A. Safonyk, O. Rogov, M. Trokhymchuc
Èlektron. model. 2023, 45(2):03-15
The main goal of this article is to design a multifactorial model for rapid evaluation of the effective operation of reactors for biological wastewater treatment, which is based on: changes in the concentration of organic pollutants in the bioreactor over time; changes in the concentration of activated sludge in the bioreactor over time; changes in the concentration of activated sludge in the reactor over time, taking into account the unevenness of the flow of wastewater to treatment facilities; the process of transporting the substrate to the bioreactor (it is possible to obtain different amounts at different times). The software implementation of the proposed algorithm for finding the appropriate model problem in the Python environment has been developed. The results of computer experiments on the study of the effectiveness of wastewater treatment in biological treatment reactors for different operating modes of the installations are given. The obtained results will be useful during calculations in the case of designing biological treatment facilities or during the reconstruction of existing bioreactors for their promising operation in new operating conditions.
mathematical model; biological wastewater treatment; non-uniformity conditions.
- Yun Y., Lee E., Kim K., Han J. (2019), Sulfate reducing bacteria-based wastewater treatment system integrated with sulfi de fuel cell for simultaneous wastewater treatment and electricity generation, Chemosphere, 233, pp. 570–578.
- Ghangrekar M.M., Shinde V.B. (2007), Performance of membrane-less microbial fuel cell treating wastewater and effect of electrode distance and area on electricity production, Bioresource Technology, 97, 2879–2885.
- Seung Hyuk Baek, Seok Ku Jeon, Krishna Pagilla (2009). Mathematical modeling of aerobic membrane bioreactor (MBR) using activated sludge model no. 1 (ASM1). Journal of Industrial and Engineering Chemistry, 15(6), pp. 835-840.
- Gladys Jiménez-García, Rafael Maya-Yescas (2019). Chapter Two - Mathematical modeling of mass transport in partitioning bioreactors. Advances in Chemical Engineering, 54, pp. 53-74.
- Hong-Gui Han, Chen-Xuan Sun, Xiao-Long Wu, Hong-Yan Yang, Nan Zhao, Jie Li, Jun-Fei Qiao (2023). Dynamic–static model for monitoring wastewater treatment processes, Control Engineering Practice, 132, 105424.
- Peng Chang, Xun Bao, FanChao Meng, RuiWei Lu (2023). Multi-objective Pigeon-inspired Optimized feature enhancement soft-sensing model of Wastewater Treatment Process, Expert Systems with Applications, 215, 119193.
- Pezhman Kazemi, Christophe Bengoa, Jean-Philippe Steyer, Jaume Giralt (2021). Data-driven techniques for fault detection in anaerobic digestion process, Process Safety and Environmental Protection, 146, pp. 905-915.
- Hongjun Xiao, Daoping Huang, Yongping Pan, Yiqi Liu, Kang Song (2017). Fault diagnosis and prognosis of wastewater processes with incomplete data by the auto-associative neural networks and ARMA model, Chemometrics and Intelligent Laboratory Systems, 161, 2017, pp. 96-107.
- Laurent Lardon, Ana Punal, Jean-Philippe Steyer (2004). On-line diagnosis and uncertainty management using evidence theory––experimental illustration to anaerobic digestion processes, Journal of Process Control, 14(7), pp. 747-763.
- Sánchez-Fernández, F.J. Baldán, G.I. Sainz-Palmero, J.M. Benítez, M.J. Fuente (2018). Fault detection based on time series modeling and multivariate statistical process control, Chemometrics and Intelligent Laboratory Systems, 182, pp. 57-69.
- Doris Brockmann, Yves Gérand, Chul Park, Kim Milferstedt, Arnaud Hélias, Jérôme Hamelin (2021), Wastewater treatment using oxygenic photogranule-based process has lower environmental impact than conventional activated sludge process, Bioresource Technology, 319, pp. 124-204.
- Andrii Safonyk, Viktor Zhukovskyy, Anna Burduk (2020). Modeling of biological wastewater treatment process taking into account reverse effect of concentration on diffusion coefficient. Conference Paper 10th International Conference on Advanced Computer Information Technologies (ACIT2020), pp. 29-35.
- Safonyk A., Bomba A., Tarhonii I. (2019) Modeling and automation of the electrocoagulation process in water treatment, Advances in Intelligent Systems and Computing, 871, pp. 451-463.
- Safonyk A., Martynov S., Kunуtskіy S. (2019) Modeling of the contact removal of iron from groundwater, International Journal of Pure and Applied Mathematics, 32, pp. 71-82.