Luca Luccarini

Learn More
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(More)
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(More)
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,(More)
The applicability of set-point titration for monitoring biological processes has been widely demonstrated in the literature. Based on published and on-going experiences, some operating procedures have been specifically developed to be applied to SBRs, so that real-time information about the process and/or the influent can be obtained. This, in turn, would(More)
In Waste-Water Treatment Plant (WWTP) automation, “soft” sensors might be used in conjunction with “hard” sensors to improve the reliability of the measurements, or even to replace the latter when they would be too expensive or difficult to maintain. Unfortunately, many soft sensors are created using black-box data mining techniques such as neural networks(More)
  • 1