Thermal properties of 3-hydroxy fatty acids and their binary mixtures as phase change energy storage materials

In the present work, we describe the chemical synthesis of 3-HFAs as prominent derivatives of fatty acids and assess if they could be applied as phase change materials (PCM). In addition, 3-HFAs were obtained by depolymerization of a bacterial biopolymeric material, polyhydroxyalkanoate. Thermal properties of 3-hydoxyoctanoic, decanoic, and dodecanoic acids are reported for the first time. These materials showed the potential to be applied as PCM in temperature range from 33°C to 66°C. In order to expand the temperature range for application of 3-HFAs as PCM, eutectic mass ratios of three kinds of binary mixtures of 3-HFAs were calculated, and their properties were predicted using the Schröder-van Laar equation. Thermal properties of these mixtures were validated by differential scanning calorimetry (DSC) analysis. These results showed that eutectics considerably expanded the scope of applications of 3-HFAs as PCMs. 3-HFAs originating from biotechnologically obtained polyhydroxyalkanoates also showed potential to be applied in development of PCMs.     

Effects of supercritical CO2 extraction parameters on soybean oil yield

A series of operational parameters of supercritical fluid extraction of soybean oil (pressure: 300–500 bar, temperature: 40–60 °C, CO₂ mass flow rate: 0.194–0.436 kg/h and characteristic particle size: 0.238–1.059 mm) were investigated in a laboratory scale apparatus. The results show that the extraction yields were significantly affected by applied operational extraction parameters. The increase in pressure, temperature and solvent flow rate improved the extraction yield. The extraction yield increased as the particle size decreased depending on decreasing intra-particle diffusion resistance. To describe the extraction process Sovova's model was used and very good agreement with the experimental results was obtained. Based on the experimental data the internal and external mass transfer coefficients were estimated. To explore the influence of the extractor size on this process, soybean samples were extracted using different extraction basket volumes (0.2 L and 5 L) and related model parameters were examined. The mass transfer coefficient in the fluid phase increased with the increase in extractor size, while the mass transfer coefficient in the solid phase was independent of the extractor size.     

Smoothing innovations and data association with IPDA

Surveillance sensors return detections from targets as well as the clutter measurements. Data association algorithms often use innovations to discriminate between the target and the clutter measurements. Reducing the covariance of innovations reduces the surveillance area from which measurements are used, reducing the number of clutter measurements. This paper introduces smoothing innovations which reduce innovation covariance, and improve the data association performance. This concept is applied to the Integrated Probabilistic Data Association (IPDA) to produce a Smoothing IPDA (sIPDA). sIPDA trajectory estimation errors are reduced with a smoothing delay. A surprising outcome is that sIPDA improves the false track discrimination in real time (without the smoothing delay).     

An algorithm for calculating the optimal reference temperature in buildings

In this paper we present an algorithm for calculating the reference temperature in the rooms of a building. The algorithm works in such a way as to properly reduce the reference temperature in the rooms when they are not occupied, and at the correct start time, before the rooms are occupied, recover the reference temperature back to the defined value. The algorithm is tested in six rooms of a simulated hotel building using the well-known TRNSYS software. Different occupancy regimes are considered in the various rooms, and simulations are performed for a period of 1 year using the weather data of the town of Portoro?, Slovenia. The results obtained with the proposed algorithm are compared to the results achieved with a constant reference temperature setting. The comparison is made with regard to the energy consumed for the heating and cooling of the rooms, and taking account of the deviations from the allowed temperature rise time. In the rooms where the occupancy is not known in advance similar results can be obtained with the proposed algorithm as with the constant reference setting. However, in the rooms where the occupancy is known in advance, desired level of guest comfort can be preserved with the proposed algorithm with an approximately 10% lower energy consumption for the heating and cooling of the rooms than with the constant reference recovery time setting.     

