Abstract
AbstractThe 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 occurred at the sub-cycle. To deal with those errors, the rule-base-compensator was developed based on operational knowledge.KeywordsWastewater Treatment PlantSequencing Batch ReactorRemote Control SystemSoft SensorPiggery WastewaterThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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