A dynamic optimization framework for integration of design, control and scheduling of multi-product chemical processes under disturbance and uncertainty

Koller, Robert W.; Ricardez‐Sandoval, Luis A.

doi:10.1016/j.compchemeng.2017.05.007

Cited by 43 publications

(32 citation statements)

References 36 publications

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“…12 For simplicity, all these variables are referred to as the decision variables E5 D; C; S; Dt f g , and they are limited by upper and lower bounds. Decisions are made on design, control, and scheduling to minimize the total cost of the process, z.…”

Section: Conceptual Formulationmentioning

confidence: 99%

“…12 The process is a first-order irreversible reaction that converts reactant A into product B. 12 The process is a first-order irreversible reaction that converts reactant A into product B.…”

Section: Case Studymentioning

confidence: 99%

“…All these aspects can impact each other, resulting in complex interactions. [11][12][13][14][15][16][17][18][19] Uncertainty and process disturbances can have a significant effect on process optimality and feasibility, as model parameters and external perturbations are typically not known a priori with absolute certainty, resulting in variability in process output. Integrated optimization of design, control, and/or scheduling frameworks are therefore sought, and deemed necessary to achieve optimal process integration.…”

Section: Introductionmentioning

confidence: 99%

“…For a process that is optimized only at nominal conditions, that is, uncertain parameters and disturbances set to their nominal (expected) values, the solution can become suboptimal or infeasible when it is subjected to parameter uncertainty and process disturbance, as operating limits are surpassed or stability is decreased. 12 The multiscenario method proposed in that work was extended from previous studies in the area that also considered a worst-case approach for process integration. Parameters that are measurable can be explicitly optimized, as their values are known, and measurement-based optimization methods can be applied.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

2018

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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

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Section: Conceptual Formulationmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

2018

Self Cite

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“…Koller et al present an optimization framework for integrated scheduling, control, and design of multi-product processes with uncertainty. Worst-case scenarios are examined in the algorithm to design a process feasible under all worst-case scenarios [52]. Grade transitions and scheduling are incorporated via flexible finite elements and dynamic process models.…”

Section: Literature Reviewmentioning

confidence: 99%

Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes

et al. 2017

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Abstract: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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A trust‐region framework for integration of design and control

Rafiei

Ricardez‐Sandoval

2020

AIChE Journal

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A trust‐region approach is presented to address the simultaneous design and control of large‐scale systems under uncertainty. The key idea is to represent the system using power series expansions (PSEs) as piecewise models in an iterative manner while the validity of those expansions is certified in a trusted region. The mean of squared errors is used as a metric to quantify the accuracy of the PSE approximations. Identified search regions specify the boundaries of the decision variables for the PSE‐based optimization problems. The proposed algorithm shows a significant accomplishment in locating dynamically feasible and near‐optimal design and operating conditions. The proposed approach was tested in a wastewater treatment plant and the Tennessee Eastman process. The results indicate that the proposed methodology leads to more economically attractive and reliable designs while maintaining the dynamic operability of the system in the presence of disturbances and uncertainty.

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A dynamic optimization framework for integration of design, control and scheduling of multi-product chemical processes under disturbance and uncertainty

Cited by 43 publications

References 36 publications

Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty

Combined Noncyclic Scheduling and Advanced Control for Continuous Chemical Processes

A trust‐region framework for integration of design and control

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