relation: https://khub.utp.edu.my/scholars/20075/
title: Explosion pressure and duration prediction using machine learning: A comparative study using classical models with Adam-optimized neural network
creator: Idris, A.M.
creator: Rusli, R.
creator: Mohamed, M.E.
creator: Ramli, A.F.
creator: Nasif, M.S.
creator: Lim, J.S.
description: The application of machine learning (ML) for the prediction of gas explosion pressure remains limited, and the prediction of the explosion duration is nearly non-existent. A series of dispersion and subsequent explosion computational fluid dynamics (CFD) simulations were conducted to determine explosion pressure and duration values. These results were used to train classical ML models, that is, support vector regression (SVR), random forest (RF), and decision tree (DT) models. Additionally, a multi-output Adam-optimized artificial neural network (ANN) model was employed for performance comparison. All the models demonstrated respectable predictions for both parameters, while the RF model demonstrated the highest performance based on the metrics analyzed, followed by the DT model. The proposed gas volume and gas volume blockage ratio (gas-VBR) emerged as the most crucial feature for predicting explosion pressure, while the monitoring point and gas-VBR was the most important feature for explosion duration. It is recommended to consider the gas-VBR feature in future studies rather than solely focusing on blockage ratio or obstacle location. The model proposed was compared with models from previous studies for predicting explosion pressure. The findings conclusively demonstrate that the multi-output model outperforms the compared models, offering a notable advantage in its ability to predict both gas explosion pressure and duration. © 2024 Canadian Society for Chemical Engineering.
date: 2024
type: Article
type: PeerReviewed
identifier:   Idris, A.M. and Rusli, R. and Mohamed, M.E. and Ramli, A.F. and Nasif, M.S. and Lim, J.S.  (2024) Explosion pressure and duration prediction using machine learning: A comparative study using classical models with Adam-optimized neural network.  Canadian Journal of Chemical Engineering.       
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189878777&doi=10.1002%2fcjce.25258&partnerID=40&md5=7fb0f1ef0b61694f307c452d7225c3f8
relation: 10.1002/cjce.25258
identifier: 10.1002/cjce.25258