Simulation and optimization of a coking wastewater biological treatment process by activated sludge models (ASM)

Application of a generalized hybrid machine learning model for the prediction of H2S and VOCs removal in a compact trickle bed bioreactor (CTBB)

This study presents a generalized hybrid model for predicting H2S and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification model, and (ii) prediction of K <100%, based on inlet concentration, time of day, pH and retention time. Global sensitivity analysis (GSA) was used to test the relationships between the inputs and outputs of the K-NN model. The results from classification model simulation showed high goodness of fit for the classification models to predict the removal of H2S and VOCs (SPEC = 0.94-0.99, SENS = 0.96-0.99). It was shown that the hybrid K-NN model applied for the "Klimzowiec" WWTP, including the pilot plant, can also be applied to the "Urbanowice" WWTP. The hybrid machine learning model enables the development of a universal system for monitoring the removal of H2S and VOCs from WWTP facilities.

Machine learning modeling for the prediction of phosphorus and nitrogen removal efficiency and screening of crucial microorganisms in wastewater treatment plants

The effectiveness of wastewater treatment plants (WWTPs) is largely determined by the microbial community structure in their activated sludge (AS). Interactions among microbial communities in AS systems and their indirect effects on water quality changes are crucial for WWTP performance. However, there is currently no quantitative method to evaluate the contribution of microorganisms to the operating efficiency of WWTPs. Traditional assessments of WWTP performance are limited by experimental conditions, methods, and other factors, resulting in increased costs and experimental pollutants. Therefore, an effective method is needed to predict WWTP efficiency based on AS community structure and quantitatively evaluate the contribution of microorganisms in the AS system. This study evaluated and compared microbial communities and water quality changes from WWTPs worldwide by meta-analysis of published high-throughput sequencing data. Six machine learning (ML) models were utilized to predict the efficiency of phosphorus and nitrogen removal in WWTPs; among them, XGBoost showed the highest prediction accuracy. Cross-entropy was used to screen the crucial microorganisms related to phosphorus and nitrogen removal efficiency, and the modeling confirmed the reasonableness of the results. Thirteen genera with nitrogen and phosphorus cycling pathways obtained from the screening were considered highly appropriate for the simultaneous removal of phosphorus and nitrogen. The results showed that the microbes Haliangium, Vicinamibacteraceae, Tolumonas, and SWB02 are potentially crucial for phosphorus and nitrogen removal, as they may be involved in the process of phosphorus and nitrogen removal in sewage treatment plants. Overall, these findings have deepened our understanding of the relationship between microbial community structure and performance of WWTPs, indicating that microbial data should play a critical role in the future design of sewage treatment plants. The ML model of this study can efficiently screen crucial microbes associated with WWTP system performance, and it is promising for the discovery of potential microbial metabolic pathways.

Assessment and modeling of effluent quality, economic benefits, and greenhouse gas reduction for receiving brewery wastewater on A2O by GPS-X

Recently, breweries have been allowed to discharge brewery wastewater (BWW) to the sewage pipe network to alleviate the shortage of carbon sources of municipal wastewater treatment plants (MWTPs) under the premise of signing a contract with MWTPs in some countries. This study aims to provide a model-based method for MWTPs to evaluate the threshold, the effluent risk, the economic benefits, and the potential greenhouse gas (GHG) emissions reduction of receiving BWW. In this research, a simulation model of an anaerobic-anoxic-oxic process (A²O) receiving BWW was established based on the data of a real MWTP and brewery using GPS-X. The sensitivity factors of 189 parameters were analyzed, and several sensitive parameters were calibrated stably and dynamically. By analyzing the errors and standardized residuals, the calibrated model was proved to be high-quality and reliable. In the next phase, the impact of receiving BWW on the A²O was evaluated in terms of effluent quality, economic benefits, and GHG emissions reduction. The results showed that receiving a certain amount of BWW can effectively reduce the carbon source cost and GHG emissions for the MWTP compared with adding methanol. Though the chemical oxygen demand (COD), biochemical oxygen demand in five days (BOD₅), and total nitrogen (TN) in the effluent increased in various degrees, the effluent quality still met the discharge standard implemented by the MWTP. The study can also facilitate the modeling work for many researchers and promote more kinds of food production wastewater to be treated equally.

