Effluent Total Suspended Solids Research Articles

Clarity tubes as effective citizen science tools for monitoring wastewater treatment works and rivers.

Improved freshwater resource management requires the implementation of widespread, effective, and timely water quality monitoring. Conventional monitoring methods are often inhibited by financial, infrastructural, and human capacity limitations, especially in developing regions. This study aimed to validate the citizen-scientist-operated transparency or clarity tube (hereafter "clarity tube") for measuring water clarity as a proxy for total suspended solids (TSS) concentration, a critical quality metric in river systems and wastewater treatment works (WWTW) effluent in Southern Africa. Clarity tubes provided a relatively accurate and precise proxy for TSS in riverine lotic systems and WWTW effluent, revealing significant inverse log-linear relationships between clarity and TSS with r2 = 0.715 and 0.503, respectively. We demonstrate that clarity-derived estimates of TSS concentration (TSScde) can be used to estimate WWTW compliance with WWTW effluent TSS concentration regulations. The measurements can then be used to engage with WWTW management, potentially affecting WWTW performance. Overall, these findings demonstrate the usefulness of clarity tubes as low-cost, accessible, and easy-to-use citizen science tools for high spatial and temporal resolution water quality monitoring, not only in rivers in Southern Africa but also in WWTW effluent for estimating compliance, with strong global relevance to the sustainable development goals (SDGs). Integr Environ Assess Manag 2024;00:1-10. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Performances of constructed wetland system to treat whey and dairy wastewater during a macrophytes life cycle

Constructed wetlands system (CWs) have been proposed as an economical, efficient and sustainable wastewater treatment technology. The aim of this paper was to provide the first published data on the efficiency of CWs for dairy wastewater treatment in Tunisia. In most CWs, good organic, total suspended solids (TSS) and nutrient removal was measured. In order to evaluate the efficiency of this process, hybrid small-scale wetland system was designed containing three horizontal subsurface flow (HSSF) and one free water surface flow (FWS). A mixture of Reeds (Phragmites australis), Cattails (Typha latifolia), and Papyrus (Cyperus papyrus) was planted. Several water quality parameters in both raw and treated wastewaters were monitored during the macrophytes life cycle. The average influent properties for 20 % of whey are: TSS (3040 mg/L), COD (5060 mg O2/L), BOD5 (3020 mg O2/L), TKN (79 mg N/L) and TP (32,2 mg P/L). Removal efficiencies of 99.6 % for TSS, 80 % for COD, 97 % for BOD5, 90.4 % for TKN and 99.6 % for TP were observed. The results reveal temporal variations in system performance according to macrophyte growth rates. Reeds, Cattails and Papyrus start their life cycle at the beginning of the winter and continue growing in summer. The effluent pH, TSS, COD,BOD5, TKN and TP concentrations were in compliance with both irrigation reuse and discharge regulations.

Machine learning-based prediction of effluent total suspended solids in a wastewater treatment plant using different feature selection approaches: A comparative study

Accurately predicting the characteristics of effluent, discharged from wastewater treatment plants (WWTPs) is crucial for reducing sampling requirements, labor, costs, and environmental pollution. Machine learning (ML) techniques can be effective in achieving this goal. To optimize ML-based models, various feature selection (FS) methods are employed. This study aims to investigate the impact of six FS methods (categorized as Wrapper, Filter, and Embedded methods) on the accuracy of three supervised ML algorithms in predicting total suspended solids (TSS) concentration in the effluent of a municipal wastewater treatment plant. Based on the features proposed by each FS method, five distinct scenarios were defined. Within each scenario, three ML algorithms, namely artificial neural network-multi layer perceptron (ANN-MLP), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost) were applied. The features utilized for predicting TSS concentration in the WWTP effluent included BOD5, COD, TSS, TN, NH3 in the influent, and BOD5, COD, residual Cl2, NO3, TN, NH4 in the effluent. To construct the models, the dataset was randomly divided into training and testing subsets, and K-fold cross-validation was employed to control overfitting and underfitting. The evaluation metrics that are used are root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R2). The most efficient scenario was identified as Scenario IV, with the Sequential Backward Selection FS method. The features selected by this method were CODe, BOD5e, BOD5i, TNi. Furthermore, the ANN-MLP algorithm demonstrated the best performance, achieving the highest R2 value. This algorithm exhibited acceptable performance in both the training and testing subsets (R2 = 0.78 and R2 = 0.8, respectively).

