Data-driven neuro-fuzzy modeling and rule optimization for intelligent prediction of bioreactor dynamics

Kamala Najaf Mammadzada

Кимёвий технология. Назорат ва бошқарув · 2026-yil

Annotatsiya

Modeling wastewater bioreactors is a challenging problem in environmental engineering because the processes are constantly changing and the microorganisms do not behave in a linear or predictable way. This makes it difficult to predict and control the system behavior. This research investigates three artificial intelligence approaches for modeling wastewater bioreactors: the Mamdani Fuzzy Inference System (FIS), the Adaptive Neuro-Fuzzy Inference System (ANFIS), and clustering-based fuzzy models. These models help us predict what is happening in the bioreactors. For instance they help us predict how the amount of substances in the water is changing over time like dS0/dt dSs/dt and dXH/dt. We used a lot of data, 15,000 points to train and test these models. The results of the models that employed neuro-clustering methods were excellent, showing high prediction accuracy. Moreover, data-driven self-training proved more efficient than manually defined rule-based training. This is especially important because the obtained models enable control of bioreactor performance by monitoring its behavior over time, thus saving energy and ensuring stable treatment process operation. Furthermore, we checked how efficient the developed models are in the case of changes in the conditions; specifically, when the properties of the influent changed. The models again showed satisfactory results, which allows for developing in the future their cooperation with control systems. This can help make the process more efficient and resilient. Since bioreactors are a core part of wastewater treatment, accurate modeling is crucial. This research contributes to improving wastewater treatment facility design and operation.

Maqola ma’lumotlari
MualliflarKamala Najaf Mammadzada
JurnalКимёвий технология. Назорат ва бошқарув
NashriyotТошкент давлат техника университети, «Ўзқурилишматериаллар» ДАК, «Ўзкимёсаноат» ДАК, «Совпластитал» ҚҚ, Ўзбекистон Интеллектуал мулк агентлиги
ISSN2181-1105, 1815-4840
Nashr sanasi2026-04-30
Jild2026
Son2
Betlar22-28
TilIngliz
DOI10.59048/2181-1105.1771
Indeksga qo‘shilgan2026-09-02

DOI: 10.59048/2181-1105.1771 · Maqolaning asl sahifasi

Iqtibos keltirish

APAKamala Najaf Mammadzada (2026). DATA-DRIVEN NEURO-FUZZY MODELING AND RULE OPTIMIZATION FOR INTELLIGENT PREDICTION OF BIOREACTOR DYNAMICS. Кимёвий технология. Назорат ва бошқарув, 2026(2), 22-28. https://doi.org/10.59048/2181-1105.1771
GOST (OAK)Kamala Najaf Mammadzada DATA-DRIVEN NEURO-FUZZY MODELING AND RULE OPTIMIZATION FOR INTELLIGENT PREDICTION OF BIOREACTOR DYNAMICS // Кимёвий технология. Назорат ва бошқарув. – 2026. – T. 2026, № 2. – P. 22-28. – DOI: 10.59048/2181-1105.1771.
MLAKamala Najaf Mammadzada. “DATA-DRIVEN NEURO-FUZZY MODELING AND RULE OPTIMIZATION FOR INTELLIGENT PREDICTION OF BIOREACTOR DYNAMICS.” Кимёвий технология. Назорат ва бошқарув, 2026(2), 22-28, 2026. https://doi.org/10.59048/2181-1105.1771
ChicagoKamala Najaf Mammadzada. “DATA-DRIVEN NEURO-FUZZY MODELING AND RULE OPTIMIZATION FOR INTELLIGENT PREDICTION OF BIOREACTOR DYNAMICS.” Кимёвий технология. Назорат ва бошқарув, 2026(2), 22-28 (2026). https://doi.org/10.59048/2181-1105.1771
HarvardKamala Najaf Mammadzada (2026) ‘DATA-DRIVEN NEURO-FUZZY MODELING AND RULE OPTIMIZATION FOR INTELLIGENT PREDICTION OF BIOREACTOR DYNAMICS’, Кимёвий технология. Назорат ва бошқарув, 2026(2), 22-28. https://doi.org/10.59048/2181-1105.1771
BibTeX
@article{kamalanajafmammadzada2026,
  title = {DATA-DRIVEN NEURO-FUZZY MODELING AND RULE OPTIMIZATION FOR INTELLIGENT PREDICTION OF BIOREACTOR DYNAMICS},
  author = {Kamala Najaf Mammadzada},
  journal = {Кимёвий технология. Назорат ва бошқарув},
  year = {2026},
  volume = {2026},
  number = {2},
  pages = {22-28},
  doi = {10.59048/2181-1105.1771},
  url = {https://doi.org/10.59048/2181-1105.1771}
}

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