he application of a multivariable predictive controller to an activated sludge process is discussed in this work. Emphasis is given to the model identification and the long term assessment of the controller efficiency in terms of economical and environmental performances. A recurrent neural network model is developed for the identification problem and the dynamic matrix control is chosen as suitable predictive control algorithm for controlling the nitrogen compounds in the bioreactor. Using the Benchmark Simulation Model No. 1 as virtual platform, different predictive controller configurations are tested and further improvements are achieved by controlling the suspended solids at the end of the bioreactor. Based on the simulation results, this work shows the potentiality of the dynamic matrix control that together with a careful identification of the process, is able to decrease the energy consumption costs and, at the same time, reduce the ammonia peaks and nitrate concentration in the effluent

No abstract

We report the relaxometric dataset obtained on renneted milk during syneresis by Time-Domain Nuclear Magnetic Resonance spectroscopy (TD-NMR). Data were obtained on cow's milk provided by two different producers in two different lactation seasons (April and October) and on a group of goat's milk samples (one season, November–December, one producer). TD-NMR data refer to spin-spin relaxation times (T2) decay curves and distributions measured at 40 °C at seven time points after rennet addition, up to 70 minutes of syneresis. Curd was cut 30 min after rennet addition without removing the NMR tube from the TD-NMR instrument. The dataset here reported is related to the research article entitled “Non invasive monitoring of curd syneresis upon renneting of raw and heat-treated cow's and goat's milk” [E. Curti, A. Pardu, S. Del Vigo, R. Sanna, R. Anedda, Non-invasive monitoring of curd syneresis upon renneting of raw and heat-treated cow's and goat's milk, Int. Dairy J. 90 (2019) 95–97].

No abstract