Optimization of energy ratio, benefit to cost and greenhouses gasses using metaheuristic techniques (genetic and particular swarm algorithms) and data envelopment analysis: Recommendations for mitigation of inputs consumption (a case crop: edible onion). Issue 6 (31st May 2022)

• Record Type: Journal Article
• Authors: Elhami, Behzad; Raeini, Mahmoud Ghasemi Nejad; Taki, Morteza; Marzban, Afshin; Heidarisoltanabadi, Mohsen
• Is Part Of: Environmental progress & sustainable energy. Volume 41:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-31
• Subjects: data envelopment analysis -- multi‐objective genetic algorithm -- multi‐objective particle swarm algorithm -- transplanting onion; Environmental engineering -- Periodicals; Sustainable engineering -- Periodicals; Environmental chemistry -- Periodicals; 333.7
• DOI: 10.1002/ep.13889
• Languages: English
• ISSNs: 1944-7442

Abstract

Abstract: The present study aimed to optimize the energy, economic, and greenhouse effects indices for edible onion production in Fereydan (85 growers), and Buin (15 growers) regions in Isfahan. Required data were collected through direct measurement, interview, and questionnaire. Total energy input was obtained before and after optimization using data envelopment analysis (DEA), multi‐objective genetic algorithm (MOGA), and multi‐objective particle swarm algorithm (MOPSA), and was determined as 236, 335, 213, 068 (8.75% saving), 125, 663 (48.63% saving), and 320, 657 MJ ha −1 (35.67%), respectively. In addition, energy ratio (ER) was improved from 0.79 to 0.84, 1.81, and 0.8 using DEA, MOGA, and MOPSA, respectively. By reducing the production costs by 10.2%, 63.12%, and 29.4% using DEA, MOGA, and MOPSA, respectively, benefit‐to‐cost ratio (BCR) index was improved from 1.44 to 1.88, 5.21, and 1.94, respectively. Also results showed that GHG emissions were mitigated by 18.39%, 47.56%, and 27.94%, respectively. Based on the findings, MOGA showed better performance compared to DEA, and MOPSA in terms of ER, BCR, and GHG factors. Despite applying metaheuristic, and artificial intelligence methods by MOPSA, this algorithm was not able to optimize the target factors, and applied the energy, and production costs in negative status (in reverse).