Compact Temporal Causal Algorithm for Modeling Prediction in the Green Ammonia Production Process
Rui Wang
Science and Technology Innovation Center, CGN Wind Power Co. Ltd, 188 Fengtai Road Street, Beijing, 100071 China
Search for more papers by this authorLili Ma
Science and Technology Innovation Center, CGN Wind Power Co. Ltd, 188 Fengtai Road Street, Beijing, 100071 China
Search for more papers by this authorYukui Ren
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
Search for more papers by this authorHang Zhao
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
Search for more papers by this authorLi Zhou
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
Search for more papers by this authorCorresponding Author
Lei Luo
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Xu Ji
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Ge He
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorRui Wang
Science and Technology Innovation Center, CGN Wind Power Co. Ltd, 188 Fengtai Road Street, Beijing, 100071 China
Search for more papers by this authorLili Ma
Science and Technology Innovation Center, CGN Wind Power Co. Ltd, 188 Fengtai Road Street, Beijing, 100071 China
Search for more papers by this authorYukui Ren
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
Search for more papers by this authorHang Zhao
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
Search for more papers by this authorLi Zhou
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
Search for more papers by this authorCorresponding Author
Lei Luo
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Xu Ji
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorCorresponding Author
Ge He
School of Chemical Engineering, Sichuan University, 24 Wangjiang Road Street, Chengdu, 610065 China
E-mail: [email protected]; [email protected]; [email protected]
Search for more papers by this authorAbstract
Because of the complexity of green ammonia production, identifying useful information from massive data to construct a streamlined and interpretable prediction model is a challenge. This paper proposed an improved transfer entropy method and enhanced the model in capturing causal relationships between variables. Furthermore, a compact temporal causal identification algorithm was introduced, combining transfer entropy and direct causal identification. This algorithm integrates the advantages of both methods, enabling rapid identification of clear causal pathways in high-dimensional variable spaces. In industrial validation of the actual green ammonia process, the average number of predictive variables based on compact temporal-dependent variables and temporal Markov blankets were 3.341 and 10.171, respectively, yielding average prediction accuracies (R2) of 0.887 and 0.905. The method proposed provides new solutions for modeling prediction in green ammonia processes.
Open Research
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Supporting Information
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