Treatment Plant Model Research Articles

Using TOXCHEM model for simulation the hydrogen sulfide behavior in a full-scale MBBR process

Odors emanating from treatment plants are a source of concern for those working in these plants. Hydrogen sulfide gas is the most dangerous aromatic compound for WWTP workers and plant equipment and facilities. In this study, hydrogen sulfide gas was modeled using the TOXCHEM model of the Aoun sewage treatment plant, which operates with the MBBR system. The TOXCHEM model was calibrated and validated and the results were within the R and RMSE parameters. Sensitivity analysis was studied on the most important factors affecting the emission of hydrogen sulfide gas in the Aoun sewage treatment plant. The results showed that the most important processes that obtain hydrogen sulfide gas are biodegradation, absorption, emission or volatilization. The results in winter, (12 °C), and summer (35 °C) about 6% and 8%, 18% and 17%, 72% and 65%, 0% and 0%, and 4% and 10% were fated as emitted to air, sorbed to sludge, discharged with effluent, biodegraded, and treated with odor removal system, respectively. The highest emission of hydrogen sulfide gas was observed in the preliminary stage (lift station, course and fine screen, API, and equalization tank), especially in the API basin, where it reached 1.8 ppm. Increasing temperatures, the air flow rate, decreasing the value of pH, increasing the flow and increasing the filling time in the pumping station all cause an increase in the emission of hydrogen sulfide gas. Modeling hydrogen sulfide gas by TOXCHEM model gave an excellent prediction to control the emission of hydrogen sulfide gas for decision makers in the plant.

Wastewater treatment plant design and modeling for the city of Erzurum

Currently large amounts of wastewater are produced by domestic and industrial activities. Discharge of wastewater to the receiving environment without treatment causes significant health and environmental problems. Modeling and optimization of Wastewater Treatment Plants (WWTP) developed to treat domestic wastewater play key roles in determining unit components, design parameters and operation conditions. Several models were proposed to predict the treatment performance in WWTP. The Activated Sludge Model No. 1 (ASM1) is one of the commonly-used standard models developed to better understand removal of carbonaceous and nitrogenous materials. In this study, a WWTP is proposed for domestic wastewater using grit chamber, circular primary and secondary clarifiers, completely-mixed aeration tank, sludge thickener, sludge dewatering and anaerobic digestion processes together. The WWTP was modeled with ASM1 noting the topographic and meteorological features of the city. The treatment performances with wastewater temperatures of 10°C and 20°C were investigated for this plant, operating at high elevation. Removal efficiencies at 20°C were 95.7%, 92.2%, 97.9% and 99.2% for MLSS, COD, BOD and NH4, while effluent concentrations were 14.83, 48.51, 6.55 and 0.3 mg L-1, respectively. At 10°C, removal efficiencies were 88.9%, 88%, 93.2%, and 26.9%, while effluent concentrations were 38, 75, 21.83 and 26.13 mg L-1, respectively. A clear reduction was observed in nitrogenous material removal at low temperatures. Additionally, keeping dissolved oxygen concentration in the aeration tank at 1.5 mg L-1 with PID control increased nitrification efficiency by 30%. The findings reveal the importance of modeling studies during planning of WWTP.

Development of groundwater pollution removal technology

A trial treatment plant model was constructed and the performance of the model was found to be effective as an effective technique for removing iron and other contaminants from groundwater. With an effective size of 0.45 mm, a uniformity factor of 1.5 and a porosity of 0.6, the filter material was able to filter water with a high iron content and other parameters. The backwash speed of the sand filter is 1.44 m/s (86.4 m/h). At this speed, the pressure was sufficient to cause the media to expand and boil, so that the iron sludge, flakes and other dirt remained in the filter. Nine (9) daily inflows and outflows at depths of 60 cm, 70 cm and 80 cm were determined. An iron removal efficiency of 99.1% was achieved at a depth of 60 cm and 90.63% at a depth of 80 cm. Similarly, the model was able to remove physical and organoleptic parameters (color, turbidity, fluorine, manganese, nitrite, Ph, zinc, E-coli and total coliforms). The technology developed in this research can be used as an alternative, simple and cost-effective way to remove iron and other contaminants from groundwater.

