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"nBackground and Objectives:. Owing to the non-seperated municipal solid wastes the leachate form in land fills contain high amounts of heavy metalls and toxic substances Hence, leachate treatment is a serious problem. In order to design leachate treatment and collection systems, estimation of quality and quantity of leachate is of high necessity. Hydrologic Evaluation of Landfill Performance (HELP) Model was used to estimate leachate generation in the lined landfill cells for a variety of conditions. The HELP program is a quasi-two-dimensional hydrologic model for conducting water balance analysis of landfills, cover systems, and other solid waste containment facilities. In this paper HELP program is used to predict leachate generating in Semnan landfill after its operational life."nMaterials and Methods: HELP model use weather, soil and design data to estimate leachate quantity. The meteorological data were obtained from semnan Atmospheric Data Centre. Soil mechanics examinations in the landfill area were applied to achieve soil data. In addition, design parameters were based on Semnan landfill design specifications. Semnan landfill capacity is designed so as to accommodate municipal solid wastes generated during the next 25 years."nResults: In this study result indicated that precipitation and evapotranspiration has the most influenced on leachate generation increase and decrease, respectively. 82% of annual precipitation isn't percolated into Semnan landfill due to evapotranspiration. HELP Model simulations were indicated that the maximum and average value of leachate height above barrier layer is 36 and 3mm,respectively."nConclusion: Semnan landfill is designed under minimum standard condition. Therefore, low height of leachate generated is due to area weather. The precipitation amount is low while the evapotranspiration amount is high in this area. High evapotranspiration is due to high temperature and solar radiation in Semnan landfill area. High evapotranspiration in the landfill cap caused 14.2% of the precipitation to infiltrate into the wastes and became leachate.

"n "nBackgrounds and Objectives:A number of different technologies have recently been studied todetermine the best use of biogas, however, to choose optimize technologies of using biogas for energy recovery it is necessary to monitor and predict the methane percentage of biogas. In this study, a method is proposed for predicting the methane fraction in landfill gas originating from Labscalelandfill bioreactors, based on neural network."nMaterials and Methods: In this study, two different systems were applied, to predict the methane fraction in landfill gas as a final product of anaerobic digestion, we used the leachate specifications as input parameters. In system I (C1), the leachate generated from a fresh-waste reactor was drained to recirculation tank, and recycled. 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."nResults: There is very good agreement in the trends between forecasted and measured data. R valuesare 0.999 and 0.997, and the obtained Root mean square error values are 1.098 and 2.387 for training and test data, respectively"nConclusion: The proposed method can significantly predict the methane fraction in landfill gasoriginating and, consequently, neural network can be use to optimize the dimensions of a plant using biogas for energy (i.e. heat and/or electricity) recovery and monitoring system.