Influence of effluent particles and particle-bound micropollutants on the removal of micropollutants and UVA254 in wastewater effluent ozonation

This study systematically investigated the influence of effluent particles and activated sludge (AS) particles on the removal of micropollutants via wastewater effluent ozonation within typical effluent total suspended solids (TSS) concentrations. A series of batch experiments revealed that particle concentrations up to 30 mg/L had a minor impact on the removal of organic micropollutants (OMPs) in the aqueous phase. Moreover, the reduction of UV absorbance at 254 nm (UVA254) was negatively correlated to the level of particle concentration at ozone doses higher than 0.5 gO3/gDOC. It indicates that UVA254 abatement was more sensitive to the presence of particles compared to OMP removal. Organic micropollutants (OMPs) sorbed on effluent particles and sludge particles were extracted before and after ozonation. OMP sorption in effluent particles was 2–5 times higher than that in sludge particles. During the ozonation of raw secondary effluent, particle-bound micropollutants were removed comparably to the micropollutants in the aqueous phase. This suggests that the boundary layer surrounding the particle didn’t affect the removal of OMPs in the particle phase. Furthermore, the removal of existing OMPs (irbesartan, sulfamethoxazole, and metoprolol) in the effluent was used to assess the ozone and •OH exposure. In water samples with and without particles, the elimination of OMPs could be reliably predicted (R² > 0.95) by calculated ozone and •OH exposures.

Operational optimization at the Nenäinniemi wastewater treatment plant's tertiary disc filter phosphorus removal installation to reduce chemical consumption.

Instrumentation, automation and control in municipal wastewater treatment can result in large resource, cost and energy savings. Feedforward and feedback control algorithms were implemented together with turbidity and phosphorus analysers to control the chemical dose at the tertiary stage of the Nenäinniemi wastewater treatment plant, consisting of coagulation, flocculation and microsieve filtration. This optimization lowered the coagulant dose by 70% and the polymer dose by 36% compared to manual adjustments of the chemical dosing. Effluent total phosphorus (TP) and total suspended solids (TSS) concentrations were lowered by 20-30%. With the control system in operation, the annual savings in coagulant and polymer were in the range of 100 and 1.4 tons, respectively. Conducting automated CIP on the media at an economical break-even interval of approximately 20 days was also important to further lower energy usage and operational costs.

Performance evaluation of an oily industrial wastewater treatment system using the application of activated sludge model No. 3

AbstractThe purpose of the present study was to adapt the activated sludge model No. 3 (ASM3) to the characteristics of oily industrial wastewater, determining the utmost significant and appropriate kinetic as well as stoichiometric parameters. An oily industrial wastewater treatment system was simulated to assess ASM3 validation and perform sensitivity analysis using the STOAT program. The obtained results revealed that the ASM3 model, which was calibrated after adding the Arrhenius equation into consideration, provided strong correlations with the analytical results of chemical oxygen demand (COD), total suspended solids (TSS), mixed liquor volatile suspended solids (MLVSS) and total suspended solids in the return activated sludge flow (TSS in RAS) concentrations. The values of modelled effluent COD and TSS are very close to those corresponding real values of the treated wastewater by a difference of between 0.5% and 1.5%. Thus, this model becomes successful in representing oily industrial wastewater treatment as a new trend added to the traditional modelling of sewage treatment.

Algal settling ponds contribute to final effluent quality of integrated algal pond systems for municipal sewage treatment

AbstractAppropriate wastewater technologies and sound management are crucial to global water quality and conservation. The integrated algal pond system (IAPS), considered an efficient, passive and low‐cost wastewater treatment technology for peri‐urban spaces, is perceived to yield a final effluent unsuitable for discharge. Experiments were carried out to challenge the prevailing perception that algal‐based wastewater treatment processes and in particular IAPS produce an effluent that does not always meet national and/or regional regulatory standards. Formation of a microalgal–bacterial floc (MaB‐flocs) and settleability together with biomass removal from algal settling ponds (ASPs) is shown to reduce total suspended solids (TSS) from >50 to <20 mg L−1. Thus, production of a readily settleable MaB‐floc coupled with removal of settled biomass from ASP ensures that final effluent TSS remains below the general limit of 25 mg L−1 and yields an effluent suitable for either irrigation or discharge.

