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Growing Science » International Journal of Industrial Engineering Computations » Developing a versatile simulation, scheduling and economic model framework for bioenergy production systems

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International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print)
Quarterly Publication
Volume 10 Issue 1 pp. 17-36 , 2019

Developing a versatile simulation, scheduling and economic model framework for bioenergy production systems Pages 17-36 Right click to download the paper Download PDF

Authors: Robert Matindi, Phil Hobson, Mahmoud Masoud, Geoff Kent, Shi Qiang Liu

DOI: 10.5267/j.ijiec.2018.5.003

Keywords: Bio-refinery, Cane harvesting, Supply chain, Genetic algorithm

Abstract: Modelling is an effective way of designing, understanding, and analysing bio-refinery supply chain systems. The supply chain is a complex process consisting of many systems interacting with each other. It requires the modelling of the processes in the presence of multiple autonomous entities (i.e. biomass producers, bio-processors and transporters), multiple performance measures and multiple objectives, both local and global, which together constitute very complex interaction effects. In this paper, simulation models for recovering biomass from the field of the biorefinery are developed and validated using some industry data and the minimum biomass recovery cost is established based on different strategies employed for recovering biomass. Energy densification techniques are evaluated for their net present worth and the technologies that offer greater returns for the industry are recommended. In addition, a new scheduling algorithm is also developed to enhance the process flow of the management of resources and the flow of biomass. The primary objective is to investigate different strategies to reach the lowest cost delivery of sugarcane harvest residue to a sugar factory through optimally located bio-refineries. A simulation /optimisation solution approach is also developed to tackle the stochastic variables in the bioenergy production system based on different statistical distributions such as Weibull and Pearson distributions. In this approach, a genetic algorithm is integrated with simulation to improve the initial solution and search the near-optimal solution. A case study is conducted to illustrate the results and to validate the applicability for the real world implementation using ExtendSIM Simulation software using some real data from Australian Mills.

How to cite this paper
Matindi, R., Hobson, P., Masoud, M., Kent, G & Liu, S. (2019). Developing a versatile simulation, scheduling and economic model framework for bioenergy production systems.International Journal of Industrial Engineering Computations , 10(1), 17-36.

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Journal: International Journal of Industrial Engineering Computations | Year: 2019 | Volume: 10 | Issue: 1 | Views: 2352 | Reviews: 0

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