Deep learning in data analysis for the classification and selection of biomass valorization routes
The industry has a high contrast between waste production and profits. Because of it, the use of biomass is an important process. However, its process is long overdue considering the time to extract the biomass, the characterization, and finally the long and privileged search to find ideas in the ro...
- Autores:
-
Suárez Díaz, Yuli Natalia
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/58621
- Acceso en línea:
- http://hdl.handle.net/1992/58621
- Palabra clave:
- Deep learning
Biomass
Protocol
Biomasa
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Deep learning in data analysis for the classification and selection of biomass valorization routes |
| dc.title.alternative.none.fl_str_mv |
Deep learning en análisis de datos para la clasificación y selección de rutas de valorización de biomasa |
| title |
Deep learning in data analysis for the classification and selection of biomass valorization routes |
| spellingShingle |
Deep learning in data analysis for the classification and selection of biomass valorization routes Deep learning Biomass Protocol Biomasa Ingeniería |
| title_short |
Deep learning in data analysis for the classification and selection of biomass valorization routes |
| title_full |
Deep learning in data analysis for the classification and selection of biomass valorization routes |
| title_fullStr |
Deep learning in data analysis for the classification and selection of biomass valorization routes |
| title_full_unstemmed |
Deep learning in data analysis for the classification and selection of biomass valorization routes |
| title_sort |
Deep learning in data analysis for the classification and selection of biomass valorization routes |
| dc.creator.fl_str_mv |
Suárez Díaz, Yuli Natalia |
| dc.contributor.advisor.none.fl_str_mv |
Sierra Ramírez, Rocío Durán Aranguren, Daniel David |
| dc.contributor.author.none.fl_str_mv |
Suárez Díaz, Yuli Natalia |
| dc.subject.keyword.none.fl_str_mv |
Deep learning Biomass Protocol |
| topic |
Deep learning Biomass Protocol Biomasa Ingeniería |
| dc.subject.armarc.none.fl_str_mv |
Biomasa |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
The industry has a high contrast between waste production and profits. Because of it, the use of biomass is an important process. However, its process is long overdue considering the time to extract the biomass, the characterization, and finally the long and privileged search to find ideas in the routes of use. The present work gives the opportunity to have consolidated the principal information about the uses of principal biomass and, after it, obtain a general view of each type of biomass for the characterization protocol used (NREL, PA, or UA). Finally, it has three excellent machine learning models that allow the categorization of each biomass into different groups, made from the unsupervised learning of K-means, deeply studied to make it as unbiased as possible. The models have more than 90% excellence in their process, so they are reliable to the industry and academic community. Its thesis shows the real relationship between the use of new technologies and the chemical process, which so far has very few and only requires an analytical view. |
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2022-06-09 |
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Trabajo de grado - Pregrado |
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Bailey, D. H., & Lopez de Prado, M. (2012). Balanced baskets: a new approach to trading and hedging risks. The Journal of Investment Strategies, 1(4). https://doi.org/10.21314/jois.2012.010 Brownlee, J. (2019). How to Choose Loss Functions When Training Deep Learning Neural Networks. Machine Learning Mastery. Darrell M. West, & Christian Lansang. (2018, July). Global manufacturing scorecard: How the US compares to 18 other nations. Brookings. Definition of Biomass. (2018). In Biomass Gasification, Pyrolysis and Torrefaction (pp. 497-499). Elsevier. https://doi.org/10.1016/b978-0-12-812992-0.00024-8 Delua, J. (2021). Supervised vs. Unsupervised Learning: What's the Difference? IBM Analytics, Data Science/Machine Learning. Dozat, T. (2016). Incorporating Nesterov Momentum into Adam. ICLR