Operation Optimization of Multiphase Pollutant Treatment Considering Carbon Emissions

Carbon emissions have become a key environmental indicator as air pollution increases. It is urgent for multiobjective optimization of chemical processes to take into account carbon emissions, while ensuring economic benefits. Aiming to improve the treatment of multiphase pollutants, this study builds a three-objective optimization model by considering the optimization of treatment effect, energy consumption, and an innovatively constructed optimization objective of direct and indirect carbon emissions. We run nondominated sorting genetic algorithm-II (NSGA-II) to optimize the operating parameters with respect to the three objectives to get a set of Pareto nondominated solutions on the established Aspen Plus steady-state process simulation, which is based on physical properties and process mechanisms. Finally, we find the optimal solutions through the technique for order preference by similarity to ideal solution (TOPSIS) and knee point. Under the same experimental conditions, we compare our model with the multiphase pollutant treatment research only considering the waste-water flow and energy consumption. Additionally, a life cycle analysis of the three treatment processes (G1, G2 and G3) was conducted. The experimental results show that the proposed model has achieved good results on the three objectives, which have demonstrated the effectiveness of our model. Overall, this study offers a new perspective to the optimization and decision-making of multiphase pollutant treatment considering carbon emissions.