Autors: Yordanova, S. T., Noikova N., Petrova R., Tzvetkov, P. M.
Title: Neuron-Fuzzy Modelling on Experimental Data in Anaerobic Digestion of Organic Waste in Waters
Keywords: Anaerobic digestion of organic waste, neuro-fuzzy modelling,

Abstract: The aim of the present paper is to develop neuro-fuzzy models of the anaerobic organic digestion process in wastewater treatment from laboratory and simulated experiments accounting for the variable organic load, ambient influence and microorganisms state in MATLAB environment. The main contributions are determination of significant model parameters via graphical sensitivity analysis, simulation experimentation and design and study in MATLAB of two “black-box” models for the biogas production rate, based respectively on classical feedforward backpropagation and Sugeno fuzzy logic neural networks. The models can find application in process prediction, optimization and control.

References

    Issue

    Third IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2005, pp. 84-88, 2005, Bulgaria, IEEE, ISBN:0-7803-9446-1;978-078039446-9, DOI: 10.1109/IDAACS.2005.282946

    Full text of the publication

    Цитирания (Citation/s):
    1. Pezhman Kazemi, Jean-Philippe Steyer, Christophe Bengoa, Josep Font, Jaume Giralt Robust Data-Driven Soft Sensors for Online Monitoring of Volatile Fatty Acids in Anaerobic Digestion Processes - 2020 - в издания, индексирани в Scopus и/или Web of Science
    2. Pengfei Yan, Minghui Gai, Yuhong Wang and Xiaoyong Gao (2021), Review of Soft Sensors in Anaerobic Digestion Process, J. Processes, MDPI 9(1434), pp. 1-21, https://doi.org/10.3390/pr9081434,https://www.mdpi.com/journal/processes - 2021 - в издания, индексирани в Scopus и/или Web of Science
    3. Wang H., Kalchev B., Tian Y., Simeonov I., Christov N. (2013) Modelling and Composed Recursive Model Free Control for the Anaerobic Digestion Process. In: Dumitrache L. (eds) Advances in Intelligent Control Systems and Computer Science. Advances in Intelligent Systems and Computing, vol. 187. Springer, Berlin, Heidelberg.pp. 265-278, https://doi.org/10.1007/978-3-642-32548-9_19 - 2013 - в издания, индексирани в Scopus и/или Web of Science
    4. H.P. Wang,Y. Tian,B. Kalchev,I. Simeonov,N. Christov, (2013). Control Engineering and Applied Informatics, Vol.15, No. 2, pp. 38-45 - 2013 - в издания, индексирани в Scopus и/или Web of Science
    5. Hou, Dong, Che, Xuanxuan, Li, Feifei, Dong, Yumi (2024). Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network. Physica Scripta 99(5) pp. , Article number 055121, ISSN 0031-8949, doi: 10.1088/1402-4896/ad3e3 - 2024 - в издания, индексирани в Scopus и/или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science