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Application of artificial neural networks in the prediction of slurry erosion performance: a comprehensive review
https://orcid.org/http://orcid.org/0000-0002-1668-8765
The prevalence of artificial intelligence (AI) is driving the acceptance of various machine learning (ML) approaches. With artificial neural networks (ANN), a comprehensive physical model of the system is not necessary, allowing for a "black-box" modeling approach. It makes more sense to only look at the data from the inputs and outputs when trying to understand how the system worked. As AI has recently emerged to predict slurry erosion rates, ANNs have been the subject of substantial research. This study provided an in-depth analysis of the roles of ANN in slurry erosion predictions. Slurry erosion causes significant damage to equipment components in workplaces, such as hydraulic turbines and slurry pipelines. This damage is so severe that the affected machine parts cannot be fixed and must be replaced with new ones promptly. The popularity of AI has increased, as it may be used for other tasks such as site selection, parameter evaluation, and optimization of operation and maintenance. This study presents a comprehensive analysis of the use of AI in slurry erosion predictions within the hydropower (turbine blades) and mining industry using heavy duty pumps or impellers. The utilization of ML and big data management techniques is becoming imperative in order to enhance industry efficiency.
Application of artificial neural networks in the prediction of slurry erosion performance: a comprehensive review
https://orcid.org/http://orcid.org/0000-0002-1668-8765
The prevalence of artificial intelligence (AI) is driving the acceptance of various machine learning (ML) approaches. With artificial neural networks (ANN), a comprehensive physical model of the system is not necessary, allowing for a "black-box" modeling approach. It makes more sense to only look at the data from the inputs and outputs when trying to understand how the system worked. As AI has recently emerged to predict slurry erosion rates, ANNs have been the subject of substantial research. This study provided an in-depth analysis of the roles of ANN in slurry erosion predictions. Slurry erosion causes significant damage to equipment components in workplaces, such as hydraulic turbines and slurry pipelines. This damage is so severe that the affected machine parts cannot be fixed and must be replaced with new ones promptly. The popularity of AI has increased, as it may be used for other tasks such as site selection, parameter evaluation, and optimization of operation and maintenance. This study presents a comprehensive analysis of the use of AI in slurry erosion predictions within the hydropower (turbine blades) and mining industry using heavy duty pumps or impellers. The utilization of ML and big data management techniques is becoming imperative in order to enhance industry efficiency.
Application of artificial neural networks in the prediction of slurry erosion performance: a comprehensive review
https://orcid.org/http://orcid.org/0000-0002-1668-8765
The prevalence of artificial intelligence (AI) is driving the acceptance of various machine learning (ML) approaches. With artificial neural networks (ANN), a comprehensive physical model of the system is not necessary, allowing for a "black-box" modeling approach. It makes more sense to only look at the data from the inputs and outputs when trying to understand how the system worked. As AI has recently emerged to predict slurry erosion rates, ANNs have been the subject of substantial research. This study provided an in-depth analysis of the roles of ANN in slurry erosion predictions. Slurry erosion causes significant damage to equipment components in workplaces, such as hydraulic turbines and slurry pipelines. This damage is so severe that the affected machine parts cannot be fixed and must be replaced with new ones promptly. The popularity of AI has increased, as it may be used for other tasks such as site selection, parameter evaluation, and optimization of operation and maintenance. This study presents a comprehensive analysis of the use of AI in slurry erosion predictions within the hydropower (turbine blades) and mining industry using heavy duty pumps or impellers. The utilization of ML and big data management techniques is becoming imperative in order to enhance industry efficiency.
Application of artificial neural networks in the prediction of slurry erosion performance: a comprehensive review
Int J Interact Des Manuf
Prashar, Gaurav (author) / Vasudev, Hitesh (author)
2025-03-01
19 pages
Article (Journal)
Electronic Resource
English
AI , Slurry erosion , ML , ANN Information and Computing Sciences , Artificial Intelligence and Image Processing , Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design
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