An experimental study on monitoring Oxidation Reduction Potential (ORP), pH, Conductivity and Dissolved Oxygen (DO) in an Enhanced Biological Nutrient Removal process has been carried out. In the anaerobic phase, while ORP and conductivity were not reliable in monitoring simultaneously denitrification and P-release, pH showed the best performances. A significant relationship between P-released and pH variation was found. During the aerobic phase both ORP and pH were able to monitor successfully the nitrification, even though pH was much more reliable. pH can be also used for monitoring and control enhanced P-uptake. It has been concluded that, for a reliable and effective control of biological N and P removal processes a more sophisticated control system seems to be necessary.

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.

The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.

An enhanced process model for SBRs has been developed. Though the basic mechanism largely draws on the Activated Sludge Model n. 2d, its new features are the splitting of the nitrification stage in a two-step process, according to the well known Nitrosomonas -Nitrobacter oxidation sequence, and an improved X PAO dynamics, involved in the anaerobic/aerobic phosphorus removal process.The model was implemented through the DLL technique allowing complied C++ modules to be linked to an ordinary Simulink block diagram. The static sensitivity study revealed that if the parameter vector is partitioned into subsets of biologically related parameters and calibrated separately, the calibration procedure does not present particularly difficult aspects. Trajectory sensitivity showed also to which extent data collection could be optimised in order to improve calibration accuracy.The study of the shape of the error functional generated by parameters couples allows a much more effective calibration strategy.

Nitrification is usually the bottleneck of biological nitrogen removal processes. In SBRs systems, it is not often enough to monitor dissolved oxygen, pH and ORP to spot problems which may occur in nitrification processes. Therefore, automated supervision systems should be designed to include the possibility of monitoring the activity of nitrifying populations. Though the applicability of set-point titration for monitoring biological processes has been widely demonstrated in the literature, the possibility of an automated procedure is still at its early stage of industrial development. In this work, the use of an at-line automated titrator named TITAAN (TITrimetric Automated ANalyser) is presented. The completely automated sensor enables us to track nitrification rate trend with time in an SBR, detecting the causes leading to slower specific nitrification rates. It was also possible to perform early detection of toxic compounds in the influent by assessing their effect on the nitrifying biomass. Nitrifications rates were determined with average errors+/-10% (on 26 tests), never exceeding 20% as compared with UV-spectrophotometric determinations.