Evaluation of an aerobic granular sludge reactor with biological filtration (AGS-BF reactor) in municipal wastewater treatment: A new configuration

Aerobic Granular Sludge (AGS) technology is considered promising, but many aspects are being studied and developed to increase system reliability. In this regard, the research assessed the performance of a conventional AGS (R1) sequencing batch reactor (SBR) system compared to a new AGS design through the inclusion of a biological filter (BF) compartment in the upper zone (R2), treating municipal wastewater. BF insertion in R2 improved total nitrogen removal (> 85 %) and effluent clarification. Effluent total suspended solids (TSS) average values of 44.1 and 17.1 mg/L were found for R1 and R2, respectively. Sludge production was evaluated, and the yield coefficient (Y) and sludge concentration were different in the two reactors. The new system (R2) can be an interesting alternative to improve some aspects of conventional AGS reactors, such as denitrification and nitrite accumulation, effluent TSS concentration, and system reliability increase during periods of instability and granules breakage.

Towards better process management in wastewater treatment plants: Process analytics based on SHAP values for tree-based machine learning methods

Understanding the mechanisms of pollutant removal in Wastewater Treatment Plants (WWTPs) is crucial for controlling effluent quality efficiently. However, the numerous treatment units, operational factors, and the underlying interactions between these units and factors usually obfuscate the comprehensive and precise understanding of the processes. We have previously proposed a machine learning (ML) framework to uncover complex cause-and-effect relationships in WWTPs. However, only one interpretable ML model, Random forest (RF), was studied and the interpretation method was not granular enough to reveal very detailed relationships between operational factors and effluent parameters. Thus, in this paper, we present an upgraded framework involving three interpretable tree-based models (RF, XGboost and LightGBM), three metrics (R2, Root mean squared error (RMSE), and Mean absolute error (MAE)) and a more advanced interpretation system SHapley Additive exPlanations (SHAP). 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. Results show that, for both labels TSSe (Total suspended solids in effluent) and PO4e (Phosphate in effluent), the XGBoost models are optimal whereas the RF models are the least optimal, due to overfitting and polarized fitting. This study has yielded multiple new and significant findings with respect to the control of TSSe and PO4e in the Umeå WWTP and other similarly configured WWTPs. Additionally, this study has produced two important generic findings relating to ML applications for WWTPs (or even other process industries) in terms of cause-and-effect investigations. First, the model comparison should be carried out from multiple perspectives to ensure that underlying details are fully revealed and examined. Second, using a precise, robust, and granular (feature attribution available for individual instances) explanation method can bring extra insight into both model comparison and model interpretation. SHAP is recommended as we found it to be of great value in this study.

Experimental and simulation study of rectangular and circular primary clarifier for wastewater treatment

Clarification is an essential process in wastewater treatment which removes suspended solids using flocculants The efficiency of the primary clarifier is important as it affects the performance of the subsequent processes. The modeling of clarifiers are carried out using one-dimensional flux theory or two dimensional computational fluid dynamics. In this paper one-dimensional modeling of sedimentation process based on flux theory applied to lab-scale circular and rectangular primary clarifiers is reported. The experiments were carried out for three operating conditions i.e. low, medium and high solid concentrations using lab-scale setup. The one-dimensional sedimentation process model was implemented on MATLAB platform and simulation was carried out. The model simulation is able to predict effluent total suspended solids as well as TSS present along the height of the lab-scale primary clarifiers. The average R 2 value 0.97 was observed between measured and simulated total suspended solids present along the height of circular clarifier for the three operating conditions whereas average R 2 value 0.96 was found in the case of rectangular clarifier for identical conditions. Based on the simulation and experimental results the removal of suspended solids is found better in circular clarifier as compared to rectangular clarifier for the influent flow rate of 134 mL/min with influent total suspended solids from 300 to 600 mg/L. • One-dimensional sedimentation process model applied to the two primary clarifiers. • Experiments were performed using lab scale setup of the two primary clarifiers. • Simulation was carried out to generate profiles of the Total Suspended Solids (TSS). • TSS removal is found better in circular as compared to the rectangular design.

