Interpretable machine learning-assisted advanced exergy optimization for carbon-neutral olefins production

Qingchun Yang, Lei Zhao, Runjie Bao, Yingjie Fan, Jianlong Zhou, Dongwen Rong, Huairong Zhou and Dawei Zhang

Renewable and Sustainable Energy Reviews, 2025, vol. 208, issue C

Abstract: The CO2-to-light olefins technology represents a significant approach to mitigating the greenhouse effect and advancing green energy solutions. However, little literature comprehensively analyzes and optimizes its thermodynamic performance. This study proposes an interpretable machine learning-assisted advanced exergy analysis and optimization framework to ascertain the actual improvement potential and determine effective strategies for optimizing this system. The advanced exergy analysis method aims to identify the avoidable exergy destruction and interactions between components of the system, while integrating an interpretable machine learning model to provide the key parameters for enhancing the system's exergy efficiency through feature importance analysis. The findings indicate that the exergy destruction of the system amounts to 656.06 MW, with 96.81 % of this exergy destruction being attributed to endogenous factors and approximately 66.51 % of it being potentially avoidable. The random forest model, exhibiting superior predictive accuracy compared to other machine learning models, is coupled with the interpretable Shapley additive explanation approach to discern the most crucial parameters of the system. Results indicated catalyst properties have the greatest impact on the output performance of the system, contributing up to 66.1 % to the predicted results. The active component type, reaction temperature, and promoter content have the largest contribution to the prediction of CO2 conversion ratio and light olefins selectivity. Furthermore, the key input features are optimized by screening for better catalysts and conducting sensitivity analysis. After optimization, the system's avoidable exergy destruction is significantly saved by 32.27 %, resulting in an enhancement in exergy efficiency by 8.12 %.

Keywords: CO2 to light olefins; Advanced exergy analysis; Machine learning; System optimization; Actual improvement potential (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032124007536
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:rensus:v:208:y:2025:i:c:s1364032124007536

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2024.115027

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

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