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Prediction of Solid Holdup in a Gas–Solid Circulating Fluidized Bed Riser by Artificial Neural Networks
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2021-02-16 , DOI: 10.1021/acs.iecr.0c05474 Hanbin Zhong 1, 2 , Zeneng Sun 2 , Jesse Zhu 2 , Chao Zhang 3
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2021-02-16 , DOI: 10.1021/acs.iecr.0c05474 Hanbin Zhong 1, 2 , Zeneng Sun 2 , Jesse Zhu 2 , Chao Zhang 3
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The artificial neural network (ANN) method was applied to predict the solid holdup in a gas–solid circulating fluidized bed (CFB) riser. All the possible ANNs were first developed by looping the hidden neurons from the minimum (3) to the maximum (number of training data) and performing 500 independent runs for the same ANN structure. Then, an improved rule for finding the best ANN was proposed with the help of the expected range of the predicted solid holdup based on the existing data under training conditions. The accuracy of the prediction for test conditions was significantly enhanced by using the improved rule. The reproducibility and applicability of the proposed ANN development process were fully examined by repeating several times on the same sample and applying to different samples, respectively.
中文翻译：
用人工神经网络预测气固循环流化床提升管中的固相含率。
人工神经网络（ANN）方法用于预测气固循环流化床（CFB）立管中的固含量。首先，通过将隐藏的神经元从最小值（3）循环到最大值（训练数据的数量）并针对相同的ANN结构执行500次独立运行，来开发所有可能的ANN。然后，根据训练条件下的现有数据，借助预测的固含量的预期范围，提出了一种用于寻找最佳人工神经网络的改进规则。通过使用改进的规则，可以大大提高测试条件预测的准确性。拟议的人工神经网络开发过程的可重复性和适用性通过分别在同一样本上重复几次并分别应用于不同样本进行了全面检查。
更新日期：2021-03-03
中文翻译：
用人工神经网络预测气固循环流化床提升管中的固相含率。
人工神经网络（ANN）方法用于预测气固循环流化床（CFB）立管中的固含量。首先，通过将隐藏的神经元从最小值（3）循环到最大值（训练数据的数量）并针对相同的ANN结构执行500次独立运行，来开发所有可能的ANN。然后，根据训练条件下的现有数据，借助预测的固含量的预期范围，提出了一种用于寻找最佳人工神经网络的改进规则。通过使用改进的规则，可以大大提高测试条件预测的准确性。拟议的人工神经网络开发过程的可重复性和适用性通过分别在同一样本上重复几次并分别应用于不同样本进行了全面检查。
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