Detailed modeling of solids separation by microsieving in a rotating belt filter: Explicit effect of particle size, mesh size, and polymer dose

Microsieving by rotating belt filters is an engineered fractionation method for particle separation from wastewater that can be used as an alternative to primary clarification due to its smaller footprint and lower total cost of ownership. Because of the multiscale nature of the filtration processes involved, and the interdependent variables controlling performance and the system design, the availability of verified and comprehensively validated models that, based on fundamental principles, can explicitly capture the effect of particle size, mesh size and polymer dose are essential for performance optimization and scale-up. In this paper, a detailed microsieving filtration model was derived by extending Darcy’s law to a dual-layer dynamic framework whose predictions were validated against batch (column) and continuous flow (RBF pilot) experimental data. The ability of the microsieving model to accurately capture observed trends confirmed that the model formulation, derived from column experiments, were sufficiently accurate and flexible to capture the relevant physics involved, with a relative error in performance scale up (from column to pilot) of 33% and 14% for filter capacity and effluent total suspended solids, respectively. Upon further calibration against pilot data, the relative error was reduced to 9% and 5%, respectively, indicating the suitability of the model structure in further adapting to filtration process conditions occurring at pilot scale. Finally, the calibrated model was used to derive guidance for future pilot studies. It was determined that, under naturally varying influent conditions, at least 8 h of continuous pilot data, with sampling frequency of at least 15 min for filter capacity and suspended solids concentration, were necessary for a satisfactory estimate of the model parameters.

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 (TSSe) and Phosphate in effluent (PO4e), 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 TSSe and PO4e, while PDP elucidates their specific effects on TSSe and PO4e. The major findings are: (1) Influent temperature is the most influential variable for both TSSe and PO4e, but it affects them in different ways; (2) PO4e 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 TSSe and PO4e 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 TSSe 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.

Wastewater lagoon solids, phosphorus, and algae removal using discfiltration

Abstract The microsieving discfilter technology was investigated at the pilot scale. The pilot was configured to treat the effluent from a municipal wastewater multi-lagoon facility consisting of two facultative lagoons and a third seasonally aerated lagoon that is aerated to mitigate hydrogen sulfide release. The 10 μm filter, operated without chemical enhancement, demonstrates 60.1 ± 22.6% removal of the lagoon effluent total suspended solids (TSS) during periods of operation without aeration of the third lagoon. Aeration of the third lagoon of the multi-lagoon system prior to discharge renders the 10 μm filter cloth ineffective with respect to solids removal. The 5 μm filter cloth performs effective nonchemically enhanced removal of solids even during aeration of the lagoon, removing 68.2 ± 9.85% of effluent TSS. The greater performance of the 5 μm filter was achieved at the expense of a lower maximum conveyance capacity than the 10 μm filter. The 10 and 5 μm filters decrease the effluent total phosphorus (TP) concentrations by 0.14 and 0.13 mg-P/L, respectively. Algae, characterized as Chlorophyll α, shows removal from influent concentrations of 10.25 ± 4.19 μg/L to concentrations of 4.61 ± 1.28 μg/L for the 10 μm filter, and 4.10 ± 0.65 μg/L for the 5 μm filter.

A novel stochastic wastewater quality modeling based on fuzzy techniques.

