Document Type: Original Article

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

2 School of Industrial Engineering, Iran University of Science & amp; Technology, Tehran, Iran.

Abstract

Using second-generation biomass and biofuel deal with environmental pollution and CO2 emissions. Therefore, this paper design an integrated multi-period bi-objective biofuel supply chain network using support vector machine (SVM) and economic analysis to reduce the cost of generating biofuels and CO2 emissions. The economic analysis consists of three scenarios for supplying biomass. The SVM method specifies the potential place to build the bio-refinery. The next step solves the model with the augmented ε-constraint method. Finally, results show that biomass production and imports simultaneously reduce costs by 24.5% compared to the production scenario and 4.3% compared to the import scenario. According to the results obtained, despite the increase in cost, it reduces the amount of CO2 emissions. So, the Pareto solution resulted from the augmented ε-constraint method for the problem is determined as one of the most effective techniques to help the decision-makers.

Keywords

Azadeh, A., Babazadeh, R., and Asadzadeh, S. M., (2013). ''Optimum estimation and forecasting of renewable energy consumption by artificial neural networks'', Renewable and Sustainable Energy Reviews. Elsevier, Vol. 27, pp. 605–612. doi: 10.1016/j.rser.2013.07.007.

Babazadeh, R., Razmi, J., Pishvaee, M. S., and Rabbani, M., (2017). ''A sustainable second-generation biodiesel supply chain network design problem under risk'', Omega. Elsevier, 66, pp. 258–277. doi: 10.1016/j.omega.2015.12.010.

Babazadeh, R., Razmi, J., Rabbani, M., and Pishvaee, M.S., (2017). ''An integrated data envelopment analysis–mathematical programming approach to strategic biodiesel supply chain network design problem'', Journal of Cleaner Production. Elsevier Ltd, Vol. 147, pp. 694–707. doi: 10.1016/j.jclepro.2015.09.038.

Bairamzadeh, S., Pishvaee, M.S., and Saidi-Mehrabad, M., (2015). ''Multiobjective robust possibilistic programming approach to sustainable bioethanol supply chain design under multiple uncertainties'', Industrial & Engineering Chemistry Research. ACS Publications, Vol. 55, No. 1, pp. 237–256.

Bharj, R. S., Singh, G.N., and Kumar, R., (2020). ''Agricultural Waste Derived 2nd Generation Ethanol Blended Diesel Fuel in India: A Perspective'', in Singh, A. P. et al. (eds) Alternative Fuels and Their Utilization Strategies in Internal Combustion Engines. Singapore: Springer Singapore, pp. 9–24. doi: 10.1007/978-981-15-0418-1_2.

Ghaderi, H., Moini, A., and Pishvaee, M.S., (2018). ''A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design'', Journal of Cleaner Production. Elsevier Ltd, Vol. 179, pp. 368–406. doi: 10.1016/j.jclepro.2017.12.218.

Goli, A., and Zare, H.K., (2018). ''A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms : A case study in dairy industry'', Journal of Industrial and Systems Engineering, Vol. 11, No. 4, pp. 190–203.

Golpira, H. et al. (2015). ''Coordination of green supply chain network, considering uncertain demand and stochastic CO2 emission level'', Journal of Industrial Engineering and Management Studies, Vol. 2, No. 2, pp. 43–54.

Hui, P.C.L., and Choi, T.M., (2016). ''Using artificial neural networks to improve decision making in apparel supply chain systems'', Information Systems for the Fashion and Apparel Industry. Woodhead Publishing, pp. 97–107. doi: 10.1016/B978-0-08-100571-2.00005-1.

Jafarnejad, E., and Aliabadi, J., (2017). ''Multi-objective optimization of costs and pollutants in order to manage the sustainable supply chain of bio-fuels'', in 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, pp. 1990–1994. doi: 10.1109/IEEM.2017.8290240.

