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[3] Zhu B, Ren S*, Weng Q, Si F. A physics-informed neural network that considers monotonic relationships for predicting NOx emissions from coal-fired boilers [J]. Fuel, 2024, 364: 131026.
[4] Ren S*, Wu S, Weng Q, Zhu B, Deng Z. Disentangled Representation Aided Physics-Informed Neural Network for Predicting Syngas Compositions of Biomass Gasification [J]. Energy & Fuels, 2024, 38(3): 2033-2045.
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[6] Ren S*, Wu S, Weng Q. Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships[J]. Bioresource Technology, 2023, 369: 128472.
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[9] Ren S*, Si F, Cao Y. Development of Input Training Neural Networks for Multiple Sensor Fault Isolation[J]. IEEE Sensors Journal, 2022, 22(15): 14997-15009.
[10] Wang P, Ren S*, Wang Y, et al. Quality-related nonlinear process monitoring of power plant by a novel hybrid model based on variational autoencoder[J]. Control Engineering Practice, 2022, 129: 105359.
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[13] Fan W, Zhu Q*, Ren S*, L Zhang, F Si. Dynamic probabilistic predictable feature analysis for multivariate temporal process monitoring[J]. IEEE Transactions on Control Systems Technology, 2022, 30(6): 2573-2584.
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[15] Ren S, Si F*, Gu H. Multiple sensor validation for natural gas combined cycle power plants based on robust input training neural networks[J]. Journal of Chemical Engineering of Japan, 2017, 50(3): 186-194.
[16] Ren S*, Charles J, Wang X C, et al. Corrosion testing of metals in contact with calcium chloride hexahydrate used for thermal energy storage[J]. Materials and Corrosion, 2017, 68(10): 1046-1056.