Multiobjective optimization of steam reformer performance using genetic algorithm

An existing side-fired steam reformer is simulated using a rigorous model w ith proven reaction kinetics, incorporating aspects of heat transfer in the furnace and diffusion in the catalyst pellet. Thereafter, "optimal" condit ions, which could lead to an improvement in its performance, are obtained. An adaptation of the nondominated sorting genetic algorithm is employed to perform a multiobjective optimization. For a fixed production rate of hydro gen from the unit, the simultaneous minimization of the methane feed rate a nd the maximization of the flow rate of carbon monoxide in the syngas are c hosen as the two objective functions, keeping in mind the processing requir ements, heat integration, and economics. For the design configuration consi dered in this study, sets of Pareto-optimal operating conditions are obtain ed. The results are expected to enable the engineer to gain useful insights into the process and guide him/her in operating the reformer to minimize p rocessing costs and to maximize profits.