Acid and greenhouse gaseous emissions as well as the physical exergy are massive challenges associated with sulfuric acid plants. Minimizing these critical problems along with maximizing the economic efficiency of the plant is of important concern. A selected plant was simulated with Aspen Plus and used for this multi-objective optimization (MOO) study. MOO was carried out with excel-based MOO with elitist non-dominated sorting genetic algorithm-II. Decision variables such as air, steam- and water- flow rates, reactors- and columns- operating pressures were considered to get the Pareto optimal front for the selected objectives (product sales, utility cost, total production cost, global warming potential, acidification potential, and physical exergy). It was found that air, steam and water flow rates have a strong impact in most cases. For example, FCI is reduced from 4.37E + 07 to 4.00E + 07 $ as air flow rate went from 3.62E + 03 to 3.40E + 03 kmol/hr in case 1. While the other objective in case 1, AP, increased from 1.68E + 05 to 2.98E + 05. The obtained trade-offs were ranked using Net Flow Method to identify the best solutions. The attained Pareto fronts provided set of equally good- and non-dominated solutions corresponding to the conflicting objectives involved in the process.
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