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One of the most important problems with designing and maintaining a landfill is managing leachate that generated when water passes through the waste. In this study, leachate samples taken from aerobic and anaerobic landfill reactors operated with and without leachate recirculation are investigated in terms of biodegradable and non-biodegradable fractions of COD. The operation time is 600 days for anaerobic reactors and 250 days for aerobic reactors. Results of this study show that while the values of soluble inert COD to total COD in the leachate of aerobic landfill with leachate recirculation and aerobic dry reactors are determined around 40%, this rate was found around 30% in the leachate of anaerobic landfill with leachate recirculation and traditional landfill reactors. The reason for this difference is that the aerobic reactors generated much more microbial products. Because of this condition, it can be concluded that total inert COD/total COD ratios of the aerobic reactors were 60%, whereas those of anaerobic reactors were 50%. This study is important for modeling, design, and operation of landfill leachate treatment systems and determination of discharge limits.

In this study, the effect of leachate recirculation on aerobic and anaerobic degradation of municipal solid wastes is determined by four laboratory-scale landfill reactors. The options studied and compared with the traditional anaerobic landfill are: leachate recirculation, landfill aeration, and aeration with leachate recirculation. Leachate quality is regularly monitored by the means of pH, alkalinity, total dissolved solids, conductivity, oxidation-reduction potential, chloride, chemical oxygen demand, ammonia, and total Kjeldahl nitrogen, in addition to generated leachate quantity. Aerobic leachate recirculated landfill appears to be the most effective option in the removal of organic matter and ammonia. The main difference between aerobic recirculated and non-recirculated landfill options is determined at leachate quantity. Recirculation is more effective on anaerobic degradation of solid waste than aerobic degradation. Further studies are going on to determine the optimum operational conditions for aeration and leachate recirculation rates, also with the operational costs of aeration and recirculation.

Copyright of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi is the property of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi is the property of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: In this study we present a neural network model for predicting the methane fraction in landfill gas originating from field-scale landfill bioreactors. Landfill bioreactors were constructed at the Odayeri Sanitary Landfill, Istanbul, Turkey, and operated with (C2) and without (C1) leachate recirculation. The refuse height of the test cell was 5m, with a placement area of 1250m2 (25m×50m). We monitored the leachate and landfill gas components for 34 months, after which we modeled the methane fraction in landfill gas from the bioreactors (C1 and C2) using artificial neural networks; leachate components were used as input parameters. To predict the methane fraction in landfill gas as a final product of anaerobic digestion, we used input parameters such as pH, alkalinity, Chemical Oxygen Demand, sulfate, conductivity, chloride and waste temperature. We evaluated the anaerobic conversion efficiencies based on leachate characteristics during different time periods. We determined the optimal architecture of the neural network, and advantages, disadvantages and further developments of the network are discussed. [Copyright &y& Elsevier]