Perspectives of nanomaterials in microbial remediation of heavy metals and their environmental consequences: A review

Nanomaterials (NMs) have diverse applications in various sectors, such as decontaminating heavy metals from drinking water, wastewater, and soil. Their degradation efficiency can be enhanced through the application of microbes. As microbial strain releases enzymes, which leads to the degradation of HMs. Therefore, nanotechnology and microbial-assisted remediation-based methods help us develop a remediation process with practical utility, speed, and less environmental toxicity. This review focuses on the success achieved for the bioremediation of heavy metals by nanoparticles and microbial strains and in their integrated approach. Still, the use of NMs and heavy metals (HMs) can negatively affect the health of living organisms. This review describes various aspects of the bioremediation of heavy materials using microbial nanotechnology. Their safe and specific use supported by bio-based technology paves the way for their better remediation. We discuss the utility of nanomaterials for removing heavy metals from wastewater, toxicity studies and issues to the environment with their practical implications. Nanomaterial assisted heavy metal degradation coupled with microbial technology and disposal issues are described along with detection methods. Environmental impact of nanomaterials is also discussed based on the recent work conducted by the researchers. Therefore, this review opens new avenues for future research with an impact on the environment and toxicity issues. Also, applying new biotechnological tools will help us develop better heavy metal degradation routes.

Modeling nutrient removal and energy consumption in an advanced activated sludge system under uncertainty

Activated sludge models are widely used to simulate, optimize and control performance of wastewater treatment plants (WWTP). For simulation of nutrient removal and energy consumption, kinetic parameters would need to be estimated, which requires an extensive measurement campaign. In this study, a novel methodology is proposed for modeling the performance and energy consumption of a biological nutrient removal activated sludge system under sensitivity and uncertainty. The actual data from the wastewater treatment plant in Slupsk (northern Poland) were used for the analysis. Global sensitivity analysis methods accounting for interactions between kinetic parameters were compared with the local sensitivity approach. An extensive procedure for estimation of kinetic parameters allowed to reduce the computational effort in the uncertainty analysis and improve the reliability of the computational results. Due to high costs of measurement campaigns for model calibration, a modification of the Generalized Likelihood Uncertainty method was applied considering the location of measurement points. The inclusion of nutrient measurements in the aerobic compartment in the uncertainty analysis resulted in percentages of ammonium, nitrate, ortho-phosphate measurements of 81%, 90%, 78%, respectively, in the 95% confidence interval. The additional inclusion of measurements in the anaerobic compartment resulted in an increase in the percentage of ortho-phosphate measurements in the aerobic compartment by 5% in the confidence interval. The developed procedure reduces computational and measurement efforts, while maintaining a high compatibility of the observed data and model predictions. This enables to implement activated sludge models also for the facilities with a limited availability of data.

A quantified nitrogen metabolic network by reaction kinetics and mathematical model in a single-stage microaerobic system treating low COD/TN wastewater

A single-stage intermittent aeration microaerobic reactor (IAMR) has been developed for the cost-effective nitrogen removal from piggery wastewater with a low ratio of chemical oxygen demand (COD) to total nitrogen (TN). In this study, a quantified nitrogen metabolic network was constructed based on the metagenomics, reaction kinetics and mathematical model to provide a revealing insight into the nitrogen removal mechanism in the IAMR. Metagenomics revealed that a complex nitrogen metabolic network, including aerobic ammonia and nitrite oxidation, anammox, denitrification via nitrate and nitrite, and nitrate respiration, existed in the IAMR. A novel method for solving kinetic parameters with high stability was developed based on a genetic algorithm. Use this method to calculate the kinetics of various reactions involved in nitrogen metabolism. Kinetics revealed that simultaneous partial nitritation-anammox (PN/A) and partial denitrification-anammox (PDN/A) were the dominant approaches to nitrogen removal in the IAMR. Finally, a kinetics-based model was proposed for quantitatively describing the nitrogen metabolic network under the limitation of COD. 58% ∼ 67% of nitrogen was removed via the anammox-based processes (PN/A and PDN/A), but only 7% ∼ 12% and 1% ∼ 2% of nitrogen were removed via heterotrophic denitrification of nitrite and nitrate, respectively. The half-inhibition constant of dissolved oxygen (DO) on anammox was simulated as 0.37 ∼ 0.60 mg L^-1, filling the gap in quantifying DO inhibition on anammox. High-frequency intermittent aeration was identified as the crucial measure to suppress nitrite-oxidizing bacteria, although it has a high affinity for DO and NO₂^--N. In continuous aeration mode, the simulated NO₃^--N in the IAMR would rise by 39.6%. The research provides a novel insight into the nitrogen removal mechanism in single-stage microaerobic systems and provides a reliable approach to practicing PN/A and PDN/A for cost-effective nitrogen removal.

