Application of Neuro-Fuzzy to the elephant grass production process: A systematic bibliographic review

Authors

DOI:

https://doi.org/10.33448/rsd-v13i2.44927

Keywords:

Rural sustainability; Biomass; Cellulose; Pennisetum purpureum.

Abstract

The transition to renewable energy sources can help combat climate change, as they emit fewer greenhouse gas emissions. Biomass is an important source of energy production, being composed of organic materials, such as residues from agricultural and forestry crops, among others, with emphasis on elephant grass. The application of Neuro-Fuzzy in production processes, especially laboratory ones, is of utmost importance, as it allows the creation of more accurate and efficient prediction and control models in the bioenergetic context. Given this context, the objective of this article is to identify how the state of knowledge is configured regarding the application of the use of Neuro-Fuzzy for the chemical quantification process of elephant grass cellulose. A Systematic Bibliographic Review was carried out to search and map published scientific data to identify numerous applications for the use of Neuro-Fuzzy, mainly in renewable energy. By carrying out the research using the Systemic Bibliographic Review, it was possible to identify several opportunities for the application of Neuro-Fuzzy in the chemical quantification of elephant grass. However, it was observed that this article presents a novelty on the application of the use of Neuro-Fuzzy for the process of chemical quantification of cellulose, in addition to the production of bioethanol from this biomass. Of the 22 documents analyzed in this research, 100% were articles in the form of applied research and literature review, demonstrating great relevance in this line of research, which is the application of Artificial Intelligence in field and laboratory production processes using elephant grass. as biomass to produce bioethanol.

References

Adedeji, P. A., Akinlabi, S. A., Madushele, N., & Olatunji, O. O. (2020). Neuro-Fuzzy resource forecast in site suitability assessment for wind and solar energy: A mini review. Journal of Cleaner Production, 269, 122104. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.122104

Adelekan, D. S., Ohunakin, O. S., & Paul, B. S. (2022). Artificial intelligence models for refrigeration, air conditioning and heat pump systems. Energy Reports, 8, 8451–8466. https://doi.org/10.1016/j.egyr.2022.06.062

Aditiya, H. B., Mahlia, T. M. I., Chong, W. T., Nur, H., & Sebayang, A. H. (2016). Second generation bioethanol production: A critical review. Renewable and Sustainable Energy Reviews, 66, 631–653. https://doi.org/10.1016/j.rser.2016.07.015

Amosov, O. S., Ivanov, Y. S., & Zhiganov, S. V. (2017). Human Localization in the Video Stream Using the Algorithm Based on Growing Neural Gas and Fuzzy Inference. Procedia Computer Science, 103, 403–409. https://doi.org/https://doi.org/10.1016/j.procs.2017.01.128

Anusree, K., & Varghese, K. O. (2016). Streamflow Prediction of Karuvannur River Basin Using ANFIS, ANN and MNLR Models. Procedia Technology, 24, 101–108. https://doi.org/https://doi.org/10.1016/j.protcy.2016.05.015

Araújo Júnior, C. A., Silva, L. F. Da, Silva, M. L. Da, Leite, H. G., Valdetaro, E. B., Donato, D. B., & Castro, R. V. O. (2016). Modelagem e prognose do preço de carvão usando um sistema Neuro-Fuzzy. Cerne, 22(2), 151–158. https://doi.org/10.1590/0104776020162222103

Bandeira, E. L., Ferreira, V. C., & Cabral, A. C. de A. (2019). [ARTIGO RETRATADO] Conflito trabalho-família: a produção científica internacional e a agenda de pesquisa nacional. REAd. Revista Eletrônica de Administração (Porto Alegre), 25(1), 49–82. https://doi.org/10.1590/1413-2311.232.87660

Borisov, V., & Luferov, V. (2020). Neuro-Fuzzy Cognitive Temporal Models for Predicting Multidimensional Time Series With Fuzzy Trends. Computación y Sistemas, 24(3), 1165–1177. https://doi.org/10.13053/cys-24-3-3477

Bressane, A., Bagatini, J. A., Biagolini, C. H., Arnaldo, J., Roveda, F., Regina, S., Roveda, M. M., Fengler, F. H., & Longo, R. M. (2018). Neuro-Fuzzy modeling: a promising alternative for risk analysis in urban afforestation management. Revista Árvore, 42(1), 420106. https://doi.org/10.1590/1806-90882018000100006

