Hybrid-modeling for PTFE polymerization reaction with deep learning-based reaction rate model

References

Azarpour, A., T. Borhani, S. R. W. Alwi, Z. A. Manan, and M. I. A. Mutalib. 2017. “A Generic Hybrid Model Development for Process Analysis of Industrial Fixed-Bed Catalytic Reactors.” Chemical Engineering Research and Design 117: 149–67. https://doi.org/10.1016/j.cherd.2016.10.024.Search in Google Scholar

Bengio, Y., P. Simard, and P. Frasconi. 1994. “Learning Long-Term Dependencies with Gradient Descent is Difficult.” IEEE Transactions on Neural Networks 5 (2): 157–66. https://doi.org/10.1109/72.279181.Search in Google Scholar PubMed

Bhaskar, V., S. K. Gupta, and A. K. Ray. 2001. “Multiobjective Optimization of an Industrial Wiped Film Poly (Ethylene Terephthalate) Reactor: Some Further Insights.” Computers & Chemical Engineering 25 (2–3): 391–407. https://doi.org/10.1016/s0098-1354(00)00665-7.Search in Google Scholar

Crowley, T. J., and K. Y. Choi. 1997. “Calculation of Molecular Weight Distribution from Molecular Weight Moments in Free Radical Polymerization.” Industrial & Engineering Chemistry Research 36 (5): 1419–23. https://doi.org/10.1021/ie960623e.Search in Google Scholar

Chowdhury, S., S. K. Lahiri, A. Hens, and S. Katiyar. 2021. “Performance Enhancement of Commercial Ethylene Oxide Reactor by Artificial Intelligence Approach.” International Journal of Chemical Reactor Engineering 20 (2): 237–50. https://doi.org/10.1515/ijcre-2020-0230.Search in Google Scholar

Dubé, M. A., J. B. Soares, A. Penlidis, and A. E. Hamielec. 1997. “Mathematical Modeling of Multicomponent Chain-Growth Polymerizations in Batch, Semibatch, and Continuous Reactors: A Review.” Industrial & Engineering Chemistry Research 36 (4): 966–1015. https://doi.org/10.1021/ie960481o.Search in Google Scholar

Embiruçu, M., D. M. Prata, E. L. Lima, and J. C. Pinto. 2008. “Continuous Soluble Ziegler-Natta Ethylene Polymerizations in Reactor Trains, 2 – Estimation of Kinetic Parameters from Industrial Data.” Macromolecular Reaction Engineering 2 (2): 142–60. https://doi.org/10.1002/mren.200700046.Search in Google Scholar

Embirucu, M., E. L. Lima, and J. C. Pinto. 1996. “A Survey of Advanced Control of Polymerization Reactors.” Polymer Engineering & Science 36 (4): 433–47. https://doi.org/10.1002/pen.10430.Search in Google Scholar

François, G., B. Srinivasan, D. Bonvin, J. Hernandez Barajas, and D. Hunkeler. 2004. “Run-to-run Adaptation of a Semiadiabatic Policy for the Optimization of an Industrial Batch Polymerization Process.” Industrial & Engineering Chemistry Research 43 (23): 7238–42. https://doi.org/10.1021/ie034330e.Search in Google Scholar

Ge, Z. 2017. “Review on Data-Driven Modeling and Monitoring for Plant-wide Industrial Processes.” Chemometrics and Intelligent Laboratory Systems 171: 16–25. https://doi.org/10.1016/j.chemolab.2017.09.021.Search in Google Scholar

Ghiba, L., E. N. Drăgoi, and S. Curteanu. 2021. “Neural Network‐based Hybrid Models Developed for Free Radical Polymerization of Styrene.” Polymer Engineering & Science 61 (3): 716–30. https://doi.org/10.1002/pen.25611.Search in Google Scholar

Hatzantonis, H. P. 2021. “Estimation of the Optimum Propane Content for the Spheripol Polypropylene Process.” Journal of Process Control 99: 1–18. https://doi.org/10.1016/j.jprocont.2021.01.002.Search in Google Scholar

Hangos, K., and I. Cameron. 2001. Process Modelling and Model Analysis. San Diego: Academic Press.Search in Google Scholar

Han, I. S., C. Han, and C. B. Chung. 2005. “Melt Index Modeling with Support Vector Machines, Partial Least Squares, and Artificial Neural Networks.” Journal of Applied Polymer Science 95 (4): 967–74. https://doi.org/10.1002/app.20979.Search in Google Scholar

