Simulation-based reinforcement learning for delivery fleet optimisation in CO2 fertilisation networks to enhance food production systems

Rajesh Govindan, Tareq Al-Ansari

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

As part of the drive for global food security, all nations will need to intensify food production, including those situated in hyper arid climates. The State of Qatar is one such example of a national system that whilst it is presented with environmental challenges, seeks to enhance food security. There is a consensus that CO2 fertilisation of agricultural systems has the potential to enhance their productivity. In this paper, the authors present a novel study that involves the development of a simulation model of a GIS-based CO2 fertilisation network comprising of power plants equipped with CO2 capture systems, transportation network, including pipeline and roadways, and agricultural sinks, such as greenhouses. The simulation model is used to specifically train the CO2 distribution agent in order to optimise the logistical performance objectives of the network, namely delivery fulfilment and network utilisation rates. The Pareto non-dominating solutions correspond to an optimal CO2 delivery fleet size of around 1-2 trucks for an average year in the simulation example considered.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages1507-1512
Number of pages6
DOIs
Publication statusPublished - 1 Jan 2019

Publication series

NameComputer Aided Chemical Engineering
Volume46
ISSN (Print)1570-7946

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Keywords

  • CO fertilisation
  • Logistics
  • Reinforcement Learning
  • Simulation

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

Cite this

Govindan, R., & Al-Ansari, T. (2019). Simulation-based reinforcement learning for delivery fleet optimisation in CO2 fertilisation networks to enhance food production systems. In Computer Aided Chemical Engineering (pp. 1507-1512). (Computer Aided Chemical Engineering; Vol. 46). Elsevier B.V.. https://doi.org/10.1016/B978-0-12-818634-3.50252-6