by a Neural Network

Abstract

Dimethyl ether (DME) from natural gas or coal via syngas (3CO + 3H 2 f DME + CO 2 ) attracts much attention as a high quality diesel fuel of the next generation. Considering the compact process based on the small-scale carbon resources, both low-pressure operation (at 1-3 MPa) and high one-pass conversion (90% CO conversion) are required to produce DME economically. A temperature gradient reactor (TGR) was effective for overcoming both the equilibrium limit of the reaction at high temperature and the low activity of the catalyst at low temperature. For example, 90% CO conversion and high STY (1.1 kg-MeOH eq./kg-cat./h) was attained at the same time in TGR at 280-240 °C, 3 MPa. For higher performance of the TGR, in the next step, optimization of the temperature gradient was required. A homemade program according to a genetic algorithm (GA) was used for the optimization. The catalyst bed was divided into 5 zones in series. The temperature of each zone was encoded to "gene" and the fitness of the "gene" was evaluated by CO conversion obtained in the reactor of which temperatures were set according to the gene. After a few generations of "evolution", CO conversion (70%) higher than that in a conventional isothermal reactor (66%) was achieved at 1 MPa. For higher CO conversion, neural network, trained by the results in the preceding generations, was used to evaluate the "gene". By this combination of GA and neural network, evolution of the temperature profile was accelerated and successfully optimized to give 71% CO conversion.

Universal characteristics of chemical synthesis and property optimization

“…Typically, ''smart'' optimization methods such as factorial design, 2 directed evolution, 3,4 or genetic algorithms 5 are used to find the best values of the variables. Efficient optimization has been observed in organic synthesis, [6][7][8] discovery of functional proteins, 9-26 optimization of catalytic activity, [27][28][29][30][31][32][33][34][35][36][37][38][39] and the properties [40][41][42][43][44][45][46][47][48][49][50] of materials. The typical numbers of variables and resulting possible experiments, as well as the number of experiments actually performed for these objectives are shown in Table 1.…”

Optimization of ODHE membrane reactor based on mixed ionic electronic conductor using soft computing techniques

“…Another interesting work was performed by Nandi et al, 2002Nandi et al, , 2004, where NN's and GA's are used for the optimization of reactor operating conditions in the hydroxylation of benzene catalyzed by titanium silicalite zeolite (TS-1). In another approach, the temperature gradient profile in the reactor for the synthesis of dim-ethyl ether was optimized using a simple binary genetic algorithm, assisted by a NN modeling the catalytic activity (Omata et al, 2003).…”

Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks

“…ANNs have proven to be suitable for creating predictive models for chemical engineering processes and several applications have been subject of research in the last decades such as the evaluation and modeling of complex kinetic data 3–6, catalyst design 7, 8, soft sensoring 1, 9, advanced process control 10, and others 11. Studies regarding the application of ANNs for the synthesis of dimethyl ether (DME) have been reported, e.g., for the screening of additives 7, 8, the optimization of temperature profiles in a temperature gradient reactor 12, and the modeling of the single process steps 13, 14. Furthermore, ANNs have been used for predicting the performance of the liquid phase direct synthesis of DME over CuO/ZnO/Al 2 O 3 and H‐ZSM‐5 catalysts 9.…”