Mathematical Modeling and Optimization of Supply Chain for Bioethanol

Year 2022, Volume: 6 Issue: 1, 33 - 42, 28.06.2022

Yunzile Dzhelil Todor Mihalev Dragomir Dobrudzhaliev

https://doi.org/10.26650/acin.817655

Abstract

Biofuels today are a good solution in the tendentiously declining stocks of raw materials for conventional fuels. They are used by adding in a certain percentage to usable fuels for transport combustion systems. Their environmental performance is also a good feature in environmental protection. European and global scale, there is an increased use in the coming years, adopted in prescriptions and directives. One of these biofuels is bioethanol, which also finds other applications in the industry and bits. For this purpose, optimal supply chains (SC) are developed, including suitable raw materials, technologies and equipment. This can be done by developing a mathematical model describing the extremely large number of parameters and factors, as well as their limits for real application. Then it is necessary to conduct numerical experiments through multifactorial and multi-critical optimization. The development presents the mathematical model and its software implementation on the GAMS platform. Modeling and optimization has been carried out according to economic and environmental criteria, and the results obtained can be used to build optimal SC for a particular territory – region, state or country.

Keywords

Modeling, Optimization, Bioethanol, Supply Chain

Mathematical Modeling and Optimization of Supply Chain for Bioethanol

Abstract

Biofuels today are a good solution in the tendentiously declining stocks of raw materials for conventional fuels. They are used by adding in a certain percentage to usable fuels for transport combustion systems. Their environmental performance is also a good feature in environmental protection. European and global scale, there is an increased use in the coming years, adopted in prescriptions and directives. One of these biofuels is bioethanol, which also finds other applications in the industry and bits. For this purpose, optimal supply chains (SC) are developed, including suitable raw materials, technologies and equipment. This can be done by developing a mathematical model describing the extremely large number of parameters and factors, as well as their limits for real application. Then it is necessary to conduct numerical experiments through multifactorial and multi-critical optimization. The development presents the mathematical model and its software implementation on the GAMS platform. Modeling and optimization has been carried out according to economic and environmental criteria, and the results obtained can be used to build optimal SC for a particular territory – region, state or country.