Workshop, 1. García, R., Pizarro, C., Lavín, A. G., & Bueno, J. L. (2014). Spanish biofuels heating value estimation. Part II: Proximate analysis data. Fuel, 117(PARTB). https://doi.org/10.1016/j.fuel.2013.08.049 GAYOSO A, J., & GUERRA C, J. (2005). Contenido de carbono en la biomasa aérea de bosques nativos en Chile. Bosque (Valdivia), 26(2). https://doi.org/10.4067/s0717-92002005000200005 37 Huang, W., Peng, Y., Ge, Y., & Kong, W. (2021). A new Kmeans clustering model and its generalization achieved by joint spectral embedding and rotation. PeerJ Computer Science, 7. https://doi.org/10.7717/PEERJ-CS.450 Maithani, M. (2021). Guide To Tensorflow Keras Optimizers. Analytics India Magazine. Maksimuk, Y., Antonava, Z., Krouk, V., Korsakova, A., & Kursevich, V. (2021). Prediction of higher heating value (HHV) based on the structural composition for biomass. Fuel, 299. https://doi.org/10.1016/j.fuel.2021.120860 Mansor, A. M., Lim, J. S., Ani, F. N., Hashim, H., & Ho, W. S. (2019). Characteristics of cellulose, hemicellulose and lignin of MD2 pineapple biomass. Chemical Engineering Transactions, 72. https://doi.org/10.3303/CET1972014 Passos, H., Freire, M. G., & Coutinho, J. A. P. (2014). Ionic liquid solutions as extractive solvents for value-added compounds from biomass. In Green Chemistry (Vol. 16, Issue 12). https://doi.org/10.1039/c4gc00236a Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12. Rajendra, P., Kumari, M., Rani, S., Dogra, N., Boadh, R., Kumar, A., & Dahiya, M. (2022). Impact of artificial intelligence on civilization: Future perspectives. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2022.01.113 Shi, C., Wei, B., Wei, S., Wang, W., Liu, H., & Liu, J. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. Eurasip Journal on Wireless Communications and Networking, 2021(1). https://doi.org/10.1186/s13638-021-01910-w The State of Food Security and Nutrition in the World 2021. (2021). In The State of Food Security and Nutrition in the World 2021. FAO, IFAD, UNICEF, WFP and WHO. https://doi.org/10.4060/cb4474en Tursi, A. (2019). A review on biomass: Importance, chemistry, classification, and conversion. In Biofuel Research Journal (Vol. 6, Issue 2, pp. 962-979). Green Wave Publishing of Canada. https://doi.org/10.18331/BRJ2019.6.2.3 Vassilev, S. v., Baxter, D., Andersen, L. K., & Vassileva, C. G. (2010). An overview of the chemical composition of biomass. In Fuel (Vol. 89, Issue 5). https://doi.org/10.1016/j.fuel.2009.10.022 Wongkaew, M., Kittiwachana, S., Phuangsaijai, N., Tinpovong, B., Tiyayon, C., Pusadee, T., Chuttong, B., Sringarm, K., Bhat, F. M., Sommano, S. R., & Cheewangkoon, R. (2021). Fruit characteristics, peel nutritional compositions, and their relationships with mango peel pectin quality. Plants, 10(6). https://doi.org/10.3390/plants10061148 Ying, X. (2019). An Overview of Overfitting and its Solutions. Journal of Physics: Conference Series, 1168(2). https://doi.org/10.1088/1742-6596/1168/2/022022 |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sierra Ramírez, Rocío76e7ff14-1374-4e6a-9728-ec0aa20f4245600Durán Aranguren, Daniel Davida720a2ef-7685-4d01-a809-c04d98fa31b5600Suárez Díaz, Yuli Natalia2e7fa9d8-e349-4274-9330-3ea56b90fdc76002022-07-08T21:14:24Z2022-07-08T21:14:24Z2022-06-09http://hdl.handle.net/1992/58621instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The industry has a high contrast between waste production and profits. Because of it, the use of biomass is an important process. However, its process is long overdue considering the time to extract the biomass, the characterization, and finally the long and privileged search to find ideas in the routes of use. The present work gives the opportunity to have consolidated the principal information about the uses of principal biomass and, after it, obtain a general view of each type of biomass for the characterization protocol used (NREL, PA, or UA). Finally, it has three excellent machine learning models that allow the categorization of each biomass into different groups, made from the unsupervised learning of K-means, deeply studied to make it as unbiased as possible. The models have more than 90% excellence in their process, so they are reliable to the industry and academic community. Its thesis shows the real relationship between