Cabeza, R. T., & Potts, A. S. (2021). Fault diagnosis and isolation based on Neuro-Fuzzy models applied to a photovoltaic system. IFAC-PapersOnLine, 54(14), 358–363. https://doi.org/10.1016/j.ifacol.2021.10.380

Conforto, E. C., Amaral, D. C., & Silva, S. L. Da. (2011). Roteiro para revisão bibliográfica sistemática: aplicação no desenvolvimento de produtos e gerenciamento de projetos. 8° Congresso Brasileiro de Gestão de Desenvolvimento de Produto - CNGDP 2011, 1–12. http://www.ufrgs.br/cbgdp2011/downloads/9149.pdf

Dasgupta, A., Grimaldi, S., Ramsankaran, R. A. A. J., Pauwels, V. R. N., & Walker, J. P. (2018). Towards operational SAR-based flood mapping using Neuro-Fuzzy texture-based approaches. Remote Sensing of Environment, 215, 313–329. https://doi.org/10.1016/j.rse.2018.06.019

Dokbua, B., Waramit, N., Chaugool, J., & Thongjoo, C. (2020). Biomass Productivity, Developmental Morphology, and Nutrient Removal Rate of Hybrid Napier Grass (Pennisetum purpureum x Pennisetum americanum) in Response to Potassium and Nitrogen Fertilization in a Multiple-Harvest System. Bioenergy Research, 14, 1106-1117. https://doi.org/10.1007/s12155-020-10212-w

Fernandes, F. R., Cardoso, T. A., Capaverde, L. Z., & Silva, H. de F. N. (2016). Comunidades de prática: uma revisão bibliográfica sistemática sobre casos de aplicação organizacional. AtoZ: Novas Práticas em Informação e Conhecimento, 5(1), 44. https://doi.org/10.5380/atoz.v5i1.46691

Godinho, E. Z., De Pietri, E., & Gasparotto, H. V. (2021). A dificuldade na aprendizagem da matemática. Studies in Education Sciences, 1(1), 2–19. https://doi.org/10.54019/sesv1n1-001

Godoy, F. O. de, Godinho, E. Z., Daltin, R. S., & Caneppele, F. D. L. (2020). Utilização da lógica fuzzy aplicada à energia solar. Cadernos de Ciência & Tecnologia, 37(2), 26663. https://doi.org/10.35977/0104-1096.cct2020.v37.26663

Heddam, S., Bermad, A., Dechemi, N., Heddam, S., Bermad, A., Dechemi, · N, & Dechemi, N. (2012). ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study. Environ Monit Assess, 184, 1953–1971. https://doi.org/10.1007/s10661-011-2091-x

Huang, H., Band, S. S., Karami, H., Ehteram, M., Chau, K., & Zhang, Q. (2022). Solar radiation prediction using improved soft computing models for semi-arid, slightly-arid and humid climates. Alexandria Engineering Journal, 61(12), 10631–10657. https://doi.org/https://doi.org/10.1016/j.aej.2022.03.078

Jamma, M., Joshi, D., Akherraz, M., & Bennassar, A. (2018). Direct Power Neuro-Fuzzy Controller Scheme of Three-Phase PWM Rectifiers for Power Quality Improvement. Procedia Computer Science, 132, 595–605. https://doi.org/10.1016/j.procs.2018.05.013

Khatibi, R., & Nadiri, A. A. (2021). Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity. Geoscience Frontiers, 12(2), 713–724. https://doi.org/https://doi.org/10.1016/j.gsf.2020.07.011

Kullavanijaya, P., & Chavalparit, O. (2020). The effect of ensiling and alkaline pretreatment on anaerobic acidification of napier grass in the leached bed process. Environmental Engineering Research, 25(5), 668–676. https://doi.org/10.4491/eer.2019.231

Lins, A. C. d. S. S., Lourençoni, D., Júnior, T. Y., Miranda, I. B., & Santos, I. E. do. A. (2021). Neuro-Fuzzy Modeling of Eyeball and Crest Temperatures in Egg-laying Hens. Engenharia Agricola, 41(1), 34–38. https://doi.org/10.1590/1809-4430-ENG.AGRIC.V41N1P34-38/2021

Macêdo, A. J. da S., Neto, J. M. C., Silva, M. A. da, & Santos, E. M. (2019). Potencialidades e limitações de plantas forrageiras para ensilagem: Revisão. Revista Brasileira de Higiene e Sanidade Animal, 13(2), 320–337.

