Improved medical waste plasma gasification modelling based on implicit knowledge-guided interpretable machine learning.

Zhou, Jianzhao; Ren, Jingzheng; He, Chang

Zhou, Jianzhao; Ren, Jingzheng; He, Chang.

Afiliación

Zhou J; Research Institute for Advanced Manufacturing, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Ren J; Research Institute for Advanced Manufacturing, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China. Electronic address: jzhren@polyu.edu.hk.
He C; School of Materials Science and Engineering, Guangdong Engineering Centre for Petrochemical Energy Conservation, Sun Yat-sen University, Guangzhou 510275, China.

Waste Manag ; 188: 48-59, 2024 Nov 15.

Article en En | MEDLINE | ID: mdl-39098272

ABSTRACT

ABSTRACT

Ensuring the interpretability of machine learning models in chemical engineering remains challenging due to inherent limitations and data quality issues, hindering their reliable application. In this study, a qualitatively implicit knowledge-guided machine learning framework is proposed to improve plasma gasification modelling. Starting with a pre-trained machine learning model, parameters are further optimized by integrating the heuristic algorithm to minimize the data fitting errors and resolving implicit monotonic inconsistencies. The latter is comprehensively quantified through Monte Carlo simulations. This framework is adaptive to different machine learning techniques, exemplified by artificial neural network (ANN) and support vector machine (SVM) in this study. Validated by a case study on plasma gasification, the results reveal that the improved models achieve better generalizability and scientific interpretability in predicting syngas quality. Specifically, for ANN, the root mean square error (RMSE) and knowledge-based error (KE) reduce by 36.44% and 83.22%, respectively, while SVM displays a decrease of 2.58% in RMSE and a remarkable 100% in KE. Importantly, the improved models successfully capture all desired implicit monotonicity relationships between syngas quality and feedstock characteristics/operating parameters, addressing a limitation that traditional machine learning struggles with.

Asunto(s)

Aprendizaje Automático; Redes Neurales de la Computación; Máquina de Vectores de Soporte; Gases; Algoritmos; Método de Montecarlo; Modelos Teóricos

Palabras clave

Implicit knowledge-based error; Interpretability; Machine learning; Medical waste; Plasma gasification

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Máquina de Vectores de Soporte / Aprendizaje Automático Idioma: En Revista: Waste Manag Asunto de la revista: SAUDE AMBIENTAL / TOXICOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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