Zeqiu Li’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.

In practical industrial applications, equipment usually operates normally and failures are relatively rare, resulting in serious imbalances in the collected data. This imbalance leads to issues such as overfitting, instability, and poor robustness, significantly reducing the accuracy and stability of fault diagnosis system. To address these challenges, this research proposes a method for imbalanced data augmentation and industrial process fault diagnosis based on improved Generative Adversarial Network (GAN). The method adopts Wasserstein distance with gradient penalty and integrates residual connections into the architecture of the generator. This innovation not only helps improve gradient transfer in the generator, but also significantly enhances the data generation capabilities of the generative model through improving the stability of training. Limited industrial process data is used by a generative model to produce synthetic samples with high similarity and diversity. These high‐quality samples improve fault diagnosis by enriching the imbalanced dataset. Experimental results on two industrial datasets confirm the method's effectiveness in enhancing fault diagnosis performance with limited data.

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.

Renewable energy integration is a crucial approach for achieving a low-carbon energy supply in industrial utility systems. However, the uncertainty of user demand often leads to a mismatch between the system’s real operating conditions and the optimal operating points, resulting in energy wastage and high emissions. This study presents a multi-source heat and power system that integrates biomass gasification, solar collecting, solid oxide fuel cell (SOFC), gas turbine, and steam power systems. A scheduling strategy that varies the heat-to-power ratio is proposed to accommodate changes in user requirements. A simulation model of this multi-source system is established and validated. The influence of three key parameters on system performance under different configurations is explored. Energy and economic evaluations are conducted for three different configurations, and the system’s energy production and adjustable range are determined. The analysis reveals that, under the optimal configuration, the system can achieve an energy efficiency of 64.51%, and it is economically feasible with the levelized cost of electricity (LCOE) of USD 0.16/kWh. The system is capable of producing an output power ranging from 11.52 to 355.53 MW by implementing different configuration strategies. The heat-to-power ratio can be adjusted from 0.91 to 28.09.

Solar power is a crucial force in renewable energy. The production of solar photovoltaic (PV) has nonnegligible effects on the environment, and the economic properties of various PV technologies range. Life Cycle Assessment (LCA) and Multi-objective Optimization (MOO) methodologies were utilized in this research to establish an optimization model of PV technology regional planning that took into account combined environmental impacts and Electricity Supply Cost (ESC). Through NSGA-II genetic algorithm, the Pareto optimal solution with the minimum environmental pollution and the lowest economic cost in the whole life cycle of solar PV technology is obtained. The results demonstrated that toxic environmental impacts are the primary categories of crystalline silicon PV panels' potential environmental impacts, and monocrystalline silicon PV technology is the more advantageous choice when considering environmental impact and ESC. Results of sensitivity analysis indicated that uncertain factors would affect the scheme selection. This research offers a planning methodology for regional sustainable development of solar PV technology considering both environmental and economic objectives.