Using new computer based techniques to optimise energy consumption in agricultural land levelling

Salim Almaliki and Nasim Monjezi
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Salim Almaliki: Department of Machinery, University of Basrah, Basrah, Iraq
Nasim Monjezi: Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Research in Agricultural Engineering, 2021, vol. 67, issue 4, 149-163

Abstract: Land levelling is one of the most energy-demanding steps in soil preparation. There are many limiting factors for a specific land levelling operation, such as fertile topsoil conservation, limited allowed slope, specific cut to fill ratio, etc. These limitations make optimisation problems of land levelling even more complicated. In this research, three computational and evolutionary methods including ICA, PSO, GA along with MLS were utilised as optimisation methods to minimise the soil cut and fill volumes and to determine the preferred levelling plane. The results indicated that ICA had the most efficient solution for the energy optimisation in the land levelling among the other investigated methods by saving 29% (17 GJ) of the total energy consumption compared with MLS. This study deals with optimising the energy consumption during land levelling projects using new computer-based techniques and compares them to the MLS method as a benchmark. All in all, ICA, PSO, and GA performed much better than MLS by saving 29, 17, and 10% of the total energy consumption in their best model (number 1 models), respectively. Nonetheless, with these great capacities for saving energy in developing countries, unfortunately, the lack of education and excess subsidies on fossil fuels nullify these potentials.

Keywords: genetic algorithm; imperialistic competitive algorithm; particle swarm optimisation; sustainable agriculture (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlrae:v:67:y:2021:i:4:id:20-2021-rae

DOI: 10.17221/20/2021-RAE

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