Addressing Health Equity in the Context of Carbon Capture, Utilization, and Sequestration Technologies.

PURPOSE OF REVIEW: To describe the role of health equity in the context of carbon capture, utilization, and sequestration (CCUS) technologies. RECENT FINDINGS: CCUS technologies have the potential to both improve and worsen health equity. They could help reduce greenhouse gas emissions, a major contributor to climate change, but they could also have negative health impacts like air and noise pollution. More research is needed to fully understand the health equity implications of CCUS technologies. CCUS technologies have both health equity risks and benefits. Implementing misguided CCUS projects in vulnerable communities could exacerbate environmental injustice and health disparities and have the potential to increase carbon emissions. However, well-conceived projects could benefit communities through economic development. Governments, industry, and society should prioritize and expedite the reduction of CO2 emissions before considering carbon reductions via CCUS. Furthermore, CCUS projects must be thoroughly evaluated and should only proceed if they have demonstrated a net reduction in CO2 emissions and provide more benefits than risks to local communities. This underscores the importance of prioritizing health equity in the planning of CCUS projects.

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities.

Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sectors to mitigate climate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. The developed AI methods can be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.