Model Of Activated Sludge Process Research Articles

Inference of the Paramentes of a Bioreactor via Approximate Bayesian Evidence

In the context of the recent water crisis, the development and improvement of technologies for efficiently treating wastewater becomes increasingly necessary. Aligned with this, the activated sludge process is widely used in biological wastewater treatment plants to remove carbon and nutrients from wastewater. Although significant advancements have been achieved in recent years in modelling these processes to improve their performance and energy efficiency, the models are still complex and difficult to calibrate using data from real plants. In this work, we use Bayesian inference frameworks to estimate the parameters of the activated sludge process. We employ Gaussian processes to estimate the solution to the differential equations of the activated sludge process model and we fit this estimation to manufactured data of the observations of the wastewater. We find that the use of the Bayesian inference framework outperforms the classical approach in scenarios where measurements are more strongly affected by noise.

Optimal detuning of multivariable proportional integral controller based on data‐driven approach for an activated sludge process

AbstractThe study deals with designing a multivariable centralized proportional‐integral (PI) controller for an activated sludge process (ASP) by adopting a new tuning technique. To overcome the tedious task of manual tuning by a classical method that was based on Ziegler–Nichols tuning, a novel approach is developed by formulating the optimization problem and considering it along with the theoretical results to calculate the controller parameters. The proposed method is then compared with the reported method to demonstrate the advantages of the proposed approach. A nonlinear ASP model is selected to evaluate the performance of the designed controller. The simulation carried through the proposed optimization technique exhibits a significant improvement in closed‐loop performance for tuning the centralized controller. It is observed that the proposed method minimizes the effort of tuning the controller significantly. The proposed controller achieves better load disturbance response and lower overshoot than the reported method. The proposed method could improve integral absolute error (IAE) and integral time absolute error (ITAE) performance indices, ranging between 18.69% to 53.80% and 16.42% to 52.97% for a step change in and , respectively, which are better compared to the reported method. The study also assesses the control system's robustness for their uncertainties with the time constant and time delay.

Self-organizing modeling and control of activated sludge process based on fuzzy neural network

The wastewater treatment process contains multiple complex biochemical reactions featured by strong nonlinear and time-varying dynamics due to the built-in discontinuity and uncertainty. Herein, a self-organizing fuzzy neural network with an efficient scheme for parsimonious (SOFNN-ESP) was orchestrated to improve the self-organizing modeling of municipal wastewater treatment process by combing predictive algorithms to deal with the complex water treatment procedure. The SOFNN-ESP algorithm could identify sewage treatment plants by a high-throughput parameter screening system and recursive least square method in real-time, which provided dynamic setting feedback and promoted water quality. The integration of the SOFNN-ESP algorithm and a model predictive control (MPC) further improved the accuracy in water quality controlling via immediately adjusting weight parameters of the network. This gradient algorithm also realized the online dynamic tracking of dissolved oxygen and nitrate nitrogen level by simultaneous tracking of multiple performance indicators and optimizing setting values of the control variable. SOFNN-ESP-MPC gave an error of <5 % when the peak error of proportional integral differential controller was >10 %. Concerning the benchmark simulation model No.1 of municipal wastewater treatment, the SOFNN-ESP-MPC method exhibited a compact network structure and outstanding generalization performance. The self-organizing modeling and predictive control strategy proposed in this study could effectively improve the prediction accuracy and control efficiency of the activated sludge process model, which is of great significance for the efficiency improvement of sewage treatment process.

Fractional-order models identification and control within a supervisory control framework for efficient nutrients removal in biological wastewater treatment plants.

Wastewater treatment plants (WWTPs) are highly non-linear processes that must be optimized to meet rigorous environmental water regulations. In this context, efficiency and costs are equally important terms. The ASM3bioP framework is employed in this study to enable simultaneous nitrogen and phosphorus removal using an activated sludge process model with seven-reactor configurations. The activated sludge process is the most complicated and energy-intensive phase of a WWTP. To control dissolved oxygen in aerobic reactors and nitrate levels in anoxic reactors, two robust PI controllers - a classical PI and a non-integer (fractional)-order PI - with both integer-order and fractional-order models are designed. The controllers are created and simulated with the use of a mathematical model that has been developed based on the input data. The lower level fractional controller with a fractional-order model improves both the effluent quality (EQI) and operational cost (OCI) indices significantly. For such biological WWTP, a hierarchical fuzzy logic controller is designed to adjust the dissolved oxygen in the seventh reactor (DO7) to control ammonia. The implemented supervisory layer control strategy improves effluent quality EQI while increasing OCI marginally.