Static calibration of wastewater treatment plant models: Investigating calibration processes and objective functions

Mathematical modeling provides a reliable tool for optimal management and operation of wastewater treatment plants (WWTPs). Accurate calibration is crucial for producing reliable results, and static calibration is a critical initial step for this purpose, ensuring enough accuracy and minimizing later difficulties. The two main areas to investigate in static calibration are the appropriate calibration process and objective function. This study proposes a three-stage methodology for static calibration of model parameters. Nine different calibration objective functions considering various target variables are investigated to evaluate how different objective functions can affect the calibration process accuracy. The proposed approach is applied in a real WWTP with a carousel oxidation ditch for secondary treatment. Benchmark Simulation Model No.2 (BSM2) is modified to be used for oxidation ditch modeling. The calibration results show less than 3 % prediction error for COD, TN, and TSS, and 6.7 % mean overall error. The validation resulted in 22.5 %, 10.6 %, and 31.5 % MARE (Mean Absolute Relative Error) for effluent COD, TN, and, TSS, respectively, indicating successful calibration. Comparing different objective functions indicates that in static calibration, the objective functions that consider more targets usually yield more accurate results in a relatively acceptable time.

Chemical composition of organic acid digest from a municipal wastewater treatment plant and chemical modeling of nuisance struvite formation and phosphorus recovery as brushite

Modern municipal wastewater treatment often includes an anaerobic digestion step with a hydraulic retention time of ~1.5 days before the main anaerobic digestors. This step, often termed the organic acid digester or “acid digester” for short, produces a sludge characterized by lower pH, higher volatile fatty acid (VFA) concentrations, and high soluble phosphorus (P) concentrations, particularly if fed sludge from an enhanced biological phosphorus removal process. Here, the analysis of major ions, organic and inorganic, in the organic acid digest from a wastewater treatment plant (WWTP), Nine Springs, Madison, Wisconsin (USA), is reported for a period of 13 weeks. The bioengineered origin of this organic acid digest makes its composition unlike natural waters of any kind. From the chemical composition, the course of subsequent processes can be predicted by chemical modelling. Methanogenesis using VFAs as substrates is predicted to raise digest pH to the point where struvite is expected to form, and nuisance struvite is routinely observed at Nine Springs. Addition of calcium hydroxide to organic acid digest is expected to precipitate ~90% of soluble P from solution as the calcium phosphate mineral brushite at near neutral pH, confirmed by a pilot plant at Nine Springs. This study demonstrates the possible application of chemical analysis and chemical modelling in wastewater systems to predict and possibly adaptively manage waste streams to curb nuisance struvite formation and predict P recovery, and could be refined through broader testing and application at other wastewater treatment facilities.

Thermal-flow calculations for a thermal waste treatment plant and CFD modelling of the spread of gases in the context of urban structures

Thermal waste treatment plants are an answer to the important problem of waste neutralization on the one hand, while inspiring various emotions in society due to their impact on their planned or existing sites on the other. The primary benefit from incinerating waste is the ability to reuse it as fuel to produce energy. The objective of the research presented in this paper was to assess the production potential of eco-incinerators and to investigate the potential impact that the operation of a thermal waste treatment plant can have on its surroundings. To this end, the study included analyses in three problem groups: urban planning, balance calculations and the numerical modeling of fouling spread. The area subjected to the analyses was a district of a model city with a plant at the center. The goal of the CFD modeling was to determine the spread of gaseous pollutants (CO, CO2, SO2, NOx) produced as a result of the incineration of municipal waste in the incineration plant, to get their approximate mass fraction values at a distance of 1 km from the smokestack outlet. The spread of gaseous pollutants in CFD modeling was simulated using the k-epsilon RNG model. It is noticeable that the largest mass fraction of the given gases has carbon dioxide, which is also characterized as a 1.5 times heavier gas than the air. The paper also presents thermal calculations that present the performance of the entire thermal waste treatment plant, which depends on the final use of the energy it produces.

