Crossflow Microfiltration of Aqueous Suspensions with Guar and Xanthan Gums: Identification of Solutions Using Artificial Neural Networks
Keywords:
Artificial Intelligence, Crossflow Microfiltration, Guar, XantanaAbstract
Artificial Neural Networks (ANNs) are mathematical models used in the computational area that act in an analogous way to the central nervous system of living beings, which possess the ability of acquiring knowledge in a technique called machine learning, allowing them to recognize patterns and stop numerous applications. Therefore, the objective was to develop Neural Networks capable of identifying aqueous solutions with Guar and Xanthan gums (widely used in the food industry) during the crossflow microfiltration process. The networks were trained in the supervised learning algorithms trainscg, trainlm and traingd, all in the 70/15/15 model, for a range of five to fifteen neurons in the hidden layer, whose datasets were found in the literature, referring to temperature, flow velocity, pressure, transmembrane flow rate, time and membrane pore size. The software used to implement the ANNs was MATLAB and the evaluation criteria consisted of the analysis of the parameters confusion matrix, error histogram,
performance and ROC curve. In summary, ten ANNs had satisfactory performances, presenting confusion matrices with accuracies above 98.8%, error histogram graphs being Gaussian centered at 0, decaying performance curves with stopping criterion equal to 6 errors in the validation set and ROC graphs similar to a square with vertices at (0,0), (1,0), (0,1) and (1,1), results considered satisfactory in the literature.
References
Barros, V. (2018). Avaliaç˜ao do desempenho de algoritmos de retropropagaçao com redes neurais artificiais para a resoluçao de problemas n˜ao-lineares. [Master’s thesis, Federal Technological University of Paran´a]. Postgraduate in Science Computing.
Belfort, G., Davis, R., & Zydney, A. (1994). The behavior of suspensions and macromolecular solutions in crossflow microfiltration. Journal of Membrane Science, 96(1-2), 1–58. DOI: https://doi.org/10.1016/0376-7388(94)00119-7
Borges, C. D. & Tondo, C. V. (2008). Xanthan gum: Characteristics and operational conditions of production. Semina: Ciˆencias Biol´ogicas e Da Sa´ude, 29(2), 171–188. DOI: https://doi.org/10.5433/1679-0367.2008v29n2p171
Casta˜neda Ovando, A., Gonz´alez-Aguilar, L. A., Granados-Delgadillo, M. A., & Ch´avez-G´omez, U. J. (2020). Goma guar: un aliado en la industria alimentaria. P¨adi Bolet´ın Cient´ıfico de Ciencias B´asicas e Ingenier´ıas Del ICBI, 7(14), 107–111. DOI: https://doi.org/10.29057/icbi.v7i14.4988
Chen, T., Kapron, N., & Chen, J. C.-Y. (2020). Using evolving ann-based algorithm models for accurate meteorological forecasting applications in vietnam. Mathematical Problems in Engineering, 1–8. DOI: https://doi.org/10.1155/2020/8179652
Chew, J. W., Kilduff, J., & Belfort, G. (2020). The behavior of suspensions and macromolecular solutions in crossflow microfiltration: An update. Journal of Membrane Science, 601, 117865. DOI: https://doi.org/10.1016/j.memsci.2020.117865
Cust´odio, C. A., Filletti, ´ E. R., & Franc¸a, V. v. (2019). Artificial neural networks for density-functional optimizations in fermionic systems. Scientific Reports, 9(1). DOI: https://doi.org/10.1038/s41598-018-37999-1
Filletti, ´ E. R. (2007). Desenvolvimento de modelos neurais para o processamento de sinais ac´usticos visando a medic¸ ˜ao de propriedades topol´ogicas em escoamentos multif´asicos. [PhD thesis].
