Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor
| Autor: | Kyung-Min Poo, Jongsoo Ko, Soo-Kyoung Kim, H.J. Woo, H. Bae, Chang-Won Kim, Yung-Jin Kim |
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| Rok vydání: | 2006 |
| Předmět: |
Engineering
Environmental Engineering Nitrogen business.industry Decision tree learning Decision Trees Sequencing batch reactor Rule-based system Waste Disposal Fluid Oxygen Set (abstract data type) Bioreactors Fuzzy Logic Ammonia Control theory Close relationship Fuzzy inference system Correlation analysis Polynomial neural network business Algorithm Algorithms Water Science and Technology |
| Zdroj: | Water Science and Technology. 53:199-207 |
| ISSN: | 1996-9732 0273-1223 |
| DOI: | 10.2166/wst.2006.022 |
| Popis: | A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable. |
| Databáze: | OpenAIRE |
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