An existing side-fired steam reformer is simulated using a rigorous model with proven reaction kinetics, incorporating aspects of heat transfer in the furnace and diffusion in the catalyst pellet. Thereafter, “optimal” conditions, 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 hydrogen from the unit, the simultaneous minimization of the methane feed rate and the maximization of the flow rate of carbon monoxide in the syngas are chosen as the two objective functions, keeping in mind the processing requirements, heat integration, and economics. For the design configuration considered in this study, sets of Pareto-optimal operating conditions are obtained. 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 processing costs and to maximize profits.

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The ant colony algorithm, mimicking the cooperative search behavior of ants in real life, has been employed for the dynamic optimization of fed-batch bioreactors. To test the capability of this new heuristic algorithm, two well-known and extensively studied systems have been chosen. The algorithm rapidly converges to optimal feed rate profiles, which maximize the overall production of the desired product and the profits in a computationally efficient and robust manner. The optimal profiles evolved are easy to implement in plant operation. The algorithm compares favorably with the other known techniques.