| Titolo | Soft sensors for control of nitrogen and phosphorus removal from wastewaters by neural networks |
|---|---|
| Tipo di pubblicazione | Articolo su Rivista peer-reviewed |
| Anno di Pubblicazione | 2002 |
| Autori | Luccarini, L., Porrà E., Spagni A., Ratini P., Grilli Selene, Longhi S., and Bortone G. |
| Rivista | Water Science and Technology |
| Volume | 45 |
| Paginazione | 101-107 |
| ISSN | 02731223 |
| Parole chiave | accuracy, activated sludge, Activated sludge process, Artificial Neural Network, concentration (parameters), conference paper, control system, denitrification, dephosphorylation, energy conservation, Hydrogen-Ion Concentration, neural network, Neural Network (NN) models, Neural networks, Neural Networks (Computer), Nitrogen, Nitrogen removal, nutrient, online monitoring, Optimal control systems, oxidation reduction potential, Oxidation-Reduction, pH, pH effects, Phosphorus, pollutant removal, prediction, Process control, process design, process model, Productivity, Redox reactions, reproducibility, Reproducibility of Results, Research, Sensitivity and Specificity, sensor, Sequencing batch reactor, Sequencing Batch Reactors (SBRs), sewage, Time Factors, waste water management, Wastewater treatment, water pollutant, Water Purification |
| Abstract | In this paper, we describe the results of research aimed to evaluate the possibility of using a neural network (NN) model for predicting biological nitrogen and phosphorus removal processes in activated sludge, utilising oxidation reduction potential (ORP) and pH as NN inputs. Based on N and P concentrations predictions obtained via the NN, a strategy for controlling sequencing batch reactors (SBRs) phases duration, optimising pollutants removal and saving energy, is proposed. The NN model allowed us to reproduce the concentration trends (change in slope, or process end), with satisfactory accuracy. The NN results were generally in good agreement with the experimental data. These results demonstrated that NN models can be used as "soft on-line sensors" for controlling biological processes in SBRs. By monitoring ORP and pH, it is possible to recognise the N and P concentrations during different SBRs phases and, consequently, to identify the end of the biological nutrient removal processes. This information can then be used to design control systems. |
| Note | cited By 30 |
| URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036201619&partnerID=40&md5=b0e8b3b0b4a4a640745cf530ca131946 |
| Citation Key | Luccarini2002101 |
