Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes
by
Damon Petersen 1, Logan D. R. Beal 1
, Derek Prestwich 1, Sean Warnick 2 and John D. Hedengren 1,*
Damon Petersen 1, Logan D. R. Beal 1, Derek Prestwich 1, Sean Warnick 2 and John D. Hedengren 1,*
1
Department of Chemical Engineering, Brigham Young University, Provo, UT 84602, USA
2
Department of Computer Science, Brigham Young University, Provo, UT 84602, USA
*
Author to whom correspondence should be addressed.
Processes 2017, 5(4), 83; https://doi.org/10.3390/pr5040083
Received: 14 November 2017 / Revised: 7 December 2017 / Accepted: 8 December 2017 / Published: 13 December 2017
(This article belongs to the Special Issue Combined Scheduling and Control)
A novel formulation for combined scheduling and control of multi-product, continuous chemical processes is introduced in which nonlinear model predictive control (NMPC) and noncyclic continuous-time scheduling are efficiently combined. A decomposition into nonlinear programming (NLP) dynamic optimization problems and mixed-integer linear programming (MILP) problems, without iterative alternation, allows for computationally light solution. An iterative method is introduced to determine the number of production slots for a noncyclic schedule during a prediction horizon. A filter method is introduced to reduce the number of MILP problems required. The formulation’s closed-loop performance with both process disturbances and updated market conditions is demonstrated through multiple scenarios on a benchmark continuously stirred tank reactor (CSTR) application with fluctuations in market demand and price for multiple products. Economic performance surpasses cyclic scheduling in all scenarios presented. Computational performance is sufficiently light to enable online operation in a dual-loop feedback structure.
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MDPI and ACS Style
Petersen, D.; Beal, L.D.R.; Prestwich, D.; Warnick, S.; Hedengren, J.D. Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes. Processes 2017, 5, 83.
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