期刊
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
卷 60, 期 8, 页码 3452-3462出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.iecr.0c05474
关键词
-
资金
- Natural Science Basic Research Plan in Shaanxi Province of China [2020JQ-764]
- State Key Laboratory of Heavy Oil Processing [SKLHOP201804]
The study applied artificial neural network (ANN) method to predict solid holdup in a gas-solid circulating fluidized bed (CFB) riser, improving prediction accuracy with an enhanced rule, and verifying the reproducibility and applicability of ANN development process.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据