{"title":"Investigation of Hydrodynamics Characteristics of Fluidized Bed with back propagation Artificial Neural Network (BPANN)","authors":"Dhamyaa Saad Khudhur, Saad Obied Esmaiel","doi":"10.30574/gjeta.2024.18.1.0251","DOIUrl":null,"url":null,"abstract":"Correlations have also been developed with system parameters by using dimensional analysis and an artificial neural network approach. The paper describes an investigation for the thermal design of a fluidized bed cooler and prediction of heat transfer rate among the media categories. It is devoted to the thermal design of such equipment and their application in the industrial fields. In the present work, an extensive ANN by using back propagation (BP) has been carried out to correlate the expansion ratio, fluctuation ratio in gas-solid fluidized bed. Back propagation network is the most well-known and widely used among the current types of neural network system, several applications of ANN for modeling of nonlinear process systems and subsequent control were reported. In back-propagation, different ANN structures (I×H×O) with varying number of neurons in the hidden layer were used as a tool for training input and output data for prediction value of hydrodynamic characteristics of the bed system. It was noted that ring models are the best ones in reducing bed expansion ratio and fluctuation ratio around (25%) and (23.22%).","PeriodicalId":402125,"journal":{"name":"Global Journal of Engineering and Technology Advances","volume":"11 22","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Journal of Engineering and Technology Advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30574/gjeta.2024.18.1.0251","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Correlations have also been developed with system parameters by using dimensional analysis and an artificial neural network approach. The paper describes an investigation for the thermal design of a fluidized bed cooler and prediction of heat transfer rate among the media categories. It is devoted to the thermal design of such equipment and their application in the industrial fields. In the present work, an extensive ANN by using back propagation (BP) has been carried out to correlate the expansion ratio, fluctuation ratio in gas-solid fluidized bed. Back propagation network is the most well-known and widely used among the current types of neural network system, several applications of ANN for modeling of nonlinear process systems and subsequent control were reported. In back-propagation, different ANN structures (I×H×O) with varying number of neurons in the hidden layer were used as a tool for training input and output data for prediction value of hydrodynamic characteristics of the bed system. It was noted that ring models are the best ones in reducing bed expansion ratio and fluctuation ratio around (25%) and (23.22%).
此外,还利用维度分析和人工神经网络方法建立了与系统参数的相关性。本文介绍了流化床冷却器的热设计调查和介质类别之间的传热率预测。它致力于此类设备的热设计及其在工业领域的应用。在本研究中,利用反向传播(BP)进行了广泛的 ANN,以关联气固流化床中的膨胀比和波动比。反向传播网络是目前各类神经网络系统中最著名、应用最广泛的一种,已有多篇关于 ANN 应用于非线性过程系统建模和后续控制的报道。在反向传播中,使用了不同的 ANN 结构(I×H×O)和不同数量的隐层神经元作为训练输入和输出数据的工具,以预测床系统的流体力学特性值。结果表明,环形模型在降低床面膨胀率和波动率方面效果最好,分别为(25%)和(23.22%)。