Life Cycle Assessment and Estimation of Environmental Pollutants Emission in Sugarcane Production (Saccharum officinarum L.) using Artificial Neural Network

Document Type : Scientific - Research

Authors

1 دانشکده مهندسی و فنـاوری کشاورزی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، ایران.

2 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, Campus of Agriculture and Natural Resources, University of Tehran, Ira

Abstract

Introduction
Sugarcane (Saccharum officinarum L.) cultivation as a strategic product with a high sugar content and having side products can play a major role in addressing this need and disconnecting dependence. Life cycle assessment in recent years has become an appropriate tool for assessing environmental impacts in agricultural and food industries. The purpose of this study was to evaluate the environmental impacts of sugarcane production in Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC) with the life cycle approach and predict the amount of contaminants released according to inputs using artificial neural network.
Materials and Methods
This research was carried out in IKSAIC as one of the seven companies affiliated to the sugarcane development and related industries of Khuzestan province during 2017-2018. In a life cycle assessment project, all production processes of a product, from the stage of materials extraction to disposal of the remaining waste from the product (from cradle to grave) are reviewed and the results of the reduction of environmental degradation are used.
Each life cycle assessment project has four essential steps as follows:

Goal and scope definition
Life cycle inventory
Environmental impact assessment
Interpretation of results

Artificial neural network models were used to predict the environmental contamination of the product due to different inputs used in sugarcane production for plant and ratoon farms.
Results and Discussion
Assessment of environmental impacts of sugarcane production
In order to evaluate the reduction of environmental pollutants in sugarcane production, the entire life cycle of the product was investigated from primary sources extraction to harvesting in a field at plant and ratoon farms, separately. Ecoinvent databases were used to access needed information and data analysis was done with Simapro software.
In this study, the global warming potential per product in plant and ratoon farms is estimated to be equal to 126.51 and 103.95 kg CO2, respectively, which is due to the burning of sugarcane branches before harvest. The studies also show that agricultural machinery and electricity have the most impact on this sector.
In order to better interpret the results of the other functional unit, it was considered as a unit of land. This means that all indicators were calculated per hectare of production. When the functional unit is calculated on the unit basis, the differences in the products are ignored for the different performance levels in the results.
Assessment modeling of environmental emissions sugarcane production
Based on the results, in modeling for sugarcane production in plant farms, 9-10-5-10 structures with nine inputs, two hidden layer with 10 as well as 5 secret neurons and 10 output parameters were identified as the best structure. For this structure, R2, RMSE and MAPE were calculated. While the R2 coefficient for the human toxicity index and the marine aquatic eco-toxicity were obtained with the lowest values of 0.960, RMSE for these indices were calculated as 0.138 and 0.126 respectively. Furthermore, highest values of R2 coefficient for the acidification and eutrophication index of the lake were resulted about 0.992, whereas RMSE were computed about 0.106 and 0.115) respectively. Also, in modeling for sugarcane production in ratoon farms, the structure of 7-9-6-10 with seven inputs, two hidden layer with 9 and 6 secret neurons, and 10 output parameters are determined as the best structure. For this structure, R2, RMSE and MAPE were calculated and R2 coefficient was obtained with the lowest value of 0.985, while RMSE for this index was 0.164. In addition, R2 for the acidification index was calculated with the highest value of 0.995, whereas the RMSE for this index was calculated to be 0.116.
Conclusion
The results of the evaluation of product life cycle showed that the global warming potential in plant and ratoon farms was estimated to be 126.51 and 103.95 kg CO2, respectively. Results of artificial neural network modeling indicated that the best structure of the neural network to predict the environmental contaminants of sugarcane production, is 9-10-5-10 and 7-9-6-10 for plant farms and ratoon farms respectively.
Acknowledgements
Thanks and appreciation from the Department Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran for the credibility of this research, as well as the officials and engineers of Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC).