Filletti, ´ E. R., & Seleghim Jr, P. (2010). Nonintrusive measurement of interfacial area and volumetric fraction in dispersed two-phase flows using a neural network to process acoustic signals—a numerical investigation. [Journal article - Please provide journal name, volume, and page numbers for complete reference]
Gul, M., Kalam, M. A., Mujtaba, M. A., Alam, S., Bashir, M. N., Javed, I., Aziz, U., Farid, M. R., Hassan, M. T., & Iqbal, S. (2020). Multi-objective-optimization of process parameters of industrial-gas-turbine fueled with natural gas by using grey-taguchi and ann methods for better performance. Energy Reports, 6, 2394–2402. DOI: https://doi.org/10.1016/j.egyr.2020.08.002
Hube, S., Jokić, A., W., Sim, L. N., O´ lafsdo´ttir, D., Chong, T. H., & Wu, B. (2021). Fouling and mitigation mechanisms during direct microfiltration and ultrafiltration of primary wastewater. Journal of Water Process Engineering, 44, 102331. DOI: https://doi.org/10.1016/j.jwpe.2021.102331
Joki´c, A., Pajˇcin, I., Grahovac, J., Luki´c, N., Ikoni´c, B., Nikoli´c, N., & Vlajkov, V. (2020). Dynamic modeling using artificial neural network of bacillus velezensis broth cross-flow microfiltration enhanced by air-sparging and turbulence promoter. Membranes, 10(12), 1–14. DOI: https://doi.org/10.3390/membranes10120372
Kürzl, C., Tran, T., & Kulozik, U. (2022). Application of a pulsed crossflow to improve chemical cleaning efficiency in hollow fibre membranes following skim milk microfiltration. Separation and Purification Technology, 302, 122123. DOI: https://doi.org/10.1016/j.seppur.2022.122123
Møller, M. F. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6(4), 525–533. DOI: https://doi.org/10.1016/S0893-6080(05)80056-5
Mykhailichenko, I., Ivashchenko, H., Barkovska, O., & Liashenko, O. (2022). Application of deep neural network for real-time voice command recognition. In 2022 IEEE 3rd KhPIWeek on Advanced Technology (KhPIWeek), pages 1–4. DOI: https://doi.org/10.1109/KhPIWeek57572.2022.9916473
Proni, C., Haneda, R. N., & Filletti, E. R. (2020). Desenvolvimento de redes neurais artificiais para an´alise do fluxo de permeado de uma bebida `a base de ac¸a´ı no processo de microfiltrac¸ ˜ao tangencial. C.Q.D.- Revista Eletrˆonica Paulista de Matem´atica, 17, 189–205. DOI: https://doi.org/10.21167/cqdvol17ermac202023169664cprnherf189205
Queiroz, V. M. S. (2004). Estudo experimental do escoamento e da concentrac¸ ˜ao de mistura no processo de filtrac¸ ˜ao tangencial de suspens˜oes macromoleculares. [PhD thesis].
Sekuli´c, Z., Antanasijevi´c, D., Stevanovi´c, S., & Trivunac, K. (2017). Application of artificial neural networks for estimating cd, zn, pb removal efficiency from wastewater using complexation-microfiltration process. International Journal of Environmental Science and Technology, 14(7), 1383–1396. DOI: https://doi.org/10.1007/s13762-017-1248-8
Viktoratos, I. & Tsadiras, A. (2021). Personalized advertising computational techniques: A systematic literature review, findings, and a design framework. Information, 12(11), 480. DOI: https://doi.org/10.3390/info12110480
Wang, G., Tan, G. W.-H., Yuan, Y., Ooi, K.-B., & Dwivedi, Y. K. (2022). Revisiting tam2 in behavioral targeting advertising: A deep learning-based dual-stage sem-ann analysis. Technological Forecasting and Social Change, 175, 121–345. DOI: https://doi.org/10.1016/j.techfore.2021.121345
Published
How to Cite
Issue
Section
Copyright (c) 2024 Matheus Nonis Passerini, Érica Regina Filletti
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Engineering & Technology Scientific Journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Disclaimer: Articles published by Engineering & Technology Scientific Journal have been pre-viewed and authenticated by the Authors before publication. The Journal, Editor and the editorial board are not entitled or liable to either justify or responsible for inaccurate and misleading data if any. It is the sole responsibility of the Author concerned. Read our Plagiarism Policy and use of this site signifies your agreement to the Terms of Use.
