Dynamic predictive scheduling of operational strategies for continuous processes using mixed-logic dynamic optimization

Busch, Jan; Oldenburg, Jan; Santos, Marcella; Cruse, Andreas; Marquardt, Wolfgang

doi:10.1016/j.compchemeng.2006.08.009

Cited by 45 publications

(19 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The Overall Structure of the Algorithm. We consider our problem using a moving horizon presented in [12]. The dynamic scheduling problem is solved on the horizon  …”

Section: Dynamic Differential Evolution Algorithmmentioning

confidence: 99%

A Dynamic Differential Evolution Algorithm for Dynamic Hybrid Flow Shop Scheduling Problem

Zhao

Wang

Yang³

et al. 2016

Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering

View full text Add to dashboard Cite

In most real-world industries, scheduling is processed in a stochastic and dynamic environment, it is necessary to generate a schedule which is suitable for the current system states. Dynamic scheduling solves unimplemented jobs and updates an existing schedule based on the real-time information with minimizing the deviation between the new and original schedules. In this paper, we investigate the dynamic hybrid flow shop scheduling problem and propose a dynamic differential evolution algorithm. In the algorithm, the search space shifts as new jobs arrive, and the problem is solved on a moving horizon based on the real-time information, after the new schedule is established, we update the current schedule. Experiments are carried out to prove the effectiveness of the proposed algorithm.

show abstract

“…This means that we need to work with production and market uncertainties, which increase with longer prediction horizons. To reduce the effect of uncertainty, the so‐called rolling horizon methodology must be adopted It is real‐time since the problem must be solved promptly to have effective support to the decision‐making. As mentioned, it is nonlinear for the nonlinear nature of industrial gases plants …”

Section: Supply Chain Managementmentioning

confidence: 99%

Raising the Decision‐Making Level to Improve the Enterprise‐Wide Production Flexibility

et al. 2012

View full text Add to dashboard Cite

When market demand significantly changes, as in the ongoing worldwide economic crisis, many production plants are forced to operate far from nominal conditions. In this case, the current plant-wide optimization of production sites is a myopic approach that could lead to plant inefficiencies and unconventional operation issues, thus, resulting in ineffective prevention of economic losses. A way to tackle low-demand conditions is to raise the decision-making process from the plant-wide (or business-wide) level to the enterprise-wide (or corporate) level by assigning a Boolean variable to each production site so as to manage their on/off status. By doing so, certain additional (social) constraints may become relevant. The case of operating industrial gases supply chains is considered. © 2012 American Institute of Chemical Engineers

show abstract

Dynamic predictive scheduling of operational strategies for continuous processes using mixed-logic dynamic optimization

Cited by 45 publications

References 22 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

A Dynamic Differential Evolution Algorithm for Dynamic Hybrid Flow Shop Scheduling Problem

Raising the Decision‐Making Level to Improve the Enterprise‐Wide Production Flexibility

Contact Info

Product

Resources

About