In this article, multi-objective optimization of a large-scale industrial ammonia converter is considered. The first-principle mathematical model was developed and validated with industrial data. Thereafter, the model was used to solve two multi-objective optimization problems. In the first case, targeted objectives were to maximize ammonia production and heat recovery simultaneously. In the second case, along with maximization of ammonia production, total catalyst loading among all converter beds was minimized. It was found for both cases that objectives were in conflict, thus resulting in Pareto optimal set of solutions implying trade-off between the objectives. Solution to the first optimization case showed that suitable selection of operation parameters allows for better value of either objectives. In the second case, it was found that one can improve both objectives if catalyst loading is properly re-distributed among beds, even at lower total catalyst loadings. All optimization problems were solved using elitist Non-dominated Sorting Genetic Algorithm, NSGA-II.
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