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Process Optimization of Carbon Elect...

Cincotta, Robert E. F.,

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Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques // Robert E. F Cincotta.
作者:	Cincotta, Robert E. F.,
面頁冊數:	1 electronic resource (215 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Chemical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31238928
ISBN:	9798382726755

Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques /
Cincotta, Robert E. F.,

Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques /Robert E. F Cincotta. - 1 electronic resource (215 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Electrospun carbon fibers from coal have been investigated as electrodes for batteries and supercapacitors. Despite the excellent properties of coal-derived carbon fibers (CCNF) for energy storage devices, there still lacks systematic understanding on how various process parameters affect final electrode performances, which poses challenges to scale from pilot to high volume manufacturing. The goals of this project are twofold. First, we focuse on process optimization for converting a new precursor from powder river basin (PRB) coal, referred to as coal-based polyurethane (CPU) to CCNF using electrospinning. Second, different machine learning techniques will be examined using experimental data from this work and open literature. Specifically, for CPU the following process parameters need to be characterized and optimized in order to produce CCNFs with desirable mechanical integrity and physiochemical properties: precursor composition and viscosity, operating voltage and distance, oxidation and carbonization temperature and duration. Consequently, physiochemical properties of the fibers were characterized to correlate these process parameters with desirable electrochemical performance. Given the complex nature of the fiber production process, ML models are assessed for their ability to capture the nonlinear relationship between process parameters and the electrochemical properties in applications including supercapacitors. As such, we applied various machine learning techniques, to determine which technique produces a model that best predicts device function.

English

ISBN: 9798382726755Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Coal-to-products

Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques /
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520 $a Electrospun carbon fibers from coal have been investigated as electrodes for batteries and supercapacitors. Despite the excellent properties of coal-derived carbon fibers (CCNF) for energy storage devices, there still lacks systematic understanding on how various process parameters affect final electrode performances, which poses challenges to scale from pilot to high volume manufacturing. The goals of this project are twofold. First, we focuse on process optimization for converting a new precursor from powder river basin (PRB) coal, referred to as coal-based polyurethane (CPU) to CCNF using electrospinning. Second, different machine learning techniques will be examined using experimental data from this work and open literature. Specifically, for CPU the following process parameters need to be characterized and optimized in order to produce CCNFs with desirable mechanical integrity and physiochemical properties: precursor composition and viscosity, operating voltage and distance, oxidation and carbonization temperature and duration. Consequently, physiochemical properties of the fibers were characterized to correlate these process parameters with desirable electrochemical performance. Given the complex nature of the fiber production process, ML models are assessed for their ability to capture the nonlinear relationship between process parameters and the electrochemical properties in applications including supercapacitors. As such, we applied various machine learning techniques, to determine which technique produces a model that best predicts device function.
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