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Identification of Granule Growth Regimes in High Shear Wet Granulation Processes Using a Physics-Constrained Neural Network. Processes (Basel) 2021. [DOI: 10.3390/pr9050737] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The digitization of manufacturing processes has led to an increase in the availability of process data, which has enabled the use of data-driven models to predict the outcomes of these manufacturing processes. Data-driven models are instantaneous in simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first-principle-based models to predict process outcomes have proven to be effective but computationally inefficient and cannot be solved in real time. Thus, there remains a need to develop efficient data-driven models with a physical understanding about the process. In this work, we have demonstrate the addition of physics-based boundary conditions constraints to a neural network to improve its predictability for granule density and granule size distribution (GSD) for a high shear granulation process. The physics-constrained neural network (PCNN) was better at predicting granule growth regimes when compared to other neural networks with no physical constraints. When input data that violated physics-based boundaries was provided, the PCNN identified these points more accurately compared to other non-physics constrained neural networks, with an error of <1%. A sensitivity analysis of the PCNN to the input variables was also performed to understand individual effects on the final outputs.
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Sampat C, Baranwal Y, Ramachandran R. Accelerating multi-dimensional population balance model simulations via a highly scalable framework using GPUs. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106935] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Santos FP, Lage PLC, Favero JL, Senocak I. GPU‐accelerated simulation of polydisperse multiphase flows using dual‐quadrature‐based moment methods. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Fabio P. Santos
- Departamento de Engenharia Química Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
| | - Paulo L. C. Lage
- Programa de Engenharia Química — COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
| | - Jovani L. Favero
- Programa de Engenharia Química — COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil
| | - Inanc Senocak
- Department of Mechanical Engineering & Materials Science University of Pittsburgh Pittsburgh Pennsylvania
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Sampat C, Bettencourt F, Baranwal Y, Paraskevakos I, Chaturbedi A, Karkala S, Jha S, Ramachandran R, Ierapetritou M. A parallel unidirectional coupled DEM-PBM model for the efficient simulation of computationally intensive particulate process systems. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.08.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Nadeem M, Banka H, Venugopal R. Estimation of pellet size and strength of limestone and manganese concentrate using soft computing techniques. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.06.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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