Liujian Yang’s research while affiliated with University of Shanghai for Science and Technology and other places

With the increasing emphasis on emission reduction targets, the low-carbon sustainable transformation of industrial energy supply systems is crucial. Addressing the urgent issue of reducing industrial carbon emissions, this study presents an integrated industrial energy supply system (IRE-CCUS-BESS-SPS) that incorporates renewable energy; calcium-based carbon capture, utilization, and storage (CCUS); and battery energy storage systems (BESSs) to improve energy efficiency and sustainability. The system model is designed to achieve a cost-effective and environmentally low-impact energy supply, validated through Aspen Plus V11.0 and Matlab R2019b simulations. The system’s performance is evaluated using a 4E index system encompassing economy, environment, energy, and exergy. The findings indicate that the system’s lifetime net present value (NPV) is positive, with a payback period of 6.09 years. Despite a 12.9% increase in the overall economic cost, carbon emissions are significantly reduced by 59.78%. The energy supply composition includes 48.60% from fuel oil and 22.10% from biomass, with an additional 270.04 kW of heat provided by waste heat boilers. The equalization costs for CO2 removal (LCCR) and methanation (LCOM) are 122.95 CNY/t and 10908.35 CNY/t, respectively, both exceeding current carbon emission trading costs and methane prices. This research offers a robust framework for designing sustainable industrial energy systems that integrate renewable energy, CCUS, and energy storage technologies for low-carbon operations. The analysis also suggests that government policies, such as direct financial subsidies or tax relief, are effective in accelerating the adoption of CCUS technology.

Renewable energy integration and operational optimization are crucial in energy sustainability and decarbonization, especially for industrial steam power systems (SPS). This study establishes an SPS superstructure that integrates wind, solar, and biomass energy. A mixed‐integer nonlinear programming (MINLP) model is developed to determine an optimal retrofit strategy that minimizes the life cycle cost, which includes carbon emission cost and energy cost. To solve this high‐dimensional complex optimization problem, a multistage strategy fusion differential evolution algorithm with dynamic partitioning of the SVM feasible domain (SVM‐MS‐DE) is developed. The results from the optimal strategy demonstrate a 9.02% reduction in the system's total cost and a 9.64% decrease in carbon emission through the incorporation of wind and solar energy. Additionally, the sensitivity analysis on biomass ratio, carbon emission price, energy demand, and carbon emission limits reveal that the region can contribute significantly to renewable energy initiatives. Several recommended policies are provided to encourage enterprises to move towards sustainable development.