Yasar Avsar

Comparison of pH Adjustment and Electrocoagulation Processes on Treatability of Metal Plating Wastewater

This study includes both the removal of heavy metals and chemical oxidation demand from metal plating wastewater with pH adjustment and electrocoagulation processes and a comparison of these two processes. For the pH adjustment, process experimental works were studied for five pH levels as 6.0, 7.0, 8.0, 9.0, and 10.0. Optimum removal rates obtained at pH 8.0. With these operating conditions, 90% COD removal, 99.9% Zn (II) removal, 92.5% Cu (II) removal, 78.6% Ni (II) removal, and 50.5% Cr (VI) removal efficiencies were gained. For the electrocoagulation process, the optimum working conditions were found using response surface methodology (RSM) with a 5-level, 6-replicate central composite design (CCD). The optimum working conditions of the electrocoagulation process for pH, reaction time, and current density were determined as 8.0, 90 min, and 250 A/m 2 , respectively. At this condition, 90% COD, 100% Zn, 97% Cu, 90% Ni, and 75% Cr removal efficiencies were obtained. Treatment costs for each process per unit cubic meter wastewater were calculated as

Determination of Biological Treatability Processes of Textile Wastewater and Implementation of a Fuzzy Logic Model

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Optimization of treatment leachates from young, middle-aged and elderly landfills with bipolar membrane electrodialysis

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Modeling of biogas production from cattle manure with co-digestion of different organic wastes using an artificial neural network

This study investigated the biological treatability of textile wastewater. For this purpose, a membrane bioreactor (MBR) was utilized for biological treatment after the ozonation process. Due to the refractory organic contents of textile wastewater that has a low biodegradability capacity, ozonation was implemented as an advanced oxidation process prior to the MBR system to increase the biodegradability of the wastewater. Textile wastewater, oxidized by ozonation, was fed to the MBR at different hydraulic retention times (HRT). During the process, color, chemical oxygen demand (COD), and biochemical oxygen demand (BOD) removal efficiencies were monitored for 24-hour, 12-hour, 6-hour, and 3-hour retention times. Under these conditions, 94% color, 65% COD, and 55% BOD removal efficiencies were obtained in the MBR system. The experimental outputs were modeled with multiple linear regressions (MLR) and fuzzy logic. MLR results suggested that color removal is more related to COD removal relative to BOD removal. A surface map of this issue was prepared with a fuzzy logic model. Furthermore, fuzzy logic was employed to the whole modeling of the biological system treatment. Determination coefficients for COD, BOD, and color removal efficiencies were 0.96, 0.97, and 0.92, respectively.

Rehabilitation of Wastewater Treatment Plant of Sakhnin City in Israel by Using Advanced Technologies

ABSTRACT In this study, a bipolar membrane electrodialysis (BMED) process, which is thought to be an effective treatment method for leachate, was evaluated for leachates of three different ages (‘young’, ‘middle-aged’ and ‘elderly’). The leachates were pretreated to eliminate membrane fouling problems prior to the BMED process. Experimental studies were carried out to determine optimal operating conditions for the three differently aged leachates in the BMED process. According to the experiment results, there was a high removal efficiency of conductivity determined at 4 membrane – 25 V for young and elderly leachate and at 1 membrane – 25 V for middle-aged leachate. It was found that the operating times required to reach the optimal endpoints (at conductivity of about 2 mS/cm) of BMED process were 90, 180 and 300 min for the middle-aged, young and elderly leachates, respectively. Under the optimum operating conditions for the BMED process, removal efficiencies of conductivity and chemical oxygen demand were determined to be 89.5% and 60% for young, 82.5% and 30% for middle-aged and 91.8% and 26% for elderly leachate, respectively.

Comparison of classical chemical and electrochemical processes for treating rose processing wastewater.

AbstractThe present study utilizes an artificial neural network (ANN) as an estimation model of biogas production from laboratory-scale up-flow anaerobic sludge blanket (UASB) reactors treating cattle manure with co-digestion of different organic wastes. It can be estimated depending on working days, influent chemical oxygen demand, influent pH, influent alkalinity, influent ammonia, influent total phosphorus, hydraulic retention time, waste adding ratio, pretreatment and additive waste sorts. The suitable architecture of an ANN for use in biogas prediction consists of 10 input factors, tangent sigmoid transfer function (tansig) at the four hidden layer neurons and a linear transfer function (purelin) at the output layer neuron. The R2 was found to equal 0.89, 0.79 and 0.75 in the training, validation and testing steps, respectively. ANN estimation modeling can effectively predict the biogas production performance of laboratory-scale UASB reactors. These results indicate that biogas production was optimized to occur in the 20–30% addition range with different organic wastes. Graphical Abstract

Treatability of water-based paint wastewater with Fenton process in different reactor types.

This study deals with the rehabilitation of waste water treatment plant (WWTP) of Sakhnin city in Israel. To increase low removal efficiency of the facultative pond (FP) and seasonal reservoir (SR) of the WWTP, different kinds of reactors having different operating conditions were established by the name of two tasks such as intermittent trickling bio filters (ITBFs) and concrete tunnels, respectively. According to the evaluation of the results, it was observed that ITBFs and tunnel units contribute to reasonable removal capacity on sCOD, TSS, NH3-N and PO4-P parameters. When it is considered establishing a full scale treatment plant to rehabilitate FP and SR units of available WWTP, this study will be able to a guide for decision makers.