Modelling of micropollutant fate in hybrid growth systems: model concepts, Peterson matrix, and application to a lab-scale pilot plant

Modelling the fate of micropollutants in different wastewater treatment processes is of present concern. Moreover, during the last few years, there has been an increasing interest in the development of hybrid reactors which contain both suspended biomass and biofilm. Here, a new model developed which tries to determine the fate of micropollutants in hybrid reactors such as moving bed biofilm reactor (MBBR) and called the ASM-biofilm-MPs model considered the main mechanisms leading to the micropollutant removal (sorption/desorption, biodegradation, cometabolism) in hybrid reactors. This dynamic model describes the fate of micropollutants in a hybrid reactor using first-order kinetics for biotransformation and sorption/desorption equations. Also, it considered the reactions for carbon oxidation, nitrification, and denitrification in attached and suspended biomass under aerobic conditions. The mathematical model consists of three connected models for the simulation of micropollutants, suspended biomass, and biofilm. Biochemical conversions are evaluated according to the Activated Sludge Model No. 1 (ASM1) for both attached and suspended biomass. The model is applied for a laboratory MBBR, which fed with synthetic wastewater containing 4-nonylphenol (4-NP) as micropollutant, and accurately describes the experimental concentrations of COD, attached and suspended biomass, nitrogen, and 4-NP micropollutant obtained during 180 days working at different loadings. The differences between simulations and experiments in all operational periods for sCOD, NH₄-N, NO₃-N, and attached and suspended biomass concentrations were less than 15%, 10%, 10%, 5% and 5%, respectively. Finally, the contribution of adsorption and biodegradation mechanisms in the fate of 4-NP was calculated, when 4-NP concentration is set to 1 µg/L (biodegradation = 86.5%, sorption = 5%) and 50 µg/L (biodegradation = 55.9%, sorption = 34.7%).

A Comprehensive View of the ASM1 Dynamic Model: Study on a Practical Case

The ASM1 model was elaborated by the IWA Task Group for Mathematical Modelling, with the aim of explaining and predicting the output values of organic matter concentration in activated sludge processes, especially for domestic wastewaters. In recent years, ASM1 has been completed with new components and extended to other biological processes, including biological membrane reactors, activated carbon filters, and microalgae bioreactors. In this article, the essentials of this model are studied by outlining the original topics that were formulated in the model, and by using a practical example of a wastewater treatment plant (WWTP), which can clarify the application of the ASM1. A protocol of approximation between the dynamic model and the experimental data for the COD effluent concentration is presented, based on three steps of tuning and fine tuning, and the corrected values of the kinetic parameters YH and μH,max are calculated in accordance with the minimum error. In the simulation procedure, the baseline and dynamism are controlled, comparing them to the experimental data line, and the values obtained for the kinetic parameters are YH = 0.60 and μH,max = 0.40 d−1. The kinetic parameters reflect the activity of the mixed community of microorganisms in the WWTP.

Optimization of the Anaerobic-Anoxic-Oxic Process by Integrating ASM2d with Pareto Analysis of Variance and Response Surface Methodology

Wastewater treatment plants (WWTPs) are high-energy-consuming units. Reasonable operation strategies can enable WWTPs to meet discharge standards while reducing the operating cost. In this study, the activated sludge model 2d (ASM2d), Pareto analysis of variance (ANOVA), and response surface methodology (RSM) were jointly used to simulate and optimize the operation of a lab-scale anaerobic-anoxic-oxic (AAO) reactor. The optimization objective was to determine the optimal design and operational parameters (DOPs) that could enhance both pollutant removal and energy saving. The DOPs that had significant influence on the optimization objective, such as sludge retention time (SRT), dissolved oxygen (DO), and the ratio of biodegradable chemical oxygen demand to total nitrogen (BCOD/TN), were identified by Pareto ANOVA. The optimal DOPs with SRT of 15 days, DO concentration of 0.5 mg/L, and BCOD/TN of 5.21 were determined by RSM. Under the optimal conditions, the removal efficiencies of NH4+-N, total nitrogen (TN), and total phosphorus (TP) were 96.2%, 76.8%, and 92.8%, respectively, and the annual operating cost was $26.4. Furthermore, this combination of DOPs was validated using a pilot-scale AAO system. The TN and TP removal efficiencies were improved by 11.0% and 5.0%, respectively, and the annual operating cost could be reduced by 15.0%. Overall, this study confirmed that the method integrating ASM2d with Pareto ANOVA and RSM was effective in optimizing wastewater treatment processes.

Inhibition of nitrite-oxidizing bacteria in automatic recycling PN/ANAMMOX under mainstream conditions

At present, sustainable and stable partial nitrification has not been widely achieved in the mainstream PN/ANAMMOX process. Here, the feasibility of sustainable and stable partial nitrification was demonstrated in automatic recycling PN/ANAMMOX reactor under mainstream conditions using both simulation and experimental methods. Stable nitrite accumulation in the aerobic zone could be achieved via regulating dissolved oxygen (DO) concentrations and sludge retention time (SRT). The DO concentrations required for the repression of nitrite-oxidizing bacteria (NOB) were lower at longer SRTs. The DO concentrations and SRTs required for NOB repression were lower at lower temperatures. However, NOB repression was diminished by a persistent low DO and short SRT under mainstream conditions. With the introduction of automatic recycling, sustainable and stable partial nitrification was achieved. Effluent recycling could limit the nitrite-nitrogen required for NOB growth. Collectively, effluent recycling may serve as a feasible and useful strategy for NOB inhibition during the PN/ANAMMOX process.

