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

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 adva...

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Detalles Bibliográficos
Autores principales: Zhou, Jianzhao, Shi, Tao, Qian, Qiming, He, Chang, Ren, Jingzheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10622306/
https://www.ncbi.nlm.nih.gov/pubmed/37905497
http://dx.doi.org/10.1016/j.xpro.2023.102685
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author Zhou, Jianzhao
Shi, Tao
Qian, Qiming
He, Chang
Ren, Jingzheng
author_facet Zhou, Jianzhao
Shi, Tao
Qian, Qiming
He, Chang
Ren, Jingzheng
author_sort Zhou, Jianzhao
collection PubMed
description 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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spelling pubmed-106223062023-11-04 Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools Zhou, Jianzhao Shi, Tao Qian, Qiming He, Chang Ren, Jingzheng STAR Protoc Protocol 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) Elsevier 2023-10-30 /pmc/articles/PMC10622306/ /pubmed/37905497 http://dx.doi.org/10.1016/j.xpro.2023.102685 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Zhou, Jianzhao
Shi, Tao
Qian, Qiming
He, Chang
Ren, Jingzheng
Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools
title Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools
title_full Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools
title_fullStr Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools
title_full_unstemmed Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools
title_short Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools
title_sort protocol for the design and accelerated optimization of a waste-to-energy system using ai tools
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10622306/
https://www.ncbi.nlm.nih.gov/pubmed/37905497
http://dx.doi.org/10.1016/j.xpro.2023.102685
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