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Quantity prediction of municipal solid waste (MSW) is crucial for design and programming municipal solid waste management system (MSWMS). Because effect of various parameters on MSW quantity and its high fluctuation, prediction of generated MSW is a difficult task that can lead to enormous error. The works presented here involve developing an improved support vector machine (SVM) model, which combines the principal component analysis (PCA) technique with the SVM to forecast the weekly generated waste of Mashhad city. In this study, the PCA technique was first used to reduce and orthogonalize the original input variables (data). Then these treated data were used as new input variables in SVM model. This improved model was evaluated by using weekly time series of waste generation (WG) and the number of trucks that carry waste in week of t. These data have been collected from 2005 to 2008. By comparing the predicted WG with the observed data, the effectiveness of the proposed model was verified. Therefore, in authors' opinion, the model presented in this article is a potential tool for predicting WG and has advantages over the traditional SVM model.

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This study presents a new approach—preprocessing for reaching the stationary chain in time series—to unravel the interpolating problem of artificial neural networks (ANN) for long‐term prediction of solid waste generation (SWG). To evaluate the accuracy of the prediction by ANN, comparison between the results of the multivariate regression model and ANN is performed. Monthly time series datasets, by the yrs 2000–2010, for the city of Mashhad, are used to simulate the generated solid waste. Different socioeconomic and environmental factors are assessed, and the most effective ones are used as input variables. The projections of these explanatory variables are used in the estimated model to predict the generated solid waste values through the yr 2032. Ultimately, various structures of ANN models are examined to select the best result based on the performance indices criteria. Research findings clearly indicate that such a new approach can be a practical method for long‐term prediction by ANNs. Furthermore, it can reduce uncertainties and lead to noticeable increase in the accuracy of the long‐term forecasting. Results indicate that multilayer perception approach has more advantages in comparison with traditional methods in predicting the municipal SWG. © 2011 American Institute of Chemical Engineers Environ Prog, 2011

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In this study, oily sludge samples generated from a Tehran oil refinery (Pond I) were evaluated for their contamination levels and to propose an adequate remediation technique for the wastes. A simple, random, sampling method was used to collect the samples. The samples were analyzed to measure Total petroleum hydrocarbon (TPH), polyaromatic hydrocarbon (PAH) and heavy metal concentrations in the sludge. Statistical analysis showed that seven samples were adequate to assess the sludge with respect to TPH analyses. The mean concentration of TPHs in the samples was 265,600 mg kg⁻¹. A composite sample prepared from a mix of the seven samples was used to determine the sludge's additional characteristics. Composite sample analysis showed that there were no detectable amounts of PAHs in the sludge. In addition, mean concentrations of the selected heavy metals Ni, Pb, Cd and Zn were 2700, 850, 100, 6100 mg kg⁻¹, respectively. To assess the sludge contamination level, the results from the analysis above were compared with soil clean-up levels. Due to a lack of national standards for soil clean-up levels in Iran, sludge pollutant concentrations were compared with standards set in developed countries. According to these standards, the sludge was highly polluted with petroleum hydrocarbons. The results indicated that incineration, biological treatment and solidification/stabilization treatments would be the most appropriate methods for treatment of the sludges. In the case of solidification/stabilization, due to the high organic content of the sludge, it is recommended to use organophilic clays prior to treatment of the wastes.

Oily sludge is a stable emulsion of water in oil, containing solid particles, oily hydrocarbons, and metals with different compositions, which is greatly hazardous to the environment; as a consequence, they must be removed or recovered. Recovery methods are usually preferable because of the possibility of valuable oily hydrocarbons recovery as well as environmental protection. Liquid extraction is one of the most effective methods of oily sludge recovery. In this research, hydrocarbon recoveries from oily sludge using liquid extraction with methyl ethyl ketone (MEK) and toluene as polar and non-polar solvents have been studied and compared to each other. Different parameters will affect the quality of extraction process, among which the temperature, time, and solvent to sludge ratio are the most important ones. Response surface methodology was used as a method of experimental design to find the optimum conditions for obtaining maximum recovery. Then, the sludge recovery efficiencies under the optimum conditions for MEK and toluene were determined by gas chromatography and compared to each other. The results showed 30.41 and 37.24% hydrocarbon recovery for MEK and toluene respectively. Since the major composition of the sludge consisted of non-polar components, therefore, non-polar solvent (MEK) shows better efficiency. The optimum conditions of the studies were 20 °C, 19 min, and 6.4/4.2 for MEK and 55 °C, 17 min, and 3.6/3.6 for toluene.

Anaerobic digestion (AD) treatment of agricultural and animal waste can be considered as a means of enabling environmental remediation. This research investigated two conditions of anaerobic co‐digestion of maize waste and cow dung. This was done by placing a mixture of cow dung and maize waste together in a floating drum digester that was run at two different cow dung/maize ratios of 10:1 and 10:5. Batch conditions were on a bench‐scale of AD, 5 L in volume, developed to operate under mesophilic (36 ± 1°C). Results showed that biogas and methane yields from mesophilic digestion at the ratio of 10:1 were lower than yields obtained at ratio tested in the second run (10:5). Biogas yields were 250 and 480 L/kg VS for first and second runs, respectively. In the case of methane, these amounts were presented at 130 and 300 L/kg VS. Furthermore, the average methane content of biogas was calculated as 51 and 62%, in first and second runs respectively. The total biogas production of the reactor increased by 92% when substrates were fed in second condition ratio compared to biogas production during the first condition ratio. In continue the effect of adding maize waste on biogas yield from cow dung was evaluated in batch digesters under mesophilic conditions. The addition of maize waste to cow dung presents a viable method to improve biogas yield, as well as a means to use maize waste. © 2013 American Institute of Chemical Engineers Environ Prog, 33: 597–601, 2014