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Application of multi-objective genetic algorithms for optimization of energy, economics and environmental life cycle assessment in oilseed production

Mousavi-Avval, Seyed Hashem, Rafiee, Shahin, Sharifi, Mohammad, Hosseinpour, Soleiman, Notarnicola, Bruno, Tassielli, Giuseppe, Renzulli, Pietro A.
Journal of cleaner production 2017 v.140 pp. 804-815
algorithms, canola, cost benefit analysis, data collection, econometrics, economic productivity, emissions, energy, farms, fertilizers, life cycle assessment, materials life cycle, mixing, models, nitrogen, production technology, Iran
In this study a multi-objective genetic algorithm (MOGA) was applied to find the best combination of mixing energy, economic and environmental indices concerning oilseed canola production. Data were collected from oilseed farming enterprises in Mazandaran province of Iran. Life cycle assessment of canola production from cradle to farm gate was investigated to calculate the environmental emissions. Econometric modelling was applied to find the relationship functions between energy inputs and three individual output parameters including environmental emissions, output energy and economic productivity. A multi-objective model was formulated in order to maximise the output energy and benefit to cost ratio, and minimise the final score of environmental emissions in order to obtain a set of Pareto frontier. When applying CML-IA methodology, multi-objective optimization resulted in a 32.1% reduction of the total environmental emissions as well as simultaneous increase of output energy and benefit cost ratio by 24.1% and 14.2%, respectively. More specifically, the reduction of chemicals by 82.2%, nitrogen by 11.1% and other chemical fertilisers by 70.7% would be beneficial from environment, energy and economic viewpoints. This work highlights the usefulness of the implementation of MOGA in agricultural production systems to find an optimized combination of mixing energy, economic and environment.