Intelligent power management in micro grids with EV penetration

Large deployment of Electric Vehicles (EVs) adds new challenges in the operation of a microgrid. Assuming that a number of EV owners allow their batteries to charge when their cars are parked, this paper proposes an approach that aims to find suitable individual active power set-points corresponding to the hourly charging rate of each EV battery connected to the microgrid. A multi agent system based controller is designed to find these active power set points for optimal power management of EVs, distributed energy resources in the microgrid, and the loads.     

Dissolution and Solubility Behavior of Fenofibrate in Sodium Lauryl Sulfate Solutions

The solubility of fenofibrate in pH 6.8 McIlvaine buffers containing varying concentrations of sodium lauryl sulfate was determined. The dissolution behavior of fenofibrate was also examined in the same solutions with rotating disk experiments. It was observed that the enhancement in intrinsic dissolution rate was approximately 500-fold and the enhancement in solubility was approximately 2000-fold in a pH 6.8 buffer containing 2% (w/v) sodium lauryl sulfate compared to that in buffer alone. The micellar solubilization equilibrium coefficient (k*) was estimated from the solubility data and found to be 30884 ± 213 L/mol. The diffusivity for the free solute, 7.15 × 10^{? 6} cm²/s, was calculated using Schroeder's additive molal volume estimates and Hayduk-Laurie correlation. The diffusivity of the drug-loaded micelle, estimated from the experimental solubility and dissolution data and the calculated value for free solute diffusivity, was 0.86 × 10^{? 6} cm²/s. Thus, the much lower enhancement in dissolution of fenofibrate compared to its enhancement in solubility in surfactant solutions appears to be consistent with the contribution to the total transport due to enhanced micellar solubilization as well as a large decrease ( ～ 8-fold) in the diffusivity of the drug-loaded micelle.     

Every team deserves a second chance: an extended study on predicting team performance

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.     

An Energy-efficient Switching Scheme with Reduced Switching Transients for a Wind-driven Induction Generator

This article presents a scheme for improving the power output of grid-connected induction generator commonly used in wind energy conversion systems. Generally, the stator of the induction generator is connected in a star with a line voltage of √3 times the rated winding voltage to reduce the line current and, hence, conductor size. To extend the generating operation over a wider speed range, delta-star switchable stator windings are also in vogue. In such cases, the stator is star connected in the lower speed range and switched to a delta connection above a threshold speed. In this study, a new switching scheme is proposed wherein the stator coils are always connected in a star, while the stator is connected to different voltages in low- and high-speed conditions. At low wind speeds, nominal winding voltage is applied to the stator, whereas at higher speeds, the stator applied voltage is √3 times higher than the rated winding voltage. The efficacy of the scheme is demonstrated experimentally with a suitable microcontroller-based switching arrangement. Typical results indicate an increase in output with reduced switching transients. A case study on a 3-Φ, 50-kW induction generator is presented to emphasize the performance improvement with the proposed scheme.     

Numerical Simulation of the Head/Disk Interface for Patterned Media

The use of patterned media is a new approach proposed to extend the recording densities of hard disk drives beyond 1 Tb/in.². Bit-patterned media (BPM) overcome the thermal stability problems of conventional media by using single-domain islands for each bit of recorded information, thereby eliminating the magnetic transition noise (Albrecht et al., Magnetic Recording on Patterned Media, 2003). Considering steady state conditions, we have transferred the pattern from the disk surface onto the slider surface and have investigated the pressure generation due to the bit pattern. To reduce the numerical complexity, we have generated the bit pattern only in the areas of the slider near the trailing edge, where the spacing is small. Cylindrical protrusions were modeled using very small mesh size on the order of nanometers to obtain the flying characteristics for the entire slider air bearing surface (ABS) using the “CMRR” finite element Reynolds equation simulator (Duwensee et al., Microsyst Technol, 2006; Wahl et al., STLE Tribol Trans, 39(1), 1996). The effect of pattern height, pattern diameter, slider skew angle, and slider pitch angle on flying height of a typical slider is investigated. Numerical results show that the flying height decreases for a patterned slider and the change in flying height is a function of the pattern height and ratio of the pattern diameter to the pattern pitch. In comparison to discrete track media, the flying height loss is larger for a patterned slider disk interface for the same recessed area of pattern.     

