Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boiler
operation, control, protection, and planning issues, particularly affecting frequency stability in the grid. In contrast to more widespread wind turbines and photovoltaic systems, biomass based electricity systems are more stable with no negative impacts on the grid stability. The efficiency of baga...
- Autores:
-
Ducardo León, Molina López
Vidal Medina, Juan Ricardo
Sagastume Gutiérrez, Alexis
Cabello Eras, Juan J.
López Sotelo, Jesús Alfonso
Hincapie, Simón
Quispe Oqueña, Enrique Ciro
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2023
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/15878
- Acceso en línea:
- https://hdl.handle.net/10614/15878
https://doi.org/10.3390/su151411290
https://red.uao.edu.co/
- Palabra clave:
- Water-tube boilers
Cogeneration
Energy efficiency
Exergy efficiency
Bagasse
- Rights
- openAccess
- License
- Derechos reservados - MDPI, 2023
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Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boiler |
| title |
Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boiler |
| spellingShingle |
Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boiler Water-tube boilers Cogeneration Energy efficiency Exergy efficiency Bagasse |
| title_short |
Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boiler |
| title_full |
Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boiler |
| title_fullStr |
Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boiler |
| title_full_unstemmed |
Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boiler |
| title_sort |
Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boiler |
| dc.creator.fl_str_mv |
Ducardo León, Molina López Vidal Medina, Juan Ricardo Sagastume Gutiérrez, Alexis Cabello Eras, Juan J. López Sotelo, Jesús Alfonso Hincapie, Simón Quispe Oqueña, Enrique Ciro |
| dc.contributor.author.none.fl_str_mv |
Ducardo León, Molina López Vidal Medina, Juan Ricardo Sagastume Gutiérrez, Alexis Cabello Eras, Juan J. López Sotelo, Jesús Alfonso Hincapie, Simón Quispe Oqueña, Enrique Ciro |
| dc.subject.proposal.eng.fl_str_mv |
Water-tube boilers Cogeneration Energy efficiency Exergy efficiency Bagasse |
| topic |
Water-tube boilers Cogeneration Energy efficiency Exergy efficiency Bagasse |
| description |
operation, control, protection, and planning issues, particularly affecting frequency stability in the grid. In contrast to more widespread wind turbines and photovoltaic systems, biomass based electricity systems are more stable with no negative impacts on the grid stability. The efficiency of bagasse boilers is essential to guaranteeing adequate economic profit and environmental performance in sugar plants. To realice universal access to affordable, reliable, and modern energy services by 2030 (SDG 7), the use of renewable energy sources in energy mixing and energy efficiency must increase globally. Sugar plants include cogeneration systems to provide heat and electricity to the process and frequently sell an electricity surplus to the grid, which depends on their energy efficiency. Boilers are an essential component of cogeneration systems in sugar plants, and their efficiency is crucial to guarantee electricity surplus. Therefore, this study assessed a bagasse boiler to optimize its operational efficiency. To this end, the exergy assessment and multiobjective optimization based on a genetic algorithm are used. The results show that the exergy efficiency of the boiler improved by 0.8% with the optimization, reducing bagasse consumption by 23 t/d |
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2023 |
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2023 |
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2024-10-30T20:02:08Z |
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2024-10-30T20:02:08Z |
