mohammad javad zoqi

Mining is one of the important economic activities all over the world. It causes the release of various emissions, especially heavy metals in the soil, due to the weak exploitation and improper disposal of mineral wastes. Different techniques are used for soil remediation and heavy metal extraction; including the electrokinetic method (so effective in fine-grained soils). In this research, the electrokinetic process was used to extract copper from the waste of the mine in Birjand.

In this research, a 24 cm long PVC reactor was used. The retention time was 2, 4, and 6 days and the voltage gradient was 1 V/cm. Graphite electrodes and electrolyte solutions of nitric acid and citric acid were investigated for copper extraction. The electrode polarity was alternately changed in order to pH control and improve the extraction process.

According to the results, the highest removal efficiency (54%) was obtained after 6 days using 0.1 M citric acid and distilled water in the anode and cathode reservoir, respectively. Further, by 24-hour polarity reversing, the copper removal efficiency increased to about 60%.

The use of citric acid in anode was more effective than nitric acid, leading to more copper removal. In addition, by periodically polarity change and keeping the soil pH in the neutral range, further dissolution of the metal and reducing its sedimentation in the soil occurred. As a result, the rate of its transfer outside the treatment area and removal efficiency increased.

Due to the water shortage crisis, the importance of wastewater treatment and wastewater reuse, today the application of advanced wastewater treatment methods has been considered. Therefore, in the present study, a rotating bio-disk system has been used to treat municipal wastewater. To conduct the research, a laboratory-scale reactor unit made of Plexiglas with 35 disks was used, and initially the system used a 20-liter tank in a completely anaerobic manner to increase the reactor efficiency. The aerobic sludge of the wastewater treatment plant (activated sludge method) of the slaughterhouse has been used for the initial inoculation of the reactor and sugar, urea and potash fertilizer have been used to feed the reactor. The study lasted 96 days in three periods. During the study, the organic load increased 575 to 1250 mg/ l.d. The reactor temperature is in the mesophilic and psychrophilic temperature range for two periods. The results showed that the thickness of the biofilm on the surface of the disks is 2 mm and the pH changes are in the range of 9 to 7. COD removal efficiency during the second period is between 19.48% and during the third period is between 50-92%. During the study of the hydraulic retention time factor and the change in the rotation speed of the disk, the highest efficiency was obtained at 24% in 24 hours and 92% at 12 rpm, respectively. Experiment with real wastewater has an efficiency of 80%, which is 12% different from the efficiency of laboratory wastewater.

The present study was conducted to compare the efficiency of normal and modified UASB reactor for the treatment of dairy wastewater. To conduct research, two reactor units with a height of 120 cm and a volume of 48 liters have been used on a laboratory scale and a tank septic tank and an additional sludge blanket have been used to optimize the UASB reactor. Initial inoculation of the reactor was carried out using sewage treatment sludge (active sludge method) slaughterhouse, along with fresh cow discharges and feeding using dry milk. The research lasted for fourteen periods for 154 days, the first period for 30 days including the design and construction of the reactor, the second period for 40 days including starting, forming granules and measuring PH, the third period for 40 days including the continuation of the process The formation of granules and sludge blankets, pH measurements, and preliminary analysis of the removal efficiency of COD and the fourth period for 44 days include the continuation of granulation sludge measurement, PH and the evaluation of COD removal efficiency. The organic loading during four periods was 5.2-11.4 kgCOD/m3.day, and the reactor temperature was in the second to third period in the mesophilic temperature range and during the fourth period at the mesophilic and psychrophilic temperature range. The retention time in the studied period is 24 hours. The output COD yields four to for normal reactor 75-60% and a modified reactor of 94-60%.

The main components of landfill biogas are methane and carbon dioxide, both of which are greenhouse gases. Methane is a greenhouse gas with global warming potential of 21 times greater compared to CO2. In this paper, a new method is proposed for landfilling to reduce landfill gas emissions and to prevent entry of air into the landfill. This paper presents a new landfill design and system for air ingress prevention and landfill gas containment. In addition, in this paper Aspen Hysyssoftware was used for the dynamic simulation of separation of CO2 from landfill gas by adsorption process in the ethanolamine solution. Findings: The new system proffers more control over the biogas extraction and processes of anaerobic digestion than conventional landfills. In the new system, the landfill gas purification process becomes cheaper due to reduction of air ingress. The new system can be applied on tipycal landfills. Using this new system, biogas purification and exploitation will become economical in more landfills, and the increased use of biogas will result in greater use of renewable energy sources and reduction of greenhouse gas emissions.

