首页|New Machine Learning Study Findings Reported from Monash Uni- versity (Fast Prediction and Control of Air Core In Hydrocyclone By Machine Learning To Stabilize Operations)
New Machine Learning Study Findings Reported from Monash Uni- versity (Fast Prediction and Control of Air Core In Hydrocyclone By Machine Learning To Stabilize Operations)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
2024 FEB 27 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Machine Learning. According to news reporting from Clayton, Australia, by NewsRx journalists, research stated, “Operation stability significantly impacts hydrocyclone separation performance during wastewater treatment, sludge processing, and microplastic removal from water. The air core inside a hydrocyclone is an important indicator of operation stability.” Financial support for this research came from Australian Research Council. The news correspondents obtained a quote from the research from Monash University, “This paper presents a machine learning model designed for fast prediction and control of air core profiles. The model is built upon a modified graph neural network (GNN). It is trained by the data generated from a well- established and validated computational fluid dynamics (CFD) model. This GNNbased surrogate model has undergone two modifications to enhance its prediction accuracy. One is data smoothing, to mitigate the adverse effects of the drastic data change in spatial distributions. The other is the loss function modification to incorporate the air core information acquired by the CFD model. The predicted air cores are compared with the original GNN and random forest (RF) against the CFD results. It shows that the new surrogate model can reproduce air profiles and have higher accuracy than other models in predicting spatial distribution results among different error metrics. Furthermore, this surrogate model is combined with the genetic algorithm to optimize the air core.” According to the news reporters, the research concluded: “The proposed machine learning model framework offers a promising avenue for the prediction and control of hydrocyclones.” This research has been peer-reviewed.
ClaytonAustraliaAustralia and New ZealandComputational Fluid DynamicsCyborgsEmerging TechnologiesFluid MechanicsMachine LearningMonash University
NETL
NSTL
Elsevier
