2023 AIChE Annual Meeting

Session: Interactive Session: Data and Information Systems

Chair

García-Muñoz, S., Eli Lilly and Company

Co-Chair

Papathanasiou, M., Imperial College London

Presentations

03:30 PM

(59am) Self-Optimizing Control Methodology Using Surrogate Models for Complex Systems: A Jupyter-Based Application for Flexible Exploration and Adjustment
Bispo, H., Tavernard, A., Maximo, M., Teixeira, H.
(59c) Towards an Integrated Approach for Upstream Field Recovery
Ramjanee, S.
(59o) Development of Algorithms for Mass and Energy Constrained Dynamic Neural Network Models
Mukherjee, A., Bhattacharyya, D.
(59t) Improving Industrial-Scale Bioreactor Performance: Development and Validation of Computationally Efficient Compartment-Based Models Using Real Plant Data
Shah, P., Kwon, J.
(59z) Combined Use of Recursive Neural Network (RNN), Convolutional Neutral Network (CNN), and Attention Mechanism on Cycling Data of Lithium Ion Battery for Lifespan Prediction
Lee, J. H., Lee, J.
(59an) Chemical Substance Diagnosis System Based on Knowledge Inference and Machine Learning for Chemical Exposure Symptoms
Kim, K., Shin, D.
(59p) Design of Microfluidic Chromatographs through Reinforcement Learning
Shahab, M., Rengaswamy, R.
(59d) Hybrid Bayesian-Based Surrogate Optimization for Sustainable Process Design within Planetary Boundaries
Jog, S., Vázquez, D., Dos Santos, L. F., Caballero, J. A., Guillén-Gosálbez, G.
(59y) Optimal Sensor Network Design for Maximizing Net Present Value and Its Application to Corrosion Monitoring in a Power Plant
Somayajula, C. S., Bhattacharyya, D., Liu, X., Hu, S.
(59aa) Adversarial Data in Demand Side Management
Cramer, E.
(59ao) Augmented Control Using Reinforcement Learning and Conventional Process Control
Bhattacharyya, D., Beahr, D., Allan, D. A., Zitney, S.
(59e) Probabilistic Lifespan Prediction of Lithium-Ion Batteries Under Varying Operating Protocols Using Gaussian Process Regression
Park, S., Lee, J., Lee, J. H.
(59ap) Intelligent Size Characterization of Granules By Machine Learning Method
Khakbiz, M., Rezaeizadeh, M., Callegari, G., Muzzio, F. Sr.
(59f) Automated Synthesis of Hybrid Models for Ionic Separations
Briceno-Mena, L., Olayiwola, T., Arges, C., Kulkarni, T., Kumar, R., Romagnoli, J. A.
(59ar) Designing pH-Temperature Responsive Microgels with Targeted Transition Temperature Using a Novel Partial Least Squares (PLS) Model Inversion Technique
Mhaskar, P., Hoare, T., Tayebi, S. S.
(59as) Prediction of Chemical Toxicity and Exposure Symptoms Based on Knowledge Graph Embedding and Language Models
Kim, K., Shin, D., Jung, Y. M., Son, J.
(59b) Real-Time Fault Detection Models for Smart Manufacturing: A Case Study with Heat Exchanger Equipment and Innovation Platform
Yerimah, L., Sontakke, M., Rebmann, A., Ghosh, S., Hedden, R., Bequette, B. W.
(59ab) Generalization Error Bounds for Neural Networks Modeling Two-Time-Scale System Dynamics with Application to Model Predictive Control of Nonlinear Processes
Alnajdi, A., Suryavanshi, A. V., Abdullah, F., Christofides, P.
(59at) Data-Driven Adaptive Sparse Identification of Time-Varying Nonlinear Dynamics for 2,3-Bdo Distillation Column
Choi, Y., Bhadriraju, B., Cho, H., Lim, J., Moon, I., Kwon, J., Kim, J.
(59g) Modeling and Predictive Control of Hybrid Dynamical Systems Using Machine Learning Methods
Wu, Z., Hu, C., Xiao, M.
(59s) Investigating the Effects of Tunable Experimental Parameters on hiPSC-Cms Maturation Via Clustering Techniques
Rajendiran, S., Cremaschi, S.
(59ac) Control Lyapunov-Barrier Function-Based Safe Reinforcement Learning for Nonlinear Optimal Control
Wu, Z., Wang, Y.
(59ad) Reinforcement Learning (RL)-Based Process Controller Design: An Implementable Approach
Hassanpour, H., Mhaskar, P., Corbett, B., Wang, X.
(59a) Measure This, Not That: Pareto Optimal Trade-Offs between Model-Based Information Content and Measurements Cost (Poster corresponding to plenary presentation)
Wang, J., Dowling, A.
(59ae) Efficient Hybrid Modeling and Sorption Model Discovery for Non-Linear Advection-Diffusion-Sorption Systems: A Systematic Scientific Machine Learning Approach
Viena Santana, V., Costa, E., Rebello, C., Ribeiro, A. M., Rackauckas, C., Nogueira, I.
(59h) Smells like AI: Harnessing Machine Learning for Advanced Olfactory Experience Reproduction and Odorant Optimization
Viena Santana, V., Rodrigues, B., Murins, S., Nogueira, I., Shardt, N.
(59af) Using Artificial Neural Networks for Real-Time Tuning of PID Controllers
Bestwick, T., Camarda, K. V.
(59i) A Novel Recurrent Neural Network for Hydroprocessing Unit Modeling Using Neural Circuit Policies and Attention-Based Encoder-Decoder
Yang, S. B., Li, Z.
(59j) A Parametric Cost Function Approximation Algorithm for Multiscale Decision-Making
Cao, K., Li, C., Ramanujam, A.
(59ag) Chemistry-Aware Retrosynthesis and Forward Reaction Prediction Using Smiles Grammar Tree Transformer
Mann, V., Zhang, K., Venkatasubramanian, V.
(59ah) Control Invariant Set Enhanced Reinforcement Learning for Process Control: Improved Sampling Efficiency and Guaranteed Stability
Bo, S., Xunyuan, Y., Liu, J.
(59k) Data-Driven Linear Predictive Control of Nonlinear Processes Based on the Reduced-Order Koopman Operator
Zhang, X., Yin, X., Han, M.
(59ai) A Comprehensive Decision Making and Networking Facility for the Biorefining Community
Kalemi, E., Cecelja, F.
(59w) Automating the Discovery of Reaction Networks for Complex Reaction Systems from Spectroscopic Measurements
Srinivasan, K., Prasad, V.
(59l) Modelling of Non-Conventional Streams in the Context of Circular Economy-the Case of Hydrothermal Liquefaction
Kokosis, A.
(59x) Data-Driven Supply Chain Monitoring Based on Canonical Variate Analysis
Wang, J., Swartz, C., Huang, K.
(59m) Data Embedding and Hybrid Modeling for Industrial Fluid Catalytic Cracking
Kokosis, A., Blitas, D.
(59ak) Structure-Based Prediction of Kinase Activation amidst a Varied Mutational Landscape Using Privileged Learning
Wang, Y., Radhakrishnan, R., de la Fuente-Nunez, C., Nukpezah, J., Chen, Z., Wan, F.
(59n) A Graph Attention Network Based Approach for Interpretable and Domain-Aware Modeling of a Wellhead Water Treatment System
Sekhon, J.
(59u) Learning Dynamical Process Models Using Plant Data: A Real-World Case Study in the Sustainable Manufacturing of Insulation Products
Kothare, M., Prabhu, S., Rangarajan, S.