Waste activated sludge reduction using sonication and cryptic growth

Differences of bacterial communities in two full-scale A2/O municipal wastewater treatment plants and their effects on effluent total nitrogen removal

The measurements ofmanykeyparameters and effluent qualities inWWTP(Wastewater Treatment Plant) are impossible duetothelackofprecise online sensors andstrong time-delay ofWW'TPprocess. TheFuzzy-Neural Network (FNN)basedeffluent COD(Chemical OxygenDemand)of activated sludge SBR(Sequential BatchReactor) prediction modelisbuilt inthis paper, before whichpreprocessing ofSBR simulation dataisdoneusingPCA (Principal Component Analysis) to extract the validinformation of vast multi-dimension data. Thegaining principal components are treated astheinputs oftheFNNmodeltopredict effluent COD withanAdaptive Genetic Algorithm (AGA)methodtorectify theprediction model. Theresult indicates thathybrid FNNcan extract valid information fromdataset anddescribe complex non-linear properties ofWWTP topredict effluent qualities accurately.

Ultrasonic reduction of excess sludge from the activated sludge system

Wastewater is created by pharma firms and has become a huge worry for the ecosystem. There is a significant amount of toxins that are being dropped continuously from numerous pharmaceutical companies that causes serious damages to the environment and public health because of its comprising high organics as well as inorganic loadings and thus requirements appropriate treatment before final disposal to the ecosystem. Goal of this approach is to treat the wastewater treatment model with industrial data. Algorithms of the artificial neural network (ANN) were employed progressively to predict parameters for wastewater plants. This provision assists users to take remedial measures and function the process by the standards. It is proven as beneficial technology because of its complicated mechanism, dynamic and inconsistent changes in aspects, to overcome some of the limitations of common mathematical models for the wastewater treatment plant. The target is to achieve better prediction accuracy in wastewater treatment model. In this paper, ANN approaches are relevant to the prediction of input and effluent chemical oxygen demand (COD) for effluent treatment procedures. Artificial neural networks (ANNs) offer accurate technique modeling for complex systems using an artificial intelligence technique. Three distinct types of back-propagation ANN were devised to avoid the concentration of wastewater treatment facilities in the concentration of COD, suspended particles, and mixed liquid solids in an epidermal water treatment tank (MLSS). To anticipate COD levels in influential and effluent areas, two ANN-based techniques have been presented. The proper structure for the neural network models was identified via a variety of training and model testing methods. An efficient and robust forecasting tool has been created for the ANN model.

The aim of this study was to interpret the development of Anammox activity by a mathematical model in an UASB reactor -originally inoculated with methanogenic granules- at which Anammox progress has been also experimentally observed while treating chicken manure digestate. Since ammonium is derived from anaerobic degradation of nitrogenous compounds in chicken manure similar to any other nitrogen-rich organic wastes; the reactor was operated intentionally at favorable conditions [i.e.; with external nitrite source for NH4 +:NO2 -≅1.0] in order to make Anammox process to prevail as operation continued. Results indicated significant ammonia removals (60% on average) although influent concentration was gradually increased up to 200 mg L-1. A modeling exercise has been undertaken to investigate the performance of the laboratory scale UASB reactor. In this scope, the experimental results were modeled by using Mantis2 model within GPS-X 6.5 simulation software that included several built in libraries. Accordingly, effluent chemical oxygen demand (COD) and total ammonia nitrogen (TAN) concentrations could be predicted with reasonably good accuracy demonstrating successful calibration. The regression coefficient (R2 ) and mean relative absolute error (MRAE) parameters were found as 0.66 and 16% and 0.70 and 19%, respectively.

