Two-Stage Multi-Period Design and Optimization for Green Ethylene Glycol Production

This study addresses the critical scientific gap in optimizing renewable-powered chemical processes under intermittent solar energy and complex material flows─a key challenge that existing steady-state or single-period models fail to resolve. To bridge this gap, we propose a novel two-stage, multiperiod optimization framework for ethylene glycol (EG) production, which innovatively integrates K-means clustering for irradiance period division, rigorous Aspen Plus process simulation, and mathematical programming. The framework first determines optimal capital cost and subsequently optimizes operational cost across varying renewable energy conditions. Results demonstrate stable EG production ranging from 17.96 t/h (high irradiation) to 8.86 t/h (low irradiation), achieving CO2 consumption through synergistic CO2 hydrogenation and reverse water–gas shift pathways. The system only utilizes 1.1% green oxygen in methyl nitrite synthesis. Economic analysis reveals methanol synthesis (92.13 M$) and steam demand (92.22 M$/y) as primary cost drivers. The proposed model successfully obtains optimal system configurations and operating conditions, establishing a crucial foundation for Power-to-X technologies. This work provides both theoretical guidance and practical solutions for renewable-integrated chemical manufacturing, with demonstrated 60–70% emission reductions, offering a scalable blueprint for industrial decarbonization.