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Lookup NU author(s): Kangcheng Wang, Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
Coal gasification technology has gained increasing popularity in recent years, butthe optimization of operating conditions remains inefficient. The operation optimizationof the Shell coal gasification process (SCGP) is investigated in this paper usingan operation optimization model integrating data analytics and mechanism analysis.The objective function contains two important indicators. One is effective syngasproductivity and the other one is specific oxygen consumption. The optimization issubject to constraints on gasifier temperature and syngas yield. The objective functionand the constraints can be calculated by six key process variables through threeconvolutional neural network (CNN) models, which can additionally utilize the correlationsbetween process variables. Prior physical knowledge and simplified mechanisticmodels of SCGP are integrated with the development of CNN models. Theeffectiveness of the proposed model is validated by an industrial case study. Afterthe operation optimization, the objective function decreases by 28.3306% comparedwith its minimum value on historical process operation data, which outperforms theoperation optimization model developed by ANN models. The sensitivities of theobjective function and effective syngas yield are analyzed. The operating conditionof SCGP can be optimized by the proposed model.
Author(s): Wang K, Zhang J, Shang C, Huang D
Publication type: Article
Publication status: Published
Journal: Applied Energy
Year: 2021
Volume: 292
Print publication date: 15/06/2021
Online publication date: 07/04/2021
Acceptance date: 16/03/2021
Date deposited: 19/03/2021
ISSN (print): 0306-2619
ISSN (electronic): 1872-9118
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.apenergy.2021.116847
DOI: 10.1016/j.apenergy.2021.116847
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