Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools

Jianzhao Zhou; Tao Shi; Qiming Qian; Chang He; Jingzheng Ren

doi:10.1016/j.xpro.2023.102685

Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools

Jianzhao Zhou, Tao Shi, Qiming Qian, Chang He, Jingzheng Ren

Research output: Journal article publication › Journal article › Academic research › peer-review

10 Citations (Scopus)

Abstract

Amid a surge in waste volume, the need to achieve sustainable waste treatment has become increasingly important. Here, we present a protocol for the design and accelerated optimization of a waste-to-energy system using artificial intelligence tools. We describe steps for waste treatment process advancement as demonstrated by the medical waste-to-methanol conversion and implementing data-driven process optimization. We then detail procedures for streamlining tasks by establishing connectivity between systems such as Aspen Plus and MATLAB. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022)¹ and Fang et al. (2022).²

Original language	English
Article number	102685
Number of pages	22
Journal	STAR Protocols
Volume	4
Issue number	4
DOIs	https://doi.org/10.1016/j.xpro.2023.102685
Publication status	Published - 15 Dec 2023

Keywords

Computer sciences
Energy
Environmental sciences

ASJC Scopus subject areas

General Neuroscience
General Biochemistry,Genetics and Molecular Biology
General Immunology and Microbiology

More information

10.1016/j.xpro.2023.102685

http://hdl.handle.net/10397/108561

Cite this

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title = "Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools",

abstract = "Amid a surge in waste volume, the need to achieve sustainable waste treatment has become increasingly important. Here, we present a protocol for the design and accelerated optimization of a waste-to-energy system using artificial intelligence tools. We describe steps for waste treatment process advancement as demonstrated by the medical waste-to-methanol conversion and implementing data-driven process optimization. We then detail procedures for streamlining tasks by establishing connectivity between systems such as Aspen Plus and MATLAB. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022)1 and Fang et al. (2022).2",

keywords = "Computer sciences, Energy, Environmental sciences",

author = "Jianzhao Zhou and Tao Shi and Qiming Qian and Chang He and Jingzheng Ren",

note = "Funding Information: The work described in this study is supported by a grant from the Research Committee of The Hong Kong Polytechnic University under the student account code RKQ1 and RK3P. The work was also supported by a grant from Research Grants Council of the Hong Kong Special Administrative Region, China (General Research Fund; project ID: P0042030 , funding body ref. no. 15304222 , project no. B-Q97U), a grant from the PROCORE – France/Hong Kong Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the Consulate General of France in Hong Kong (ref. no. F-PolyU501/22 ), and a grant from Research Institute for Advanced Manufacturing (RIAM), The Hong Kong Polytechnic University (PolyU) (project no. 1-CD4J, project ID: P0041367). Publisher Copyright: {\textcopyright} 2023 The Author(s)",

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AU - Zhou, Jianzhao

AU - Shi, Tao

AU - Qian, Qiming

AU - He, Chang

AU - Ren, Jingzheng

N1 - Funding Information: The work described in this study is supported by a grant from the Research Committee of The Hong Kong Polytechnic University under the student account code RKQ1 and RK3P. The work was also supported by a grant from Research Grants Council of the Hong Kong Special Administrative Region, China (General Research Fund; project ID: P0042030 , funding body ref. no. 15304222 , project no. B-Q97U), a grant from the PROCORE – France/Hong Kong Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the Consulate General of France in Hong Kong (ref. no. F-PolyU501/22 ), and a grant from Research Institute for Advanced Manufacturing (RIAM), The Hong Kong Polytechnic University (PolyU) (project no. 1-CD4J, project ID: P0041367). Publisher Copyright: © 2023 The Author(s)

PY - 2023/12/15

Y1 - 2023/12/15

N2 - Amid a surge in waste volume, the need to achieve sustainable waste treatment has become increasingly important. Here, we present a protocol for the design and accelerated optimization of a waste-to-energy system using artificial intelligence tools. We describe steps for waste treatment process advancement as demonstrated by the medical waste-to-methanol conversion and implementing data-driven process optimization. We then detail procedures for streamlining tasks by establishing connectivity between systems such as Aspen Plus and MATLAB. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022)1 and Fang et al. (2022).2

AB - Amid a surge in waste volume, the need to achieve sustainable waste treatment has become increasingly important. Here, we present a protocol for the design and accelerated optimization of a waste-to-energy system using artificial intelligence tools. We describe steps for waste treatment process advancement as demonstrated by the medical waste-to-methanol conversion and implementing data-driven process optimization. We then detail procedures for streamlining tasks by establishing connectivity between systems such as Aspen Plus and MATLAB. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022)1 and Fang et al. (2022).2

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KW - Energy

KW - Environmental sciences

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ER -

Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools

Abstract

Keywords

ASJC Scopus subject areas

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