CO₂ sequestration in sediments as solid hydrate is considered a potential way to capture and store anthropogenic CO₂. When CO₂ hydrate is formed in front of CO₂ migration, the injection channel will be blocked, and the removal of hydrate blockage becomes the first problem that must be faced. This work proposed an N₂ injection method to remove CO₂ hydrate blockage. Based on numerical simulation, a study was conducted using TOUGH+MIXHYD v.1.0 to confirm the feasibility of N₂ injection and compare it to depressurization. The spatial and temporal distribution characteristics of pressure, temperature, hydrate saturation, and gas saturation were investigated. Under the combined effects of temperature, pressure, and gas composition, secondary CO₂-N₂ hydrate can form far from the injection point, causing an increase in local temperature and hydrate saturation. The rate of CO₂ hydrate dissociation using direct depressurization is significantly slower compared to N₂ injection methods. As the pressure of N₂ injection increases, the rate of CO₂ hydrate dissociation notably accelerates, which does not show a significant increase with increasing injection temperature. This work introduced a novel approach to addressing the issue of CO₂ hydrate blockage, which holds prominent significance for the advancement of hydrate-based CO₂ geological sequestration.

Despite a growing body of research on supercritical carbon dioxide (SCO₂) heat transfer in microchannels, comprehensive reviews remain scarce. Existing studies predominantly focus on isolated experiments or simulations, yielding inconsistent findings and lacking a unified theory or optimization framework. This review systematically consolidates recent SCO₂ microchannel heat transfer advancements, emphasizing key performance factors, degradation mechanisms, and optimization strategies. We critically analyze over 260 studies (1962–2024), evaluating the experimental and numerical methodologies, heat transfer deterioration (HTD) phenomena, and efficiency enhancement techniques. Key challenges include the complexity of heat transfer mechanisms, discrepancies in experimental outcomes, and the absence of standardized evaluation criteria. Future research directions involve refining predictive models, developing mitigation strategies for HTD, and optimizing microchannel geometries to enhance thermal performance. This work not only integrates the current knowledge but also provides actionable insights for advancing SCO₂-based technologies in energy systems.

In recent years, the manufacturing industry and power sector have collectively accounted for nearly 60% of global carbon emissions, presenting a formidable obstacle to achieving net-zero targets by 2050. To address the urgent need for industrial decarbonization, this paper proposes a profit-driven framework for low-carbon manufacturing that synergistically integrates green certificates, demand response, distributed generation, and carbon capture, utilization, and storage (CCUS) technologies. A comprehensive optimization model is formulated to enable manufacturers to maximize profits through strategic participation in electricity, carbon, green certificate, and industrial manufacturing product markets simultaneously. By solving this optimization problem, manufacturers can derive optimal production decisions. The framework’s effectiveness is demonstrated through a case study on lithium-ion battery manufacturing, which reveals promising outcomes: meaningful profit growth, substantial carbon emission reductions, and only minimal impacts on production output. Furthermore, the proposed demand response strategy achieves significant reductions in electricity consumption during peak hours, while the integration of distributed generation systems markedly decreases reliance on the main grid. The incorporation of CCUS extends the clean operation periods of thermal power units, generating additional revenue from carbon trading and CO₂ utilization. In summary, the proposed model represents the first unified profit-maximizing optimization framework for low-carbon manufacturing industries, shifting from traditional cost minimization to profitability optimization, addressing gaps in fragmented low-carbon strategies, and providing a replicable blueprint for carbon-neutral operations while enhancing profitability.

This paper addresses high electricity costs in long-distance crude oil transmission systems due to limited renewable energy integration. A mixed-integer linear programming (MILP) model is formulated to maximize renewable energy use and minimize purchased electricity costs, considering pump station and pipeline constraints. To solve this problem, a hybrid artificial bee colony algorithm with variable neighborhood search (HABC-VNS) is proposed, incorporating memory guidance, discrete uniform crossover, and three neighborhood structures. The algorithm is compared with standard ABC, binary PSO, and GA. In two experimental setups (four pumps/eight stations, 24 h horizon), HABC-VNS achieves average total costs of 33,000 CNY and 43,000 CNY, respectively, compared to 61,000–368,000 CNY for the other methods. Average green power integration rates reach 61.6% and 59.6%, outperforming all baselines. The proposed approach provides effective scheduling under strong operational constraints.