Integrated soft sensor of COD for WWTP based on ASP model and RBF neural network

For wastewater treatment process (WWTP), mechanism model for activated sludge process (ASP) is unsuitable for estimating the effluent COD (Chemical Oxygen Demand) as the parameters of ASM (Activated Sludge Model) series models are varying with operating conditions. This paper presents an integrated model to predict the effluent COD. The model consists of two sub-models which are simplified mechanism model of ASP and RBFNN (RBF Neural Network) with variable structure (VSRBFNN). ASP model can express the dynamic biochemical reactions occurred in WWTP, and VSRBFNN is used to reduce the prediction error of the ASP model as an error compensation model. To reduce the complexity of the mechanism model of ASP, the parameters of mechanism model are fixed. The layout and the parameters of VSRBFNN can be adjusted according to the training data, and the stable learning algorithm can restrict the modeling error of VSRBFNN within a bounded domain. The output value of the integrated model is weighted sum of those of two sub-models, where the weights denote the contributions of the two “sub-models” to the prediction error of integrated model and are rectified according to the relative prediction error online. The structure of the integrated soft sensor is concise and real-time capability is improved. Simulations show that the presented soft sensor has satisfactory prediction accuracy under various operating characteristics.

Teaching–Learning-Based Optimization for Parameter Identification of an Activated Sludge Process Model

Teaching–Learning-Based Optimization for Parameter Identification of an Activated Sludge Process Model

Systematic Modeling of Municipal Wastewater Activated Sludge Process and Treatment Plant Capacity Analysis Using GPS-X

Mathematical modeling has become an indispensable tool for sustainable wastewater management, especially for the simulation of complex biochemical processes involved in the activated sludge process (ASP), which requires a substantial amount of data related to wastewater and sludge characteristics as well as process kinetics and stoichiometry. In this study, a systematic approach for calibration of the activated sludge model one (ASM1) model for a real municipal wastewater ASP was undertaken in GPS-X. The developed model was successfully validated while meeting the assumption of the model’s constant stoichiometry and kinetic coefficients for any plant influent compositions. The influences of vital ASP parameters on the treatment plant performance and capacity analysis for meeting local discharge limits were also investigated. Lower influent chemical oxygen demand in mgO2/L (COD) could inhibit effective nitrification and denitrification, while beyond 250 mgO2/L, there is a tendency for effluent quality to breach the regulatory limit. The plant performance can be satisfactory for handling even higher influent volumes up to 60,000 m3/d and organic loading when Total Suspended Solids/Volatile Suspended Solids (VSS/TSS) and particulate COD (XCOD)/VSS are maintained above 0.7 and 1, respectively. The wasted activated sludge (WAS) has more impact on the effluent quality compared to recycle activated sludge (RAS) with significant performance improvement when the WAS was increased from 3000 to 9000 m3/d. Hydraulic retention time (HRT) &gt; 6 h and solids retention time (SRT) &lt; 7 days resulted in better plant performance with the SRT having greater impact compared with HRT. The plant performance could be sustained for a quite appreciable range of COD/5-day Biochemical Oxygen Demand (BOD5 in mgO2/L) ratio, Mixed Liquor Suspended Solid (MLSS) of up to 6000 mg/L, and when BOD5/total nitrogen (TN) and COD/TN are comparatively at higher values. This work demonstrated a systematic approach for estimation of the wastewater treatment plant (WWTP) ASP parameters and the high modeling capabilities of ASM1 in GPS-X when respirometry tests data are lacking.

Anatomy of the organic carbon in an industrial wastewater: Implications of particle size distribution, respirometry and process modelling

This study evaluated the fate of chemical oxygen demand (COD) fractions of an organized industrial district effluent treated with pilot membrane bioreactor (MBR) system. A series of respirometric analyses were performed on raw wastewater together with 450 nm, 100 kDa and 1 kDa filtered samples obtained from particle size distribution (PSD) experiments. The PSD analysis revealed that more than 300 mg/L of the influent COD accumulated in the lowest size range (<2 nm), accounting for 49–52 % of the total COD. The bulk (64 %) of the influent COD consisted of soluble hydrolysable COD (SH1). The hydrolysis rates for SH1 (0.80–1.28 1/day) were slightly below the levels suggested for domestic wastewaters, indicating the presence of inhibitory compounds in industrial wastewaters. The COD removal efficiency of MBR pilot was found as 85 %; however, 43 % of effluent COD (106 mg/L) still included rapidly hydrolysable organics in the size bracket of 5–30 nm. In this respect, the COD fractions for activated sludge process models used for MBR applications require a new-insight to correctly reflect organic matter balances in the treatment system.

Design of control strategies for nutrient removal in a biological wastewater treatment process.

