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Development of Carbon Capture, Utilization and Storage Technology in China

Zhang Xian, Li Yang, Ma Qiao, Liu Lingna

Strategic Study of Chinese Academy of Engineering 2021, Volume 23, Issue 6,   Pages 70-80 doi: 10.15302/J-SSCAE-2021.06.004

Abstract:

Carbon capture, utilization and storage (CCUS) is an indispensable optiontechnical development level, demonstration progress, cost effectiveness, and CO2 reduction potential of CCUSin China to review the status of CCUS and identify its future direction of development.The conclusion indicates that China’s deployment of CCUS projects has developed rapidly and isIn terms of cost and benefit, although the current cost of CCUS technology is high, CCUS remains a cost-effective

Keywords: carbon neutrality,carbon capture     utilization and storage (CCUS),technology research and demonstration    

System integration of CCS, CCUS, CCRS, CMC

Jin Yong, Zhu Bing, Hu Shanying, Hong Liyun

Strategic Study of Chinese Academy of Engineering 2010, Volume 12, Issue 8,   Pages 49-55

Abstract: Carbon Capture Use and Storage (CCUS) and Carbon Capture Reuse and Storage (CCRS), as the two concepts

Keywords: low carbon economy     Carbon Capture and Storage     Carbon Capture Use and Storage     Carbon Capture Reuse and Storage     Comprehensive Management of Carbon    

Research on Technology Directions and Roadmap of CCS/CCUS for Coal-Fired Power Generation in China

Ye Yunyun, Liao Haiyan, Wang Peng, Wang Junwei, Li Quansheng

Strategic Study of Chinese Academy of Engineering 2018, Volume 20, Issue 3,   Pages 80-89 doi: 10.15302/J-SSCAE-2018.03.012

Abstract: development directions of a carbon capture and storage (CCS) / carbon capture, utilization and storage (CCUScalculated, contributions of energy saving and consumption reduction, energy structure adjustment, the CCS/CCUSemission reduction are evaluated, and recommendations for popularization and application of the CCS/CCUS

Keywords: coal-fired generation     carbon capture     utilization and storage     technology roadmap     emission forecast     policy proposal    

Technical Perspective of Carbon Capture, Utilization, and Storage Perspectives

Qingyang Lin, Xiao Zhang, Tao Wang, Chenghang Zheng, Xiang Gao

Engineering 2022, Volume 14, Issue 7,   Pages 27-32 doi: 10.1016/j.eng.2021.12.013

Abstract: Carbon capture, utilization, and storage (CCUS) is a sustainability technology promising in terms of

Keywords: CCUS     Carbon capture     Carbon utilization     Carbon storage     Chemical absorption     Electrochemical conversion    

A Comparison of Incentive Policies for the Optimal Layout of CCUS Clusters in China’s Coal-Fired Power

Wenhui Chen, Xi Lu, Yalin Lei, Jian-Feng Chen

Engineering 2021, Volume 7, Issue 12,   Pages 1692-1695 doi: 10.1016/j.eng.2021.11.011

Bayesian Optimization for Field-Scale Geological Carbon Storage

Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan

Engineering 2022, Volume 18, Issue 11,   Pages 96-104 doi: 10.1016/j.eng.2022.06.011

Abstract:

We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization (BO) for injection well scheduling optimization in geological carbon sequestration. This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage. The implicit parallel accurate reservoir simulator (IPARS) is utilized to accurately capture the underlying physical processes during CO2 sequestration. IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations. In this work, we use the compositional flow module to simulate the geological carbon storage process. The compositional flow model, which includes a hysteretic three-phase relative permeability model, accounts for three major CO2 trapping mechanisms: structural trapping, residual gas trapping, and solubility trapping. Furthermore, IPARS is coupled to the International Business Machines (IBM) Corporation Bayesian Optimization Accelerator (BOA) for parallel optimizations of CO2 injection  strategies during field-scale CO2 sequestration. BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm—the Gaussian process regression, and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample. The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel (batch) executions, scales better for high-dimensional problems, and is more robust to initializations. We demonstrate these merits by applying the algorithm in the optimization of the CO2 injection schedule in the Cranfield site in Mississippi, USA, using field data. The optimized injection schedule achieves 16% more gas storage volume and 56% less water/surfactant usage compared with the baseline. The performance of BO is compared with that of a genetic algorithm (GA) and a covariance matrix adaptation (CMA)-evolution strategy (ES). The results demonstrate the superior performance of BO, in that it achieves a competitive objective function value with over 60% fewer forward model evaluations. 

Keywords: Compositional flow     Bayesian optimization     Geological carbon storage     CCUS     Machine learning     AI for    

Title Author Date Type Operation

Development of Carbon Capture, Utilization and Storage Technology in China

Zhang Xian, Li Yang, Ma Qiao, Liu Lingna

Journal Article

System integration of CCS, CCUS, CCRS, CMC

Jin Yong, Zhu Bing, Hu Shanying, Hong Liyun

Journal Article

Research on Technology Directions and Roadmap of CCS/CCUS for Coal-Fired Power Generation in China

Ye Yunyun, Liao Haiyan, Wang Peng, Wang Junwei, Li Quansheng

Journal Article

Technical Perspective of Carbon Capture, Utilization, and Storage

Qingyang Lin, Xiao Zhang, Tao Wang, Chenghang Zheng, Xiang Gao

Journal Article

A Comparison of Incentive Policies for the Optimal Layout of CCUS Clusters in China’s Coal-Fired Power

Wenhui Chen, Xi Lu, Yalin Lei, Jian-Feng Chen

Journal Article

Bayesian Optimization for Field-Scale Geological Carbon Storage

Xueying Lu, Kirk E. Jordan, Mary F. Wheeler, Edward O. Pyzer-Knapp, Matthew Benatan

Journal Article