Automatic set-point titration for monitoring nitrification in SBRs.

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.

SBR on-line monitoring by set-point titration.

The applicability of set-point titration for monitoring biological processes has been widely demonstrated in the literature. Based on published and on-going experiences, some operating procedures have been specifically developed to be applied to SBRs, so that real-time information about the process and/or the influent can be obtained. This, in turn, would allow plant operators to select the most appropriate actions properly and timely. Five operating modes are described for the monitoring of (1) influent toxicity, (2) influent N-content, (3) nitrification capacity, (4) end of the nitrification reaction, and (5) nitrate effluent concentration, and are currently tested on the on-line titrator TITAAN (TITrimetric Automated ANalyser) which is in operation on a pilot scale SBR.

On-line supervision and control of an aerobic SBR process.

This paper presents a new software developed in MATLAB for analyzing on-line data of an aerobic SBR, detecting faults and, in this case, proposing the most probable causes of fault. Process diagnosis is achieved using a statistical method divided in two main phases: off-line model building and on-line data diagnosis. The off-line model identifies the correct working conditions of the system (standard operative conditions). It includes the characterization of the deviation of the system from these standard conditions in the case of changing in the biomass properties or carbon and nitrogen load characteristics. The on-line diagnosis aims at collecting and analyzing all the available data available through industrial sensors, and at classifying the behavior of each treatment cycle. The diagnosis performance of the proposed method is tested using a data set of an aerobic SBR pilot plant.