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The effect of settling on mass balance and biodégradation characteristics of domestic wastewater and on denitrification potential was studied primarily using model calibration and evaluation of oxygen uptake rate profiles. Raw domestic wastewater was settled for a period of 30 minutes and a period of 2 hours to assess the effect of primary settling on wastewater characterization and composition. Mass balances in the system were made to evaluate the effect of primary settling on major parameters.Primary settling of the selected raw wastewater for 2 hours resulted in the removal of 32% chemical oxygen demand (COD), 9% total Kjeldahl nitrogen, 9% total phosphorus, and 47% total suspended solids. Respirometric analysis identified COD removed by settling as a new COD fraction, namely settleable slowly biodegradable COD (XssX characterized by a hydrolysis rate of 1.0 day\"⁻¹ and a hydrolysis half-saturation coefficient of 0.08. A model simulation to test the fate and availability of suspended (Xs ) and settleable (Xss) COD fractions as carbon sources for denitrification showed that both particulate COD components were effectively removed aerobically at sludge ages higher than 1.5 to 2.0 days. Under anoxic conditions, the biodegradation of both COD fractions was reduced, especially below an anoxic sludge retention time of 3.0 days. Consequently, modeling results revealed that the settleable COD removed by primary settling could represent up to approximately 40% of the total denitrification potential of the system, depending on the specific configuration selected for the nitrogen removal process. This way, the results showed the significant effect of primary settling on denitrification, indicating that the settleable COD fraction could contribute an additional carbon source in systems where the denitrification potential associated with the influent becomes rate-limiting for the denitrification efficiency.

Water quality parameters like temperature, pH, total dissolved solids (TDS), total suspended solids (TSS), dissolved oxygen (DO), oil and grease, etc., are calculated from the field while parameters like biological oxygen demand (BOD) and chemical oxygen demand (COD) are interpreted through the laboratory tests. On one hand parameters like temperature, pH, DO, etc., can be accurately measured with the exceeding simplicity, whereas on the other hand calculation of BOD and COD is not only cumbersome but also inaccurate many times. A number of previous researchers have tried to use different empirical methods to predict BOD and COD but these empirical methods have their limitations due to their less versatile application. In this paper, an attempt has been made to calculate BOD and COD from simple field parameters like temperature, pH, DO, TSS, etc., using Artificial Neural Network (ANN) method. Datasets have been obtained from analysis of mine water discharge of one of the mines in Jharia coalfield, Jharkhand, India. 73 data sets were used to establish ANN architecture out of which 58 datasets were used to train the network while 15 datasets for testing the network. The results show encouraging similarity between experimental and predicted values. The RMSE values obtained for the BOD and COD are 0.114 and 0.983 %, respectively.

We evaluated the feasibility of using waste activated sludge (WAS) from a wastewater treatment plant as an internal electron donor to fuel denitrification, by increasing its bioavailability with Focused-Pulsed (FP) technology. The focused-pulsed treatment of WAS (producing FP-WAS) increased the semi-soluble chemical oxygen demand (SSCOD) by 26 times compared with the control WAS. The maximum denitrification rate of FP-WAS (0.25 g nitrate-nitrogen [NO3--N]/g volatile suspended solids [VSS]·d) was greater than for untreated WAS (0.05 g NO3--N/g VSS·d) and methanol (0.15 NO3--N/g VSS·d). Centrifuging out the larger suspended solids created FP-centrate, which had a rate (0.14 g NO3--N/g VSS·d) comparable with that of methanol. Thus, FP treatment of WAS created SSCOD, which was an internal electron donor that was able to drive denitrification at a rate similar to or greater than methanol. One trade-off of using FP-WAS for denitrification is an increase in total Kjeldahl nitrogen (TKN) loading. While FP-WAS achieved the lowest total nitrogen and NO3--N concentrations in the batch denitrification test, its final ammonia-nitrogen (NH3-N) concentration was the highest, as a result of the release of organic nitrogen from the FP-treated biomass; FP-centrate had less release of total soluble nitrogen. While the return of total nitrogen (TN) is small compared with the SSCOD, the effects of the added nitrogen loading need to be considered. Water Environ.

In this study, the artificial neural network (ANN) technique was employed to derive an empirical model to predict and optimize landfill leachate treatment. The impacts of H[sub.2]O[sub.2]:Fe[sup.2+] ratio, Fe[sup.2+] concentration, pH and process reaction time were studied closely. The results showed that the highest and lowest predicted chemical oxygen demand (COD) removal efficiency were 78.9% and 9.3%, respectively. The overall prediction error using the developed ANN model was within −0.625%. The derived model was adequate in predicting responses (R[sup.2] = 0.9896 and prediction R[sup.2] = 0.6954). The initial pH, H[sub.2]O[sub.2]:Fe[sup.2+] ratio and Fe[sup.2+] concentrations had positive effects, whereas coagulation pH had no direct effect on COD removal. Optimized conditions under specified constraints were obtained at pH = 3, Fe[sup.2+] concentration = 781.25 mg/L, reaction time = 28.04 min and H[sub.2]O[sub.2]:Fe[sup.2+] ratio = 2. Under these optimized conditions, 100% COD removal was predicted. To confirm the accuracy of the predicted model and the reliability of the optimum combination, one additional experiment was carried out under optimum conditions. The experimental values were found to agree well with those predicted, with a mean COD removal efficiency of 97.83%.

