Description:
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. Contributions may incorporate (i) model-free methods such as hardware-in-the-loop for process development, (ii) the development and use of surrogate models, (iii) methodologies for dealing with large-scale data sets, extracted from simulation or industrial historical data, and the information content in these data sets.
Sponsor:
Systems and Process Operations
Co-Sponsor(s):
Information Management and Intelligent Systems (10E)
Co-chairs:
Atharv Bhosekar
GAMS Development Corporation, Bear, DE
Panagiotis Petsagkourakis
School of Chemical Engineering and Analytical Science, University College of London, London, United Kingdom
Abstract Submissions Closed
See more of this Group/Topical: Computing and Systems Technology Division