In this thesis, the application of upflow anaerobic sludge bed (UASB) reactors for the treatment of low-strength domestic sewage was studied for the city ofSalta, in northwesternArgentina. The climate in this region can be defined as subtropical with a dry season. Mean ambient temperature in the city is 16.5ºC. Mean sewage temperature during the experiments was 23.0°C (monthly minimum: 17.2°C; daily minimum: 12.6°C). A literature review on the use of upflow reactors for sewage treatment was performed, and a brief description of laboratory, pilot-scale, and full-scale applications from all over the world is presented.Experiments were performed in two pilot plants especially built for this thesis. The first pilot plant was a UASB reactor installed after a conventional full-scale sedimentation tank (settler). The second pilot plant was a two-stage UASB system with posttreatment in five waste stabilization ponds (WSP) in series. In the first pilot plant, chemical oxygen demand (COD) removal efficiencies up to 84% in total COD and 92% in suspended COD were achieved at a mean hydraulic retention time (HRT) of 2 h in the settler and 5.6 h in the reactor, equivalent to an upflow velocity (V up ) of 0.71 m/h. A granular sludge developed in the UASB reactor probably due to the low concentration of suspended solids (SS) and COD in the influent and an adequate combination of HRT and V up . Some of the granules were surprisingly big (up to 5 cm in diameter). The system studied was highly robust andefficient,it consistently delivered a final effluent in compliance with discharge standards for COD and SS, and produced a small amount of well-stabilized sludge. In the two-step UASB system of the second pilot plant, COD removal efficiencies up to 89% were obtained at mean HRTs of 6.4 + 5.6 h (V up = 0.62 + 0.70 m/h), with 83 and 36% removal in the first and second steps, respectively. The effluent concentration was similar to that obtained in the first pilot plant. The performance of the two-step system was not affected during the coldest period of the year, which usually lasts about three months. The anaerobic sludge showed good stability, especially in summer time, and could be directly disposed of without further treatment. After the posttreatment, the effluent also complied with discharge standards for pathogenic microorganisms. It was concluded that a single-stage UASB reactor followed by a series of WSP could be a very efficient, reliable, compact, and simple system for the treatment of raw sewage in subtropical regions likeSalta.Finally, a comparative assessment of the sustainability of three technological options for sewage treatment was performed, in terms of a series of technical, environmental, social, and economic criteria and indicators. In this preliminary assessment it was found that, under local conditions, a system UASB + WSP was more sustainable than (a) an aerobic high-rate treatment system based on trickling filters, and (b) a system of conventional WSP. The assessment method used (a multi-criteria weighted-scale matrix) was simple to perform and sensitive enough to detect differences in sustainability between the options compared. A representative panel of local stakeholders must perform the actual assessment in a transparent and participatory way before any concrete policy decision is taken. The final plead of this thesis is that sustainable development will only be achieved through a fully democratic way of decision-making that can go beyond political and economic motivations and that may be able to solve environmental problems and social injustices.

Modeling of effluent quality parameters in a submerged membrane bioreactor with simultaneous upward and downward aeration treating municipal wastewater using hybrid models

High-performance Multi-stage Baffled A2O Treatment Process for Domestic Sewage on Plateaus

With increasingly stringent guidelines for treated wastewater discharge in the UAE, several existing wastewater treatment plants (WWTP) may require future expansion and upgradation. The use of advanced simulation software packages such as GPS-X can aid in those decision-making processes and provide cost savings by reliably estimating the treated wastewater quality under varying scenarios. This study simulated the performance of a sequencing batch reactor (SBR) at a WWTP located in UAE using actual field data. Water quality parameters measured during winter months such as total suspended solids (TSS), chemical oxygen demand (COD), Total Kjeldahl Nitrogen (TKN), and five-day biochemical oxygen demand (cBOD5) were employed in the calibration of the model, whereas the summer season data set was used in the validation of the calibrated model. This ensured robustness of the developed model in capturing any seasonal variations at the site. A sensitivity analysis was also conducted to simulate the effect of varying physicochemical parameters on the WWTP performance. The findings indicated that TKN and cBOD5 were relatively insensitive to changes in the examined parameters. In contrast, the plant treatment performance declined with respect to the effluent TSS and COD, wherein sharp increases were observed under higher influent wastewater flows and COD levels. The analysis also suggested that up-gradation of the SBR treatment tank size can potentially aid in reducing the effluent TSS and COD, thus improving the plant performance.

