Function to smooth the RODTOX signal and to obtain the first and second derivative

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Basic background

According to Spanjers et al. (1998), respirometry is the measurement and interpretation of the respiration rate of activated sludge. In turn, the respiration rate is defined as the rate at which oxygen (O2), or any other electron acceptor for that matter, is consumed by activated sludge to support its metabolic processes. Though wastewater and activated sludge hold a complex ecosystem with several trophic levels, the bulk of its biomass is composed of bacteria and archaea. Taken as a whole, this biomass can be separated into two categories. The heterotrophic organisms use external sources of organic carbon as an electron donor.

On the other hand, autotrophic organisms use inorganic carbon dioxide as their carbon source, and other substrates such as ammonia, nitrite, iron III or sulfur as the electron donor for their metabolic processes (Metcalf & Eddy, 2013). In wastewater, however, the most prevalent of these autotrophic substrates is ammonia. The primary electron acceptor used by microorganisms, regardless of whether they are heterotrophic or autotrophic, is dissolved oxygen. However, some organisms, such as facultative or anoxic heterotrophs, use nitrate (NO? 3 ) as an electron acceptor. These organisms are instrumental to wastewater treatment, as they are responsible for denitrification.

Finally, anaerobic bacteria and archaea, which are responsible for the fermentation and methanogenesis occurring in anaerobic processes, use inorganic molecules such as CO2 as their final electron acceptor. Though they do play a key role in sludge treatment, anaerobic organisms are not involved in the activated sludge process (Spanjers and Vanrolleghem, 2016). Given the role of O2 as the primary electron acceptor in activated sludge, its importance in activated sludge treatment cannot be overstated. Respirometry can, therefore, provide relevant information about the processes occurring within activated sludge by allowing one to assess these processes’ oxygen consumption.

Uses for stBOD

The main advantage of stBOD in comparison with other biological parameters is the speed at which it is measured, which is counted in minutes rather than hours, days or even weeks for BOD5=u. The fact that it can either include or exclude nitrogenous demand also gives the test flexibility. Most importantly, however, is the fact that by automatically filtering out the demand coming from slowly biodegradable compounds, the test shows how much substrate inside the sample is directly available to the biomass. This information is of great importance for some of the processes being used in wastewater treatment plants.

For instance, the denitrification process, which relies on heterotrophic bacteria, requires easily biodegradable substrate for rapid nitrate removal while using a small reactor (Metcalf & Eddy, 2013). Depending on the plant design being used, the carbon source used can differ. In post-anoxic configurations (in which the anoxic tank, which is where denitrification occurs, is placed downstream of the aerobic tank and their associated secondary clarifiers), external carbon sources are often employed, as most of the wastewater’s organic matter is destroyed inside the aeration tank upstream of the denitrifiers.

Pre-anoxic configurations, on the other hand, place the anoxic tank upstream of the aeration tank, and heavily recycle aerobic sludge to the former. In this configuration, wastewater still contains biodegradable compounds when it comes into contact with denitrifying organisms, thus reducing the need for external carbon. However, incoming readily biodegradable carbon may still not be sufficient to achieve complete denitrification. stBOD measurement can, therefore, help to dose appropriate amounts of external carbon for optimal denitrification in pre-anoxic plants by showing how much readily biodegradable carbon is being fed to the denitrifiers by the influent (Copp et al., 2002). Phosphorus accumulating organisms (PAOs) also need readily biodegradable organic matter to function effectively.

These organisms, as their name suggests, store phosphorus inside themselves as poly-phosphates, and the energy required to fuel this process comes from cell internal storage products. In turn, these internal storage products are created when cells assimilate fermentation products, which are a type of readily biodegradable organic matter. Therefore, the ability of PAOs to accumulate phosphorus hinges on the availability of readily biodegradable matter in the wastewater (Gujer et al., 1995). Thus, stBOD can help determine whether conditions are favourable for dephosphatation even though it is not a selective enough indicator to specifically quantify fermentescible organic matter or to distinguish between carbon-related and nitrifiable nitrogen-related oxygen demand, for that matter. The RODTOX does not differentiate between readily fermentescible organic matter and other types of readily biodegradable matter.

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Similarly, it also has no way to distinguish between ammonia nitrogen and other forms of nitrifiable nitrogen present in wastewater, e.g., organic nitrogen coming from dead cells’ lysis products or organic molecules present in the water (e.g., proteins). These other types of nitrifiable nitrogen are hydrolyzed, ammonified and finally oxidized by the autotrophic organisms in the same way as the ammonia nitrogen present as such in the influent. Therefore, telling apart those sources of nitrogen is simply impossible using only the respirometer’s DO or OUR measurements. However, this catch-all approach to nitrifiable nitrogen detection may prove to be an advantage for the RODTOX versus other types of nitrogen sensors (i.e., ion-sensitive electrodes), as it allows it to estimate the total nitrification load of a wastewater sample, instead of only that coming from ammonia nitrogen specifically.

Building a mathematical model of the RODTOX sensor

The array of analysis tools described in the preceding section have allowed the extraction of stBOD measurements from RODTOX raw data. Conveniently, this stBOD evaluation has had the side effect of unearthing a large amount of information which can be used to describe the features of the respirograms being studied, and also the physical process of oxygen transfer to the RODTOX reactor. One might put this data to use to create a mathematical model of the processes occurring within the RODTOX reactor. Such a modelling exercise would, moreover, extract even more information from the respirograms regarding the analyzed wastewater samples (i.e., determining which portion of each respirogram is caused by either carbonaceous or nitrogenous oxygen demand). This modelling endeavour would also have the side-effect of producing additional information about the RODTOX sludge’s biokinetic characteristics (Spanjers and Vanrolleghem, 1995).

Thus, this section proposes a procedure for creating a mathematical model of the RODTOX sensor, and then to extract estimations of stBODC and stBODN using both the WEST modelling suite and the RODTOX data which was processed using the tools described above, and some additional tools described below.The respirograms of the sample injections used in this section are shown in Figure 4.25. In this figure, one may see two calibration experiments, followed by the addition of 3 wastewater samples, one toxicity check (which corresponds to a calibration solution feed), and 3 more wastewater samples.

Introduction
1 Respirometry in literature
1.1 Basic background
1.2 Usage of different water quality parameters
1.3 Respirometer designs and their use
1.4 Overview of the Activated Sludge Models
1.5 Structure of ASM1
1.6 Wastewater and biomass characterization for ASM1
2 Objectives
3 Methodology
3.1 Study site
3.2 Anatomy of the RODTOX
3.3 Fast loop
3.4 Operation of the RODTOX
3.5 Data processing
3.6 Modelling tools
4 Implementation of the RODTOX sensor
4.1 Installation of the fast loop system
4.2 Start-up tests of the RODTOX sensor
4.3 Default output of the RODTOX sensor
4.4 Python-based data analysis
4.5 Building a mathematical model of the RODTOX sensor
5 Conclusions and perspectives
5.1 Conclusions
5.2 Paths for improvement
Bibliographie
A RODTOX python functions
A.1 Function to import RODTOX data into a python environment
A.2 Function to smooth the RODTOX signal and to obtain the first and second derivative
A.3 Function to analyze every respirogram to determine KLa and stBOD
A.4 Function to plot respirograms and their associated parameters
A.5 Function to create WEST-compatible input files
A.6 Function to create WEST-compatible objective DO and OUR time series for calibration
A.7 Function to automatically create WEST-compatible objective DO and OUR time series from wastewater respirograms

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