A fuzzy optimization approach to carbon capture and utilization in industrial parks

As the main part of economic development, carbon dioxide (CO2) emission reduction in industrial parks is crucial in assisting a country in achieving its national targets. In this paper, a multi-objective optimization model based on mixed-integer nonlinear programming (MINLP) is proposed for CO2 integration in an industrial park, where overall costs and CO2 emissions are minimized through optimal matching of carbon sources, carbon sinks and carbon capture technologies. In order to find a compromise solution, fuzzy optimization method is used to deal with the conflict between economic and environmental objectives. Based on the CO2 reduction target determined by fuzzy optimization, the ε-constraint method is utilized to yield the Pareto solutions. The proposed model was applied in a case study based on a chemical industry park in Fuyang City, China. The results show that for cost minimization scenario, the minimum total annualized cost and CO2 emission was identified as 6.38 M$/y, and 44.53 t/h, respectively, and its CO2 reduction target is 10 %. On the other hand, for the CO2 emissions minimization scenario, the CO2 emission was minimized to 8.79 t/h with CO2 reduction target is 10 %, while its total annualized cost was identified as 21.91 M$/y. Next, fuzzy optimization identifies a compromise solution with a total annualized cost of 86.08 M$/y and CO2 emission of 56.70 t/h, and the corresponding CO2 reduction target is 42.61 %. The findings indicate that the proposed model could effectively reduce total cost and environmental impact, and provide a decision-making tool for CO2 emission reduction in industrial parks.