Keywords

Modeling, Optimization, Bioethanol, Supply Chain

References

• Balat, M., Balat, H., 2008, Progress in bioethanol processing. Progress in Energy and Combustion Science, 34, 551-573. google scholar
• Balat, M., Balat, H., 2009, Recent trends in global production and utilization of bioethanol fuel. Applied Energy, 86(11), 2273-2282. google scholar
• Biofuels in the European Union. A vision for 2030 and beyond- final report of the Biofuels Research Advisory Council, ftp://ftp.cordis.europa.eu/pub/ fp7/energy/docs/ biofuels_vision_2030_en.pdf 2006 [accessed 28.07.08]. google scholar
• Bowling, I.M., Ponce-Ortega, J.M., El-Halwagi, M.M., 2011, Facility location and supply chain optimization for a biorefinery. Industrial&Engineering Chemistry Research 50(10), 6276-6286. google scholar
• De Meyer, A, Cattrysse, D., Rasinmaki, J., Van Orshoven, J., 2014, Methods to optimise the design and management of biomass-for-bioenergy supply chains. Renewable and Sustainable Energy Reviews 31, 657-670. google scholar
• Development of an Optimization Model for the Location of Biofuel Production Plants, http://pure.ltu.se/portal/files/2745819/Sylvain_Leduc_DOC2009.pdf, [last visited: Feb. 1, 2014]. google scholar
• Digital Library of National Statistical Institute-Online Catalogue,http://statlib.nsi.bg:8181/FullT/FulltOpen/SRB_7_5_2012_2013.pdf, [last visited: Feb.1, 2014]. google scholar
• European Commission, Well-to-wheels analysis of future automotive fuels and powertrains in the European context. (Online). (2006), Available from: <http://www. europabio.org/Biofuels%20reports/well-to-wheel.pdf> Accessed July 2011. google scholar
• European Parliament. Directive 2003/30/CE, eur-lex.europa. eu/LexUriServ/LexUriServ.do?uri%OJ: L:2003:123:0042:0046: IT:PDF [accessed 28.07.08]. google scholar
• Gupta, H., Kusi-Sarpong, S., Rezaei, J., 2020, Barriers and overcoming strategies to supply chain sustainability innovation. Resources, Conservation and Recycling 161, 104819 google scholar
• Hamelinck, C.N., van Hooijdonk, G., Faaij, A.P.C., 2005, Ethanol from lignocellulosic biomass: techno-economic performance in short-, middle- and long-term. Biomass Bioenergy 28, 384-410. google scholar
• Rahemi H., Ali Torabi S., Avami A., Jolai F., 2020, Bioethanol supply chain network design considering land characteristics. Renewable and Sustainable Energy Reviews 119, 109517. google scholar
• Harahap, F., Leduc, S., Mesfun, S., Khatiwada, D., Kraxner, F., Silveira, S., 2020, Meeting the bioenergy targets from palm oil based biorefineries: An optimal configuration in Indonesia. Applied Energy 278, 115749 google scholar
• Hsieh, W.D., Chen, R.H, Wu, T.L, Lin, T.H., 2002. Engine performance and pollutant emission of an SI engine using ethanol-gasoline blended fuels. Atmos Environ 36, 403-410. google scholar http://www.biofuels.apec.org/pdfs/ewg_2010_biofuel-production-cost.pdf. google scholar
• Ivanov B., Dimitrova B., Dobrudzhaliev D., 2013, Optimal location of biodiesel refineries the Bulgarian scale. Journal of Chemical Technology and Metallurgy 48 (5), 513-523. google scholar
• Ivanov B., Dimitrova B., Dobrudzhaliev D., 2014, Optimal design and planning of biodiesel supply chain considering crop rotation model. Part 2. Location of biodiesel production plants on the Bulgarian scale. Bulgarian Chemical Communications 46(2), 306 - 319. google scholar
• Kim, S., Dale, B.E., 2005. Environ mental aspects of ethanol derived from no-tilled corn grain: nonrenewable energy consumption and greenhouse gas emissions. Biomass Bioenergy 28(5), 475-489. google scholar
• Ko, J.K., Lee, J.H., Jung, J.H., Lee, S.M., 2020, Recent advances and future directions in plant and yeast engineering to improve lignocellulosic biofuel production. Renewable and Sustainable Energy Reviews 134, 110390. google scholar
• Kondili E., Kaldellis J., 2007, Biofuel implementation in East Europe: Current status and future prospects. Renewable and Sustainable Energy Reviews 11, 2137-2151. google scholar
• Lennartsson P. R., Erlandsson P., Taherzadeh M. J., 2014, Integration of the first and second generation bioethanol processes and the importance of by-products. Bioresource Technology 165, 3-8. google scholar
• McCarl, B., Meeraus, A., Eijk P., Bussieck M., Dirkse, S., Steacy, P., 2008, McCarl Expanded GAMS user Guide Version 22.9. GAMS Development Corporation. google scholar
• Miret, C., Chazara, P., Montastruc, L., Negny, S., Domenech, S., 2016, Design of bioethanol green supply chain: Comparison between first and second generation biomass concerning economic, environmental and social criteria. Computers and Chemical Engineering 85, 16-35. google scholar
• Najafi, G., Ghobadian, B., Tavakoli, T., Buttsworth, D., Yusaf, T., Faizollahnejad, M., 2009, Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy 86, 630-639. google scholar
• Osmani A., 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 61, 420-450. google scholar
• REPUBLIC OF BULGARIA National statistical institute, http://www.nsi.bg, [last visited:Feb. 1, 2015]. google scholar
• Sassner, P., Galbe, M., Zacchi, G., 2008, Techno-economic evaluation of bioethanol production from three different lignocellulosic materials. Biomass and Bioenergy 32, 422-430. google scholar
• Sharma, B., Ingalls, R., Jones, C., Khanchi, A., 2013, Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future. Renewable and Sustainable Energy Reviews 24, 608-627. google scholar
• Sun, Y., Cheng, J., 2002, Hydrolysis of lignocellulosic materials for ethanol production: a review. Bioresource Technology 83, 1-11. google scholar