522 Advances in Machine Learning and Intelligent Systems II

Wednesday, November 18, 2020: 8:00 AM - 9:00 AM
Computing and Systems Technology Division (10) (PreRecorded+)

Description:
Data-driven approaches are playing an increasingly significant role in chemical engineering. This session solicits submissions pertaining to both methodological advances in machine learning as well as application-driven case studies demonstrating the use data and machine learning to infer correlations, develop models, as well as to improve processes/systems through data-driven optimization and control. Particular emphasis will be given to applications which employ an adaptive data-driven approach, through which data-mining and machine learning are used to create intelligent systems, which adaptively learn from the data.

Sponsor:
Information Management and Intelligent Systems
Co-Sponsor(s):
Pharmaceutical Discovery, Development and Manufacturing Forum (26)
Chair:
Zhenyu Wang Email: zwang12@dow.com
Co-Chair:
Joseph Sang-Il Kwon Email: kwonx075@tamu.edu


(522a) Comparison of Surrogate Modeling Techniques for Design Space Approximation and Surrogate-Based Optimization: Effect of Sampling Technique and Sample Size
Bianca Williams and Selen Cremaschi


(522c) Investigating Performance Metrics and Machine Learning Models Towards Automating the Diagnosis of Membranous Nepthropathy
Antonios Armaou and Yao Xue


(522d) High-Throughput Adsorbent Screening for Gas Separation Via Artificial Neural Networks
Akhil Arora and M. M. Faruque Hasan


(522e) A Hybrid Modelling Framework for on-Line Data-Driven Modelling of Particle Processes
Rasmus Fjordbak Nielsen, Nima Nazemzadeh, Martin Peter Andersson, Krist V. Gernaey and Seyed Soheil Mansouri


(522f) Integration of Control with Process Operation through Reinforcement Learning
Panagiotis Petsagkourakis, Vasileios Charitopoulos, Federico Galvanin and E. Antonio del Rio Chanona


(522g) Control Lyapunov-Barrier Function-Based Predictive Control of Nonlinear Processes Using Real-Time Machine Learning Modeling
Zhe Wu, David Rincon and Panagiotis D. Christofides


(522h) Constrained Reinforcement Learning for Process Optimization and Control
Panagiotis Petsagkourakis, Eric Bradford, Ilya Orson Sandoval Cardenas, Dongda Zhang, Federico Galvanin and E. Antonio del Rio Chanona
See more of this Group/Topical: Computing and Systems Technology Division
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