Advanced process integration and machine learning-based optimization to enhance techno-economic-environmental performance of CO2 capture and conversion to methanol

Zhiwei Zhang, Dat-Nguyen Vo, Tuan B.H. Nguyen, Jinsheng Sun and Chang-Ha Lee

Energy, 2024, vol. 293, issue C

Abstract: Developing economically sustainable CO2 capture and conversion processes is essential to realize carbon neutrality. This study proposed an integrated process for CO2 capture and conversion-to-methanol (CCTM) and applied machine learning-based optimization to enhance techno-economic-environmental performance. After validating CO2 capture and CO2-to-methanol sections, an advanced CCTM design was developed and compared with conventional one regarding techno-economic-environmental performance across various operating scenarios. The advanced CCTM exhibited significant improvements in energy consumption (14.73–16.30%), production cost (0.81–1.28%), and net CO2 reduction (3.13–3.38%) owing to efficiently reusing waste heat, off-gas, and water resources. The one-at-a-time sensitivity analysis revealed roles of each variable and nonlinear variable-performance tendencies among operating variables in the advanced CCTM process. Subsequently, a well-developed deep neural network (DNN) model precisely formulated the relationship between key variables and performances. The DNN-based optimization provided optimum operating conditions within a minute, resulting in an 8.21 $/tMeOH (∼0.81%) reduction in production cost compared to base case of CCTM. Notably, the total CO2 capture rate of 92.53% at an optimal condition highlighted the significant contribution of advanced CCTM to carbon neutrality. The findings provide a viable reference for the effective and sustainable design and operation of an integrated CCTM process.

Keywords: CO2 capture and conversion to methanol; High CO2 capture rate; Process integration; Machine learning-based optimization; Techno-economic-environmental performance (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224005309
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:293:y:2024:i:c:s0360544224005309

DOI: 10.1016/j.energy.2024.130758

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().