The present paper investigates the effect of dilution rate on the removal of total chemical oxygen demand and nitrate in the draft tube spouted bed reactor and morphological characteristics of biofilms formed by microorganisms of mixed culture on granular activated carbon (GAC). The nitrate and total chemical oxygen demand (COD) decreased from 97 to 81% and 95% to 87% respectively with increase in dilution rate from 0.6/h to 1.5/h showing that residence time in the reactor governs the nitrate and total COD reduction efficiency. Lower dilution rates favor higher production of biomass and extracellular polymeric substances (EPS). It was observed that the nitrate and total COD reduction rate increased with time along with simultaneous increase in EPS production. Thus, the performance of a reactor in terms of dynamic and steady-state biofilm characteristics associated with nitrate and organic reduction is a strong function of dilution rate. Hence these findings indicate that a draft tube spouted bed reactor is capable of simultaneously reducing total organics and nitrogen in industrial/municipal wastewater, as this reactor possesses two distinct regions aerobic and anoxic conditions which can prevail in different parts of a reactor.

In this study, a high-concentration simulated organic wastewater, made by dissolving methyl violet in water, was degraded using dielectric barrier discharge (DBD) plasma generated in air and O respectively. The decoloration rate and chemical oxygen demand (COD) of wastewater were evaluated during plasma treatments with the initial concentration of methyl violet of 300 mg L . Results showed that the highest decoloration rate of around 100% within 10 min and the highest COD decrease of 33% within 60 min could be achieved with the O plasma treatment at the discharge voltage of 10 kV, while air plasma treatment showed lower efficiency in decolorizing the methyl violet solution and lower COD decrease (24%) after 60 min treatment. UV-Vis spectroscopy and chemical analysis of generated by-products during the plasma-enabled degradation process revealed that the methyl violet molecules could be completely decomposed into some refractory organics in the solution. Based on the experimental results and literature review, a pathway of methyl violet degradation attributed to energetic electrons and highly reactive species generated by DBD was proposed.

Conventional activated sludge (CAS) process is one of the most commonly applied processes for municipal wastewater treatment. However, it requires a high energy input and does not promote energy recovery. Currently, high-rate activated sludge (HRAS) process is gaining importance as a good option to reduce the energy demand of wastewater treatment and to capture organic matter for valorizing through anaerobic digestion (AD). Besides, food waste addition to wastewater can help to increase the organic matter content of wastewater and thus, energy recovery in AD. The objective of this study is to evaluate the applicability of co-treatment of municipal wastewater and food waste in a pilot-scale HRAS system as well as to test the minimal hydraulic retention times (HRTs) such as 60 and 30 min. Food waste addition to the wastewater resulted in a 10% increase in chemical oxygen demand (COD) concentration of influent. In the following stages of the study, the pilot-scale system was operated with wastewater solely under the HRTs of 60 and 30 min. With the decrease of HRT, particulate COD removal increased; however, soluble COD removal decreased. The results demonstrated that if the settling process is optimized, more particulate matter can be diverted to sludge stream.

The microbial fuel cell coupled constructed wetland (CW-MFC) was used for treatment sewage and simultaneously generating electricity. The main aim of this study was to explore the optimal conditions for the treatment of hexavalent chromium (Cr (VI)) wastewater by the CW-MFC system. The performance of CW-MFC in removing Cr (VI) and chemical oxygen demands (COD) contained in wastewater and its electricity generation were studied. Electrode spacing, Cr (VI) and COD concentration, and hydraulic retention time (HRT) had certain effects on the performance of CW-MFC. For the electrode spacing of 10 cm, the highest power density of 458.2 mW/m 3 could be obtained with the influent concentration of Cr (VI) (60 mg/L) and COD (500 mg/L). The highest Cr (VI) and COD removal rate were obtained with the HRT of 3 days. Compared with CW system, the electrical energy generated in CW-MFC was beneficial to improving the removal efficiency of COD and Cr (VI). Thus, the results confirmed that CW-MFC is a promising technology to remove Cr (VI) from wastewater and achieve bioelectricity production simultaneously.

Common to all National Pollutant Discharge Elimination System (NPDES) permits in the United States is a limit on biochemical oxygen demand (BOD). Chemical oxygen demand (COD), total organic carbon (TOC), and fluorescence spectroscopy are also capable of quantifying organic content, although the mechanisms of quantification and the organic fractions targeted differ for each test. This study explores correlations between BOD₅ and these alternate test procedures using facility influent, primary effluent, and facility effluent samples from a full-scale water resource recovery facility. Relative reductions of the water quality parameters proved to be strong indicators of their suitability as surrogates for BOD₅. Suitable correlations were generally limited to the combined datasets for the three sampling locations or the facility effluent alone. COD exhibited relatively strong linear correlations with BOD₅ when considering the three sample points (r = 0.985) and the facility effluent alone (r = 0.914), while TOC exhibited a suitable linear correlation with BOD₅ in the facility effluent (r = 0.902). Exponential regressions proved to be useful for estimating BOD₅ based on TOC or fluorescence (r > 0.95).

Abstract In our era, many technical applications are being used. Artificial Neural Networks (ANNs) as one of the artificial intelligence tools have emerged to learn and discover a model of dynamic nonlinear. In this study, six input parameters were taken to predict the value of the Chemical Oxygen Demand (COD) in the wastewater before and after the treatment at the North Gas Company/Kirkuk, by using the standard back propagation algorithm. The network was trained with the 150 data collected from the quality indices of the untreated and treated wastewater, such as total chloride ions Cl-, nitrate ions NO 3 − , phosphate ions PO 4 −3 , sulfate ions SO 4 −2 , ammonia NH 3 , Biochemical Oxygen Demand (BOD5) to predict one element, that is the COD. After properly training of the neural network, it was tested by using the test data, and the best results were selected by the consideration of the mean square error and the regression coefficient, where the best result appeared before wastewater treatment is 0. 98235 and the best result after wastewater treatment is 0.999 99 . The findings of this study suggest that artificial neural networks are accurate and effective tools for predicting the COD values of treated wastewater.