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Data Driven Optimization

Chair(s):
Co-chair(s):
Hasan, F., Texas A&M University

Data from simulations or from process industries can be high-dimensional, sparse, uncertain, heterogeneous, multi-scale and represent discontinuous nonlinear functions. Novel methodology is required for data-driven optimization for applications in design, real-time optimization, scheduling, and process operations. This session seeks presentations on new mathematical optimization algorithms for data-driven optimization and/or applications to the process industries.

Papers:
8:00 AM
(578a) The Melon Toolbox: Machine Learning Models for Optimization
8:15 AM
(578b) Surrogate-Based Optimization Using Random Forests
8:30 AM
(578c) Data-Driven Process Optimization Under Neural Network Surrogate Model Uncertainty
8:45 AM
(578d) A Unified Framework for Adjustable Robust Optimization with Endogenous Uncertainty and Active Learning
9:00 AM
(578e) Simulation-Based Optimization with Embedded Dynamic Models Using a Data-Driven Spatial Branch-and-Bound Algorithm
9:15 AM
(578f) Data-Driven Strategies for Optimization of Integrated Chemical Plants
9:45 AM
(578h) On the Acceleration of quadratic Semidefinite Outer-Approximation Using Combinations of Cutting Plane Approximations and Data Analytics
Topics: 
Computing and Systems Engineering

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Individuals

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