Estudios de inteligencia artificial para mejorar el desempeño de modelos estadísticos en codigestión anaerobia
Artificial intelligence studies to improve the performance of statistical models in anaerobic codigestion
DOI:
https://doi.org/10.32870/e-cucba.vi23.359Palabras clave:
Análisis estadístico, optimización, red neuronal artificialResumen
Este trabajo busca aplicar inteligencia artificial para mejorar los resultados obtenidos por modelos basados en sistemas estadísticos como la metodología de superficie de respuesta (MSR) en la literatura, empleando técnicas computacionales basadas en IA, que han demostrado ser valiosas para el diseño y predicción del comportamiento de los sistemas de digestión anaerobios, se empleó una matriz de 3 por 3 para encontrar la mejor arquitectura para la red neuronal, logrando mejorar el rendimiento del modelo propuesto en la literatura que logra de R² de 0.90 y con esta metodología empleada en esta investigación se logra un ajuste R² de 0.99.
Citas
Abdelhay, A., Albsoul, A., Hadidi, F. y Abuothman, A. (2016). Optimization and Modeling of Biogas Production From Green Waste/Biowaste Co‐Digestion Using Leachate and Sludge. CLEAN–Soil, Air, Water, 44(11), 1557-1563. https://doi.org/10.1002/clen.201500514
Ahmad, A., Yadav, A. K., Singh, A. y Singh, D. K. (2023). Optimisation of biogas yield from anaerobic co-digestion of dual waste for environmental sustainability: ANN, RSM and GA approach. International Journal of Oil, Gas and Coal Technology, 33(1), 75-101. https://doi.org/10.1504/IJOGCT.2023.130377
Almomani, F. y Bhosale, R. R. (2020). Enhancing the production of biogas through anaerobic co-digestion of agricultural waste and chemical pre-treatments. Chemosphere, 255, 126805. https://doi.org/10.1016/j.chemosphere.2020.126805
Chen, Y., Song, L., Liu, Y., Yang, L. y Li, D. (2020). A review of the artificial neural network models for water quality prediction. Applied Sciences, 10(17), 5776. https://doi.org/10.3390/app10175776
Chow, W. L., Chong, S., Lim, J. W., Chan, Y. J., Chong, M. F., Tiong, T. J., ... y Pan, G. T. (2020). Anaerobic co-digestion of wastewater sludge: A review of potential co-substrates and operating factors for improved methane yield. Processes, 8(1), 39. https://doi.org/10.3390/pr8010039
Cruz, I. A., Chuenchart, W., Long, F., Surendra, K. C., Andrade, L. R. S., Bilal, M., ... y Ferreira, L. F. R. (2022). Application of machine learning in anaerobic digestion: Perspectives and challenges. Bioresource Technology, 345, 126433. https://doi.org/10.1016/j.biortech.2021.126433
Haffiez, N., Chung, T. H., Zakaria, B. S., Shahidi, M., Mezbahuddin, S., Maal-Bared, R. y Dhar, B. R. (2022). Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion. Science of The Total Environment, 839, 156211. https://doi.org/10.1016/j.scitotenv.2022.156211
Hatata, A., Galal, O. H., Said, N. y Ahmed, D. (2021). Prediction of biogas production from anaerobic co-digestion of waste activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network. Renewable Energy, 178, 226-240. https://doi.org/10.1016/j.renene.2021.06.050
Iweka, S. C., Owuama, K. C., Chukwuneke, J. L. y Falowo, O. A. (2021). Optimization of biogas yield from anaerobic co-digestion of corn-chaff and cow dung digestate: RSM and python approach. Heliyon, 7(11). https://www.cell.com/heliyon/pdf/S2405-8440(21)02358-6.pdf
Karki, R., Chuenchart, W., Surendra, K. C., Shrestha, S., Raskin, L., Sung, S., ... y Khanal, S. K. (2021). Anaerobic co-digestion: Current status and perspectives. Bioresource Technology, 330, 125001. https://doi.org/10.1016/j.biortech.2021.125001
Ma, G., Ndegwa, P., Harrison, J. H. y Chen, Y. (2020). Methane yields during anaerobic co-digestion of animal manure with other feedstocks: A meta-analysis. Science of the Total Environment, 728, 138224. https://doi.org/10.1016/j.scitotenv.2020.138224
Mohamadou, Y., Halidou, A. y Kapen, P. T. (2020). A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19. Applied Intelligence, 50(11), 3913-3925. https://doi.org/10.1007/s10489-020-01770-9
Mougari, N. E., Largeau, J. F., Himrane, N., Hachemi, M. y Tazerout, M. (2021). Application of artificial neural network and kinetic modeling for the prediction of biogas and methane production in anaerobic digestion of several organic wastes. International Journal of Green Energy, 18(15), 1584-1596. https://doi.org/10.1080/15435075.2021.1914630
Neto, J. G., Ozorio, L. V., de Abreu, T. C. C., Dos Santos, B. F. y Pradelle, F. (2021). Modeling of biogas production from food, fruits and vegetables wastes using artificial neural network (ANN). Fuel, 285, 119081. https://doi.org/10.1016/j.fuel.2020.119081
Zeb, A., Alzahrani, E., Erturk, V. S. y Zaman, G. (2020). Mathematical model for coronavirus disease 2019 (COVID‐19) containing isolation class. BioMed research international, 2020(1), 3452402. https://doi.org/10.1155/2020/3452402
Zaied, B. K., Rashid, M., Nasrullah, M., Bari, B. S., Zularisam, A. W., Singh, L., ... y Krishnan, S. (2020). Prediction and optimization of biogas production from POME co-digestion in solar bioreactor using artificial neural network coupled with particle swarm optimization (ANN-PSO). Biomass Conversion and Biorefinery, 1-16. https://link.springer.com/article/10.1007/s13399-020-01057-6
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2024 Juan Pablo Morales Rivera, Jean Michelle Flores Gómez, Francisco González Torres
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