Glucose Addition Enhanced the Advanced Treatment of Coking Wastewater

Biological processes have high removal efficiencies and low operational costs, but the secondary effluent of coking wastewater (CWW), even at a low concentration, is difficult for microorganisms to degrade directly. In this study, glucose was used as a carbon source and co-metabolic substrate for microbial acclimation in order to enhance the advanced treatment of coking wastewater (CWW). The removal performance of the pollutants, especially recalcitrant compounds, was studied and the changes in the microbial community structure after activated sludge acclimation were analyzed. The effect of glucose addition on the secondary biochemical effluent of coking wastewater (SBECW) treatment by the acclimated sludge was further studied by a comparison between the performance of two parallel reactors seeded with the acclimated sludge. Our results showed that the concentrations of chemical oxygen demand (COD), total organic carbon (TOC), and UV absorption at 254 nm (UV254) of the wastewater decreased in the acclimation process. Refractory organic matter, such as polycyclic aromatic hydrocarbons and nitrogen-containing heterocyclics, in the SBECW was effectively degraded by the acclimated sludge. High-throughput sequencing revealed that microbes with a strong ability to degrade recalcitrant compounds were enriched after acclimation, such as Thauera (8.91%), Pseudomonas (3.35%), and Blastocatella (10.76%). Seeded with the acclimated sludge, the reactor with the glucose addition showed higher COD removal efficiencies than the control system without glucose addition (p < 0.05). Collectively, glucose addition enhanced the advanced treatment of coking wastewater (CWW).

Incorporation of the complete ammonia oxidation (comammox) process for modeling nitrification in suspended growth wastewater treatment systems

The newly discovered process complete ammonia oxidation (comammox) has changed the traditional understanding of nitrification. In this study, three possible concepts of comammox were developed and incorporated as part of an extended two-step nitrification model. For model calibration and validation, two series of long-term biomass washout experiments were carried out at 12 °C and 20 °C in a laboratory sequencing batch reactor. The inoculum biomass was withdrawn from a large biological nutrient removal wastewater treatment plant. The efficiency of the examined models was compared based on the behaviors of ammonia, nitrite, and nitrate in the studied reactor. Predictions of the conventional approach to comammox, assuming the direct oxidation of ammonia to nitrate, were slightly better than the two other approaches. Simulation results revealed that comammox could be responsible for the conversion of >20% of the influent ammonia load. Therefore, the role of commamox in the nitrogen mass balance in activated sludge systems should not be neglected and requires further investigation. Furthermore, sensitivity and correlation analysis revealed that the maximum growth rates (μ), oxygen half-saturation (K_O), and decay rates (b) of the canonical nitrifiers and comammox were the most sensitive factors, and the highest correlation was found between μ and b among all considered kinetic parameters. The estimated μ values by the best model were 0.57, 0.11, and 0.15 d^-1 for AOB, NOB, and comammox bacteria, respectively.

Dynamic modeling of the activated sludge microbial growth and activity under exposure to heavy metals

The presence of heavy metals in the environment can lead to ecological and health problems. The evolution of biological systems, such as activated sludge, exposed to heavy metals is still underexplored. Therefore, this study sought to develop a model of microorganism activity and growth in activated sludge and used it to investigate the toxicity of five metals: Cu, Cd, Ni, Zn, and Ag. Patterns in the evolution of the toxic effects caused by these metals were similar at the beginning of exposure. Differences in toxicity between metal ions were noted for longer exposure times. Changes in model parameters indicate the influence of metal ions on the mass and energy balance of living cells. Decreases in new enzyme units and biomass production yields in contaminated activated sludge indicate a shift from anabolic reactions to metal homeostasis and resistance.

Thermal Analysis and Energy Efficiency Improvements in Tunnel Kiln for Sustainable Environment

Kiln is a prime need in the ceramics industry, where energy loss is a major part which consumes about 60% production cost through thermal energy for different applications. Higher density of fired and tunnel kiln refractory material lowers the thermal diffusivity and the proper selection of fired material minimizes the energy loss along the kiln. In particular, this research analysed the results of a heat recovery system comprised of a metallic recuperator which gives around 8% energy savings in natural gas consumption. In this work, detailed power quality analysis of low-power factor motors of a tunnel kiln was carried out and a power factor improvement solution was suggested to save electrical energy with payback period of 0.8 y. The motor operating at a low-power factor consumes more reactive power which does not produce beneficial work. A low-power factor around 0.4 causes network power loss, increases in transformer loss and voltage drops. The solution with accumulative capacitance power of 148.05 uF was installed to achieve the power factor to 0.9. Flu gas analyzer was installed to monitor the range of O2 in pre-heating, oxidation, and firing zones of the kiln which should be ≥8% and 3%, respectively. Regression analysis for thermal energy consumption of a tunnel kiln is done to find the forecast thermal energy consumption. This analysis can be used to find operational efficiency, supporting decisions regarding dependent variable of thermal energy consumption and independent variable of production. This research is very helpful for the ceramics industry to mitigate the energy loss at SMEs as well as in mass production level.

