Kim, Yejin Bae, Hyeon Poo, Kyungmin Kim, Jongrack Moon, Taesup Kim, Sungshin Kim, Changwon 2024-01-21T03:01:55Z 2024-01-21T03:01:55Z 2022-01-11 2006-06 0302-9743 https://pubs.kist.re.kr/handle/201004/135415 The biological wastewater treatment plant, which uses microbial community to remove organic matter and nutrients in wastewater, is known as its nonlinear behavior and uncertainty to operate. In spite of strong needs of automatic monitoring of nutrients, it is thought that tremendous expense may be required to install equipments related with remote control system, especially on-line sensors for monitoring organic and nutrient concentrations in the treatment processes. In this research, as a cost-effective tool for replacing expensive on-line sensor, PNN(Polynomial Neural Network) models were developed to estimate the NOx-N and ammonia concentrations by only using on-line values of ORP, DO and pH at the wastewater treatment plant. Developed PNN model could estimate the NOx-N and ammonia profile well. However, the error was increased at the first anoxic period of the first sub-cycle and NOx-N accumulation was occur-red at the sub-cycle. To deal with those errors, the rule-base-compensator was developed based on operational knowledge. English SPRINGER-VERLAG BERLIN NEURAL-NETWORK Soft sensor using PNN model and rule base for wastewater treatment plant Article 1 ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS, v.3973, pp.1261 - 1269 ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS 3973 1261 1269 scie scopus 000239485300184 2-s2.0-33745925556 Computer Science, Artificial Intelligence Computer Science, Theory & Methods Computer Science Article; Proceedings Paper NEURAL-NETWORK