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Data Driven Optimization

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.



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


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