Multiobjective Optimization of a Light Olefin Production Process via CO₂ Hydrogenation Considering Yield and Energy Efficiency

Producing light olefins via CO2 hydrogenation can prospectively reduce net CO2 emissions, but the process suffers from low productivity and high energy demands. Herein, the process efficiency was improved by minimizing the entropy generation rate while maximizing the product yield. Modeling and simulation of CO2 hydrogenation indicated a trade-off between minimizing the entropy generation rate and maximizing the product yield. The optimal operating conditions were derived through multiobjective optimization (MOO). Specifically, the weighted-sum method was employed, which assigns weight to each objective function and combines them into a single function. Light olefins and exergy prices were then factored into the MOO weights to assess the economic value of each objective function based on annual operating profit (AOP). The optimization result yielded a 20.3% increase in AOP and a 15.6% reduction in entropy generation rate compared to the base case. Additionally, a Pareto front analysis was performed with various weight values for the MOO problem. As a result, each point on the Pareto front can serve as an economic indicator that simultaneously considers yield and energy efficiency in response to economic fluctuations.