Performance improvement of textile wastewater treatment plant design by STOAT model simulation

Background: To control pollution, wastewater treatment from textile plays an important role in treating wastewater to meet quality standards before it is discharged into the environment. Without properly treated wastewater from the textile industry, it contains organic and inorganic pollutants that cause environmental problems such as water pollution, loss of marine life, and soil and air pollution. The aim of this study was to design and simulate a textile sewage treatment plant. Methods: This study was conducted by simulating the process and operation of a wastewater treatment plant using STOAT software. In addition, STOAT’s graphical and static data analysis models are efficient in removing multi-component pollutants from the textile industry. Results: Some pollutant parameters prior to the design model are suspended solids (SS) (260 mg/L), DS (3600 mg/L), ammonia (65 mg/L), biochemical oxygen demand (BOD) (430 mg/L), nitrate (35 mg/L), and dissolved Oxygen (DO) (12 mg/L). The wastewater of the simulation result of the sewage treatment plant model contained SS (3.3 mg/L), ammonia (25 mg/L), BOD (4 mg/L), nitrate (61.3 mg/L), and the removal percentage of total suspended solids (TSS), BOD, and Ammonia was 99.75, 99.1, 61.33 mg/L, respectively. Through the treatment process, Ammonia was oxidized and nitrification was processed rather than denitrification. Conclusion: Using the stoat modeling software, wastewater treatment plant design is very effective in removing contaminants from textile wastewater by selecting specific parameters.

Textile Wastewater Treatment Modelling and Design Using Stoat Graph and Summary Statistic Model

Without properly treated wastewater released from the textile industry contains organic and inorganic pollutants that cause environmental problems like water body contamination, which causes marine life disappearance, soil contamination, and air pollution. Treatment of textile industry wastewater is difficult due to pollutant types existing like BOD, COD, toxic heavy metals, organic particle matter, inorganic particle matter, color, etc. The multi-component wastewater pollutant needs proper designs to remove such pollutants. Dire Dawa textile wastewater treatment plant (design) and simulation was the objective of this study. The method of this study was experimental and software modelling. STOAT model software is the best capable of simulation treatments plants, and the time to complete the simulation was 3 days. The STOAT graphic and statically data analysis model efficiently removed the multi-components of pollutants effluent from Dire Dawa textile industry. Some pollutant parameter measures before design model are SS 350 mg/L, DS 2000 mg/L, ammonia 55 mg/L, BOD 350 mg/L, nitrate 0 mg/L and DO 12 mg/L. The effluent of wastewater treatment plant model simulation results are 2 mg/L SS, ammonia 8.82 mg/L, BOD 2.5 mg/L, and nitrate increases from 0 to 58 mg/L by ammonia oxidation. Totally 98.7% SS, 99% BOD, and 84% ammonia were removed in the design model. In the sludge outlet, nitrate contents increase due to nitrification being processed rather than denitrification.

Modelling cyber resilience in a water treatment and distribution system

Considering the increasing introduction of cyber-physical systems in modern industrial plants, the analysis of systems’ performance pushes for developing a cyber resilience perspective to complement a traditional physical resilience assessment. This point of view becomes central for critical infrastructures, considering the potential societal and economic consequences a disruption may have. This work provides a cyber-resilience simulation-based assessment for a seawater desalination plant and its connected distribution system. For this purpose, a digital twin has been developed. It integrates a MATLAB/Simulink model of the reverse osmosis treatment plant with a georeferenced water distribution network designed in EPANET. Four stochastic cyber resilience metrics have been proposed and computed to assess the impact of a successful replay cyber attack. The results exemplify the benefits of cyber-physical simulations to understand the behavior of modern water treatment plants, to identify system's criticalities, and eventually to support decision making by identifying hotspots and prioritizing mitigating actions.

Modeling of Water Treatment Plant using Hybrid Petri Nets

Hybrid Petri nets are modeling framework for a system that contains discrete and continuous states. In this paper, we construct a model of water treatment plant using Hybrid Petri nets. We choose hybrid petri nets because the water treatment plant contains discrete and continuous parts. The discrete part consists of the state of pumps and the continuous part consists of water volume in each processing unit. The Petri Nets obtained were further simplified to facilitate simulation. Through simulation we can determine the amount of water production with certain specifications.

