Optimal Design of Hybrid Energy-Driven Multiproduct Chemical Processes under Uncertainty

Promoted by sustainable development and carbon neutral initiatives, converting CO2 into valuable chemicals with renewable energy sources has received extensive attention from both academic and industrial communities. However, the inherent uncertainty and volatility are still the main barriers to achieving cost-efficient scenarios. This paper mainly focuses on the systematic design optimization of hybrid energy-driven multiproduct chemical processes under uncertainty. To generate representative scenarios of renewable energy availability, which are used in stochastic programming, an unsupervised K-means clustering approach based on historical data is implemented. Based on the postulated superstructure of the production process, a two-stage stochastic programming model is then formulated and solved to optimality. Such proposed framework is applied to three cases that are derived by considering different regional data around China. The results demonstrate that by taking into account the uncertainty, the optimal process superstructure performs with high efficiency of hedging against the uncertainty and variability of renewable energy sources.