Keywords


Antanasijević, D., Pocajt, V., Ristić, M., and Perić-Grujić, A., 2015. Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. Energy 84: 816-824.
Auer, J., Bey, N., and Schäfer, J.M., 2017. Combined life cycle assessment and life cycle costing in the Eco-Care-Matrix: A case study on the performance of a modernized manufacturing system for glass containers. Journal of Cleaner Production 141: 99-109.
Cochran, W.G., 1977. The estimation of sample size. Sample Technology 3: 72-90.
Elhami, B., Akram, A., and Khanali, M., 2016b. Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) approaches. Information Processing in Agricultures 3(3): 190-205.
Elhami, B., Khanali, M., and Akram, A., 2016a. Combined application of artificial neural networks and life cycle assessment in lentil farming in Iran. Information Processing in Agricultures 4: 18–32.
FAO., 2016. Food and Agricultural Organization Statistical Yearbook, http://www.fao.org.
Ghaderpour, O., Rafiee, SH., and Sharifi, M., 2018. Life cycle assessment of alfalfa production and prediction of emissions using multi-layer Adaptive Neuro-Fuzzy Inference System in Bukan Township. Journal of Agricultural Machinery 8(1): 119-136. (In Persian)
Gonzalez-Garcia, S., Bacenetti, J., Negri, M., Fiala, M., and Arroja, L., 2013. Omparative environmental performance of three different annual energy crops for biogas production in Northern Italy. Journal of Cleaner Production 43: 71-83.
Guinee, J. B., Gorree, M., Heijungs, R., Huppes, G., de Koning, K.R.A., Wegener Sleeswijk, A., Suh, S., Udo de Haes, H., Bruijn, H., Duin, R.V., and Huijbregts, M.A.J., 2002. Handbook on life cycle assessment. Operational guide to the ISO standards. Kluwer, Dordrecht, the Netherlands.
Haroni, S., Shiekhdavoodi, M.G., and Kiani, M., 2015. Modeling of energy consumption and greenhouse gas emissions in the sugarcane production process in ratoon farms using artificial neural networks. A case study in Debel Khazai Agro-industry in Iran. Iranian Journal of Agricultural Machinery 4(2): 11-19. (In Persian with English Summary)
Iriarte, A., Rieradevall, J., and Gabarrell, X., 2010. Life cycle assessment of sunflower and rapeseed as energy crops under Chilean conditions. Journal of Cleaner Production 18(4): 336-345.
ISO., 2006. 14040 International standard. Environmental Management–Life Cycle Assessment–Principles and Framework, International Organisation for Standardization, Geneva, Switzerland.
Kaab, A., Sharifi, M., and Mobli. H., 2019a. Analysis and optimization of energy consumption and greenhouse gas emissions in sugarcane production using data envelopment analysis. Iranian Journal of Biosystem Engineering 50(1):19–30. (In Persian with English Summary)
Kaab, A., Sharifi, M., Mobli, H., Nabavi-Pelesaraei, A., and Chau, K.W., 2019b. Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production. Science of the Total Environment 664:1005–19.
Khanali, M., and Hosseinzadeh-Bandbafha, H., 2017. Evaluation of energy flow and environmental impacts of greenhouse production of medicinal plants with life cycle assessment approach case study of Aloe vera plant. Iranian Journal of Biosystem Engineering 48(3): 361-377. (In Persian with English Summary)
Khanali, M., Mobli, H., and Hosseinzadeh-Bandbafha, H., 2017. Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks. Environmental Science and Pollution Research 24(34): 26324-26340.
Khoshnevisan, B., Rafiee, S.H., Omid, M., and Mousazadeh, H., 2013. Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production. Energy 58: 588-593.
Khoshnevisan, B., Rafiee, S.H., Omid, M., Mousazadeh, H., and Clark, S., 2014. Environmental impact assessment of tomato and cucumber cultivation in greenhouses using life cycle assessment and adaptive neuro-fuzzy inference system. Journal of Cleaner Production 73: 183-192.
Kiani, M.K.D., Ghobadian, B., Tavakoli, T., Nikbakht, A., and Najafi, G., 2010. Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol-gasoline blends. Energy 35: 65-69.
Kouchaki-Penchah, H., Sharifi, M., Mousazadeh, H., and Zarea-Hosseinabadi, H., 2016a. Life cycle assessment of medium-density fiberboard manufacturing process in Iran. Journal of Cleaner Production 112: 351-358.