In this study, The Group Method of Data Handling (GMDH) type neural networks whit genetic algorithm was applied to estimate the methane fraction in landfill gas originating from Lab-scale landfill bioreactors. In this study, to predict the methane fraction in landfill gas as a final product of anaerobic digestion, we used input parameters such as pH, Chemical Oxygen Demand, NH4+N and waste temperature.
To this Purpose, two different systems were applied for neural network’s data obtained. In system I (C1), the leachate generated from a fresh-waste reactor was drained to recirculation tank, and recycled every two days. In System II (C2), the leachate generated from a fresh waste landfill reactor was fed through a well-decomposed refuse landfill reactor, and at the same time, the leachate generated from a well-decomposed refuse landfill reactor recycled to a fresh waste landfill reactor. leachate and landfill gas components were monitored for 132 days.
Findings: The study results indicate that GMDH is able to predict the methane fraction in landfill gas. The correlation between the observed and predicted values for the training data is 0.98 and for the testing data, it is 0.99.
Discussion and The proposed method can significantly predict the methane fraction in landfill gas originating and, consequently, GMDH can be use to optimize the dimensions of a plant using biogas for energy (i.e. heat and/or electricity) recovery and monitoring system.

Solidification/Stabilization (S/S) is used for treatment of various types of hazardous wastes. A systematic study was conducted for treatment of electroplating sludge by Solidification/Stabilization (S/S) with Ordinary Portland Cement (OPC), with the addition of distilled water, lime and magnesium oxide (MgO). Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in combination with Central Composite Design (CCD) were employed to develop the predictive models for simulation and optimization of S/S process. The independent variables were the amount of MgO, electroplating dried sludge, lime, distilled water, while the compressive strength, concentration of zinc and chromium in Toxicity Characteristic Leaching Procedure (TCLP) leachate of the solidified waste after 28 days of hydration were the response variables of 30 specimens. Both RSM and ANN models have been developed based on the experimental designs. The generalization and predictive capabilities of both RSM and ANN were compared by unseen data. Therefore, ten experiments were designed randomly and the predicted responses obtained by ANN and RSM were compared with the experimental results. The results show that the optimized method by ANN or RSM can be used as a confident method for determination of compressive strength of the solidified wastes and concentration of zinc and chromium in TCLP leachates. However, the data calculated by ANN model are more similar to experiments results than that of RSM predicted results. according to RSM and ANN, optimum combination were found 15.3 g, 100 g, 3.6 g, 19 g and 49.5 mL and 14.9 g, 100 g, 3.4 g, 18.5 g, 46 mL of MgO, waste, lime, cement and distilled water, respectively.

In this study a neural network model is proposed for modeling tri-halo-methane concentration in drinking water. After training, the neural network model predicts tri-halo-methane concentration based on input data. Parameters such as pH, Temperature, free chlorine residue and TOC were used as input data. To validate the proposed method, a case study was carried out, based on the data obtained from Guilan grand treatment plant (Sangar). The Levenberg-Marquardt algorithm was selected as the best of thirteen back-propagation algorithms. The optimal neuron number for Levenberg Marquardt algorithm is 8 neurons. The performance of modeling was determined. The trends of the forecast and measured data were in good agreement.

One of the most popular types of bioreactor landfills is the landfill with leachate recirculation.In this way, an anaerobic was operated by leachate recirculation to remove organic matters. In this study two different systems were applied for leachate treatment. In system (1), the leachate generated from a fresh-waste reactor was drained to recirculation tank, and recycled every two days. In System (2), the leachate generated from a fresh waste landfill reactor was fed through a well-decomposed refuse landfill reactor, and at the same time, the leachate generated from a well-decomposed refuse landfill reactor recycled to a fresh waste landfill reactor.

In this study, a neural network model is proposed for modeling leachate flow-rate in a municipal solid waste landfill site. After training, the neural network model predicts leachate generation based on meteorological data and leachate characteristics. Parameters such as pH, temperature, conductivity and meteorological data were used as input data. To validate the proposed method, a case study was carried out based on the data obtained from city of Beirut landfill site. While waste disposal at the site started in October 1997, measuring leachate generation rates was not initiated until April 1998. The Levenberg-Marquardt algorithm was selected as the best of thirteen backpropagation algorithms. The optimal neuron number for Levenberg-Marquardt algorithm is 10. The performance of modeling was determined. According to the statistical performance indices (R=0.976, ARE=0.089), the results of the forecast model were in good agreement with measured data.