Hinton, G. E., S. Osindero, and Y. W. Teh. 2006. “A Fast Learning Algorithm for Deep Belief Nets.” Neural Computation 18 (7): 1527–54. https://doi.org/10.1162/neco.2006.18.7.1527.Search in Google Scholar PubMed

Hosen, M. A., A. Khosravi, S. Nahavandi, and D. Creighton. 2014. “Prediction Interval-Based Neural Network Modelling of Polystyrene Polymerization Reactor – A New Perspective of Data-Based Modelling.” Chemical Engineering Research and Design 92 (11): 2041–51. https://doi.org/10.1016/j.cherd.2014.02.016.Search in Google Scholar

Hosen, M. A., M. A. Hussain, and F. S. Mjalli. 2011. “Hybrid Modelling and Kinetic Estimation for Polystyrene Batch Reactor Using Artificial Neutral Network (ANN) Approach.” Asia-Pacific Journal of Chemical Engineering 6 (2): 274–87. https://doi.org/10.1002/apj.435.Search in Google Scholar

Jiang, L., W. Huang, X. Xue, H. Yang, B. Jiang, D. Zhang, and S. Wang. 2012. “Radical Polymerization in the Presence of Chain Transfer Monomer: An Approach to Branched Vinyl Polymers.” Macromolecules 45 (10): 4092–100. https://doi.org/10.1021/ma300443g.Search in Google Scholar

Jiang, Q., X. Yan, H. Yi, and F. Gao. 2019. “Data-driven Batch-End Quality Modeling and Monitoring Based on Optimized Sparse Partial Least Squares.” IEEE Transactions on Industrial Electronics 67 (5): 4098–107. https://doi.org/10.1109/tie.2019.2922941.Search in Google Scholar

Khatibisepehr, S., B. Huang, and S. Khare. 2013. “Design of Inferential Sensors in the Process Industry: A Review of Bayesian Methods.” Journal of Process Control 23 (10): 1575–96. https://doi.org/10.1016/j.jprocont.2013.05.007.Search in Google Scholar

Kumar, B. S., and C. Venkateswarlu. 2012. “Estimating Biofilm Reaction Kinetics Using Hybrid Mechanistic-Neural Network Rate Function Model.” Bioresource Technology 103 (1): 300–8. https://doi.org/10.1016/j.biortech.2011.10.006.Search in Google Scholar PubMed

Lemoine-Nava, R., A. Flores-Tlacuahuac, and E. Saldívar-Guerra. 2006. “Optimal Operating Policies for the Nitroxide-Mediated Radical Polymerization of Styrene in a Semibatch Reactor.” Industrial & Engineering Chemistry Research 45 (13): 4637–52. https://doi.org/10.1021/ie050849u.Search in Google Scholar

Li, H. X., and H. Si. 2017. “Control for Intelligent Manufacturing: A Multiscale Challenge.” Engineering 3 (5): 608–15. https://doi.org/10.1016/j.eng.2017.05.016.Search in Google Scholar

Luo, N., W. Du, Z. Ye, and F. Qian. 2012. “Development of a Hybrid Model for Industrial Ethylene Oxide Reactor.” Industrial & Engineering Chemistry Research 51 (19): 6926–32. https://doi.org/10.1021/ie202619d.Search in Google Scholar

Mejdell, T., T. Pettersen, C. Naustdal, and H. F. Svendsen. 1999. “Modeling of Industrial S-PVC Reactor.” Chemical Engineering Science 54 (13–14): 2459–66. https://doi.org/10.1016/s0009-2509(98)00400-x.Search in Google Scholar

Noor, R. M., Z. Ahmad, M. M. Don, and M. H. Uzir. 2010. “Modelling and Control of Different Types of Polymerization Processes Using Neural Networks Technique: A Review.” Canadian Journal of Chemical Engineering 88 (6): 1065–84. https://doi.org/10.1002/cjce.20364.Search in Google Scholar

Piuleac, C. G., and S. Curteanu. 2010. “Different Methods of Neural Network Based Modelling for Polymerization Process.” Materiale Plastice 47 (3): 311–8.Search in Google Scholar

Plehiers, P. P., S. H. Symoens, I. Amghizar, G. B. Marin, C. V. Stevens, and K. M. Van Geem. 2019. “Artificial Intelligence in Steam Cracking Modeling: A Deep Learning Algorithm for Detailed Effluent Prediction.” Engineering 5 (6): 1027–40. https://doi.org/10.1016/j.eng.2019.02.013.Search in Google Scholar

