Data Science/Analytics for Process Applications | AIChE

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Data Science/Analytics for Process Applications

Chair(s)

Ellis, M., University of California, Davis

Co-chair(s)

Papers are sought on the topic of analysis of large data sets and information management for improving process design or operations. Submissions from industry or collaborations between academia and industry are highly encouraged. Topics can cover both methods for data-analysis and information management, as well as applications.

Presentations

3:30 PM
(185a) Data Fusion and Feature Selection for Process Monitoring at Hanford
3:49 PM
(185b) A Comprehensive Causation Prediction Model of Pipeline Incidents Using Artificial Neural Network
4:08 PM
(185c) Data-Driven Feasibility and Performance Prediction of Production Scheduling MIP Models
4:27 PM
(185d) Machine Learning-Based Operational Modeling of an Electrochemical Reactor: Handling Data Variability and Improving Empirical Models
4:46 PM
(185e) A Graph-Based Software Framework for Data Science
5:05 PM
(185f) Machine Learning-Based Ethylene Concentration Estimation, Real-Time Optimization and Feedback Control of an Experimental Electrochemical Reactor
5:24 PM
(185g) Optimization Methods for Exploring Accuracy Versus Robustness of a Regression Prediction in Process Analytical Technology
5:43 PM
(185h) Data-Driven Techniques Towards the Efficient Integration of Planning, Scheduling and Control

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Paper abstracts are public but to access Extended Abstracts, you must first purchase the conference proceedings.

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Pricing

Individuals

AIChE Pro Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
AIChE Explorer Members $225.00
Non-Members $225.00

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