Automatic extruder for processing recycled polymers with ultrasound and temperature control system

Melt viscosity is one of the main factors affecting product quality in extrusion processes particularly with regard to recycled polymers. However, due to wide variability in the physical properties of recycled feedstock, it is difficult to maintain the melt viscosity during extrusion of polymer blends and obtain good quality product without generating scrap. This research investigates the application of ultrasound and temperature control in an automatic extruder controller, which has ability to maintain constant melt viscosity from variable recycled polymer feedstock during extrusion processing. An ultrasonic modulation system has been developed and fitted to the extruder prior to the die to convey ultrasonic energy from a high power ultrasonic generator to the polymer melt. Two separate control loops have been developed to run simultaneously in one controller: the first loop controls the ultrasonic energy or temperature to maintain constant die pressure, the second loop is used to control extruder screw speed to maintain constant throughput at the extruder die. Time response and energy consumption of the control methods in real-time experiments are also investigated and reported this paper.

Free-roaming dogs control activities in one Italian province (2000-2013): Is the implemented approach effective?

In Italy, standards for the management of free-roaming dogs (FRDs) are defined by regional norms, generating a high variability of approaches around the country. Despite efforts carried out by the competent authorities, FRDs are still a reality impacting upon animal health and welfare and public costs. A similar scenario can be found in many other Mediterranean and Balkan counties. Here we present 14 years of data (2000–2013) retrieved from the admission dog registry of a public shelter (PS) responsible for the collection of stray dogs from one Italian province. The aim of this retrospective study was to describe the local FRD population, identifying its source and to evaluate the effectiveness of the actions implemented by the local authorities. In the investigated period, 7,475 dogs were admitted to the PS. Despite the intense sterilisation plan (mean 381.7 sterilisations per year), the overall number of dogs entering PS did not decrease consistently across the years. Results highlighted a lack of responsibility of owners by failing to sterilise and identify their dogs and allowing intact animals to roam free, therefore producing uncontrolled and unwanted litters. The current dog population management strategy, based on both sheltering and capture-neuter-release programmes, is insufficient to tackle the straying phenomenon. Educational and sterilisation programmes should be an integral part of a successfully implemented FRD control plan. Our results provide further insight on free-roaming dog population dynamics and control systems, and may have important implications for many other local contexts across Europe trying to overcome the straying phenomenon.

A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements.

In this brief, a hybrid filter algorithm is developed to deal with the state estimation (SE) problem for power systems by taking into account the impact from the phasor measurement units (PMUs). Our aim is to include PMU measurements when designing the dynamic state estimators for power systems with traditional measurements. Also, as data dropouts inevitably occur in the transmission channels of traditional measurements from the meters to the control center, the missing measurement phenomenon is also tackled in the state estimator design. In the framework of extended Kalman filter (EKF) algorithm, the PMU measurements are treated as inequality constraints on the states with the aid of the statistical criterion, and then the addressed SE problem becomes a constrained optimization one based on the probability-maximization method. The resulting constrained optimization problem is then solved using the particle swarm optimization algorithm together with the penalty function approach. The proposed algorithm is applied to estimate the states of the power systems with both traditional and PMU measurements in the presence of probabilistic data missing phenomenon. Extensive simulations are carried out on the IEEE 14-bus test system and it is shown that the proposed algorithm gives much improved estimation performances over the traditional EKF method.