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Molina, D. L., et. al. (2023). Multiobjective Optimization of the Energy Efficiency and the Steam Flow in a Bagasse Boiler. Sustainability. 15(14). 17 p. https://doi.org/10.3390/su151411290 |
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https://hdl.handle.net/10614/15878 |
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https://doi.org/10.3390/su151411290 |
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20711050 |
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Universidad Autónoma de Occidente |
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Respositorio Educativo Digital UAO |
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https://red.uao.edu.co/ |
| identifier_str_mv |
Molina, D. L., et. al. (2023). Multiobjective Optimization of the Energy Efficiency and the Steam Flow in a Bagasse Boiler. Sustainability. 15(14). 17 p. https://doi.org/10.3390/su151411290 20711050 Universidad Autónoma de Occidente Respositorio Educativo Digital UAO |
| url |
https://hdl.handle.net/10614/15878 https://doi.org/10.3390/su151411290 https://red.uao.edu.co/ |
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eng |
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eng |
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17 |
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14 |
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1 |
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15 |
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Sustainability |
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1. Taheri, K.; Gadow, R.; Killinger, A. Exergy Analysis as a Developed Concept of Energy Efficiency Optimized Processes: The Case of Thermal Spray Processes. Procedia CIRP 2014, 17, 511–516. [CrossRef] 2. IEA Key World Energy Statistics 2021—Analysis—IEA. Available online: https://www.iea.org/reports/key-world-energystatistics- 2021 (accessed on 26 December 2022). 3. Gupta, S.; Fügenschuh, A.; Ali, I. A Multi-Criteria Goal Programming Model to Analyze the Sustainable Goals of India. Sustainability 2018, 10, 778. [CrossRef] 4. Khan, M.F.; Pervez, A.; Modibbo, U.M.; Chauhan, J.; Ali, I. Flexible Fuzzy Goal Programming Approach in Optimal Mix of Power Generation for Socio-Economic Sustainability: A Case Study. Sustainability 2021, 13, 8256. [CrossRef] 5. Barroso, J.; Barreras, F.; Amaveda, H.; Lozano, A. On the Optimization of Boiler Efficiency Using Bagasse as Fuel. Fuel 2003, 82, 1451–1463. [CrossRef] 6. Zabat, L.H.; Akli Sadaoui, N.; Abid, M.; Sekrafi, H. Threshold Effects of Renewable Energy Consumption by Source in U.S. Economy. Electr. Power Syst. Res. 2022, 213, 108669. [CrossRef] 7. Arshad, M.; Ahmed, S. Cogeneration through Bagasse: A Renewable Strategy to Meet the Future Energy Needs. Renew. Sustain. Energy Rev. 2016, 54, 732–737. [CrossRef] 8. Khan, Y.; Oubaih, H.; Elgourrami, F.Z. The Effect of Renewable Energy Sources on Carbon Dioxide Emissions: Evaluating the Role of Governance, and ICT in Morocco. Renew Energy 2022, 190, 752–763. [CrossRef] 9. Saha, S.; Saleem, M.I.; Roy, T.K. Impact of High Penetration of Renewable Energy Sources on Grid Frequency Behaviour. Int. J. Electr. Power Energy Syst. 2023, 145, 108701. [CrossRef] 10. Castro, L.M. Simulation Framework for Automatic Load Frequency Control Studies of VSC-Based AC/DC Power Grids. Int. J. Electr. Power Energy Syst. 2022, 141, 108187. [CrossRef] 11. Østergaard, P.A. Comparing Electricity, Heat and Biogas Storages’ Impacts on Renewable Energy Integration. Energy 2012, 37, 255–262. [CrossRef] 12. Monshizadeh, P.; de Persis, C.; Stegink, T.; Monshizadeh, N.; van der Schaft, A. Stability and Frequency Regulation of Inverters with Capacitive Inertia. In Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, Melbourne, VIC, Australia, 12–15 December 2017; pp. 5696–5701. [CrossRef] 13. Chen, T.; Zhang, Y.-J.; Liao, M.-R.; Wang, W.