Influence of spent drilling mud on sedimentation time of activated sludge flocs

Co-treatment of drilling muds with municipal wastewater in the reactors operating on the activated sludge principle constitutes a potentially safe method of their disposal. The method is based on the process of biological degradation of pollutants by assemblages of activated sludge organisms (prokaryotic and eukaryotic), which include different species described as functional and trophic groups. When the ecosystem in the bioreactor is in equilibrium, high wastewater treatment efficiency and process stability can be achieved. Analysis of qualitative and quantitative changes occurring in assemblages of activated sludge organisms may facilitate understanding the causes and mechanisms involved in the observed processes. In such a context, using a model of an SBR wastewater treatment plant, a study was performed to assess the feasibility of co-treating spent drilling mud with municipal wastewater using the activated sludge method. The floc constitutes the basic structural and physiological unit forming activated sludge. In this study, the sedimentation velocity of activated sludge flocs was analysed, and the obtained results were subjected to statistical analysis.

Constrained Active Fault Tolerant Control Based on Active Fault Diagnosis and Interpolation Optimization.

A new active fault tolerant control scheme based on active fault diagnosis is proposed to address the component/actuator faults for systems with state and input constraints. Firstly, the active fault diagnosis is composed of diagnostic observers, constant auxiliary signals, and separation hyperplanes, all of which are designed offline. In online applications, only a single diagnostic observer is activated to achieve fault detection and isolation. Compared with the traditional multi-observer parallel diagnosis methods, such a design is beneficial to improve the diagnostic efficiency. Secondly, the active fault tolerant control is composed of outer fault tolerant control, inner fault tolerant control and a linear-programming-based interpolation control algorithm. The inner fault tolerant control is determined offline and satisfies the prescribed optimal control performance requirement. The outer fault tolerant control is used to enlarge the feasible region, and it needs to be determined online together with the interpolation optimization. In online applications, the updated state estimates trigger the adjustment of the interpolation algorithm, which in turn enables control reconfiguration by implicitly optimizing the dynamic convex combination of outer fault tolerant control and inner fault tolerant control. This control scheme contributes to further reducing the computational effort of traditional constrained predictive fault tolerant control methods. In addition, each pair of inner fault tolerant control and diagnostic observer is designed integratedly to suppress the robust interaction influences between estimation error and control error. The soft constraint method is further integrated to handle some cases that lead to constraint violations. The effectiveness of these designs is finally validated by a case study of a wastewater treatment plant model.

Integrated theoretical and data-driven Gaussian Process NARX Model for the Simulation of Effluent Concentrations in Wastewater Treatment Plant

Abstract This paper presents the data-driven modelling part of a multi-input multi-output hybrid model of the Wastewater Treatment Plant (WWTP) and the simulation of the WWTP effluent. The information about the future effluent concentrations is important since it is used for efficient managing of the plant, for example in decision making, predictive control, quality control, and detection of violation of effluent limits. The hybrid model consists of a theoretical model based on first principles upgraded with a probabilistic data-driven model. The integrated model is based on a multi-input multi-output autoregressive Gaussian process (GP) model where the exogenous inputs include the predictions from the theoretical model. This approach allows us to use all available information in a single integrated model. We show significant improvement over the theoretical model for one-day-ahead prediction and validate the model for simulation, which can also be used when the effluent concentrations are not measured in an on-line fashion.

Design of a hybrid mechanistic/Gaussian process model to predict full-scale wastewater treatment plant effluent

This paper presents the design of a hybrid model of a wastewater treatment plant (WWTP), which is meant to improve the quality of effluent prediction. By combining mechanistic, i.e. activated sludge model, and data-driven model it is expected to retain physical transparency and achieve good prediction accuracy. For the data-driven model, a state-of-the-art machine learning approach based on Gaussian process (GP) model was applied. GP models systematically address model uncertainty when lacking identification data and are applicable also for small data-sets, which both are encountered in WWTP modelling. Serial and parallel hybrid structures were designed to address the challenges of missing input data, insufficient mechanistic model accuracy and demanding model parameter estimation. Results of full-scale effluent predictions show that, by applying hybrid models, the accuracy of the model is improved. Good results were obtained also for default values of activated sludge model parameters, which significantly simplifies the model design process.

