Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production

Ali Kaab; Mohammad Sharifi; Hossein Mobli; Ashkan Nabavi-Pelesaraei; Kwok-Wing Chau

doi:10.1016/j.scitotenv.2019.02.004

Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production

Sci Total Environ. 2019 May 10:664:1005-1019. doi: 10.1016/j.scitotenv.2019.02.004. Epub 2019 Feb 6.

Authors

Ali Kaab¹, Mohammad Sharifi², Hossein Mobli³, Ashkan Nabavi-Pelesaraei⁴, Kwok-Wing Chau⁵

Affiliations

¹ Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. Electronic address: kaab.ali@ut.ac.ir.
² Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. Electronic address: m.sharifi@ut.ac.ir.
³ Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.
⁴ Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran; Coordination Affairs Expert, Management of Fruit and Vegetables Organizations, Tehran Municipality, Tehran, Iran. Electronic address: ashkan.nabavi@ut.ac.ir.
⁵ Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

PMID: 30769303
DOI: 10.1016/j.scitotenv.2019.02.004

Abstract

This study aims to employ two artificial intelligence (AI) methods, namely, artificial neural networks (ANNs) and adaptive neuro fuzzy inference system (ANFIS) model, for predicting life cycle environmental impacts and output energy of sugarcane production in planted or ratoon farms. The study is performed in Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC) in Khuzestan province of Iran. Based on the cradle to grave approach, life cycle assessment (LCA) is employed to evaluate environmental impacts and study environmental impact categories of sugarcane production. Results of this study show that the consumed and output energies of sugarcane production are in average 172,856.14 MJ ha^-1, 120,000 MJ ha^-1 in planted farms and 122,801.15 MJ ha^-1, 98,850 MJ ha^-1 in ratoon farms, respectively. Results show that, in sugarcane production, electricity, machinery, biocides and sugarcane stem cuttings have the largest impact on the indices in planted farms. However, in ratoon farms, electricity, machinery, biocides and nitrogen fertilizers have the largest share in increasing the indices. ANN model with 9-10-5-11 and 7-9-6-11 structures are the best topologies for predicting environmental impacts and output energy of sugarcane production in planted and ratoon farms, respectively. Results from ANN models indicated that the coefficient of determination (R²) varies from 0.923 to 0.986 in planted farms and 0.942 to 0.982 in ratoon farms in training stage for environmental impacts and outpt energy. Results from ANFIS model, which is developed based on a hybrid learning algorithm, showed that, for prediction of environmental impacts, R² varies from 0.912 to 0.978 and 0.986 to 0.999 in plant and ratoon farms, respectively, and for prediction of output energy, R² varies from 0.944 and 0.996 in planted and ratoon farms. Results indicate that ANFIS model is a useful tool for prediction of environmental impacts and output energy of sugarcane production in planted and ratoon farms.

Keywords: Artificial intelligence; Energy; Life cycle assessment; Modeling; Sugarcane.

MeSH terms

Agriculture / statistics & numerical data*
Algorithms
Artificial Intelligence
Environment
Environmental Monitoring / methods*
Farms / statistics & numerical data
Fertilizers / statistics & numerical data
Fuzzy Logic
Iran
Neural Networks, Computer
Saccharum / growth & development*

Substances

Fertilizers