2023 AIChE Annual Meeting

Session: Interactive Session: Systems and Process Control

Presentations

03:30 PM

(149l) Harnessing Feedback Control Strategy for Improved Core Annular Flow Stability in Heavy Oil Transportation
Nogueira, I., Lima, P., Schnitman, L., Paiva Guimarães Mendes, T., Costa, E., Skogestad, S.
(149ae) Bayesian Identification of Nonlinear, Sparse, Dynamic Models
Adeyemo, S., Bhattacharyya, D.
(149ac) Optimization-Based State and Parameter Estimation for Distributed Parameter Pipeline Systems
Xie, J., Dubljevic, S., Zhang, L.
(149w) Parameter Estimation in Bioprocesses Using Bayesian Inference
Mathias, N., Mhaskar, P., Corbett, B., Weir, L.
(149f) Dynamic Analysis and Model Predictive Control of a Biochemical Reactor Under Delayed Uncertain Measurement and Multi-Rate Actuation/Sampling
Ozorio Cassol, G. Sr., Dubljevic, S.
(149g) Multiscale Kinetic Modeling and Optimization: In-Depth Analysis of Cellulose Degradation for Enhanced Pulping Process and Superior Paper Quality
Kim, J., Pahari, S., Ji, A., Zhang, M., Yoo, C. G., Kwon, J.
(149x) Hybrid Series/Parallel All-Nonlinear Dynamic-Static Stochastic Neural Networks: Development, Training and Application to Chemical Processes
Mukherjee, A., Bhattacharyya, D.
(149j) Practical Control Laws with Quantum Computation
Abou Halloun, J., Kasturi Rangan, K., Durand, H.
(149ab) Considerations of Closed-Loop Control on Quantum Computers Using a Modified Grover’s Algorithm for Simulation of a Chemical Process
Nieman, K., Durand, H.
(149y) Physics-Informed Neural Networks (PINNs) for Process Systems with Model Plant Mismatch
Mhaskar, P., Rasmussen, S., Moayedi, F., Sarna, S., Corbett, B.
(149d) Model Predictive Control of an Axial Dispersion Tubular Reactor with Recycle: A Distributed Parameter System with State Delay
Moadeli, B., Dubljevic, S., Ozorio Cassol, G. Sr.
(149ad) A Constraint-Based Modeling Framework with Deep Reinforcement Learning and Multi-Objective Optimization for Control of Mammalian Cell Cultures
Almutar, E., Parulekar, S.
(149k) Considerations of Space Manufacturing: Utilizing Earth-Based Resources for Modeling and Control Applications While Considering Communication Delay
Nieman, K., Durand, H.
(149a) Case Studies on the Combined Identification and Offset-Free Control of Chemical Processes (Poster corresponding to plenary presentation)
Kuntz, S., Downs, J. J., Miller, S. M., Rawlings, J.
(149h) The Theoretical Basis of Ratio Control
Skogestad, S.
(149i) Self-Optimizing Control for Secondary Controlled Variable Selection
Zhou, C., Su, H., Zhu, X., Pan, F., Cao, Y., Yang, S., Shen, K., Hu, M., Jiao, K., Tang, X.
(149b) Fast Zone-Model Predictive Control for Full Battery Pack of Electric Vehicles
Kim, Y., Hong, C., Oh, S. K., Hong, D., Cho, H., Shin, S.
(149c) A Machine Learning Assisted Approach to Model Predictive Control with Multi-Objective Optimization and Multi-Criteria Decision Making
Wang, Z., Tan Gian Yion, W., Rangaiah, G. P., Wu, Z.
(149u) Advanced Model Predictive Control Strategies for Large-Scale Dynamic Systems Based on Data-Driven Artificial Neural Networks
Xie, W.
(149aa) Synthesis of Decarbonized Hydrogen Production Processes Using the Attainable Region Framework
Masuku, C.
(149e) Distributed Control of Integrated Process Systems – an Experimental Study
Sukhadeve, P., Jogwar, S.
(149n) Machine Learning-Based Predictive Irrigation Scheduling
Agyeman, B., Liu, J.
(149o) Data Driven Economic Model Predictive Control of a Rotational Molding Process
Chandrasekar, A., Garg, A., Abdulhussain, H., Gritsichine, V., Thompson, M. R., Mhaskar, P.
(149p) Hybrid Subspace-Rnn Based Approach for Modelling of Non-Linear Processes
Chandrasekar, A., Mhaskar, P.
(149s) Parameter Estimation for Bioprocesses Cognizant of Measurement Noise Distribution
Weir, L., Mathias, N., Mhaskar, P., Corbett, B.
(149z) Reinforcement Learning Based Control of Fed-Batch Production Reactor
Monteiro, M., Kontoravdi, C., Fadda, S.
(149q) Model-Based Control for Liquid-Liquid Extraction
Moser, D., Boehm, J., Neugebauer, P., Sacher, S., Horn, M., Rehrl, J., Steinberger, M.
(149v) Physics-Informed Neural Networks (PINNs) for Modeling Dynamic Processes Based on Limited Physical Knowledge and Data
Velioglu, M., Dahmen, M., Mitsos, A.