-Z. Coupled Modeling of Combustion and Hydrodynamics for a Coal-Fired Supercritical Boiler. Fuel 2019, 240, 49–56. [CrossRef] 14. Taler, D.; Trojan, M.; Dzierwa, P.; Kaczmarski, K.; Taler, J. Numerical Simulation of Convective Superheaters in Steam Boilers. Int. J. Therm. Sci. 2018, 129, 320–333. [CrossRef] 15. Zima, W. Simulation of Steam Superheater Operation under Conditions of Pressure Decrease. Energy 2019, 172, 932–944. [CrossRef] 16. Mali, C.R.; Vinod, V.; Patwardhan, A.W. New Methodology for Modeling Pressure Drop and Thermal Hydraulic Characteristics in Long Vertical Boiler Tubes at High Pressure. Prog. Nucl. Energy 2019, 113, 215–229. [CrossRef] 17. Sunil, P.U.; Barve, J.; Nataraj, P.S.V. Mathematical Modeling, Simulation and Validation of a Boiler Drum: Some Investigations. Energy 2017, 126, 312–325. [CrossRef] 18. Chodankar, B.M. Energy and Exergy Analysis of a Captive Steam Power Plant. In Proceedings of the International Conference on Energy and Environment, Chandigarh, India, 19–21 March 2009; pp. 263–266. 19. Hajebzadeh, H.; Ansari, A.N.M.; Niazi, S. Mathematical Modeling and Validation of a 320 MW Tangentially Fired Boiler: A Case Study. Appl. Therm. Eng. 2019, 146, 232–242. [CrossRef] 20. Centeno-González, F.O.; Lora, E.E.S.; Nova, H.F.V.; Reyes, A.M.M.; Jaén, R.L. Programming the Inverse Thermal Balance for a Bagasse-Fired Boiler, Including the Application of a Optimization Method in MATLAB. Sugar Tech. 2018, 20, 585–590. [CrossRef] 21. Sosa-Arnao, J.H.; Nebra, S.A. First and Second Law to Analyze the Performance of Bagasse Boilers. Int. J. Thermodyn. 2011, 14, 51–58. [CrossRef] 22. Parvez, Y.; Hasan, M.M. Exergy Analysis and Performance Optimization of Bagasse Fired Boiler. IOP Conf Ser Mater Sci Eng 2019, 691, 012089. [CrossRef] 23. Pellegrini, L.F.; de Oliveira Junior, S. Combined Production of Sugar, Ethanol and Electricity: Thermoeconomic and Environmental Analysis and Optimization. Energy 2011, 36, 3704–3715. [CrossRef] 24. Colombo, G.; Ocampo-Duque, W.; Rinaldi, F. Challenges in Bioenergy Production from Sugarcane Mills in Developing Countries: A Case Study. Energies 2014, 7, 5874–5898. [CrossRef] 25. Poli, M.; Bustamante, G.; Rivero, S.; Lagunes, M.; Pineda, N.; Escobedo, A.; Sustainability, C.; Manzini Poli, F.L.; Islas-Samperio, J.M.; García Bustamante, C.A.; et al. Sustainability Assessment of Solid Biofuels from Agro-Industrial Residues Case of Sugarcane Bagasse in a Mexican Sugar Mill. Sustainability 2022, 14, 1711. [CrossRef] 26. Hao, Y.S.; Chen, Z.; Sun, L.; Liang, J.; Zhu, H. Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer. Sustainability 2020, 12, 8235. [CrossRef] 27. Varshney, D.; Mandade, P.; Shastri, Y. Multi-Objective Optimization of Sugarcane Bagasse Utilization in an Indian Sugar Mill. Sustain. Prod. Consum. 2019, 18, 96–114. [CrossRef] 28. Birru, E.; Erlich, C.; Herrera, I.; Martin, A.; Feychting, S.; Vitez, M.; Abdulhadi, E.B.; Larsson, A.; Onoszko, E.; Hallersbo, M.; et al. A Comparison of Various Technological Options for Improving Energy and Water Use Efficiency in a Traditional Sugar Mill. Sustainability 2016, 8, 1227. [CrossRef] 29. Coello, C.A.C.; Lamont, G.B.; van Veldhuizen, D.A. Evolutionary Algorithms for Solving Multi-Objective Problems; Springer: Berlin/Heidelberg, Germany, 2007; ISBN 9780387310299. 30. Carrillo Caballero, G.E.; Mendoza, L.S.; Martinez, A.M.; Silva, E.E.; Melian, V.R.; Venturini, O.J.; del Olmo, O.A. Optimization of a Dish Stirling SystemWorking with DIR-Type Receiver Using Multi-Objective Techniques. Appl. Energy 2017, 204, 271–286. [CrossRef] 31. Vidal, J. Motor Stirling: Uma Alternativa Para a Geração de Eletricidade a Partir Da Biomassa Autónoma de Occidente; Universidad Autónoma de Occidente: Cali, Colombia, 2017. 