the use of new technologies and the chemical process, which so far has very few and only requires an analytical view.Ingeniero QuímicoPregrado38 páginasapplication/pdfengUniversidad de los AndesIngeniería QuímicaFacultad de IngenieríaDepartamento de Ingeniería Química y de AlimentosDeep learning in data analysis for the classification and selection of biomass valorization routesDeep learning en análisis de datos para la clasificación y selección de rutas de valorización de biomasaTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPDeep learningBiomassProtocolBiomasaIngenieríaBailey, D. H., & Lopez de Prado, M. (2012). Balanced baskets: a new approach to trading and hedging risks. The Journal of Investment Strategies, 1(4). https://doi.org/10.21314/jois.2012.010Brownlee, J. (2019). How to Choose Loss Functions When Training Deep Learning Neural Networks. Machine Learning Mastery.Darrell M. West, & Christian Lansang. (2018, July). Global manufacturing scorecard: How the US compares to 18 other nations. Brookings.Definition of Biomass. (2018). In Biomass Gasification, Pyrolysis and Torrefaction (pp. 497-499). Elsevier. https://doi.org/10.1016/b978-0-12-812992-0.00024-8Delua, J. (2021). Supervised vs. Unsupervised Learning: What's the Difference? IBM Analytics, Data Science/Machine Learning.Dozat, T. (2016). Incorporating Nesterov Momentum into Adam. ICLR Workshop, 1.García, R., Pizarro, C., Lavín, A. G., & Bueno, J. L. (2014). Spanish biofuels heating value estimation. Part II: Proximate analysis data. Fuel, 117(PARTB). https://doi.org/10.1016/j.fuel.2013.08.049GAYOSO A, J., & GUERRA C, J. (2005). Contenido de carbono en la biomasa aérea de bosques nativos en Chile. Bosque (Valdivia), 26(2). https://doi.org/10.4067/s0717-92002005000200005 37Huang, W., Peng, Y., Ge, Y., & Kong, W. (2021). A new Kmeans clustering model and its generalization achieved by joint spectral embedding and rotation. PeerJ Computer Science, 7. https://doi.org/10.7717/PEERJ-CS.450Maithani, M. (2021). Guide To Tensorflow Keras Optimizers. Analytics India Magazine.Maksimuk, Y., Antonava, Z., Krouk, V., Korsakova, A., & Kursevich, V. (2021). Prediction of higher heating value (HHV) based on the structural composition for biomass. Fuel, 299. https://doi.org/10.1016/j.fuel.2021.120860Mansor, A. M., Lim, J. S., Ani, F. N., Hashim, H., & Ho, W. S. (2019). Characteristics of cellulose, hemicellulose and lignin of MD2 pineapple biomass. Chemical Engineering Transactions, 72. https://doi.org/10.3303/CET1972014Passos, H., Freire, M. G., & Coutinho, J. A. P. (2014). Ionic liquid solutions as extractive solvents for value-added compounds from biomass. In Green Chemistry (Vol. 16, Issue 12). https://doi.org/10.1039/c4gc00236aPedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12.Rajendra, P., Kumari, M., Rani, S., Dogra, N., Boadh, R., Kumar, A., & Dahiya, M. (2022). Impact of artificial intelligence on civilization: Future perspectives. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2022.01.113Shi, C., Wei, B., Wei, S., Wang, W., Liu, H., & Liu, J. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. Eurasip Journal on Wireless Communications and Networking, 2021(1). https://doi.org/10.1186/s13638-021-01910-wThe State of Food Security and Nutrition in the World 2021. (2021). In The State of Food Security and Nutrition in the World 2021. FAO, IFAD, UNICEF, WFP and WHO. https://doi.org/10.4060/cb4474enTursi, A. (2019). A review on biomass: Importance, chemistry, classification, and conversion. In Biofuel Research Journal (Vol. 6, Issue 2, pp. 962-979). Green Wave Publishing of Canada. https://doi.org/10.18331/BRJ2019.6.2.3Vassilev, S. v., Baxter, D., Andersen, L. K., & Vassileva, C. G. (2010). An overview of the chemical composition of biomass. In Fuel (Vol. 89, Issue 5). https://doi.org/10.1016/j.fuel.2009.10.022Wongkaew, M., Kittiwachana, S., Phuangsaijai, N., Tinpovong, B., Tiyayon, C., Pusadee, T., Chuttong, B., Sringarm, K., Bhat, F. M., Sommano, S. R., & Cheewangkoon, R. (2021). Fruit characteristics, peel nutritional compositions, and their relationships with mango peel pectin quality. Plants, 10(6). https://doi.org/10.3390/plants10061148Ying, X. (2019). An Overview of Overfitting and its Solutions. 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