Use of optimization techniques for energy use efficiency and environmental life cycle assessment modification in sugarcane production

Abstract

The objective of this work is to apply optimization techniques (OT) including Multi-Objective Genetic Algorithm (MOGA) and Data Envelopment Analysis (DEA) for environmental impact category reduction and energy use optimization in planted and ratoon farms of sugarcane production at Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC) in southern Iran. Results demonstrate that energy savings by applying MOGA and DEA in planted farms are 20.90% and 8.52%, respectively whilst the corresponding values in ratoon farms are 2.61% and 13.90%, respectively. The increase of energy use efficiency is mainly attributed to electricity, diesel fuel, human labor and nitrogen fertilizer in sugarcane production (planted and ratoon). Furthermore, most environmental impacts under MOGA condition are considerably lower than those under DEA, which are in turn less than the present conditions for both farms (planted and ratoon). The largest variations between MOGA and DEA are on terrestrial ecotoxicity and photochemical oxidation in planted farms and ratoon farms, respectively. MOGA is a feasible OT to assign the best input combinations for planted and ratoon sugarcane productions, by reducing environmental impacts and simultaneously enhancing farms productivity and energy use efficiency. Results are useful to authorities in making decision regarding sustainable expansion of sugarcane production in Iran.