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Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty
Author(s) -
Koller Robert W.,
RicardezSandoval Luis A.,
Biegler Lorenz T.
Publication year - 2018
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16092
Subject(s) - flexibility (engineering) , monte carlo method , mathematical optimization , scheduling (production processes) , stochastic process , computer science , set (abstract data type) , algorithm , mathematics , statistics , programming language
An algorithm that employs the back‐off method to provide optimal solutions for integration of design, control, and scheduling for multiproduct systems is presented, featuring a flexibility and feasibility analysis. The algorithm employs Monte Carlo (MC) sampling to generate a large number of random realizations, and simulate the system to determine feasibility. Back‐off terms are determined and incorporated into a new flexibility analysis to approximate the effect of stochastic uncertainty and disturbances. Through successive iterations, the algorithm converges, terminating on a solution that is robust to a specified level of process variability due to stochastic realizations in the disturbances and uncertain parameters. The proposed algorithm has been successfully applied to a multiproduct continuous stirred tank reactor for which optimal design, control, and scheduling decisions are identified, subject to stochastic uncertainty and disturbance. The present approach has been compared to a critical‐set (multiscenario) method showing the benefits and limitations of both approaches. © 2018 American Institute of Chemical Engineers AIChE J , 64: 2379–2389, 2018