Operation optimization of Shell coal gasification process based on convolutional neural network models

Kangcheng Wang, Jie Zhang, Chao Shang and Dexian Huang

Applied Energy, 2021, vol. 292, issue C, No S030626192100341X

Abstract: Coal gasification technology has gained increasing popularity in recent years, but the optimization of operating conditions remains inefficient. The operation optimization of the Shell coal gasification process (SCGP) is investigated in this paper using an operation optimization model integrating data analytics and mechanism analysis. The objective function contains two important indicators. One is effective syngas productivity and the other one is specific oxygen consumption. The optimization is subject to constraints on gasifier temperature and syngas yield. The objective function and the constraints can be calculated by six key operating parameters through three convolutional neural network (CNN) models, which can additionally utilize the correlations between process variables. Prior physical knowledge and a simplified mechanistic model of SCGP are integrated with the development of CNN models. The effectiveness of the proposed model is validated by an industrial case study. After the operation optimization, the objective function decreases by 28.3306% compared with its minimum value on historical process operation data, which outperforms the operation optimization model developed by artificial neural network models. The sensitivities of the objective function and effective syngas yield are analyzed. The operating condition of SCGP can be optimized by the proposed model.

Keywords: Convolutional neural network; Operation optimization; Shell coal gasification process; Prior physical knowledge; Simplified mechanistic model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192100341X
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:292:y:2021:i:c:s030626192100341x

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.2021.116847

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 ().