Effects of side-stream operation on the mainstream biological phosphorus metabolic pathway for phosphorus recovery: Simulation by an extended ASM2d model

An extended activated sludge model (E-ASM2d) was established by including the metabolic processes of double-layer extracellular polymeric substances (EPS) and glycogen-accumulating organisms (GAOs) into the existing ASM2d model for describing and predicting the metabolic processes of the side-stream phosphorus (P) recovery reactor. A sensitivity analysis of model parameters on S_PO4(soluble phosphate), X_LEPS (loosely-bound EPS), X_TEPS (tightly-bound EPS), COD, and S_NH4 (soluble ammonia nitrogen) outputs was conducted for identifying influential parameters. The predicted effluent values of COD, ammonia nitrogen (NH₄), and P corresponded well with actual measured values and all the model performance coefficient values for COD, NH_4, and P were higher than 0.65, implying the E-ASM2d model could accurately simulate the metabolic processes of the side-stream P recovery process under different COD:P ratio conditions. The variations in the mainstream biological P metabolic pathway under different COD:P conditions were investigated by the E-ASM2d model. At COD:P ratios of 30, 20, and 10, the values of f_PP,TEPS (fraction of X_TEPS in polyphosphate metabolic process) increased from 0.092, 0.094, and 0.096 in the initial phase to 0.107, 0.124, and 0.187 in the side-stream phase, respectively, demonstrating that the fraction of P removal by tightly-bound EPS was improved by the side-stream operation.

A machine learning framework to improve effluent quality control in wastewater treatment plants

Due to the intrinsic complexity of wastewater treatment plant (WWTP) processes, it is always challenging to respond promptly and appropriately to the dynamic process conditions in order to ensure the quality of the effluent, especially when operational cost is a major concern. Machine Learning (ML) methods have therefore been used to model WWTP processes in order to avoid various shortcomings of conventional mechanistic models. However, to the best of the authors' knowledge, no ML applications have focused on investigating how operational factors can affect effluent quality. Additionally, the time lags between process steps have always been neglected, making it difficult to explain the relationships between operational factors and effluent quality. Therefore, this paper presents a novel ML-based framework designed to improve effluent quality control in WWTPs by clarifying the relationships between operational variables and effluent parameters. The framework consists of Random Forest (RF) models, Deep Neural Network (DNN) models, Variable Importance Measure (VIM) analyses, and Partial Dependence Plot (PDP) analyses, and uses a novel approach to account for the impact of time lags between processes. Details of the framework are provided along with a demonstration of its practical applicability based on a case study of the Umeå WWTP in Sweden involving a large number of samples (105763) representing the full scale of the plant's operations. Two effluent parameters, Total Suspended Solids in effluent (TSS_e) and Phosphate in effluent (PO4_e), and thirty-two operational variables are studied. RF models are developed, validated using DNN models as references, and shown to be suitable for VIM and PDP analyses. VIM identifies the variables that most strongly influence TSS_e and PO4_e, while PDP elucidates their specific effects on TSS_e and PO4_e. The major findings are: (1) Influent temperature is the most influential variable for both TSS_e and PO4_e, but it affects them in different ways; (2) PO4_e depends strongly on the TSS in aeration basins - higher TSS concentrations in aeration basins generally promote PO₄ removal, but excess TSS can have negative effects; (3) In general, the impact of TSS in aeration basins on TSS_e and PO4_e increases with the distances of the basin from the merging outlet, so more attention should be paid to the TSS concentration in the third or fourth aeration basins than the first and second ones; (4) Returning excessive amounts of sludge through the second return sludge pipe should be avoided because of its adverse impact on TSS_e removal. These results could support the development of more advanced control strategies to increase control precision and reduce running costs in the Umeå WWTP and other similarly configured WWTPs. The framework could also be applied to other parameters in WWTPs and industrial processes in general if sufficient high-resolution data are available.

Model-based strategy for nitrogen removal enhancement in full-scale wastewater treatment plants by GPS-X integrated with response surface methodology

Model simulation is an effective approach to optimize the operational performance of wastewater treatment plants (WWTPs). This study presents a novel strategy to enhance the total nitrogen (TN) removal in WWTPs by GPS-X integrated with response surface methodology. The sensitivities of 61 parameters were screened and analyzed, and 6 critical parameters (i.e., μ_{max A}, K_A/a, μ_{max H}, K_H/ss, Y_H and μ_maxPAO) were selected for further adjustment. The accuracy of GPS-X for WWTPs modeling was validated by static and dynamic simulations with actual operational data. The results showed that the DO concentration diffused in different biological compartments exhibited significant effects on the denitrification rate. The TN removal is also associated with SRT. The significance and optimization orders of key parameters were analyzed. With the optimization of DO in biological units and SRT, the nitrification and denitrification rates were improved to 97.1 and 85.3% respectively, saving 17.9% energy consumption.

