Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/108561
| Title: | Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools | Authors: | Zhou, J Shi, T Qian, Q He, C Ren, J |
Issue Date: | 15-Dec-2023 | Source: | STAR protocols, 15 Dec. 2023, v. 4, no. 4, 102685 | 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. Graphical abstract: [Figure not available: see fulltext.] | Publisher: | Cell Press | Journal: | STAR protocols | EISSN: | 2666-1667 | DOI: | 10.1016/j.xpro.2023.102685 | Rights: | © 2023 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). The following publication Zhou, J., Shi, T., Qian, Q., He, C., & Ren, J. (2023). Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools. STAR Protocols, 4(4), 102685 is available at https://doi.org/10.1016/j.xpro.2023.102685 |
| Appears in Collections: | Journal/Magazine Article |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 1-s2.0-S2666166723006524-main.pdf | 4.76 MB | Adobe PDF | View/Open |
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