TY - GEN
T1 - A Hybrid Modeling Approach for Batch Process Based on LSTM-RNN
AU - Xuesong, Wang
AU - Chao, Dong
AU - Cuimei, Bo
AU - Xiangyu, Zeng
AU - Chengzhi, Wang
AU - Jun, Li
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Batch process is characterized by a complex reaction mechanism and has nonlinear and time-varying properties, which makes it more difficult to establish a mathematical model of the process. Therefore, this paper proposes a hybrid model for aniline hydrogenation. Firstly, the input and output variables of the LSTM-RNN are determined, and the model is trained using the production process data to obtain the data-driven reactivity model for aniline hydrogenation. Then, the reactivity is fitted using the trained model based on the reaction temperature, concentration and other information measured in real time during the production process, and the results are passed to the mechanistic model. Finally, the validation of the model was verified by comparing the hybrid model prediction results with the industrial data of aniline hydrogenation.
AB - Batch process is characterized by a complex reaction mechanism and has nonlinear and time-varying properties, which makes it more difficult to establish a mathematical model of the process. Therefore, this paper proposes a hybrid model for aniline hydrogenation. Firstly, the input and output variables of the LSTM-RNN are determined, and the model is trained using the production process data to obtain the data-driven reactivity model for aniline hydrogenation. Then, the reactivity is fitted using the trained model based on the reaction temperature, concentration and other information measured in real time during the production process, and the results are passed to the mechanistic model. Finally, the validation of the model was verified by comparing the hybrid model prediction results with the industrial data of aniline hydrogenation.
KW - Batch process
KW - Hybrid model
KW - LSTM-RNN
KW - Mechanism model
UR - http://www.scopus.com/inward/record.url?scp=85136104956&partnerID=8YFLogxK
U2 - 10.1109/ICCIA55271.2022.9828435
DO - 10.1109/ICCIA55271.2022.9828435
M3 - 会议稿件
AN - SCOPUS:85136104956
T3 - 2022 7th International Conference on Computational Intelligence and Applications, ICCIA 2022
SP - 1
EP - 5
BT - 2022 7th International Conference on Computational Intelligence and Applications, ICCIA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Computational Intelligence and Applications, ICCIA 2022
Y2 - 24 June 2022 through 26 June 2022
ER -