Performance and process simulation of membrane bioreactor (MBR) treating petrochemical wastewater

Mathematical modelling of biological treatment is an effective tool to predict effluent quality. Model calibration is critical to improve the accuracy of simulation, which is normally carried out by fine-tuning the values of parameters according to the practical data. It indicated that huge amount of practical date will be consumed, and it cannot predict the treatment performance of new wastewater. In this study, the main objective was to investigate the feasibility of application BioWin software coupled with determination of sensitive parameters to predict the treatment performance of membrane biological reactors (MBRs) treating real petrochemical wastewater (PW). Model calibrations, i.e., COD fractions of petrochemical wastewater and kinetic parameters of biomass, were carried out using the respirometry method and the relationship between observed and true growth yield coefficients of the three lab-scale MBRs which were operated under different solid retention time (SRT). All the three MBRs had good organic and ammonium removal, with removal efficiencies higher than 80% and 99.9%, respectively. Simulation using the calibrated model also obtained good fit for effluent COD concentration, effluent nitrate concentration and bioreactor's MLSS concentration of all the three MBRs. The mean absolute percentage errors (MAPE) of the simulation mostly were lower than 22%. The results indicated that it is feasible to using BioWin, incorporated with appropriate determination methods of sensitive parameters, to simulate and monitor the treatment performance of MBR treating petrochemical wastewater. This is more time-saving and effective than fine-tuning values of all parameters. This study provides a valuable reference for simulation of industrial wastewater treatment using BioWin.

Mutual Interaction between Temperature and DO Set Point on AOB and NOB Activity during Shortcut Nitrification in a Sequencing Batch Reactor in Terms of Energy Consumption Optimization

Recently, many wastewater treatment plants (WWTPs) have had to deal with serious problems related to the restrictive requirements regarding the effluent quality, as well as significant energy consumption associated with it. In this situation, mainstream deammonification and/or shortened nitrification-denitrification via nitrite (so-called “nitrite shunt”) is a new promising strategy. This study shows the mechanisms and operating conditions (e.g., dissolved oxygen (DO) concentration, temp.), leading to the complete domination of ammonium oxidizing bacteria (AOB) over nitrite oxidizing bacteria (NOB) under aerobic conditions. Its successful application as shortcut nitrification in the sequencing batch reactor (SBR) technology will represent a paradigm shift for the wastewater industry, offering the opportunity for efficient wastewater treatment, energy-neutral or even energy-positive facilities, and substantial reductions in treatment costs. In this study, under low and moderate temperatures (10–16 °C), averaged DO concentrations (0.7 mg O2/L) were preferable to ensure beneficial AOB activity over NOB, by maintaining reasonable energy consumption. Elevated temperatures (~30 °C), as well as increased DO concentration, were recognized as beneficial for the NOB activity stimulation, thus under such conditions, the DO limitation seems to be a more prospective approach.

An improved ASM-GDA approach to evaluate the production kinetics of loosely bound and tightly bound extracellular polymeric substances in biological phosphorus removal process

This study established an extended activated sludge model no. 2 (ASM2) for providing a new recognition of the contributions of both loosely-bound EPS (LB-EPS) and tightly-bound EPS (TB-EPS) into phosphorus (P) removal by incorporating their formation and degradation processes during the anaerobic-aerobic cycle. For determining the best-fit values for the new model parameters (k _h,TB-EPS, k _h,LB-EPS, f _PP,TB-EPS, and f _PP,LB-EPS) in this extended ASM2, a novel and convenient gradient descent algorithm (GDA) based ASM (ASM-GDA) method was developed. Sensitivity analysis of f _PP,TB-EPS, f _PP,LB-EPS, k _h,TB-EPS, and k _h,LB-EPS on the model target outputs of S _PO4 , X _TB-EPS, X _LB-EPS, and X _PP proved the accuracy of the chosen parameters. Eight batch experiments conducted under different influential chemical oxygen demand (COD) and P conditions were quantitatively and qualitatively analyzed. Respectively, 9.37-9.64% and 4.17-4.29% of P removal by TB-EPS and LB-EPS were achieved. Self-Organizing Map (SOM) has shown its high performance for visualization and abstraction for exhibiting the high correlations of the influential COD/P concentrations and the P% removal by TB-EPS (and LB-EPS). Comprehensive analyses of the influences of influential COD and P concentration on the biological phosphorus removal process help us in successfully establishing the mechanism kinetics of production and degradation of P in a dynamic P biological-treatment model.