Panahi, H. K. S., Dehhaghi, M., Aghbashlo, M., Karimi, K., and Tabatabaei, M., (2020). ''Conversion of residues from agro-food industry into bioethanol in Iran: An under-valued biofuel additive to phase out MTBE in gasoline'', Renewable Energy. Elsevier Ltd, Vol. 145, pp. 699–710. doi: 10.1016/j.renene.2019.06.081.

Kim, J., Realff, M. J., and Lee, J.H., (2011). ''Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty'', Computers & Chemical Engineering. Elsevier Ltd, Vol. 35, No. 9, pp. 1738–1751. doi: 10.1016/j.compchemeng.2011.02.008.

Li, Q., and Hu, G., (2014). ''Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification'', Energy, Vol. 74(C), pp. 576–584. doi: 10.1016/j.energy.2014.07.023.

Marufuzzaman, M., Eksioglu, S.D., and (Eric) Huang, Y., (2014). ''Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment'', Computers & Operations Research. Elsevier, Vol. 49, pp. 1–17. doi: 10.1016/j.cor.2014.03.010.

Mirkouei, A., and Haapala, K.R., (2014). ''Integration of machine learning and mathematical programming methods into the biomass feedstock supplier selection process'', in Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing. DEStech Publications, Inc., pp. 443–450. doi: 10.14809/faim.2014.0443.

Mostafaeipour, A., Goli, A., and Qolipour, M., (2018). ''Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study'', The Journal of Supercomputing. Springer US, 74(10), pp. 5461–5484. doi: 10.1007/s11227-018-2452-0.

Nguyen, D.H., and Chen, H., (2018). ''Supplier selection and operation planning in biomass supply chains with supply uncertainty'', Computers & Chemical Engineering. Elsevier Ltd, Vol. 118, pp. 103–117. doi: 10.1016/j.compchemeng.2018.07.012.

Osmani, A., and Zhang, J., (2017). ''Multi-period stochastic optimization of a sustainable multi-feedstock second generation bioethanol supply chain − A logistic case study in Midwestern United States'', Land Use Policy. Elsevier Ltd, Vol. 61, pp. 420–450. doi: 10.1016/j.landusepol.2016.10.028.

Poudel, S. R., Marufuzzaman, M., and Bian, L., (2016). ''Designing a reliable bio-fuel supply chain network considering link failure probabilities'', Computers & Industrial Engineering. Elsevier Ltd, Vol. 91, pp. 85–99. doi: 10.1016/j.cie.2015.11.002.

Rabbani, M., Saravi, N. A., Farrokhi-Asl, H., Lim, S. F. W., and Tahaei, Z., (2018). ''Developing a sustainable supply chain optimization model for switchgrass-based bioenergy production: A case study'', Journal of Cleaner Production. Elsevier Ltd, Vol. 200, pp. 827–843. doi: 10.1016/j.jclepro.2018.07.226.

Roni, M. S., Eksioglu, S. D., Cafferty, K. G., and Jacobson, J.J., (2017). ''A multi-objective, hub-and-spoke model to design and manage biofuel supply chains'', Annals of Operations Research. Springer US, Vol. 249(1–2), pp. 351–380. doi: 10.1007/s10479-015-2102-3.

Sangaiah, A. K., Tirkolaee, E. B., Goli, A., and Dehnavi-Arani, S., (2019). ''Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem'', Soft Computing. Springer Berlin Heidelberg, 6. doi: 10.1007/s00500-019-04010-6.

Vapnik, V., (1998). ''The Support Vector Method of Function Estimation'', in Suykens, J. A. K. and Vandewalle, J. (eds) Nonlinear Modeling. Boston, MA: Springer US, pp. 55–85. doi: 10.1007/978-1-4615-5703-6_3.

Zhang, J., Osmani, A., Awudu, I., and Gonela, V., (2013). ''An integrated optimization model for switchgrass-based bioethanol supply chain'', Applied Energy. Elsevier Ltd, Vol. 102, pp. 1205–1217. doi: 10.1016/j.apenergy.2012.06.054.