A computational framework for integrating campaign scheduling, dynamic optimization and optimal control in multi-unit batch processes

Rossi, Francesco; Casas-Orozco, Daniel; Reklaitis, Gintaras V.; Manenti, Flavio; Buzzi‐Ferraris, Guido

doi:10.1016/j.compchemeng.2017.05.024

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…In addition, it can also relieve control room operators of some of their duties, thus increasing the level of automation in chemical processes. Although these considerations have been shown through a simple example, they apply to many different application scenarios, as the scientific literature has demonstrated over the last two decades (Balasubramhanya and Doyle, 1997;Busch et al, 2007;Gopalakrishnan and Biegler, 2013;Mahadevan et al, 2001;Manenti and Rovaglio, 2008;Pierucci et al, 1996;Rossi et al, 2015Rossi et al, , 2017Viganò et al, 2010;Weng et al, 2014). Therefore, this case study confirms that DRTO is expected to take over RTO in the near future, especially in those industrial sectors that deal with high value-added goods.…”

Section: C43 Results Of the Application Of Rto And Drto To The Rasupporting

confidence: 60%

Model predictive control and dynamic real-time optimization of steam cracking units

Rossi

Rovaglio²,

Manenti

2019

Computer Aided Chemical Engineering

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Unlike other parts of this book, this chapter does not deal with mathematical procedures for modeling complex reaction systems, e.g. strategies for construction/reduction of kinetic schemes, approaches to the estimation of their parameters (pre-exponential factors, activation energies, etc.), and so on. Conversely, it shows how to use accurate mathematical models of chemical processes, based on detailed kinetic schemes, for advanced control and optimization purposes. To this end, it describes two advanced model-based optimization/control strategies, called model predictive control and dynamic real-time optimization, and demonstrates the extent to which they can benefit chemical processes. The process unit, utilized to show the potential of model predictive control and dynamic real-time optimization, is a steam cracking furnace.

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Section: C43 Results Of the Application Of Rto And Drto To The Rasupporting

confidence: 60%

Model predictive control and dynamic real-time optimization of steam cracking units

Rossi

Rovaglio²,

Manenti

2019

Computer Aided Chemical Engineering

Self Cite

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“…Their application on air separation units is not yet real‐time applicable, but shows a better performance compared to the two‐level formulations. Alternatively, Rossi et al 55 present an integration strategy with an offline and an online phase. In the offline phase, a conventional campaign scheduling problem is solved, while the online phase amounts to an eNMPC formulation, which also updates the campaign schedule and avoids the solution of mixed‐integer problems in real time.…”

Section: Methods For Optimal Dynamic Process Operationmentioning

confidence: 99%

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Esche

Repke

2020

Chemie Ingenieur Technik

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Participating in electricity markets through demand response causes new requirements for optimizing process control of chemical plants. The last ten years have brought great advances in the formulation and solution of economic nonlinear model predictive control and state estimation to support operation of processes under dynamic constraints. However, gaps remain regarding the availabilities of suitable plant models capable of describing processes active in demand response as well as of robust schemes for state estimation and economic nonlinear model predictive control in commercial tools.

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“…Recent research continues to explore integrating process dynamics into batch scheduling [2]. Multi-parametric model predictive control [39], state equipment networks [40], and two-phase (off-line and on-line) architectures [41] have been applied to integrate scheduling and control for batch processes. Chu and You investigated scheduling and control in batch processes, investigating moving horizon approaches [30], decomposition through surrogate modeling [42], and decomposition into a bi-level problem solvable with a game theory approach [43].…”

Section: Previous Workmentioning

confidence: 99%

Integrated scheduling and control in discrete-time with dynamic parameters and constraints

Beal

Petersen

Grimsman

et al. 2018

Computers & Chemical Engineering

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Integrated scheduling and control (SC) seeks to unify the objectives of the various layers of optimization in manufacturing. This work investigates combining scheduling and control using a nonlinear discrete-time formulation, utilizing the full nonlinear process model throughout the entire horizon. This discrete-time form lends itself to optimization with time-dependent constraints and costs. An approach to combined SC is presented, along with sample pseudo-binary variable functions to ease the computational burden of this approach. An initialization strategy using feedback linearization, nonlinear model predictive control, and continuous-time scheduling optimization is presented. The formulation is applied with a generic continuous stirred tank reactor (CSTR) system in open-loop simulations over a 48-hour horizon and a sample closed-loop implementation. The value of timebased parameters is demonstrated by applying cooling constraints and dynamic energy costs of a sample diurnal cycle, enabling demand response via combined scheduling and control.

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A computational framework for integrating campaign scheduling, dynamic optimization and optimal control in multi-unit batch processes

Cited by 14 publications

References 34 publications

Model predictive control and dynamic real-time optimization of steam cracking units

Model predictive control and dynamic real-time optimization of steam cracking units

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

Integrated scheduling and control in discrete-time with dynamic parameters and constraints

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