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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

88 Citations (Scopus)

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 (R 2 ) 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, R 2 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, R 2 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.

Original languageEnglish
Pages (from-to)1005-1019
Number of pages15
JournalScience of the Total Environment
Volume664
DOIs
Publication statusPublished - 10 May 2019

Keywords

  • Artificial intelligence
  • Energy
  • Life cycle assessment
  • Modeling
  • Sugarcane

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

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