Publication detail

Process Network Design and Optimisation Using P-graph: The Success, the Challenges and Potential Roadmap

Varbanov, P.S. Friedler, F. Klemeš, J.J.

English title

Process Network Design and Optimisation Using P-graph: The Success, the Challenges and Potential Roadmap

Type

conference paper

Language

en

Original abstract

The P-graph framework is a combinatorial approach to synthesising and optimising process networks. It is very efficient in handling problems with high combinatorial complexity and has shown great superiority in reducing the related computational burden. Over the years, it has proven its efficiency in solving topologically and combinatorically challenging problems. Many successful applications to scientific and real-life problems have been produced, demonstrating the benefit potential. The application areas range from the initial chemical process synthesis to identifying the mechanisms of chemical and biochemical reactions, supply chains optimisation, regional resource planning, crisis management, evacuation planning and business process modelling. There have been tools of several generations implementing the P-graph framework, with a simple user interface, but featuring serious data input requirement. The P-graph framework also allows sensitivity analysis and produces usually a set of recommended solutions as opposed to the usual single output from straight applications of MP. The current contribution makes a critical overview of the achievements from applying the P-graph framework and the main issues to be dealt with. Based on that, a set of recommendations is made on the necessary future development of the implementations regarding modelling capability, data and algorithmic interfaces, representation of the modelled networks, as well as complexity management.

English abstract

The P-graph framework is a combinatorial approach to synthesising and optimising process networks. It is very efficient in handling problems with high combinatorial complexity and has shown great superiority in reducing the related computational burden. Over the years, it has proven its efficiency in solving topologically and combinatorically challenging problems. Many successful applications to scientific and real-life problems have been produced, demonstrating the benefit potential. The application areas range from the initial chemical process synthesis to identifying the mechanisms of chemical and biochemical reactions, supply chains optimisation, regional resource planning, crisis management, evacuation planning and business process modelling. There have been tools of several generations implementing the P-graph framework, with a simple user interface, but featuring serious data input requirement. The P-graph framework also allows sensitivity analysis and produces usually a set of recommended solutions as opposed to the usual single output from straight applications of MP. The current contribution makes a critical overview of the achievements from applying the P-graph framework and the main issues to be dealt with. Based on that, a set of recommendations is made on the necessary future development of the implementations regarding modelling capability, data and algorithmic interfaces, representation of the modelled networks, as well as complexity management.

Keywords in English

Network function virtualization; Parallel processing systems; Sensitivity analysis; Supply chains; Systems engineering; User interfaces; Biochemical reactions; Business process modelling; Combinatorial approach; Combinatorial complexity; Complexity management; Computational burden; Evacuation planning; Modelling capabilities; Complex networks;

Released

01.10.2017

ISSN

1974-9791

Book

Chemical Engineering: General Chemical Engineering

Number

61

Pages from–to

1549–1554

Pages count

6

BIBTEX


@inproceedings{BUT145997,
  author="Petar Sabev {Varbanov} and Jiří {Klemeš},
  title="Process Network Design and Optimisation Using P-graph: The Success, the Challenges and Potential Roadmap",
  booktitle="Chemical Engineering: General Chemical Engineering",
  year="2017",
  number="61",
  month="October",
  pages="1549--1554",
  issn="1974-9791"
}