Study on Optimal Setting of Calciner Temperature Under Typical Abnormal Working Conditions Based on Rule Based Reasoning

Zhao, Chuanhua; Yuan, Zhugang; Zhang, Qiang; Yin, Zhuangzhuang; Chen, Xiaotong

doi:10.1109/itoec53115.2022.9734713

Cited by 1 publication

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“…Li [ 5 ] proposed a method based on case reasoning, which establishes a knowledge base of cases through experiences, and then provides a value setting according to certain matching rules. Zhao [ 6 ] proposed a rule-based reasoning method and improved Li’s work by adding suggestions on the identification and treatment of abnormal working conditions. However, the principles of these methods are still partially based on operating experiences, and cannot fully reflect the operator’s judgement on the working condition.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Temperature Setting for Decomposition Furnace Based on Attention Mechanism and Neural Networks

Liu,

Shen,

et al. 2023

Sensors

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The temperature setting for a decomposition furnace is of great importance for maintaining the normal operation of the furnace and other equipment in a cement plant and ensuring the output of high-quality cement products. Based on the principles of deep convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms, we propose a CNN-LSTM-A model to optimize the temperature settings for a decomposition furnace. The proposed model combines the features selected by Least Absolute Shrinkage and Selection Operator (Lasso) with others suggested by domain experts as inputs, and uses CNN to mine spatial features, LSTM to extract time series information, and an attention mechanism to optimize weights. We deploy sensors to collect production measurements at a real-life cement factory for experimentation and investigate the impact of hyperparameter changes on the performance of the proposed model. Experimental results show that CNN-LSTM-A achieves a superior performance in terms of prediction accuracy over existing models such as the basic LSTM model, deep-convolution-based LSTM model, and attention-mechanism-based LSTM model. The proposed model has potentials for wide deployment in cement plants to automate and optimize the operation of decomposition furnaces.

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Section: Introductionmentioning

confidence: 99%

“…According to our survey of the state of the arts in this field [ 5 , 6 , 7 ], we believe that this paper presents pioneering work on the applications of neural networks to optimizing the temperature settings of decomposition furnace.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Temperature Setting for Decomposition Furnace Based on Attention Mechanism and Neural Networks

Liu,

Shen,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Study on Optimal Setting of Calciner Temperature Under Typical Abnormal Working Conditions Based on Rule Based Reasoning

Cited by 1 publication

References 0 publications

Optimizing Temperature Setting for Decomposition Furnace Based on Attention Mechanism and Neural Networks

Optimizing Temperature Setting for Decomposition Furnace Based on Attention Mechanism and Neural Networks

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