Malami, S. I., Anwar, F. H., Abdulrahman, S., Haruna, S. I., Ali, S. I. A., & Abba, S. I. (2021). Implementation of hybrid Neuro-Fuzzy and self-turning predictive model for the prediction of concrete carbonation depth: A soft computing technique. Results in Engineering, 10, 100228. https://doi.org/10.1016/j.rineng.2021.100228

Millward-Hopkins, J., & Purnell, P. (2019). Circulating blame in the circular economy: The case of wood-waste biofuels and coal ash. Energy Policy, 129, 168–172. https://doi.org/10.1016/j.enpol.2019.02.019

MME, M. de M. e E. (2020). BALANÇO ENERGÉTICO NACIONAL. In Empresa de Pesquisa Energética- EPE (p. 264).

Olatunji, O. O., Adedeji, P. A., Madushele, N., Akinlabi, S., & Dicarlo, A. A. (2022). Modelling Biomass Elemental Composition: A Neurofuzzy Approach. Procedia Computer Science, 200, 1736–1745. https://doi.org/10.1016/j.procs.2022.01.374

Palacio, J. C. (2020). Application of Neuro-Fuzzy systems in the classification of reports in scheduling problems Introducción. Revista Cubana de Ciencias Informáticas, 14(4), 34–47.

Pereira, W., & Paula, N. de. (2017). Fomento federal ao etanol de segunda geração no Brasil: um exame da atuação da FINEP e do BNDES. Revista de Políticas Públicas, 20(2), 805. https://doi.org/10.18764/2178-2865.v20n2p805-824

Puri, M., Abraham, R. E., & Barrow, C. J. (2012). Biofuel production: Prospects, challenges and feedstock in Australia. Renewable and Sustainable Energy Reviews, 16(8), 6022–6031. https://doi.org/10.1016/j.rser.2012.06.025

Saleem, B., Badar, R., Judge, M. A., Manzoor, A., Islam, S. ul, & Rodrigues, J. J. P. C. (2021). Adaptive recurrent NeuroFuzzy control for power system stability in smart cities. Sustainable Energy Technologies and Assessments, 45, 101089. https://doi.org/https://doi.org/10.1016/j.seta.2021.101089

Santos Cabral, M. M., de Souza Abud, A. K., de Farias Silva, C. E., & Garcia Almeida, R. M. R. (2016). Bioethanol production from coconut husk fiber. Ciência Rural, 46(10), 1872–1877.

Singh, H., & Bharadvaja, N. (2021). Treasuring the computational approach in medicinal plant research. Progress in Biophysics and Molecular Biology, 164, 19–32. https://doi.org/https://doi.org/10.1016/j.pbiomolbio.2021.05.004

Suparta, W., & Samah, A. A. (2020). Rainfall prediction by using ANFIS times series technique in South Tangerang, Indonesia. Geodesy and Geodynamics, 11(6), 411–417. https://doi.org/10.1016/j.geog.2020.08.001

Tenorio, C., Moya, R., Filho, M. T., & Valaert, J. (2015). Quality of pellets made from agricultural and forestry crops in Costa Rican tropical climates. BioResources, 10(1), 482–498. https://doi.org/10.15376/biores.10.1.482-498

Tojeiro, D. O., Cabeza, R. T., & Potts, A. S. (2021). Fault detection based on Neuro-Fuzzy models and residual evaluation with fuzzy thresholds applied to a photovoltaic system. IFAC-PapersOnLine, 54(20), 717–722. https://doi.org/10.1016/j.ifacol.2021.11.256

Winchester, N., & Reilly, J. M. (2015). The feasibility, costs, and environmental implications of large-scale biomass energy. Energy Economics, 51, 188–203. https://doi.org/10.1016/j.eneco.2015.06.016

Yadav, P. K., Bhasker, R., & Upadhyay, S. K. (2022). Comparative study of ANFIS fuzzy logic and neural network scheduling-based load frequency control for two-area hydro thermal system. Materials Today: Proceedings, 56, 3042–3050. https://doi.org/https://doi.org/10.1016/j.matpr.2021.12.041

Downloads

Published

17/02/2024

How to Cite

GODINHO, E. Z. .; CANEPPELE, F. de L. .; FLORIANO, C. Application of Neuro-Fuzzy to the elephant grass production process: A systematic bibliographic review. Research, Society and Development, [S. l.], v. 13, n. 2, p. e6613244927, 2024. DOI: 10.33448/rsd-v13i2.44927. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/44927. Acesso em: 15 nov. 2024.

Issue

Section

Exact and Earth Sciences