Aiming at the problem that the detection accuracy of effluent COD (chemical oxygen demand) in sewage treatment needs to be further improved, a combined model based on support vector machine and neural network is proposed to predict effluent COD. It can reduce the influence of local optimum on the global scope so as to improve the accuracy of prediction. Firstly, the sample data are divided into two categories by support vector machine. Then the BP neural network model and the Echo State Network (ESN) model are established on two sub-samples respectively. Compared with single neural network model, the mean absolute error and root mean square error of combined model are both reduced. Besides, the proposed model has better comprehensive prediction performance and can meet the actual demand of effluent COD prediction in sewage treatment.

BACKGROUND: This study considers batch treatment of saline wastewater in an upflow anaerobic packed bed reactor by salt tolerant anaerobic organisms Halanaerobium lacusrosei.RESULTS: The effects of initial chemical oxygen demand (COD) concentration (COD0 = 1880–9570 mg L−1), salt concentration ([NaCl] = 30–100 g L−1) and liquid upflow velocity (Vup = 1.0–8.5 m h−1) on COD removal from salt (NaCl)‐containing synthetic wastewater were investigated. The results indicated that initial COD concentration significantly affects the effluent COD concentration and removal efficiency. COD removal was around 87% at about COD0 = 1880 mg L−1, and efficiency decreased to 43% on increasing COD0 to 9570 mg L−1 at 20 g L−1 salt concentration. COD removal was in the range 50–60% for [NaCl] = 30–60 g L−1 at COD0 = 5200 ± .100 mg L−1. However, removal efficiency dropped to 10% when salt concentration was increased to 100 g L−1. Increasing liquid upflow velocity from Vup = 1.0 m h−1 to 8.5 m h−1 provided a substantial improvement in COD removal. COD concentration decreased from 4343 mg L−1 to 321 mg L−1 at Vup = 8.5 m h−1, resulting in over 92% COD removal at 30 g L−1 salt‐containing synthetic wastewater.CONCLUSION: The experimental results showed that anaerobic treatment of saline wastewater is possible and could result in efficient COD removal by the utilization of halophilic anaerobic bacteria. Copyright © 2008 Society of Chemical Industry

This work presents a comprehensive assessment of the environmental impact of winery wastewater (WWW) and methods to optimize its co-treatment at municipal wastewater treatment plants (WWTPs). A comprehensive review of full-scale treatment at 53 wineries in Niagara Region, Canada finds constructed wetlands are the most common type of on-site treatment. On-site systems do not address all treatment needs, requiring a portion of the WWW to be co-treated at WWTPs. From freshwater resource impact perspective, the grey water footprint (WF) associated with treated WWW effluents is often neglected from wine-making WF assessments. However, co-treated WWW effluents from WWTPs in Niagara Region are found to exert a substantial grey WF equivalent to over 960 times their volume. While full-scale operating data indicate that anaerobic co-digestion with municipal sludges is effective (89% chemical oxygen demand (COD) removal), co-digestion capacity is limited. Bench-scale co-treatment trials confirm that WWTPs’ aerobic activated sludge systems can effectively co-treat WWW provided organic loading rates are limited. A combined Michaelis-Menten-University of Cape Town kinetic model is found to best describe the pH-inhibited oxidation by heterotrophs, and the associated specific rate of substrate consumption is highest in biomass that had been exposed to WWW (57.3 mg COD/g MLVSS·h) compared to biomass that had not (20.7 mg COD/g MLVSS·h). The feasibility of using the Fenton-like process to pre-treat WWW to enhance co-treatment is assessed. Solubilization of particulate COD and total organic carbon (TOC), and sample handling requirements prior to analysis, are identified as factors affecting their apparent removal rates. Inert suspended solids generated during sample handling is found to be the variable best suited to quantifying the extent of reaction. However, the Fenton-like process provides limited opportunity to optimize co-treatment at WWTPs. A novel pre-treatment method, the Waste Activated Sludge-High Rate (WASHR) process, is proposed to optimize the co-treatment. The WASHR process combines the contact stabilization and sequencing batch reactor processes. It utilizes waste activated sludge from the WWTP as its biomass source, allowing rapid start-up. Bench-scale trials confirm that the WASHR process, vs. direct co-treatment, can reduce COD and total suspended solids loadings to the WWTP’s liquid treatment train by more than 81% and 92%, respectively, and to the solids treatment train by more than 59% and 30%, respectively. A case study is used to confirm the economic viability and environmental sustainability of the WASHR process compared with direct co-treatment. Finally, robust correlations between easily measured parameters and key organic and nutrient parameters are developed. The correlations provide a promising, rapid and cost-effective means of characterising WWW to allow improved process control.

