Water reduction in waste-activated sludge by resettling and filtration in batch. Phase (1): pilot-scale experiments to optimize performance

This article describes an effective procedure for reducing the water content of excess sludge production from a wastewater treatment plant by increasing its concentration and, as a consequence, minimizing the volume of sludge to be managed. It consists of a pre-dewatering sludge process, which is used as a preliminary step or alternative to the thickening. It is made up of two discontinuous sequential stages: the first is resettling and the second, filtration through a porous medium. The process is strictly physical, without any chemical additives or electromechanical equipment intervening. The experiment was carried out in a pilot-scale system, consisting of a column of sedimentation that incorporates a filter medium. Different sludge heights were tested over the filter to verify the influence of hydrostatic pressure on the various final concentrations of each stage. The results show that the initial sludge concentration may increase by more than 570% by the end of the process with the final volume of sludge being reduced in similar proportions and hydrostatic pressure having a limited effect on this final concentration. Moreover, the value of the hydrostatic pressure at which critical specific cake resistance is reached is established.

Influence of sludge retention time on filtration performance and biomass characteristics in a hollow fiber membrane bioreactor

In this study, the filtration process and the biomass characteristics in a laboratory-scale submerged membrane bioreactor (MBR) equipped with a hollow fiber (HF) microfiltration membrane were studied at different solid retention times (SRT). The MBR was fed by synthetic wastewater and the organic loading rate (OLR) was 0.5, 0.2, 0.1, and 0.08 kg COD kg VSS−1 d−1 for 10, 30, 60, and 90 days of SRT, respectively. The hydraulic retention time was 8.4 h and the permeate flux was 6 L m−2 h−1(LMH). Data analysis confirmed that at all the studied SRTs, the HF-MBR operated very good obtaining of high quality permeates. Chemical Oxygen Demand (COD) removal efficiencies were higher than 95%. The best filtration performance was reached at SRT of 30 d. On the other hand, the respirometric analysis showed that biomass was more active and there was more biomass production at low SRTs. The concentration of soluble extracellular polymeric substances (EPS) decreased with increasing SRT. A decrease of soluble EPS caused a decrease of membrane fouling rate, decreasing the frequency of chemical cleanings. The floc size decreased with SRT increasing. At high SRTs, there was more friction among particles due to the increase of the cellular density and the flocs broke decreasing their size.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.