Electrochemical and AFM study of cobalt nucleation mechanisms on glassy carbon from ammonium sulfate solutions

Nucleation mechanisms of cobalt on a glassy carbon electrode (gce) from aqueous ammonium sulfate solutions were investigated through the electrochemical techniques of cyclic voltammetry (cv) and chronoamperometry (ca), coupled with atomic force microscopy (AFM) studies. The studied parameters were pH, cobalt concentration, temperature, scanning rate, and deposition potential. It was found that scanning in the cathodic direction produced two peaks, corresponding to cobalt and hydrogen reduction, respectively. Scanning in the anodic direction was characterized by cobalt dissolution, which was interrupted by formation of cobalt hydroxide, causing a second anodic peak. The amperometric study found progressive nucleation mechanisms, in contrast to the instantaneous nucleation mechanisms determined by the AFM study. An explanation for the contradictory nucleation mechanisms shown in the two studies is provided.     

Improving the operation of a fuel-cell power unit with supervision control - A simulation study

Polymer electrolyte membrane fuel cells are proving to be a clean and efficient source of energy. Nowadays, extensive research efforts are being focused on bringing this technology to everyday use. An important aspect when integrating fuel cells in practical applications is their ability to respond to load demand. With respect to this, due to their complex internal dynamics, fuel cells belong to the group of more slowly responding sources. In order to make them more generally applicative they are often connected with a battery or a super-capacitor via a power converter to form a hybrid power source. A control algorithm, designed for such a system, represents an interesting challenge: it has to adapt to varying working conditions and operate optimally in terms of efficiency and reliability, while minimizing any impacts on the degradation of the components. Here, we present an approach using supervisory control automaton that switches between the system's operational modes and sets the references for the lower-level control loops. The evaluation of the efficiency and degradation is carried out in a simulation using a model of the widely used 1.2-kW Ballard Nexa power module.     

TiO2 as a sintering additive for KNbO3 ceramics

Improved densification during the conventional sintering of KNbO₃ ceramics was achieved by using small additions of TiO₂. This improved densification can be explained on the basis of high-temperature chemical reactions in the system. X-ray diffractometry and electron microscopy were used in combination with diffusion-couple experiments in order to elucidate the chemical reactions between KNbO₃ and TiO₂. TiO₂ reacts with KNbO₃ forming KNbTiO₅, and a low concentration of Ti incorporates in the KNbO₃ structure resulting in the formation of oxygen vacancies and, consequently, in an improvement in the densification. At ∼1037 °C eutectic melting between the KNbO₃ and the KNbTiO₅ further improves the densification of the KNbO₃ ceramics.     

Prediction of sulphur content in the industrial hydrotreatment process

The artificial neural network models were developed to determine sulphur content in the hydrotreatment product. Two models for two different types of feed were developed: light gas oil and vacuum gas oil. The developed ANN models use 6 input variables that are continuously measured in the process and are in accordance with the engineering knowledge and thermodynamics of hydrotreatment processes. Given models show good predictability of sulphur content in the hydrotreatment product and are, therefore, used in practice for continuous monitoring and optimization. This kind of application can be easily developed in any other hydrotreatment process with the available adequate historical data.     

Shear resistance of longitudinally stiffened panels—Part 1: Tests and numerical analysis of imperfections

This paper deals with the results of four full-scale tests, numerical simulation of tests and initial geometric imperfection analysis for longitudinally stiffened panels in shear. The tests examine the influence of varying position and bending stiffness of one trapezoidal longitudinal stiffener on the panel shear resistance and its buckling behaviour. The stiffeners were designed such as to obtain both global and local buckling shapes. Numerical simulations (FEA), based on the test girder geometry, the measured initial geometric imperfections and elastic-plastic material characteristic from the tensile tests, demonstrate a very good agreement with the tests. The initial geometric imperfection study on different verified numerical models shows a limited sensitivity of the panel shear capacity to any kind of imperfection shape variation with amplitude at the allowable fabrication tolerances. Finally, the paper offers some ideas for modelling geometric imperfections with regard to the design or research demands.     

Training an asymmetric signal perceptron through reinforcement in an artificial chemistry

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be reprogrammed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. In this paper, we begin addressing these challenges with a novel chemical perceptron that can solve all 14 linearly separable logic functions. The system performs asymmetric chemical arithmetic, learns through reinforcement and supports both Michaelis–Menten as well as mass-action kinetics. To enable cascading of the chemical perceptrons, we introduce thresholds that amplify the outputs. The simplicity of our model makes an actual wet implementation, in particular by DNA-strand displacement, possible.