32. Ohijeagbon, I.O.; Waheed, M.A.; Jekayinfa, S.O. Methodology for the Physical and Chemical Exergetic Analysis of Steam Boilers. Energy 2013, 53, 153–164. [CrossRef] 33. Herrera Palomino, M.; Castro Pacheco, E.; Duarte Forero, J.; Fontalvo Lascano, A.; Vásquez Padilla, R. Análisis Exergético de Un Ciclo Brayton Supercrítico Con Dióxido de Carbono Como Fluido de Trabajo. INGE CUC 2018, 14, 159–170. [CrossRef] 34. Feng,W.; Gong, D.; Yu, Z. Multi-Objective Evolutionary Optimization Based on Online Perceiving Pareto Front Characteristics. Inf. Sci. (N Y) 2021, 581, 912–931. [CrossRef] 35. Limleamthong, P.; Guillén-Gosálbez, G. Combined Use of Bilevel Programming and Multi-Objective Optimization for Rigorous Analysis of Pareto Fronts in Sustainability Studies: Application to the Redesign of the UK Electricity Mix. Comput. Aided Chem. Eng. 2018, 43, 1099–1104. [CrossRef] 36. Jiang, S.; Yang, S. An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts. IEEE Trans. Cybern. 2016, 46, 421–437. [CrossRef] 37. Ahmadi, P.; Khanmohammadi, S.; Musharavati, F.; Afrand, M. Development, Evaluation, and Multi-Objective Optimization of a Multi-Effect Desalination Unit Integrated with a Gas Turbine Plant. Appl. Therm. Eng. 2020, 176, 115414. [CrossRef] 38. Dincer, I.; Rosen, M.A.; Ahmadi, P. Optimization of Energy Systems; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2015; p. 472. 39. Hua, Y.; Liu, Q.; Hao, K.; Jin, Y. 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Triple-Objective Optimization of a Double-Tube Heat Exchanger with Elliptic Cross Section in the Presence TiO2 Nanofluid. J. Therm. Anal. Calorim. 2020, 140, 477–488. [CrossRef] 45. Emmerich, M.T.M.; Deutz, A.H. A Tutorial on Multiobjective Optimization: Fundamentals and Evolutionary Methods. Nat. Comput. 2018, 17, 585–609. [CrossRef] [PubMed] 46. Verma, S.; Pant, M.; Snasel, V. A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems. IEEE Access 2021, 9, 57757–57791. [CrossRef] 47. Hojjati, A.; Monadi, M.; Faridhosseini, A.; Mohammadi, M. Application and Comparison of NSGA-II and MOPSO in Multi- Objective Optimization ofWater Resources Systems. J. Hydrol. Hydromech. 2018, 66, 323–329. [CrossRef] 48. Ferreira, J.C.; Fonseca, C.M.; Gaspar-Cunha, A. Methodology to Select Solutions from the Pareto-Optimal Set: A Comparative Study. In Proceedings of the GECCO 2007: Genetic and Evolutionary Computation Conference, London, UK, 7–11 July 2007; pp. 789–796. 49. Costa, N.; Lourenço, J. Responses’ Prediction Standard Error Analysis in Pareto Solutions. In Proceedings of the MATEC Web of Conferences, EDP Sciences, Malacca, Malaysia, 31 May 2017; p. 10007. 50. Rao, R.V.; Lakshmi, R.J. Ranking of Pareto-Optimal Solutions and Selecting the Best Solution in Multi- and Many-Objective Optimization Problems Using R-Method. Soft Comput. Lett. 2021, 3, 100015. [CrossRef] 51. Martí, R.; Sandoya, F. GRASP and Path Relinking for the Equitable Dispersion Problem. Comput. Oper. Res. 2013, 40, 3091–3099. [CrossRef] 52. Thunuguntla, V.K.; Injeti, S.K. Butterfly Optimizer Assisted Max–Min Based Multi-Objective Approach for Optimal Connection of DGs and Optimal Network Reconfiguration of Distribution Networks. J. Electr. Syst. Inf. Technol. 2022, 9, 1–25. [CrossRef] 53. Dinçer, I.; Rosen, M. Exergy, Energy, Environment and Sustainable Development, 1st ed.; Elsevier: Amsterdam, The Netherlands, 2007; ISBN 9780128243930. 54. Kotas, T.J. The Exergy Method of Thermal Plant Analysis; Butterworths: Great Britain, UK, 1985; ISBN 0408013508. 55. Hugot, E. Manual Para Ingenieros Azucareros; Continental: Mexico City, Mexico, 1982. 56. Mendoza Baeza, J.; Rojas Lago, F. Restauración de Servicio Multiobjetivo En Redes de Distribución Utilizando NSGA-II. Ingeniare. Rev. Chil. Ing. 2009, 17, 337–346. [CrossRef] 57. Vrajitoru, D. Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval; Springer: Berlin/Heidelberg, Germany, 2000; pp. 199–222. [CrossRef] |