Wastewater treatment plants (WWTP) are highly non-linear operations concerned with huge disturbances in flow rate and concentration of pollutants with uncertainties in the composition of influent wastewater. In this work, the activated sludge process model with seven reactor configuration in the ASM3bioP framework is used to achieve simultaneous removal of nitrogen and phosphorus. A total of 8 control approaches are designed and implemented in the advanced simulation framework for assessment of the performance. The performance of the WWTP (effluent quality index and global plant performance) and the operational costs are also evaluated to compare the control approaches. Additionally, this paper reports a comparison among proportional integral (PI) control, fuzzy logic control, and model-based predictive control (MPC) configurations framework. The simulation outcomes indicated that all three control approaches were able to enhance the performance of WWTP when compared with open loop operation.

Dissolved Oxygen Model Predictive Control for Activated Sludge Process Model Based on the Fuzzy C-means Cluster Algorithm

In this work, the problem of predictive control of dissolved oxygen for the activated sludge process model with high nonlinearity and strong coupling is addressed. Firstly, the determination of the structure of fuzzy rules is displayed established upon Activated sludge model 1 (ASM1). Besides, the fuzzy space is divided through the clustering algorithm of fuzzy C-means. The corresponding parameters are estimated by means of the well-known least squares method. Subsequently, a fuzzy predictive model of dissolved oxygen is established by using the historical data. The aim is to design a predictive controller that is capable of performing the online track of dissolved oxygen attributed to better dynamic response and steadier output in different weather. Ultimately, the availability and validity of the developed technique are verified by a comparison example.

The application of multi-objective optimization method for activated sludge process: a review.

The activated sludge process (ASP) is the most generally applied biological wastewater treatment approach. Depending on the design and specific application, activated sludge wastewater treatment plants (WWTPs) can achieve biological nitrogen (N) and phosphorus (P) removal, besides the removal of organic carbon substances. However, the effluent N and P limits are getting tighter because of increased emphasis on environmental protection, and the needs for energy conservation as well as the operational reliability. Therefore, the balance between treatment performance and cost becomes a critical issue for the operations of WWTPs, which necessitates a multi-objective optimization (MOO). Recent studies in this field have shown promise in utilizing MOO to address the multiple conflicting criteria (i.e. effluent quality, operation cost, operation stability), including studying the ASP models that are primarily responsible for the process, and developing the method of MOO in the wastewater treatment process, which facilitates better optimization of process performance. Based on a better understanding of the application of MOO for ASP, a comprehensive review is conducted to offer a clear vision of the advances, and potential areas for future research are also proposed in the field.

Developing a Standardized Protocol for Assessing the Biodegradability of Trace Organic Compounds

This work was performed to better understand the factors that impact trace organic compound (TOrC) biodegradation kinetics in mixed microbial cultures. To accomplish this goal, optimized short-term batch tests using TOrC and identified TOrC surrogates were conducted using lab-scale activated sludge cultures. In these optimization experiments, the initial TOrC/surrogate concentration, readily biodegradable carbon concentration (rbCOD) and dissolved oxygen (DO) concentrations were varied. Once developed, these batch tests were deployed at three full-scale wastewater treatment facilities to estimate in-situ biotransformation kinetics. This work confirms that TOrC biotransformation is best described using pseudo-first order kinetics. In contrast, results from surrogate experiments indicate that the current approach to aerobic respirometry is not sufficiently sensitive for resolving TOrC biotransformation versus endogenous respiration. Results from short-term batch test optimization also indicate that parameters estimated from depletion tests performed at artificially high TOrC concentrations are statistically similar to parameters estimated at low TOrC concentrations. This suggests that interrogation of the biomass can be performed at high TOrC concentrations, which would reduce the cost of sample processes associated with working at lower concentrations. It was also found that the presence of excess readily biodegradable substrates suppress biotransformation rates for specific TOrC under aerobic and anoxic conditions. These results indicate that accurate estimation of biotransformation parameters requires careful design of the batch test to match the in-situ carbon conditions. An initial attempt to incorporate existing biotransformation and sorption parameters into current activated sludge process models was also performed. Results from this simulation indicate that modeling results are highly sensitive to the processes of sorption and desorption. This title belongs to WERF Research Report Series . ISBN: 9781780405162 (eBook)

Sensitivity Analysis of Optimal Operation of an Activated Sludge Process Model for Economic Controlled Variable Selection

This paper describes a systematic sensitivity analysis of optimal operation conducted on an activated sludge process model based on the test-bed benchmark simulation model no. 1 (BSM1) and the activated sludge model no. 1 (ASM1). The objective is to search for a control structure that leads to optimal economic operation, while promptly rejecting disturbances at lower layers in the control hierarchy avoiding thus violation of the more important regulation constraints on effluent discharge. We start by optimizing a steady-state nonlinear model of the process. Here, a new steady-state secondary settler mathematical model is developed based on the theory of partial differential equations applied to the conservation law with discontinuous fluxes. The resulting active constraints must be chosen as economic controlled variables. These are the effluent ammonia from the bioreaction section and the final effluent total suspended solids at their respective upper limits, in addition to the internal recycle flow rate ...

