Effects of energy and protein status on reproductive parameters and presence of subclinical ketosis in Brown Swiss cows postpartum

This study aimed to determine protein and energy metabolites in postpartum Brown Swiss cows and their effect on the presence of subclinical ketosis and reproductive parameters. One hundred cows from four farms in the Mantaro Valley (Junín, Perú) were grouped according to parity (1, 2, 3 and 4). The variables of the metabolic profile (total proteins, blood urea nitrogen [BUN], glucose, βhydroxybutyrate [B-HB]), milk production and body condition were estimated between 7 to 60 days postpartum in 7-day intervals. Likewise, the calving-first heat interval and the pregnancy rate at first service were recorded. The determination of subclinical ketosis was based on a semi-quantitative analysis by levels of B-HB in milk (µmol/l) using a commercial kit. BUN levels ranged between 11.74 and 15.92 mg/dl, being higher in fourth parity cows (p<0.05). The averages of total protein (6.54-7.90 g/dl) were homogeneous between calvings. The glucose values presented an inverse response, being lower in cows of third and fourth parity compared to cows of first and second parity (p<0.05). Similarly, the highest levels of milk production were observed in third parity cows (14.41 ± 5.42 l/d) and fourth parity (15.43 ± 4.36 l/d) with respect to first and second calvers (p<0.05). Body condition was lower in cows with subclinical ketosis. The calving - first heat interval and the pregnancy rate at first service were lower in cows with subclinical ketosis (p<0.05).

Spiking PID Control Applied in the Van de Vusse Reaction

Artificial neural networks (ANN) can approximate signals and give interesting results in pattern recognition; some works use neural networks for control applications. However, biological neurons do not generate similar signals to the obtained by ANN. The spiking neurons are an interesting topic since they simulate the real behavior depicted by biological neurons. This paper employed a spiking neuron to compute a PID control, which is further applied to the Van de Vusse reaction. This reaction, as the inverse pendulum, is a benchmark used to work with systems that has inverse response producing the output to undershoot. One problem is how to code information that the neuron can interpret and decode the peak generated by the neuron to interpret the neuron's behavior. In this work, a spiking neuron is used to compute a PID control by coding in time the peaks generated by the neuron. The neuron has as synaptic weights the PID gains, and the peak observed in the axon is the coded control signal. The neuron adaptation tries to obtain the necessary weights to generate the peak instant necessary to control the chemical reaction. The simulation results show the possibility of using this kind of neuron for control issues and the possibility of using a spiking neural network to overcome the undershoot obtained due to the inverse response of the chemical reaction.

Design and simulation of a novel FOIMC-PD/P double-loop control structure for CSTRs and bioreactors

The design of control methods for unstable plants is somewhat complex than that of stable plants. This is because unstable process models contain one or more poles lying on the right of the s-plane which yields unbounded closed-loop response. Further, the presence of the dead-time induces more complexity as it decreases the gain and phase margins which in turn deteriorates the closed-loop performance. The design of control strategies become more challenging for plants of unstable nature with positive zeros because they exhibit a phenomenon called inverse response. This paper suggests a method to design a double-loop scheme for unstable plants with/without inverse response. Accordingly, a proportional-derivative (PD)/proportional (P) controllers are used in the inner-loop for stabilizing the plant. A fractional order internal model controller (FOIMC) scheme is used to obtain the outer-loop controller using the stabilized plant model. The P/PD controller settings have been obtained by using the Routh-stability criteria and the maximum sensitivity approach. Procedure for selecting the outer-loop tuning parameter and fractional order is also given. Linear and nonlinear models of unstable plants including bioreactors and isothermal chemical reactors are used to demonstrate the merits of the suggested strategy. Robustness of the design and effect of measurement noise are also studied. Integrated absolute/squared error measures are also calculated. The suggested design is found to be more effective in controlling unstable processes than some reported works.

A simulation study of nonideal mixing effect on the dynamic response of an exothermic CSTR with Cholette’s model

The study of nonideal mixing effect on the dynamic behaviors of CSTRs has very rarely been published in the literature. In this work, Cholette’s model is employed to explore the nonideal mixing effect on the dynamic response of a nonisothermal CSTR. The analysis shows that the mixing parameter n (the fraction of the feed entering the zone of perfect mixing) and m (the fraction of the total volume of the reactor), indeed affect the characteristic roots of transfer function of a real CSTR, which determine the system stability. On the other hand, the inverse response and overshoot response are also affected by the nonideal mixing in a nonisothemal CSTR. These results are of much help for the design and control of a real CSTR.