Optimization of a large industrial wastewater treatment plant using a modeling approach: A case study

The objective of this paper is to find the optimum solid retention time (SRT) of a wastewater treatment plant (WWTP), which minimizes operating costs, using a modeling approach with WEST software by MIKE DHI®. For the determination of the kinetic and stoichiometric parameters (used for the correct calibration of the model implemented), respirometric and kinetic batch tests were carried out. Each Oxidation ditch was modeled by a sequence of four aerated activated sludge units (ASUs) and four anoxic ASUs with recirculation. The model is able to simulate the separation efficiency of the secondary settler, which is generally quite low: in fact, the industrial origin of the wastewater induces the formation of small flocs, the dimensions of which can be further reduced by the presence of surface aerators. The nitrification/denitrification process is also accurately predicted. Using data obtained from the model, mass balances at the steady state for COD and N were made and compared to the ones obtained using measured data. After calibration and validation of the model, steady-state simulations were carried out by increasing and decreasing the SRT of the system under two different operational conditions used by the managing company and by evaluating the costs related to the water treatment line and the sludge treatment line for each scenario. It is interesting to note how the total costs are lower in summer than in winter (7.2 €cent/m³ in summer and 8.7 €cent/m³ in winter, in scenario 0). In general, the increase in the SRT led to a decrease in the total management costs. In fact, differences between scenario 0 and the scenario with the lowest total treatment costs (corresponding to an SRT of 11.4 d in winter and 10.0 d in summer) could give rise to total savings of about 44·000€/year in summer and 93·000€/year in winter.

Intelligent Control/Operational Strategies in WWTPs through an Integrated Q-Learning Algorithm with ASM2d-Guided Reward

The operation of a wastewater treatment plant (WWTP) is a typical complex control problem, with nonlinear dynamics and coupling effects among the variables, which renders the implementation of real-time optimal control an enormous challenge. In this study, a Q-learning algorithm with activated sludge model No. 2d-guided (ASM2d-guided) reward setting (an integrated ASM2d-QL algorithm) is proposed, and the widely applied anaerobic-anoxic-oxic (AAO) system is chosen as the research paradigm. The integrated ASM2d-QL algorithms equipped with a self-learning mechanism are derived for optimizing the control strategies (hydraulic retention time (HRT) and internal recycling ratio (IRR)) of the AAO system. To optimize the control strategies of the AAO system under varying influent loads, Q matrixes were built for both HRTs and IRR optimization through the pair of <max reward-action> based on the integrated ASM2d-QL algorithm. 8 days of actual influent qualities of a certain municipal AAO wastewater treatment plant in June were arbitrarily chosen as the influent concentrations for model verification. Good agreement between the values of the model simulations and experimental results indicated that this proposed integrated ASM2d-QL algorithm performed properly and successfully realized intelligent modeling and stable optimal control strategies under fluctuating influent loads during wastewater treatment.

Application of the Mathematical Simulation Methods for the Assessment of the Wastewater Treatment Plant Operation Work Reliability

The aim of the present work was the modeling of the wastewater treatment plant operation work using Monte Carlo method and different random variables probability distributions modeling. The analysis includes the following pollutants indicators; BOD5 (Biochemical Oxygen Demand), CODCr (Chemical Oxygen Demand), Total Suspended Solids (SSt), Total Nitrogen (TN), and Total Phosphorus (TP). The Anderson–Darling (A–D) test was used for the assessment of theoretical and empirical distributions compatibility. The selection of the best-fitting statistical distributions was performed using peak-weighted root mean square (PWRMSE) parameter. Based on the performed calculations, it was stated that pollutants indicators in treated sewage were characterized by a significant variability. Obtained results indicate that the best-fitting pollutants indicators statistical distribution is Gauss Mixed Model (GMM) function. The results of the Monte Carlo simulation method confirmed that some problems related to the organic and biogenic pollutants reduction may be observed in the Wastewater Treatment Plant, in Jaworzno.

Modeling and performance improvement of an anaerobic-anoxic/nitrifying-induced crystallization process via the multi-objective optimization method

The trade-off between energy savings and emission reductions of an activated sludge process is a multi-objective problem relating to several potentially conflicting objectives. Therefore, the optimal modification of an anaerobic-anoxic/nitrifying/induced crystallization (A2N-IC) process by multi-objective optimization method was studied in this work. The multi-objective optimization model comprised three evaluative indices, (effluent quality (EQ), operation cost (OC), and total volume (TV) of structures), and 14 process parameters (decision variables) solving by non-dominated sorting genetic algorithm II (NSGA-II) in MATLAB. The trade-off relationships among EQ, OC, and TV were investigated under 30 days of dynamic influent with different constraint conditions. A series of Pareto solutions were obtained, and one Pareto solution was selected for further analysis. Results showed improved effluent concentrations of chemical oxygen demand (COD), total nitrogen (TN), ammonia-nitrogen (NH₄⁺-N), and total phosphorous (TP) under the optimized strategy compared to the original strategy, where the average effluent concentrations decreased by 2.22, 0.47, 0.13, and 0.02 mg/L, respectively. The values of EQ and OC decreased from 0.015 kg/day and 0.15 ¥/m³ to 0.0023 kg/day and 0.12 ¥/m³, respectively, while the TV increased from 0.31 to 0.33 m³. These results indicated that the multi-objective optimization method is useful for modifying activated sludge processes.