ℋ −/ℋ ∞ fault detection filter for a class of nonlinear descriptor systems

This article addresses the problem of designing a robust fault detection filter (RFDF) for a class of nonlinear descriptor systems whose nonlinear term is globally Lipschitz and linear term is described by a linear parameter varying form. The effect of perturbations on the residuals is minimised using the ℋ∞ norm while the sensitivity performance is guaranteed using the ℋ− index. Thanks to a particular Lyapunov function: which depends on the faults and the filter design problem is formulated as a convex optimisation problem solved via linear matrix inequality techniques. Performances of the proposed filter are shown through the application of an activated sludge process model.

Verification of WWTP Design Guidelines with Activated Sludge Process Models

There is significant interest in using process models for WWTP design to complement the traditional design guidelines. However, transparent quantification of the differences in the design results obtained with the two approaches has not been done yet. The objective of this paper is to define a methodology to evaluate the expected performance of a designed WWTP. The use of this methodology will enable to identify inherent safety incorporated in design guidelines by verification with dynamic process models. The methodology includes two parts: first, multiple designs are made using a design guideline for different combinations of influent characteristics, effluent requirements, safety factors and operating preferences. Second, the results of the designs (e.g. reactor volumes) are applied in a process model that emulates WWTP behaviour. The effluent concentrations predicted by the process model are then compared to the effluent requirements which were imposed for the designs. The methodology is illustrated using the Metcalf & Eddy design guidelines for a nitrogen-removing process and the ASM1 process model.

A nonlinear observer design for an activated sludge wastewater treatment process

This paper treats the problem of estimating simultaneously the state and the unknown inputs of a class of nonlinear discrete-time systems. An observer design method for nonlinear Lipschitz discrete-time systems is proposed. By assuming that the linear part of this class of systems is time-varying, the state estimation problem of nonlinear system is transformed into a state estimation problem for LPV system. The stability analysis is performed using a Lyapunov function that leads to the solvability of linear matrix inequalities (LMIs). Performances of the proposed observer are shown through the application to an activated sludge process model.

Modeling, Instrumentation, Automation, and Optimization of Wastewater Treatment Facilities

A review of the literature published in 2008 on topics relating to wastewater treatment innovations in the areas of modeling, instrumentation, automation and optimization of wastewater treatment is presented.

Reduced modeling and state observation of an activated sludge process

This article first proposes a reduction strategy of the activated sludge process model with alternated aeration. Initiated with the standard activated sludge model (ASM1), the reduction is based on some biochemical considerations followed by linear approximations of nonlinear terms. Two submodels are then obtained, one for the aerobic phase and one for the anoxic phase, using four state variables related to the organic substrate concentration, the ammonium and nitrate-nitrite nitrogen, and the oxygen concentration. Then, a two-step robust estimation strategy is used to estimate both the unmeasured state variables and the unknown inflow ammonium nitrogen concentration. Parameter uncertainty is considered in the dynamics and input matrices of the system.

Stoichiometric deduction of activated sludge process for organic carbon and nitrogen removal

The activated sludge process (ASP) is the most generally applied biological wastewater treatment method. The ASP for the removal of organic carbon and nitrogen can be looked as the combination of eight processes. In order to set up an ASP model, the stoichiometric coefficients should be deduced so that the stoichiometric matrix can be presented. The important assumptions and simplifications behind the model for ASP are enumerated. Using the matrix, mass balance equation and consistent units, the stoichiometric coefficients in the eight processes are exclusively deduced one by one.

An Updated Process Model for Carbon Oxidation, Nitrification, and Denitrification

The currently available comprehensive activated sludge models, ASM#1 (Grady et al., 1986) and its successor ASM#3 (Gujer et al., 1999), do not adequately describe nitrification and denitrification, with respect to ammonia oxidation inhibition, nitrite accumulation, or emissions of nitric oxide and nitrous oxide. A new comprehensive activated sludge process model, the Activated Sludge Model for Nitrogen (ASMN), is presented. The ASMN incorporates two nitrifying populations-ammonia-oxidizing bacteria and nitrite-oxidizing bacteria-using free ammonia and free nitrous acid, respectively, as their true substrates. The ASMN incorporates four-step denitrification (sequential reduction of nitrate to nitrogen gas via nitrite, nitric oxide, and nitrous oxide) using individual, reaction-specific parameters. Simulation results for ammonia, nitrate, soluble substrate, and biomass concentrations determined by using ASMN for three activated sludge process configurations under steady-state and dynamic municipal-type influent conditions are shown to be comparable with ASM#1 results.