Pal, M. 2021. “On Application of Machine Learning Method for History Matching and Forecasting of Times Series Data from Hydrocarbon Recovery Process Using Water Flooding.” Petroleum Science and Technology 39 (15–16): 519–49. https://doi.org/10.1080/10916466.2021.1918712.Search in Google Scholar

Ray, W. H. 1972. “On the Mathematical Modeling of Polymerization Reactors.” Journal of Macromolecular Science—Reviews in Macromolecular Chemistry 8 (1): 1–56. https://doi.org/10.1080/15321797208068168.Search in Google Scholar

Sun, Q., and Z. Ge. 2021. “A Survey on Deep Learning for Data-Driven Soft Sensors.” IEEE Transactions on Industrial Informatics 17 (9): 5853–66. https://doi.org/10.1109/tii.2021.3053128.Search in Google Scholar

Shewalkar, A., D. Nyavanandi, and S. A. Ludwig. 2019. “Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU.” Journal of Artificial Intelligence and Soft Computing Research 9 (4): 235–45. https://doi.org/10.2478/jaiscr-2019-0006.Search in Google Scholar

Sun, J. G., and Q. Cao. 2014. “The Research on Modeling and Simulation of TFE Polymerization Process.” Mathematical Problems in Engineering 2014: 365486, https://doi.org/10.1155/2014/365486.Search in Google Scholar

Tobita, H., and N. Hamashima. 2000. “Monte Carlo Simulation of Size Exclusion Chromatography for Branched Polymers Formed through Free‐radical Polymerization with Chain Transfer to Polymer.” Macromolecular Theory and Simulations 9 (8): 453–62. https://doi.org/10.1002/1521-3919(20001101)9:8<453::aid-mats453>3.0.co;2-a.10.1002/1521-3919(20001101)9:8<453::AID-MATS453>3.0.CO;2-ASearch in Google Scholar

Tian, Y., J. Zhang, and J. Morris. 2002. “Optimal Control of a Batch Emulsion Copolymerisation Reactor Based on Recurrent Neural Network Models.” Chemical Engineering and Processing: Process Intensification 41 (6): 531–8. https://doi.org/10.1016/s0255-2701(01)00173-8.Search in Google Scholar

Thomas, S., A. Hamielec, and J. Soares. 1997. “Free Radical Polymerization – an Elegant Method of Solving the Population Balance Equations with Chain Transfer to Polymer.” Polymer Reaction Engineering 5 (4): 183–203.Search in Google Scholar

Vouyiouka, S. N., E. K. Karakatsani, and C. D. Papaspyrides. 2005. “Solid State Polymerization.” Progress in Polymer Science 30 (1): 10–37.10.1016/j.progpolymsci.2004.11.001Search in Google Scholar

Willis, M. J., and M. Stosch. 2017. “Simultaneous Parameter Identification and Discrimination of the Nonparametric Structure of Hybrid Semi-parametric Models.” Computers & Chemical Engineering 104: 366–76. https://doi.org/10.1016/j.compchemeng.2017.05.005.Search in Google Scholar

Yuan, X., L. Li, Y. A. Shardt, Y. Wang, and C. Yang. 2020. “Deep Learning with Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development.” IEEE Transactions on Industrial Electronics 68 (5): 4404–14. https://doi.org/10.1109/tie.2020.2984443.Search in Google Scholar

Zorina, L. B., and V. P. Mel’nikov. 2008. “Polymerization of Tetrafluoroethylene Initiated by Fluorinated Petroleum Coke.” Polymer Science – Series A 50 (9): 942–7. https://doi.org/10.1134/s0965545x08090022.Search in Google Scholar

Zendehboudi, S., N. Rezaei, and A. Lohi. 2018. “Applications of Hybrid Models in Chemical, Petroleum, and Energy Systems: A Systematic Review.” Applied Energy 228: 2539–66. https://doi.org/10.1016/j.apenergy.2018.06.051.Search in Google Scholar

Zhou, Y. N., and Z. H. Luo. 2016. “State-of-the-art and Progress in Method of Moments for the Model-Based Reversible-Deactivation Radical Polymerization.” Macromolecular Reaction Engineering 10 (6): 516–34. https://doi.org/10.1002/mren.201500080.Search in Google Scholar

Zhang, M., H. Liu, M. Wang, X. Lan, X. Shi, and J. Gao. 2021. “Intelligence Hybrid Modeling Method and Applications in Chemical Process.” Chemical Industry and Engineering Progress 40 (04): 1765–76.Search in Google Scholar