External Carbon Source Dosage Control in Denitrification Biofilter Based on Artificial Neural Network
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第36卷
Number:
7 7
Page:
19-25
Column:
Date of publication:
2020-04-01
- Keywords:
- denitrification biofilter; advanced nitrogen removal; external carbon source; artificial neural network
- Abstract:
- Excessive dosage of carbon source in the denitrification biofilter will result in total carbon over set standard in the effluent and waste of carbon source.Therefore, factors influencing the optimal dosage of external carbon source were explored in the laboratory test device feeding actual sewage, and the models of external carbon source dosage and denitrification performance prediction were built by applying artificial neural network.The problem of waste and pollution of carbon source could be alleviated by adding external carbon sources based on the total nitrogen load of influent and the conservation of carbon nitrogen biochemical reaction. However, the denitrification performance was not stable, and it could be improved by the combined effects of ORP, pH, DO and temperature. The adaptive learning rate momentum gradient descent algorithm was used to establish a carbon source dosage model with input of five influent indexes and output of an optimal dosage of external carbon source. The correlation coefficient was 0.964 8,indicating that there was a good correlation between the influent parameters and the optimal carbon dosage and the improvement of the model was feasible. The Bayesian-regularization algorithm was used to establish the denitrification performance prediction model with input of five influent indexes and output of NO3- -N and NO2- -N concentration.The correlation coefficient was 0.908 5, indicating that it was feasible to predict the performance of the denitrification biofilter. The external carbon source dosage control system of denitrification biofilter could be established by cooperation of the carbon source dosage model and the denitrification performance prediction model, in order to improve the automatic control of the sewage treatment plant.
Last Update:
2020-04-01