Nitroarenes as Antitubercular Agents: Stereoelectronic Modulation to Mitigate Mutagenicity

Nitroarenes are less preferred in drug discovery due to their potential to be mutagenic. However, several nitroarenes were shown to be promising antitubercular agents with specific modes of action, namely, nitroimidazoles and benzothiazinones. The nitro group in these compounds is activated through different mechanisms, both enzymatic and non‐enzymatic, in mycobacteria prior to binding to the target of interest. From a whole‐cell screening program, we identified a novel lead nitrobenzothiazole (BT) series that acts by inhibition of decaprenylphosphoryl‐β‐d ‐ribose 2′‐epimerase (DprE1) of Mycobacterium tuberculosis (Mtb). The lead was found to be mutagenic to start with. Our efforts to mitigate mutagenicity resulted in the identification of 6‐methyl‐7‐nitro‐5‐(trifluoromethyl)‐1,3‐benzothiazoles (cBTs), a novel class of antitubercular agents that are non‐mutagenic and exhibit an improved safety profile. The methyl group ortho to the nitro group decreases the electron affinity of the series, and is hence responsible for the non‐mutagenic nature of these compounds. Additionally, the co‐crystal structure of cBT in complex with Mtb DprE1 established the mode of binding. This investigation led to a new non‐mutagenic antitubercular agent and demonstrates that the mutagenic nature of nitroarenes can be solved by modulation of stereoelectronic properties.     

Structure,properties, and applications of polyacrylonitrile/carbon nanotube (CNT) fibers at low CNT loading

Bi-component, polyacrylonitrile (PAN)/carbon nanotube (CNT) fibers were processed, at different core-sheath area ratios, by gel spinning. A percolated CNT network at 10 wt% CNT in the sheath enhanced electrical conductivity as compared to the neat PAN fiber, while PAN polymer in the core contributed to the good mechanical properties. Fibers with relatively thin sheath allowed overall CNT loading as low as 3.7 wt% to be made with good electrical conductivity, and PAN stabilization by Joule heating was demonstrated. Such fibers with combined good mechanical properties and electrical conductivity can also potentially be used for electrical heating of fabrics, for making smart textiles, and for electromagnetic interference shielding.     

Simulation of population balance model-based particulate processes via parallel and distributed computing

Population Balance Models (PBMs), a class of integro partial differential equations, are utilized for simulating dynamics of numerous particulate systems. PBMs describe the time evolutions and distributions of many particulate processes and their efficient and quick simulation are critical for enhanced process control and optimization, especially for real-time applications. However, their intensive computational resource requirement is largely a prohibitive factor in the utility of PBMs for control and optimization. This paper describes how distributed computing systems may be leveraged to execute PBM-based simulations thus achieving time savings, using MATLAB's Distributed Computing Toolbox. A parallel computing algorithm was developed for a three dimensional and four dimensional population balance model with built-in constructs such as SPMD that ran efficiently on a cluster of two quad-core machines linked via a gigabit ethernet cable. Speedup of 6.2 and 5.7 times were achieved with 8 workers, over an un-parallelized code, for a 3 and 4 dimensional PBM respectively. Evaluations on work efficiency and scalability further affirm the algorithms’ respectable performance on larger clusters despite significant memory transfer overheads.     

Influence of Media Composition on the Production of alpha-Amylase and Amyloglucosidase by a Strain of Aspergillus niger

The nature of thermophilic amylolytic enzymes produced by a strain of Aspergillus niger Van Tieghem was influenced by media composition. While glucose, maltose, dextrin and soluble starch induced the production of amyloglucosidase, pectin and sodium polypectate almost exclusively induced the formation of α-amylase. Alkali ions suppressed the formation of amyloglucosidase. NH₄NO₃, (NH₄)₂HPO₄ and (NH₄)₂C₄H₄O₆ served as good inorganic nitrogen sources, while casein, groundnut protein and corn steep liquor served to a similar capacity as nitrogen sources from organic material. Deletion of the growth factors pyridoxine-hydrochloride and inositol suppressed enzyme production, while the absence of riboflavin had the opposite effect.     

Mechanical behaviour of an austempered ductile iron

Austempering of a ferrite-pearlitic grade of ductile iron was carried out to assess the potential use of the material for crank shaft application reported. A commercial material was austempered at 340°C to realize the properties. The austempered ductile iron gave good strength although the ductility values were lower. The material developed had complete ausferritric structure free of pearlite. The various phase constitution and phase transformation associated with the treatment and during mechanical deformation was examined. Using XRD analysis the volume fraction of the austenite in the matrix was estimated. The various aspects of processing a commercial cast iron during ausetmpering, the phase transformation, microstructural evolution have been examined along with the property of the material. The mechanical behaviour of the material and the scope for further improvement is discussed.