No abstract

Biological nitrogen removal via nitrite may represent a promising process for the optimization of nitrogen removal, in particular in the presence of a low biodegradable COD/TKN ratio. In the present study a lab-scale sequencing batch reactor (SBR) was monitored for approximately 2 years to evaluate the use of dissolved oxygen (DO), pH and oxidation-reduction potential (ORP) as monitoring parameters in order to optimize nitrogen removal via nitrite from leachate generated in old sanitary landfills. The SBR manifested a nitrification efficiency exceeding 99% whereas, due to the low biodegradability of the organic matter presents in the leachates, COD removal reached approximately 40% and the addition of external COD was required to accomplish denitrification process. Moreover, the results demonstrate that DO, pH and ORP are reliable parameters for use in the monitoring of nitritation and denitritation processes in SBRs treating landfill leachates. Through manual modification of the length of the SBR phases to achieve nitrogen removal via nitrite, the nitritation and denitritation processes were rendered unstable leading to the saving of 20% in addition of external COD, almost half the theoretically achievable value. Furthermore, the low dissolved oxygen concentration applied during the oxic phases in an attempt to increase the nitritation process would appear to cause the settling characteristics of the activated sludge to deteriorate.

In this paper, we describe the results of research aimed to evaluate the possibility of using a neural network (NN) model for predicting biological nitrogen and phosphorus removal processes in activated sludge, utilising oxidation reduction potential (ORP) and pH as NN inputs. Based on N and P concentrations predictions obtained via the NN, a strategy for controlling sequencing batch reactors (SBRs) phases duration, optimising pollutants removal and saving energy, is proposed. The NN model allowed us to reproduce the concentration trends (change in slope, or process end), with satisfactory accuracy. The NN results were generally in good agreement with the experimental data. These results demonstrated that NN models can be used as "soft on-line sensors" for controlling biological processes in SBRs. By monitoring ORP and pH, it is possible to recognise the N and P concentrations during different SBRs phases and, consequently, to identify the end of the biological nutrient removal processes. This information can then be used to design control systems.

Digital image analysis is a useful tool to estimate some morphological parameters of flocs and filamentous species in activated sludge wastewater treatment processes. In this work we found the correlation between some morphological parameters and sludge volume index (SVI). The sludge was taken from a pilot-scale activated sludge plant, owned by ENEA, located side stream to the Trebbo di Reno (Bologna, Italy) municipal WWTP and fed by domestic wastewater. In order to use image analysis, we developed a correct method to acquire digital microbiological observations and to obtain images altogether representative of the sludge properties. We identified and assessed the parameters needed to estimate the settleability of the sludge and evaluated the morphological filamentous features. It is known that several conditions (i.e. low F/M, nutrient deficiency, low dissolved oxygen) select specific filamentous species and their excessive growth decrease floc-forming/filaments ratio, correspond to the worse settleability properties; we found a relationship between the relative abundance of filamentous species and SVI. We also evaluated the fractal dimension parameter (FD) and determined a threshold value useful to distinguish between the "weak" and "firm" floc and we found a correlation between FD and SVI.

BACKGROUND: In recent years water scarcity has become more prominent, increasing the need for new practices in terms of efficient water management. Reuse and valorization of water from wastewater treatment plants (WWTPs) can help solve the problem. RESULTS:The traditional denitrification/nitrification scheme followed by a natural disinfection/storage tank has been studied in order to optimize the process for irrigation reuse. The study is based on data produced by an experimental pilot-scale plant, located in Trebbo di Reno (BO, Italy) and from a recent field campaign carried out on internal fresh water of the Allacciamento Channel inside the basin of Porto Canale Cesenatico (FC, Italy). Using WEST software to model the first phase and a dispersed flow equation to model the Escherichia coli (E. coli) removal rate in the following basin, the overall process has been optimized to test the capacity of a Business Process Management (BPM) approach to model the water storage management for irrigation reuse.CONCLUSIONS: High pollutants removal efficiencies and a water storage system able to fulfill the irrigation needs are two essential requirements for water reuse. The study shows how to achieve those aims using a combined system with a natural disinfection basin analyzing the hydraulic parameters effects on pollutants removal throughout the water column. Moreover, the management optimization of the process has been studied suggesting different management policies for different scenarios.