Multiplicity of solutions in model-based multiobjective optimization of wastewater treatment plants

Wastewater treatment process design involves the optimization of multiple conflicting objectives. The detection of different equivalent solutions in terms of objective values is crucial for designers in order to efficiently switch to the new optimal operation policies if changes in the process conditions or new constraints occur. In this work, the dynamic multi-objective optimization of a municipal wastewater treatment plant model is carried out. The aim is to simultaneously optimize an economic cost term and an effluent quality index. The selected process variables for the optimization are (1) an aeration factor in the aerated tank previous to the clarifier, and (2) an internal recycle flow rate. Their time profiles are approximated using the control vector parameterization technique. To solve the multi-objective problem and find the Pareto front, the NSGA-II algorithm has been used. The simulation of different realistic scenarios which impose operational constraints (e.g., maintenance operations) reveals that, indeed, multiple solutions exist at least in some areas of the Pareto front. It is observed that different control profiles can produce nearly identical results in terms of Pareto solutions. The a priori knowledge of these equivalent solutions for different scenarios provides the decision makers with alternative choices to be adapted to their organizations policies when events altering decision variables bounds or adding new constraints to the process model occur.

Evaluation of environmental impacts for future influent scenarios using a model-based approach.

Changes in dilution of wastewater to a treatment plant due to infiltration or surface runoff can have a great impact on treatment process performance. This paper presents a model-based approach in which realistic influent scenarios are generated and used as inputs to a dynamic plant-wide process model of the wastewater treatment plant. The simulated operation is subsequently evaluated using life-cycle assessment (LCA) to quantify the environmental impacts of the future influent scenarios. The results show that increased infiltration led to higher environmental impact per kg nitrogen removed. The increase in surface runoff had a minor impact.

Experimental Water Treatment Plant from Agrozootechnical Farm

According to the existing statistical data at the country level, over 65% of the wastewater (rural from agricultural livestock farms) is discharged without purification, 61% are insufficiently purified and only 10-15% are properly purified. The pollution effect of wastewater not purified or insufficiently purified on surface waters, is mainly manifested by the content of suspended matter, of organic matter, in nutritional salts, ammonium and in pathogenic microorganisms. It is well-known that the nutritional salts of nitrogen and phosphorus cause the eutrophication of surface waters, with the effect of consuming the dissolved oxygen needed to sustain the aquatic life. Ammonia is particularly toxic to aquatic life. Unsaturated or insufficiently purified wastewater pollutes groundwater, among others with nitrates, ammonium and bacteriologically. From the presented results, the purification of waste water is an essential requirement of the development of human civilization. Being a necessity with special social and ecological implications, the unitary regulation and the general provision of the necessary infrastructure is a priority, in this sense an article will be presented in an experimental treatment station that performs the collection of waste water from an agrozootechnical farm. The purification of water within this experimental model of treatment plant will be done autonomously from an energy point of view (electricity supply is carried out through a hybrid system based on photovoltaic panels and wind turbine), and the recovery. The use of purified water is done in a greenhouse, using a mixed irrigation system (dripping / spray irrigation).

Efficient calibration methodology of the wastewater treatment plant model based on ASM3 and application to municipal wastewater

Efficient calibration methodology of the wastewater treatment plant model based on ASM3 and application to municipal wastewater

Identification of behavioural model input data sets for WWTP uncertainty analysis.

Uncertainty analysis is important for wastewater treatment plant (WWTP) model applications. An important aspect of uncertainty analysis is the identification and proper quantification of sources of uncertainty. In this contribution, a methodology to identify an ensemble of behavioural model representations (combinations of input data, model structure and parameter values) is presented and evaluated. The outcome is a multivariate conditional distribution of input data that is used for generating samples of likely inputs (such as Monte Carlo input samples) to perform WWTP model uncertainty analysis. This article presents an approach to verify uncertainty distributions of input data (otherwise often assumed) by using historical observations and actual plant data.

Performance comparison of SVM and ANN for aerobic granular sludge

To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.