Anaerobic removal of COD from high strength fresh and partially stabilized leachates and applications of multi stage Kinetic model

High strength fresh leachates generated at a new disposal trench, compost plant and partially stabilized leachate of an older trench were characterized in terms of anaerobic degradation at laboratory batch scale at 35 [degree sign] C. Fresh leachate had extremely high COD of 66,710 - 89,501 mg/L along with low pH of 4.1-5.9 in contrast to older and therefore partially stabilized leachate with a COD of about 19,000 mg/L and higher pH of 8.4. Filtration of fresh leachate samples showed to have considerable effect on continuation of degradation as for the unfiltered samples, degradation nearly stopped after a slight reduction in COD. As a first attempt, it was shown that a considerably better fit was achieved for COD variations of filtered fresh leachate samples using first order multistage kinetic model based on which hydrolysis was found to have the smallest rate, therefore being the rate limiting stage in anaerobic degradation process

Comparison of neural network and principal component-regression analysis to predict the solid waste generation in Tehran

Municipal solid waste [MSW] is the natural result of human activities. MSW generation modeling is of prime importance in designing and programming municipal solid waste management system. This study tests the short-term prediction of waste generation by artificial neural network [ANN] and principal component-regression analysis. Two forecasting techniques are presented in this paper for prediction of waste generation [WG]. One of them, multivariate linear regression [MLR], is based on principal component analysis [PCA]. The other technique is ANN model. For ANN, a feed-forward multi-layer perceptron was considered the best choice for this study. However, in this research after removing the problem of multicolinearity of independent variables by PCA, an appropriate model [PCA-MLR] was developed for predicting WG. Correlation coefficient [R] and average absolute relative error [AARE] in ANN model obtained as equal to 0.837 and 4.4% respectively. In comparison whit PCA-MLR model [R= 0.445, MARE= 6.6%], ANN model has a better results. However, threshold statistic error is done for the both models in the testing stage that the maximum absolute relative error [ARE] for 50% of prediction is 3.7% in ANN model but it is 6.2% for PCA-MLR model. Also we can say that the maximum ARE for 90% of prediction in testing step of ANN model is about 8.6% but it is 10.5% for PCA-MLR model. The ANN model has better results in comparison with the PCA-MLR model therefore this model is selected for prediction of WG in Tehran