Image based Modeling and Control for Batch Processes DOI Creative Commons
Aswin Chandrasekar,

Kevork Baghdassarian,

Farshad Moayedi

et al.

Journal of Process Control, Journal Year: 2024, Volume and Issue: 143, P. 103314 - 103314

Published: Sept. 16, 2024

Language: Английский

Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation DOI
Parth Shah,

Silabrata Pahari,

Raj Bhavsar

et al.

Computers & Chemical Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 108926 - 108926

Published: Nov. 1, 2024

Language: Английский

Citations

11
Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes DOI
Zhaoyang Li, Minghao Han, Dat-Nguyen Vo

et al.

Computers & Chemical Engineering, Journal Year: 2024, Volume and Issue: 191, P. 108854 - 108854

Published: Aug. 24, 2024

Language: Английский

Citations

7
Multi-objective optimization for sustainable and economical polycarbonate production with reaction kinetics inference for real-world industrial process DOI

Eunbyul Lee,

Minsu Kim, Il Moon

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 490, P. 151484 - 151484

Published: April 25, 2024

Language: Английский

Citations

6
Physics-informed neural networks for state reconstruction of hydrogen energy transportation systems DOI Creative Commons
Lu Zhang, Junyao Xie,

Qingqing Xu

et al.

Computers & Chemical Engineering, Journal Year: 2024, Volume and Issue: 192, P. 108898 - 108898

Published: Nov. 2, 2024

Language: Английский

Citations

4
Advancing Kinetic Study of Catalytic Reaction: A Hybrid Modeling Approach for Predicting Effective Activation Energies DOI

Silabrata Pahari,

Chi H. Lee, Denis Johnson

et al.

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 9544 - 9554

Published: May 20, 2025

Language: Английский

Citations

0
Data-driven plant-model mismatch detection for dynamic matrix control systems using sum-of-norms regularization DOI
Yimiao Shi, Xiaodong Xu, Yuan Yuan

et al.

Computers & Chemical Engineering, Journal Year: 2024, Volume and Issue: 190, P. 108823 - 108823

Published: Aug. 13, 2024

Language: Английский

Citations

2
Integrating Deep Neural Networks for Hybrid Modeling of Complex Chemical Processes: Estimation of Spatiotemporally Varying Parameters in Moving Boundary Problems DOI

Silabrata Pahari,

Parth Shah, Joseph Sang‐Il Kwon

et al.

2022 American Control Conference (ACC), Journal Year: 2024, Volume and Issue: 2, P. 5370 - 5375

Published: July 10, 2024

Language: Английский

Citations

1
Leveraging neural networks to estimate parameters with confidence intervals DOI Creative Commons

Nigel Mathias,

Lauren Weir, Brandon Corbett

et al.

The Canadian Journal of Chemical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 7, 2024

Abstract This manuscript presents a proof of concept for the estimation parameters in bioprocess while providing reliable confidence intervals. Specifically, Bayesian inference is used to estimate uncertainty prediction parameter due presence measurement noise process. The resultant joint probability distribution utilized infer interval estimates. method numerically applied using technique known as nested sampling. algorithm iteratively samples from pre‐determined range values compare model predictions and obtain density function. One challenge typically associated with this determination error, especially when high‐fidelity dynamic being utilized. For motivating example present manuscript, where simulated considered, use provided by Sartorius AG part poses computational challenges. To overcome challenge, universal approximator such parameterized neural network used. designed simulate results first principles (while also capturing dependence on output), once trained can provide near instantaneous making sampling computationally tractable application. Simulation demonstrate feasibility capability proposed approach.

Language: Английский

Citations

0
Empowering Hybrid Models with Attention-Based Time-series Transformers: A Case Study in Batch Crystallization DOI
Niranjan Sitapure, Joseph Sang‐Il Kwon

2022 American Control Conference (ACC), Journal Year: 2024, Volume and Issue: 2004, P. 62 - 67

Published: July 10, 2024

Language: Английский

Citations

0
A Hybrid Modeling Framework for Catalytic Systems: Sensitivity Analysis and Estimation of Activation Energies DOI

Silabrata Pahari,

Parth Shah, Chi H. Lee

et al.

2022 American Control Conference (ACC), Journal Year: 2024, Volume and Issue: 24, P. 5376 - 5381

Published: July 10, 2024

Language: Английский

Citations

0