On multi-parametric programming and its applications in process systems engineering

Oberdieck, Richard; Diangelakis, Nikolaos A.; Nașcu, Ioan; Papathanasiou, Maria M.; Sun, Muxin; Avraamidou, Styliani; Pistikopoulos, Efstratios N.

doi:10.1016/j.cherd.2016.09.034

Cited by 74 publications

(29 citation statements)

References 199 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this section we present the idea behind the design of multisetpoint explicit controllers. Multi-parametric programming (mp-P) as an optimisation based methodology has found numerous applications in the field of process systems engineering with the design of explicit controllers probably being the most popular ( Charitopoulos and Dua, 2016;Dua et al, 2008;Oberdieck et al, 2016 ). Within the context of explicit MPC, one considers as uncertain parameters the initial states of the system at each sampling instance and thus an mp-P problem with uncertainty in the right hand side (RHS) of the constraints is formulated ( Bemporad et al, 2002 ).…”

Section: Multi-parametric Model Predictive Controlmentioning

confidence: 99%

Closed-Loop Integration of Planning, Scheduling and Multi-parametric Nonlinear Control

Charitopoulos¹

2020

Springer Theses

View full text Add to dashboard Cite

In this article, motivated by the need for efficient closed-loop implementation of the control objectives set within the integrated planning, scheduling and control (iPSC) problem we introduce a novel framework that enables its online solution under dynamic disturbances. We introduce the concept of multi-setpoint explicit controllers through the use of a new multi-parametric nonlinear programming algorithm and develop a rigorous rescheduling mechanism that mitigates the impact of the dynamic disruptions on the operational decisions of planning and scheduling. The overall closed-loop problem is formulated as mixed integer linear program with the control problem integrated via an outer loop. The benefits of the proposed framework are highlighted through two case studies and the results indicate the necessity of considering dynamic disruptions within the scope of the integrated problem.

show abstract

Section: Multi-parametric Model Predictive Controlmentioning

confidence: 99%

Closed-Loop Integration of Planning, Scheduling and Multi-parametric Nonlinear Control

Charitopoulos¹

2020

Springer Theses

View full text Add to dashboard Cite

show abstract

“…For a complete review of multi-parametric programming, properties of the solution and available solution techniques and algorithms the reader is referred to Ref. 118. From the derivation of the explicit/multi-parametric solution and the critical regions the following can be stated in context with the framework presented here:…”

Section: Simultaneous Design and Control Optimization Via Parocmentioning

confidence: 99%

Process design and control optimization: A simultaneous approach by multi‐parametric programming

et al. 2017

Self Cite

View full text Add to dashboard Cite

We present a framework for the application of design and control optimization via multi‐parametric programming through four case studies. We develop design dependent multi‐parametric model predictive controllers that are able to provide the optimal control actions as functions of the system state and the design of the process at hand, via our recently introduced PAROC framework (Pistikopoulos et al, Chem Eng Sci. 2015;136:115–138). The process and the design dependent explicit controllers undergo a mixed integer dynamic optimization (MIDO) step for the determination of the optimal design. The result of the MIDO is the optimal design of the process under optimal operation. We demonstrate the framework through case studies of a tank, a continuously stirred tank reactor, a binary distillation column and a residential cogeneration unit. © 2017 American Institute of Chemical Engineers AIChE J, 2017

show abstract

“…So reichen wenige Datenpunkte, um die molekularen Modelle zu parametrieren, die wiederum ein breites Spektrum thermodynamischer Eigenschaften über einen weiten Zustandsbereich vorhersagen können , . Auch durch die intelligente simulationsgestützte Regelung von Anlagen lassen sich Einsparpotenziale realisieren, sofern die Genauigkeit der Modelle und der Simulationsergebnisse bekannt ist und den quantitativen Anforderungen der Ingenieurpraxis genügt , .…”

Section: Introductionunclassified

SkaSim – Scalable HPC Software for Molecular Simulation in the Chemical Industry

Vrabec

Bernreuther

Bungartz

et al. 2018

Chemie Ingenieur Technik

View full text Add to dashboard Cite

Der vorliegende Übersichtsartikel berichtet über Fortschritte in der molekularen Modellierung und Simulation mittels massiv‐paralleler Hoch‐ und Höchstleistungsrechner (HPC). Im SkaSim‐Projekt arbeiteten dazu Partner aus der HPC‐Community mit Anwendern aus Wissenschaft und Industrie zusammen. Ziel dabei war es mittels HPC‐Methoden die Vorhersage von thermodynamischen Stoffdaten in Bezug auf Effizienz, Qualität und Zuverlässigkeit weiter zu optimieren. In diesem Zusammenhang wurden verschiedene Themen bearbeitet: Atomistische Simulation der homogenen Gasblasenbildung, Oberflächenspannung klassischer Fluide und ionischer Flüssigkeiten, multikriterielle Optimierung molekularer Modelle, Weiterentwicklung der Simulationscodes ls1 mardyn und ms2, atomistische Simulation von Gastrennprozessen, molekulare Membran‐Strukturgeneratoren, Transportwiderstände und gemischtypenspezifische Bewertung prädiktiver Stoffdatenmodelle.

show abstract

On multi-parametric programming and its applications in process systems engineering

Cited by 74 publications

References 199 publications

Closed-Loop Integration of Planning, Scheduling and Multi-parametric Nonlinear Control

Closed-Loop Integration of Planning, Scheduling and Multi-parametric Nonlinear Control

Process design and control optimization: A simultaneous approach by multi‐parametric programming

SkaSim – Scalable HPC Software for Molecular Simulation in the Chemical Industry

Contact Info

Product

Resources

About