This work presents a comprehensive assessment of the environmental impact of winery wastewater (WWW) and methods to optimize its co-treatment at municipal wastewater treatment plants (WWTPs). A comprehensive review of full-scale treatment at 53 wineries in Niagara Region, Canada finds constructed wetlands are the most common type of on-site treatment. On-site systems do not address all treatment needs, requiring a portion of the WWW to be co-treated at WWTPs. From freshwater resource impact perspective, the grey water footprint (WF) associated with treated WWW effluents is often neglected from wine-making WF assessments. However, co-treated WWW effluents from WWTPs in Niagara Region are found to exert a substantial grey WF equivalent to over 960 times their volume. While full-scale operating data indicate that anaerobic co-digestion with municipal sludges is effective (89% chemical oxygen demand (COD) removal), co-digestion capacity is limited. Bench-scale co-treatment trials confirm that WWTPs’ aerobic activated sludge systems can effectively co-treat WWW provided organic loading rates are limited. A combined Michaelis-Menten-University of Cape Town kinetic model is found to best describe the pH-inhibited oxidation by heterotrophs, and the associated specific rate of substrate consumption is highest in biomass that had been exposed to WWW (57.3 mg COD/g MLVSS·h) compared to biomass that had not (20.7 mg COD/g MLVSS·h). The feasibility of using the Fenton-like process to pre-treat WWW to enhance co-treatment is assessed. Solubilization of particulate COD and total organic carbon (TOC), and sample handling requirements prior to analysis, are identified as factors affecting their apparent removal rates. Inert suspended solids generated during sample handling is found to be the variable best suited to quantifying the extent of reaction. However, the Fenton-like process provides limited opportunity to optimize co-treatment at WWTPs. A novel pre-treatment method, the Waste Activated Sludge-High Rate (WASHR) process, is proposed to optimize the co-treatment. The WASHR process combines the contact stabilization and sequencing batch reactor processes. It utilizes waste activated sludge from the WWTP as its biomass source, allowing rapid start-up. Bench-scale trials confirm that the WASHR process, vs. direct co-treatment, can reduce COD and total suspended solids loadings to the WWTP’s liquid treatment train by more than 81% and 92%, respectively, and to the solids treatment train by more than 59% and 30%, respectively. A case study is used to confirm the economic viability and environmental sustainability of the WASHR process compared with direct co-treatment. Finally, robust correlations between easily measured parameters and key organic and nutrient parameters are developed. The correlations provide a promising, rapid and cost-effective means of characterising WWW to allow improved process control.

The coinage refractory wastewater treated by electrocatalytic-membrane process (ECMP) integrated with chemical- or electro-coagulation techniques

Oxidation-enhanced diffusion (OED) of ion-implanted boron in heavily phosphorus-doped silicon is studied by measuring boron depth profiles using secondary ion mass spectroscopy for phosphorus concentration from intrinsic conditions to 1.2×1020 cm−3. OED is observed for the whole phosphorus concentration range investigated in the present work. However, both the diffusion coefficient in N2 ambient DN and that in dry O2 ambient DO decrease with an increase in substrate phosphorus concentration in extrinsic conditions, i.e., for phosphorus concentration larger than the intrinsic carrier concentration. Diffusion coefficient increment due to OED ΔD(=DO−DN) decreases with an increase in phosphorus concentration. The decrease in ΔD with an increase in phosphorus concentration is attributed to a decrease in excess silicon interstitials due to recombination with acceptor-type vacancies.