Influence of Integrated Membrane Treatment on the Phytotoxicity of Wastewater from the Coke Industry

In this paper, coke wastewater that had passed through biological and integrated membrane processes (filtration on sand bed-reverse osmosis) was chosen to assess the phytotoxicity of selected industrial wastewater with regard to the test plant-Vicia faba. An innovative research technique in vitro test was conducted in a large scale phytothrone chamber on two matrices: cotton and Murashige and Skoog Basal Medium (MSBM). The toxicity of wastewater was evaluated for samples: (1) treated in the treatment plant by biological processes, (2) filtrated through a sand bed and filtrated (3) reverse osmosis (RO) membrane. The results showed that there is a noticeable correlation between increasing concentrations of wastewater and seed germination of the test plant. Although the wastewater collected from the coke plant was treated biologically, it showed very high levels of germination inhibition (90-98% for cotton matrix and 92-100% for MSBM matrix) and strong toxic effects. The wastewater collected from the coke plant showed a significantly greater phytotoxic effect compared with those obtained from the effluent treated on a sand bed and in RO. However, wastewater, even after treatment on a sand bed (reduction of COD-39%, TN-46%, TOC-42%, TC-47%, SS-50%, 16PAHs-53%), was still toxic and germination inhibition was in the range of 24-48% for the cotton matrix and 14-54% for the MSBM matrix. The toxicity of wastewater treated in the membrane process was the lowest (reduction of COD-85%, TN-95%, TOC-85%, TC-86%, SS-98%, 16PAHs-67%). The germination inhibition was in the range of 4-10% for the cotton matrix and 2-12% for the MSBM matrix. These samples are classified as non-toxic or slightly toxic to the model plant. The present study highlights the necessity of monitoring not only the basic physical and chemical indicators (including the level of toxic substances as PAHs), but also their effect on the test organisms in wastewater samples.

Cometabolic degradation of low-strength coking wastewater and the bacterial community revealed by high-throughput sequencing

Cometabolism technology was employed to degrade low-strength coking wastewater (CWW) in Sequencing Batch Reactor (SBR). The bacterial community compositions were monitored by high-throughput sequencing. Cometabolic substrate effectively improved the chemical oxygen demand (COD) removal efficiency in glucose-added system (A1) compared to glucose-free system (A0). Meanwhile, A1 exhibited larger biomass, better settlement performance, and higher dehydrogenase activity (DHA). More importantly, high-throughput sequencing revealed that dominant populations in A1 were quite different with A0. Thauera (9.27%), Thermogutta (7.58%), and Defluviimonas (4.6%) began to enrich in A1 after cometabolic substrate supplement. Especially, Thauera, as the most dominant populations in Al, could degrade a wide spectrum of aromatic compounds, which may contribute to the better system performance. This work would provide a novel option to treat low-strength CWW, discern the relationship between bacterial community and CWW quality, and further explore the cometabolic degradation through bacterial community structures.

Four-stage biofilm anaerobic–anoxic–oxic–oxic system for strengthening the biological treatment of coking wastewater: COD removal behaviors and biokinetic modeling

Biokinetic models of high-strength coking wastewater with a high chemical oxygen demand (COD) was efficiently treated by a novel pilot-scale four-stage biofilm anaerobic–anoxic–oxic–oxic (FB-A²/O²) system.

Transformation and utilization of slowly biodegradable organic matters in biological sewage treatment of anaerobic anoxic oxic systems

This study examined the distribution of carbon sources in two anaerobic anoxic oxic (AAO) sewage treatment plants in Xi'an and investigated the transformation characteristics and utilization potential of slowly biodegradable organic matters (SBOM). Results indicated under anaerobic and aerobic conditions, SBOM could be transformed at a rate of 65% in 8h into more readily biologically utilizable substrates such as volatile fatty acids (VFAs), polysaccharides and proteins. Additionally, non-biodegradable humus-type substances which are difficult to biodegrade and readily accumulate, were also generated. These products could be further hydrolyzed to aldehyde and ketone compounds and then transformed into substances with significant oxygen-containing functional groups and utilized subsequently. The molecular weights of proteinoid substances had a wide distribution and tended to decrease over time. Long hours of microbial reaction increased the proportion of micromolecular substances. This particular increase generated significant bioavailability, which can greatly improve the efficiency of nitrogen removal.