Diseño de un biorreactor aerobio automatizado para la bioconversión de materia orgánica
| dc.contributor.author | Castañeda-Arias, Nelson | |
| dc.contributor.author | Florián, Daniel | |
| dc.contributor.author | López, Fernando | |
| dc.contributor.author | Siaucho, Darío | |
| dc.date.accessioned | 2023-03-14T23:13:59Z | |
| dc.date.available | 2023-03-14T23:13:59Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14329/510 | |
| dc.description.abstract | Los procesos de bioconversión de materia orgánica permiten el aprovechamiento de residuos orgánicos para aprovecharlos de formas diversas. Una de las aplicaciones convencionales es el compostaje. Pero este, suele realizarse de manera manual o con técnicas de control poco efectivas. Se propone la integración de técnicas derivadas de la Industria 4.0 para mejorar los rendimientos de estos procesos de bioconversión. | |
| dc.format.extent | 1 página | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | spa | spa |
| dc.publisher | Escuela Tecnológica Instituto Técnico Central | spa |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/ | spa |
| dc.title | Diseño de un biorreactor aerobio automatizado para la bioconversión de materia orgánica | |
| dc.type | Informe de investigación | spa |
| dc.rights.license | Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) | spa |
| dc.rights.accessrights | info:eu-repo/semantics/closedAccess | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_18ws | spa |
| dc.type.driver | info:eu-repo/semantics/report | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| dc.contributor.researchgroup | Semillero ISQUA | spa |
| dc.publisher.place | Bogotá | spa |
| dc.relation.references | [1] Guan Wang, Cees Haringa, Henk Noorman, Ju Chu, Yingping Zhuang. (2020). Developing a computational framework to advance bioprocess scale-up. Trends in Biotechnology. [2] Mohamed Helmy, Derek Smith, Kumar Selvarajoo. (2020). Systems biology approaches integrated with artificial intelligence for optimized food-focused metabolic engineering. Metabolic Engineering Communications, e00149. [3] Sin Yong Teng, Guo Yong Yew, Kateina Sukaová, Pau Loke Show, Vítzslav Máa, Jo-Shu Chang,. (2020). Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products.ăBiotechnology advances, 107631. [4] Dutta, S., Madan, S., Parikh, H., Sundar, D. (2016). An ensemble micro neural network approach for elucidating interactions between zinc finger proteins and their target DNA. Bmc Genomics, 17(13), 97-107. [5] Feng, Z., Zhang, B., Ding, W., Liu, X., Yang, D. L., Wei, P., ... Zhu, J. K. (2013). Efficient genome editing in plants using a CRISPR/Cas system. Cell research, 23(10), 1229-1232. [6] Patnaik, P. R. (2006). Synthesizing cellular intelligence and artificial intelligence for bioprocesses.ăBiotechnology advances, 24(2), 129-133. [7] Janmenjoy Nayak, Kanithi Vakula, Paidi Dinesh, Bighnaraj Naik, Danilo Pelusi. (2020). Intelligent food processing: Journey from artificial neural network to deep learning. Computer Science Review, 38, 124-127. | spa |
| dc.subject.unesco | Diseño industrial | |
| dc.subject.unesco | Materia orgánica | |
| dc.subject.unesco | Conversión de energía | |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/IFI | spa |
| dc.rights.coar | http://purl.org/coar/access_right/c_14cb | spa |
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