Measurement and prediction of wastewater quality parameters are crucial for evaluating the risk to the receiving waters. This study presents new methods for the identification of outlier data and smoothing as an effective pre-processing technique prito to modelling. This new data processing method uses a combination of the autoregressive integrated moving average (ARIMA) model and -the adaptive neuro fuzzy inference system with fuzzy C-means clustering (FCM) (ANFIS-FCM). These new pre-processing methodsare compared to previously employed non-linear approaches for modelling of wastewater influent/effluent 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD) and total suspended solids (TSS). Linear modelling of each parameter, 242 linear models, were investigated, and a linear model for each parameter was selected. The results of the non-linear models led to an acceptable prediction for qualitative parameters so that the high coefficient of determination (R 2 ) was observed for the influent and effluent BOD and TSS, respectively. The range of the R 2 for all models was recorded as 0.8-0.87 and 0.83-0.89, respectively. By a combination of the linear and non-linear mothods a hybrid model was introduced. The proposed hybrid model for the influent BOD with the highest correlation between the observed and predicted values, and limited scattering was identified as the optimal model (R2 = 0.95). The use of hybrid models to predict wastewater quality parameters improved the performance and efficiency of the models. In addition, a comparison of the hybrid model with the recently developed models in the literature indicates that the developed ARIMA-ANFIS-FCM outperformed other models.

Combining Chemical Coagulation Process and Innovative Aerobic Reactor for the Treatment of De-Hairing Wastewater

The tanning industry causes great damage and pollution to the environment. It generates tanning effluents and solid waste which, without pretreatment, discharged directly into the environment. Beam-house operations are among the important process of this industry which generates the biggest amount of toxic effluents. These wastes are considered as the most odious materials in the leather industry. The effluent is characterized by alkaline pH value, high loads of oil&grease, sulfides, total suspended solids, chemical oxygen demands and biological oxygen demands. Integration of chemical coagulation using ferric chloride aided with lime and down flow hanging non-woven (DHNW) reactor was applied in the present study. Coagulation was performed using a continuous coagulation reactor (CCT). The DHNW was packed with non-woven poly ethylene terephthalate (PET) fabric produced from waste plastic bottles. The efficiency of the integrated treatment system was assessed using the reduction of sulfide, chemical and biological oxygen demands (COD & BOD), total Kjeldahl nitrogen (TKN) and total suspended solids (TSS). Residual concentrations of COD, BOD and TSS in the CCT effluent were 985, 440 and 25 mg/L (96.1%, 85.2% and 99.3% removal), respectively. The effluent of CCT reactor was feed to the DHNW reactor. Concentrations of COD, BOD and TSS were 145, 55 and 4 mg/L, respectively in the final treated effluent. While, the sulfide concentration had been greatly reduced to 0.5 mg/L. With respect to the parameters studied, the treated effluent might meet the National regulatory standards for discharging into the sewerage systems.

Performance evaluation and modelling of an integrated municipal wastewater treatment system using neural networks

AbstractThis study evaluates and models the impacts of employing biofilm carriers in sequencing batch reactors (SBR). A neural network (NN) was used to predict contaminants in the effluent and analyse the importance of operating parameters. With a hydraulic retention time of 7 h, the removal efficiency of chemical oxygen demand (COD), total phosphorous (TP), and total suspended solids (TSS) were 85, 82, and 98.9%, respectively. The removal efficiency of COD, TP, and TSS in our hybrid system was superior to regular single SBR systems. The training procedure of the NN model was successful and almost a perfect match was achieved between predicted values and experimental values. For all models predicting effluent COD, TP, and TSS, the correlation coefficient was higher than 0.99, and mean squared error approached zero. The analysis of input parameters demonstrated that influent concentration is a significant factor in the modelling of effluent characteristics.

Prediction of total suspended solids present in effluent of primary clarifier of industrial common effluent treatment plant: Mechanistic and fuzzy approach

Primary clarifier plays vital role in removal of pollutants present as total suspended solids (TSS) in wastewater. In this research work prediction of effluent TSS present in the sedimentation tank of clariflocculator is reported. The data of common effluent treatment plant (CETP), Ahmedabad, India has been used in the development of soft sensor. The mechanistic and fuzzy inference system (FIS) soft sensor models have been developed. The models predict the effluent total suspended solids (TSSe) of clariflocculator process based on the measurement of influent flow rate (Qf) and influent total suspended solids (TSSin). The root mean square error (RMSE) of mechanistic model and fuzzy inference system were 30 and 48 respectively for the data samples Qf ranging from 800 to 900 m3/h and TSSin from 300–600 mg/L. These ranges are typically observed throughout the year at CETP, Ahmedabad, India. The percentage mean accuracy for mechanistic model was 88 %. The difference between predicted and actual measured values of RMSE and percentage mean accuracy of mechanistic model is acceptable as system is highly sensitive. Mechanistic model gives information of total suspended solids present in retentate as well as TSS present at different height of the sedimentation tank in addition to TSS present in effluent.

Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions models

In the present study, a three-layer artificial neural network (ANN) and nonlinear regression models were developed to predict the performance of biogas production from the anaerobic hybrid reactor (AHR). Firstly, the performance of an AHR which is filled with perlite (2.38–4.36 mm) at fill rates of 1/3, 1/4 and 1/5 for the treatment of synthetic wastewater was investigated at a loading rate of 5, 7.5, 10, 12.5 and 15 kg COD m−3 day with 12, 24, 36 and 48 h of hydraulic retention time (HRT) under mesophilic conditions (37 ± 1 °C). In this study, experimental data were used to estimate the biogas production rate with models produced using both ANNs and nonlinear regression methods. Moreover, ten related variables, such as reactor fill ratio, influent pH, effluent pH, influent alkalinity, effluent alkalinity, organic loading rate, effluent chemical oxygen demand, effluent total suspended solids, effluent suspended solids and effluent volatile suspended solids, were selected as inputs of the model. Finally, ANN and nonlinear regression models describing the biogas production rate were developed. The R2, IA, FA2, RMSE, MB for ANNs and nonlinear regression models were found to be 0.9852 and 0.9878, 0.9956 and 0.9945, 0.9973 and 0.9254, 217.4 and 332, 36 and 222, respectively. The statistical quality of ANNs and nonlinear regression models were found to be significant due to its high correlation between experimental and simulated biogas values. The ANN model generally showed greater potential in determining the relationship between input data and the biogas production rate according to statistical parameters (except R2 and R). The results showed that the proposed ANNs and nonlinear regression models performed well in predicting the biogas production rate of AHR on behalf of avoiding economic and environmental sustainability problems.

Developing a multi-criteria decision support system based on fuzzy analytical hierarchical process (AHP) method for selection of appropriate high-strength wastewater treatment plant

The selection of an optimum treatment process for high-strength wastewater is complicated. Familiarity with wastewater treatment methods is not enough to design a plant and requires a multidisciplinary knowledge base. In this research, five alternative wastewater treatment methods for high-strength wastewater were investigated and ranked based on the analytic hierarchy process (AHP) fuzzy method: upflow anaerobic sludge blanket (UASB) + membrane bioreactor (MBR), UASB + extended aeration (EA), anaerobic baffled reactor (ABR), anaerobic lagoon (ANL) + aerated lagoon (AL), and sequencing batch reactor (SBR) + ABR. These treatment methods were ranked based on five criteria, namely energy consumption, effluent total suspended solids (TSS), effluent chemical oxygen demand (COD), cost, and level of technology. The different options of the wastewater treatment plant were rated by expert decision-makers in this field. The results show that for typical high-strength wastewater, the use of an UASB reactor followed by a MBR is the most appropriate alternative for treating the wastewater.

Transesterification of Waste Activated Sludge for Biosolids Reduction and Biodiesel Production

Transesterification of waste activated sludge (WAS) was evaluated as a cost-effective technique to reduce excess biosolids and recover biodiesel feedstock from activated sludge treatment processes. A laboratory-scale sequencing batch reactor (SBR) was operated with recycling transesterification-treated WAS back to the aeration basin. Seventy percent recycling of WAS resulted in a 48% reduction of excess biosolids in comparison with a conventional SBR, which was operated in parallel as the control SBR. Biodiesel recovery of 8.0% (dried weight basis) was achieved at an optimum transesterification condition using acidic methanol and xylene as cosolvent. Average effluent soluble chemical oxygen demand (COD) and total suspended solids (TSS) concentrations from the test SBR and control SBR were comparable, indicating that the recycling of transesterification-treated WAS did not have detrimental effect on the effluent quality. This study demonstrated that transesterification and recycling of WAS may be a feasible technique for reducing excess biosolids, while producing valuable biodiesel feedstock from the activated sludge process.