Kouchaki-Penchah, H., Sharifi, M., Mousazadeh, H., Zarea-Hosseinabadi, H., and Nabavi-Pelesaraei, A., 2016b. Gate to gate life cycle assessment of flat pressed particleboard production in Islamic Republic of Iran. Journal of Cleaner Production 112: 343-350.
Kylili, A., Christoforou, E., and Fokaides, P.A., 2016. Environmental evaluation of biomass pelleting using life cycle assessment. Biomass Bioenergy 84: 107-117.
Lin, P.L., Petway, J., and Settele, J., 2017. Train artificial intelligence to be fair to farming, nature international Journal of Science 334-552.
Manfredi, M., and Vignali, G., 2014. Life cycle assessment of a packaged tomato puree: a comparison of environmental impacts produced by different life cycle phases. Journal of Cleaner Production 73: 275-284.
Mila, I., Canals, L.M., Burnip, G.M., and Cowell, S.J., 2006. Evaluation of the environmental impacts of apple production using life cycle assessment (LCA): Case study in New Zealand. Agriculture, ecosystems and environment 114(2): 226-238.
Milutinović, B., Stefanović, G., Dekić, PS., Mijailović, I., and Tomić, M., 2017. Environmental assessment of waste management scenarios with energy recovery using life cycle assessment and multi-criteria analysis. Energy 137:917-926.
Ministry of Jihad-e-Agriculture of Iran., 2018. Center for Information and Communication Technology - Ministry of Agriculture, amar.maj.ir. (In Persian)
Mirhaji, H., Khojastehpour, M., Abbaspour-Fard, M.H., and Mahdavi-Shahri, S.M., 2012. Environmental impact assessment of sugarbeet (Beta vulgaris L.) production with life cycle assessment method (Case study: South Khorasan province farms). Journal of Agroecology 4(2): 112-120. (In Persian with English Summary)
Mollafilabi, A., 2019. Comparison of environmental impacts for rice (Oryza sativa L.) agroecosystems in the first and second planting patters by using life cycle assessment (Case study: Sari county). Journal of Agroecology 10(4): 949-964. (In Persian with English Summary)
Mousavi-Avval, S.H., Rafiee, SH., Sharifi, M., and Hoseynpour, S., 2015. Energy and environmental life cycle assessment of canola production in Mazandaran province of Iran by applying two different approaches. Iranian Journal of Biosystem Engineering 46(3): 265-274. (In Persian with English Summary)
Mousazadeh, H., Keyhani, A., Javadi, A., Mobli, H., Abrinia, K., and Sharifi, A., 2011. Life cycle assessment of a solar assist plug-in hybrid electric tractor (SAPHT) in comparison with a conventional tractor. Energy Convers Manag 52(3): 1700-1710.
Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S.S., Hosseinzadeh-Bandbafha, H., and Chau K.W., 2018. Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production. Science of the Total Environment 631–632: 1279–94.
Nasrollahi-Sarvaghaji, S., Alimardani, R., Sharifi, M., and Taghizadeh Yazdi, M.R., 2016. Comparison of the environmental impacts of different municipal solid waste treatments using life cycle assessment (LCA) (Case study: Tehran). Iranian Journal of Health and Environment 9(2): 273-88. (In Persian with English Summary)
Nemecek, T., Dubois, D., Huguenin-Elie, O., and Gaillard, G., 2011. Life cycle assessment of Swiss farming systems: I. Integrated and organic farming. Agricultural Systems 104(3): 217-232.
Renno, C., Petito, F., and Gatto, A., 2016. ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building. Journal of Cleaner Production 135: 1298-1316.
Renouf, M.A., Wegener, M.K., and Pagan, R.J., 2010. Life cycle assessment of Australian sugarcane production with a focus on sugarcane growing. The International Journal of Life Cycle Assessment 15(9): 927-937.
Romero-Gamez, M., Audsley, E., and Suarez-Rey, E.M., 2012. Life cycle assessment of cultivating lettuce and escarole in Spain. Journal of Cleaner Production 73:193-203.
Safa, M., and Samarasinghe, S., 2011. Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”. Energy 36: 5140-5147.
Soleymani, M., Keyhani, A., and Omid, M., 2017. Life cycle assessment of ethanol produced from sugarcane molasses in Iran. Journal of Agricultural Engineering 40(2): 13-27. (In Persian with English Summary)
Sugarcane Research Institue of Iran., 2018. Annual Statistics Report, www.sugarcane.ir (In persian)
Yousefi, R., 2011. Agricultural Mechanization, First Edition. Publication of Applied Higher Education Institution of Jihad Agriculture, Iran. p.361. (In Persian)
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