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Ducardo León, Molina LópezVidal Medina, Juan Ricardovirtual::5737-1Sagastume Gutiérrez, AlexisCabello Eras, Juan J.López Sotelo, Jesús Alfonsovirtual::5738-1Hincapie, SimónQuispe Oqueña, Enrique Cirovirtual::5739-12024-10-30T20:02:08Z2024-10-30T20:02:08Z2023Molina, D. L., et. al. (2023). Multiobjective Optimization of the Energy Efficiency and the Steam Flow in a Bagasse Boiler. Sustainability. 15(14). 17 p. https://doi.org/10.3390/su151411290https://hdl.handle.net/10614/15878https://doi.org/10.3390/su15141129020711050Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/operation, control, protection, and planning issues, particularly affecting frequency stability in the grid. In contrast to more widespread wind turbines and photovoltaic systems, biomass based electricity systems are more stable with no negative impacts on the grid stability. The efficiency of bagasse boilers is essential to guaranteeing adequate economic profit and environmental performance in sugar plants. To realice universal access to affordable, reliable, and modern energy services by 2030 (SDG 7), the use of renewable energy sources in energy mixing and energy efficiency must increase globally. Sugar plants include cogeneration systems to provide heat and electricity to the process and frequently sell an electricity surplus to the grid, which depends on their energy efficiency. Boilers are an essential component of cogeneration systems in sugar plants, and their efficiency is crucial to guarantee electricity surplus. Therefore, this study assessed a bagasse boiler to optimize its operational efficiency. To this end, the exergy assessment and multiobjective optimization based on a genetic algorithm are used. The results show that the exergy efficiency of the boiler improved by 0.8% with the optimization, reducing bagasse consumption by 23 t/d17 páginasapplication/pdfengMDPIBasel, SwitzerlandDerechos reservados - MDPI, 2023https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Multiobjective optimization of the energy efficiency and the steam flow in a bagasse boilerArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a851714115Sustainability 1. Taheri, K.; Gadow, R.; Killinger, A. Exergy Analysis as a Developed Concept of Energy Efficiency Optimized Processes: The Case of Thermal Spray Processes. Procedia CIRP 2014, 17, 511–516. [CrossRef] 2. IEA Key World Energy Statistics 2021—Analysis—IEA. Available online: https://www.iea.org/reports/key-world-energystatistics- 2021 (accessed on 26 December 2022). 3. Gupta, S.; Fügenschuh, A.; Ali, I. A Multi-Criteria Goal Programming Model to Analyze the Sustainable Goals of India. Sustainability 2018, 10, 778. [CrossRef] 4. Khan, M.F.; Pervez, A.; Modibbo, U.M.; Chauhan, J.; Ali, I. Flexible Fuzzy Goal Programming Approach in Optimal Mix of Power Generation for Socio-Economic Sustainability: A Case Study. Sustainability 2021, 13, 8256. [CrossRef] 5. Barroso, J.; Barreras, F.; Amaveda, H.; Lozano, A. On the Optimization of Boiler Efficiency Using Bagasse as Fuel. Fuel 2003, 82, 1451–1463. [CrossRef] 6. Zabat, L.H.; Akli Sadaoui, N.; Abid, M.; Sekrafi, H. Threshold Effects of Renewable Energy Consumption by Source in U.S. Economy. Electr. Power Syst. Res. 2022, 213, 108669. [CrossRef] 7. Arshad, M.; Ahmed, S. Cogeneration through Bagasse: A Renewable Strategy to Meet the Future Energy Needs. Renew. Sustain. Energy Rev. 2016, 54, 732–737. [CrossRef] 8. Khan, Y.; Oubaih, H.; Elgourrami, F.Z. The Effect of Renewable Energy Sources on Carbon Dioxide Emissions: Evaluating the Role of Governance, and ICT in Morocco. Renew Energy 2022, 190, 752–763. [CrossRef] 9. Saha, S.; Saleem, M.I.; Roy, T.K. Impact of High Penetration of Renewable Energy Sources on Grid Frequency Behaviour. Int. J. Electr. Power Energy Syst. 2023, 145, 108701. [CrossRef] 10. Castro, L.M. Simulation Framework for Automatic Load Frequency Control Studies of VSC-Based AC/DC Power Grids. Int. J. Electr. Power Energy Syst. 2022, 141, 108187. [CrossRef] 11. Østergaard, P.A. Comparing Electricity, Heat and Biogas Storages’ Impacts on Renewable Energy Integration. Energy 2012, 37, 255–262. [CrossRef] 12. Monshizadeh, P.; de Persis, C.; Stegink, T.; Monshizadeh, N.; van der Schaft, A. 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