6 research outputs found
Quality and quantity of municipal solid waste in Mashhad
The yield of human activities created solid waste which nowadays changed a lot in compared to the past because of various in life style. Municipal solid waste is one of the serious environmentconcerns throughoutthe regions of Mashhad. In Mashhad lots of wasteis produced daily. In this study we collect data from various municipal regions in Mashhad. In this paper quality, average generation rate, physical composition, organicmaterial, inorganic material and chemical analysis in all of municipal regions of Mashhad also were investigated. According to the different area in Mashhad the wastes can be divided into eight groups of organic materials, paper and cardboard, rubber and plastics, wood, glass, metals, construction wastes and others. According to obtained results from this study the amount of MSW generated in regions of Mashhad was 585972420 kg per year, and the average generation rate of MSW was 262.2 kg/capita/year. Results showed that the amount of organic material in the regionsof Mashhad is 67.5 % and the amount of inorganic material is 32.5%
Response Surface Methodology (RSM)-Based Prediction and Optimization of the Fenton Process in Landfill Leachate Decolorization
As an advanced oxidative processes, the Fenton process is receiving popularity as a wastewater treatment technique that can be used for hazardous landfill leachate. The treatment is simple, yet involves complex interactions between the affecting parameters including reaction time, H2O2/Fe2+ ratio, pH, and iron (II) ion concentration. Hence, the purpose of this present study was to analyze the factors affecting landfill leachate treatment as well as their interaction by means of response surface methodology (RSM) with central composite design. The independent variables were reaction time, H2O2/Fe2+ ratio, iron (II) ion concentration, and pH, and the dependent variable (response) was color-removal percentage. The optimum treatment conditions for pH, H2O2/Fe2+ ratio, Fe2+ concentration, and reaction time were 8.36, 3.32, 964.95 mg/L, and 50.15 min, respectively. The model predicted 100% color removal in optimum conditions, which was close to that obtained from the experiment (97.68%). In conclusion, the optimized Fenton process using the RSM approach promotes efficient landfill leachate treatment that is even higher than that already reported
Determination of cod and color reduction of stabilized landfill leachate by Fenton process
The Fenton process on landfill leachate was done in a batch reactor. The application of Fenton treatment technology for landfill leachate treatment greatly depends on the optimum Fenton operating conditions. The present study investigated the chemical oxygen demand (COD) and color removal efficiency of a non-biodegradable leachate by Fenton process. Determining optimum Fenton conditions requires multiple experiments using variable reaction parameters (pH, temperature, and H2O2 and Fe2+ doses).The treatment showed that the leachate organics can be broke down by the use of Fenton’s reagent. The result showed that with increasing H2O2 dosage, H2O2/Fe2+ ratio and decreasing the pH and contact time, the COD and color removal increased. The laboratory result determined that the best functional situation to catch the goals _ (i.e., BOD5/COD =0.1) resulted; pH=3, Contact time=30 minutes, H2O2 concentration= 40000 mgL-1 and the favorable H2O2/Fe2+ ratio = 10
Prediction and Optimization of the Fenton Process for the Treatment of Landfill Leachate Using an Artificial Neural Network
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 H2O2:Fe2+ ratio, Fe2+ 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 (R2 = 0.9896 and prediction R2 = 0.6954). The initial pH, H2O2:Fe2+ ratio and Fe2+ concentrations had positive effects, whereas coagulation pH had no direct effect on COD removal. Optimized conditions under specified constraints were obtained at pH = 3, Fe2+ concentration = 781.25 mg/L, reaction time = 28.04 min and H2O2:Fe2+ 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%
Prediction and Optimization of the Fenton Process for the Treatment of Landfill Leachate Using an Artificial Neural Network
Application of response surface method for total organic carbon reduction in leachate treatment using Fenton process
Removal of the untreated landfill leachate can be a wellspring of risk to accepting waters. Hence, the treatment of landfill leachate is considered environmentally essential. Response surface methodology (RSM) was used in this study to obtain the best result from the treatment of landfill leachate, particularly the total organic carbon (TOC) removal. Various parameters such as the hydraulic retention time, pH, Fe2+ concentration and H2O2: Fe2+ ratio on the total organic carbon removal were investigated. Response surface methodology was connected to enhance the treatment procedure and characterise the parameters playing the most important role. It was evident that the underlying pH had the most noteworthy negative impact, while retention time had a positive effect. The Fe2+ concentration had a negative effect, and the ratio of H2O2: Fe2+ did not have a significant impact on the removal of organic carbon. Results showed that the optimum total organic carbon removal was observed at a retention time of 37.31 min, pH of 3, H2O2:Fe2+ ratio of 4.75, and Fe2+ concentration of 634.49 mg/L. The experimental values of the total organic carbon removal were validated through the response surface methodology and at the optimum settings, whereby up to 95.16% of total organic carbon removal was observed and thus confirmed the results of the developed model. A high R2 value of 95.29% coefficient close to 1 confirmed some of the close similarities in the results of both experimental and model analyses. The generated empirical model can be used by landfill operators to estimate the removal of total organic carbon from the leachate by using Fenton treatment