On-line estimation and detection of abnormal substrate concentrations in WWTPs using a software sensor: a benchmark study

Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning

Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies.

Active biomass estimation based on ASM1 and on-line OUR measurements for partial nitrification processes in sequencing batch reactors

The main challenge for partial nitrification is to reach stable nitrite accumulation, which strongly depends on the nitrite-oxidizing bacteria (NOB) growth in the reactor. The on-line estimation of active biomass may enhance the decision-making process to maintain a high nitrite accumulation in the reactor. In this work, we propose an active biomass estimator based on ASM1 and on-line oxygen uptake rate measurements (OUR-E) in a sequencing batch reactor. In order to validate the OUR-E, two operating scenarios were applied during 200 days of operation: unfavorable (sludge retention time (SRT) = 40 d, pH = 7.6, dissolved oxygen (DO) = 2 mg/L) and favorable for partial nitrification (SRT = 10 d, pH = 8.5, DO = 2 mg/L). Furthermore, a second estimation method based on off-line measurements of N-species concentrations (Nsp-E) was implemented to evaluate the performance of the OUR-E. The OUR-E was able to predict a reduction in the NOB active fraction from 10.3% to 1.6% with nitrite accumulation over 80% when we shifted the operating scenario. Although both estimators predicted similar results, the OUR-E showed a better prediction quality than the Nsp-E, according to Theil's coefficient of inequality.

An online method for estimation of degradable substrate and biomass in an aerated activated sludge process

The activated sludge process for degradation of organic matter is one of the main processes commonly used in biological wastewater treatment, and aeration in that process stands for a large part of the energy consumed in a plant. Hence, there have been many attempts to improve the operation of the activated sludge process using mathematical models of the process. The advanced models used has in general their origin in IWA (former IAWQ) activated sludge model no 1 (ASM1). Unfortunately, optimization w.r.t. discharge and economy is limited for municipal wastewater treatment plants because several of the most important variables; heterotrophic biomass, readily biodegradable soluble substrate, and slowly biodegradable substrate cannot be reliably measured online because of their complexity hiding behind their notation. With the predenitrifying WWTP in Göteborg having post nitrification in trickling filters as an example, we resolve this problem by deriving an observer that estimates these concentrations in the aerobic parts based on only the commonly available online measurements of oxygen, water flows, TSS concentration and supplied air. Based on control theory analysis and simulations it is concluded that estimation does not work for an activated sludge process with aeration in one stirred tank alone, but when the activated sludge process can be described by at least two tanks in series, with oxygen measurements in each tank, the estimates converge. A sensitivity analysis with respect to deviations in model parameters reveals that the derived estimator is also fairly robust to model errors.

Online nonlinear sequential Bayesian estimation of a biological wastewater treatment process

Online estimation of unknown state variables is a key component in the accurate modelling of biological wastewater treatment processes due to a lack of reliable online measurement systems. The extended Kalman filter (EKF) algorithm has been widely applied for wastewater treatment processes. However, the series approximations in the EKF algorithm are not valid, because biological wastewater treatment processes are highly nonlinear with a time-varying characteristic. This work proposes an alternative online estimation approach using the sequential Monte Carlo (SMC) methods for recursive online state estimation of a biological sequencing batch reactor for wastewater treatment. SMC is an algorithm that makes it possible to recursively construct the posterior probability density of the state variables, with respect to all available measurements, through a random exploration of the states by entities called 'particle'. In this work, the simplified and modified Activated Sludge Model No. 3 with nonlinear biological kinetic models is used as a process model and formulated in a dynamic state-space model applied to the SMC method. The performance of the SMC method for online state estimation applied to a biological sequencing batch reactor with online and offline measured data is encouraging. The results indicate that the SMC method could emerge as a powerful tool for solving online state and parameter estimation problems without any model linearization or restrictive assumptions pertaining to the type of nonlinear models for biological wastewater treatment processes.

Development of PLS calibration models from UV-Vis spectra for TOC estimation at the outlet of a fuel park wastewater treatment plant

In the present work ultraviolet (UV)-visible spectra of water samples collected at the outlet of a fuel park wastewater treatment plant, including biological treatment, were acquired and used for the development of partial least squares (PLS) calibration models for the fast and simple estimation of total organic carbon (TOC). Three different PLS models were developed and compared on the basis of a common spectral range. The first model was obtained using spectra of raw samples, the second using spectra of diluted samples, to assess signal saturation in the UV region, and the third using spectra of both diluted and raw samples, in order to expand the narrow interval of TOC concentration values present in the original dataset. The root mean squared error of cross-validation values for the developed PLS models were 2.3, 1.0 and 4.4 mg Cl(-1), respectively, and the validation results where highly satisfactory (root mean squared error of prediction values of 1.8, 0.8 and 4.5 mg Cl(-1), respectively).