Tool for optimization of energy consumption of membrane-based carbon capture

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.

Achieving sustainable emissions in China: Techno-economic analysis of post-combustion carbon capture unit retrofitted to WTE plants

China's aims of achieving CO2 emissions peak by 2030 and carbon neutrality by 2060 are crucial in guiding international efforts to mitigate climate change. Amine-based solvent technologies for capturing CO2 on a large scale have been implemented as retrofits in various industrial facilities, with a particular focus on coal-fired power plants. Nonetheless, its implementation within the waste-to-energy (WTE) industry is considerably limited and non-existent in China. This work presents a technical and economic evaluation of retrofitting a generic WTE facility in China with a carbon capture system. A rate-based process simulation model of the capture plant was developed in Aspen Plus, and the effect of equipment installation factors on capital cost was evaluated via the enhanced detailed factor (EDF) method. A set of key performance indicators were evaluated. The findings indicate that the energy demand linked to the capture system caused a decrease in efficiency by 13.17%, 14.85%, and 16.56% at 85%, 90%, and 95% capture rates, respectively, and the overall exergy efficiency of the system was reduced by 5.5%, 8.27%, and 10.63%, respectively. The estimated CO2 captured costs range from €56.41/tCO2 to €58.95/tCO2, while CO2 avoided costs range from €153.33/tCO2 to €236.47/tCO2. Retrofitting a CO2 capture unit at WTE facilities has the potential to substantially contribute to achieving the country's emission reduction targets. However, the successful implementation requires a comprehensive policy structure. This work offers some insights into the prospective integration of CO2 capture technology in China's WTE industry.

Climate crisis and recent developments in bio-based restoration of ecosystems

Over the years, due to the climate crisis, sustainable economic growth and biodiversity protection have been increasingly promoted. Scientists, researchers, and experts in the field of sustainable development highlighted that bio-based restoration of ecosystems and responsible management of existing resources are needed to meet the needs of future generations. This paper discusses some of the latest developments in three main areas of sustainability, i.e., energy, water and environment, that emerged from the "16th Sustainable Development of Energy, Water and Environment Systems Conference - SDEWES 2021". The purpose of this introduction article is to briefly review the articles included in this Virtual Special Issue. As such, it acts as an editorial paper for the virtual special issue of the Journal of Environmental Management, dedicated to the SDEWES 2021 conference.