Advanced integration strategies and machine learning-based superstructure optimization for Power-to-Methanol

Dat-Nguyen Vo, Meng Qi, Chang-Ha Lee and Xunyuan Yin

Applied Energy, 2025, vol. 378, issue PA, No S0306261924021147

Abstract: The Power-to-methanol (PtMe) process faces significant challenges, including high production costs, low energy efficiency, and a lack of systematic and applicable integrated design and superstructure optimization methods. This study proposes advanced integration and machine learning (ML)-based superstructure optimization approaches that aim to enhance the performance of the PtMe process. Alkaline water electrolyzer (AWE), polymer electrolyte membrane electrolyzer (PEM), and solid oxide electrolyzer (SOE) are chosen for investigation due to their high technology readiness levels. The validated mathematical models for these electrolyzers are integrated with other units to form 3 conventional and 12 advanced designs. The conventional designs comprise electrolyzer-based H2 and CO2-to-methanol sections. In contrast, the advanced designs integrate these sections with four waste-utility reutilization strategies, including heat (H), heat and steam (HS), heat and power (HP), and heat, steam, and power (HSP) generations. A techno-economic analysis demonstrates the pivotal role of electrolyzers in the PtMe process. Two deep neural networks (DNN) models are developed to represent the superstructure design of the PtMe process. With marginal training and test errors (0.28% and 1.03%), the one-hot vector-DNN (OHV-DNN) model is selected to formulate four optimization problems, identifying the PtMe-SOE-HSP and PtMe-AWE-HSP designs as optimal solutions for minimizing energy consumption and production cost considering carbon tax. The PtMe-AWE and PtMe-SOE designs are the best candidates among the conventional designs. Compared to the optimal conventional designs, the optimal advanced designs improve the techno-economic-environmental performance by 1.8–29.7%. Additionally, compared to the PtMe-AWE-HSP design, the PtMe-SOE-HSP design achieves a 4.3% reduction in net CO2 reduction and a 10.2% reduction in energy consumption. Then, an economic analysis reveals the PtMe-SOE-HSP design as the superior design under scenarios of reduced electrolyzer CAPEX and increased electrolyzer lifetime. These findings are valuable for improving the techno-economic-environmental performance of the PtMe process. Moreover, the proposed integration strategies and ML-based superstructure optimization approach hold the promise for enhancing other power-to-liquid processes.

Keywords: Power-to-Methanol; Electrolyzer; Process design and integration; Machine learning-based superstructure optimization; One-hot vector-deep neural networks (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924021147
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:appene:v:378:y:2025:i:pa:s0306261924021147

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124731

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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