Material and energy coupling systems optimization for large-scale industrial refinery with sustainable energy penetration under multiple uncertainties using two-stage stochastic programming

The coupling of multimedia materials and energy with sustainable energy penetration in large-scale industrial refineries significantly lowers energy consumption and greenhouse gas (GHG) emissions. This paper presents a sustainable retrofitting framework for coupled production materials and steam systems (CPMSS) utilising two-stage stochastic programming (TSSP). A novel sustainable energy-integrated CPMSS (SEICPMSS) model that includes wind, solar, and hydrogen energy and considers multiple operating modes and hydrogen consumption of the process units was established. First-principles models of wind turbines, solar thermal collectors, and proton exchange membrane electrolysers were adopted to sustainably retrofit the CPMSS. Latin hypercube sampling and Gaussian mixture model methods were applied to classify high volumes of uncertain wind speed and solar radiation data. A SEICPMSS optimisation model formulated as a mixed-integer nonlinear programming problem was developed by considering the investment costs, carbon taxes, and operational costs. Finally, a series of case studies from the industrial refinery's CPMSS were conducted to illustrate the effectiveness of the proposed method. The optimisation results indicate that the TSSP method can reduce operating costs by 5.3355 × 108 CNY/year and decrease GHG emissions by 7.4104 × 106 t/year compared to the traditional CPMSS without the integration of sustainable energy systems.