Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model

Wang, Yaolin ORCID: 0000-0003-1932-9810, Liao, Zinan, Mathieu, Stephanie, Bin, Feng and Tu, Xin ORCID: 0000-0002-6376-0897
(2021) Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model. JOURNAL OF HAZARDOUS MATERIALS, 404 (Pt A). 123965-.

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We have developed a hybrid machine learning (ML) model for the prediction and optimization of a gliding arc plasma tar reforming process using naphthalene as a model tar compound from biomass gasification. A linear combination of three well-known algorithms, including artificial neural network (ANN), support vector regression (SVR) and decision tree (DT) has been established to deal with the multi-scale and complex plasma tar reforming process. The optimization of the hyper-parameters of each algorithm in the hybrid model has been achieved by using the genetic algorithm (GA), which shows a fairly good agreement between the experimental data and the predicted results from the ML model. The steam-to-carbon (S/C) ratio is found to be the most critical parameter for the conversion with a relative importance of 38%, while the discharge power is the most influential parameter in determining the energy efficiency with a relative importance of 58%. The coupling effects of different processing parameters on the key performance of the plasma reforming process have been evaluated. The optimal processing parameters are identified achieving the maximum tar conversion (67.2%), carbon balance (81.7%) and energy efficiency (7.8 g/kWh) simultaneously when the global desirability index I<sub>2</sub> reaches the highest value of 0.65.

Item Type: Article
Uncontrolled Keywords: Machine learning, Non-thermal plasma, Biomass gasification, Tar reforming, Naphthalene
Depositing User: Symplectic Admin
Date Deposited: 21 Sep 2020 07:44
Last Modified: 18 Jan 2023 23:32
DOI: 10.1016/j.jhazmat.2020.123965
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