The influence of raw material characteristics on the industrial pelletizing process and pellet quality

Industrial pelletizing of sawdust was carried out as a designed experiment in the factors: sawdust moisture content, fractions of fresh pine, stored pine and spruce. The process parameters and response variables were energy consumption, pellet flow rate, pellet bulk density, durability and moisture content. The final data consisted of twelve industrial scale runs. Because of the many response variables, data evaluation was by principal component analysis of a 12 × 9 data matrix. The two principal component model showed a clustering of samples, with a good reproducibility of the center points. It also showed a positive correlation of energy consumption, bulk density and durability all negatively correlated to flow rate and moisture content. The stored pine was more related to high durability and bulk density. The role of the spruce fraction was unclear. The design matrix, augmented with the process parameters was a 12 × 6 matrix. Partial least squares regression showed excellent results for pellet moisture content and bulk density. The model for durability was promising. A 12 × 21 data matrix of fatty- and resin acid concentrations measured by GC–MS showed the differences between fresh and stored pine very clearly. The influence of the spruce fraction was less clear. However, the influence of the fatty- and resin acids on the pelletizing process could not be confirmed, indicating that other differences between fresh and stored pine sawdust have to be investigated. This work shows that it is possible to design the pelletizing process for moderate energy consumption and high pellet quality.     

Study of the morphology and mechanical properties of polypropylene composites with silica or rice‐husk

The mechanical, morphological behavior and water absorption characteristics of polypropylene (PP) and silica, or PP and rice‐husk, composites have been studied. The silica used in this study as filler was a commercial type produced from soluble glass or rice husks. The compatibilizing effect of PP grafted with monomethyl itaconate (PP‐g‐MMI) and/or with vinyltriethoxysilane (PP‐g‐VTES) as polar monomers on the mechanical properties and water absorption was also investigated. In general, a high loading of the studied fillers in the polymer matrix increases the stiffness and the water absorption capacity. This effect is more noticeable in the tensile modulus of the PP/silica composite with PP‐g‐VTES as compatibilizer. However, the increase of the rice‐husk charge as a natural filler in the PP matrix decreases the stiffness, and in the presence of PP‐g‐MMI as compatibilizer in PP/rice‐husk, the tensile modulus and water absorption of the composite were improved. The better adhesion and phase continuity in the PP/silica and PP/rice‐husk composites with different compatibilizers was confirmed by the morphological study. Copyright © 2004 Society of Chemical Industry     

Avoiding negative elastic moduli when using Lagrange interpolation for material grading in finite element analysis

Polynomial interpolations, one of the most common interpolations used in finite element methods (FEMs), are a workhorse of many FEM codes. These interpolations are readily available for all kinds of elements, and using them for modeling the variation of elastic moduli in graded elements is thus both convenient and natural. Yet, like all polynomial interpolations, they can be prone to oscillations that can result in regions of negative elastic modulus in the element, even with only positive nodal values of elastic moduli. The result of these negative modulus regions, even if the region is small, can be unexpected singularities in the solution. This defeats the purpose of using polynomial interpolations for capturing material grading in the element. We demonstrate the issue using three-node quadratic Lagrange interpolations of material grading in otherwise isoparametric p-type elements and show how to avoid this problem.     

Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.     

Axialer Verlauf der Geschwindigkeit in Luft-Freistrahlen

Zusammenfassung Die bisher bekannten Beziehungen zur Berechnung des axialen Geschwindigkeitsverlaufs in isothermen Luft-Freistrahlen gelten nur für das Strahlende etwa im Gebiet von x/d>50, in dem die Reynoldszahl keinen Einflu? mehr auf den Geschwindigkeits-oder Impulsabfall hat. In dem Gebiet4<x/d<50 nimmt das Verh?ltnis mit wachsender Reynoldszahl bis zu einer Grenz-Reynoldszahl zu. Durch Definition einer axialen Halbwertl?nge x_0,5 (x_0,5=x bei ) wurde eine Beziehung gefunden, die den Einflu? der Reynoldszahl am Strahlanfang berücksichtigt. Mitteilung aus dem Engler-Bunte-Institut, Bereich Feuerungstechnik, der Universit?t Karlsruhe (TH), Direktor des Instituts: Prof. Dr.-Ing. R. Günther.     

Axial offset control of PWR nuclear reactor core using intelligent techniques

Improved load following capability is one of the main technical performances of advanced PWR (APWR). Controlling the nuclear reactor core during load following operation encounters some difficulties. These difficulties mainly arise from nuclear reactor core limitations in local power peaking, while the core is subject to large and sharp variation of local power density during transients. Axial offset (AO) is the parameter usually used to represent of core power peaking, in form of a practical parameter. This paper, proposes a new intelligent approach to AO control of PWR nuclear reactors core during load following operation. This method uses a neural network model of the core to predict the dynamic behavior of the core and a fuzzy critic based on the operator knowledge and experience for the purpose of decision-making during load following operations. Simulation results show that this method can use optimum control rod groups maneuver with variable overlapping and may improve the reactor load following capability.     

Power efficiency enhancement analysis of an inverse class D power amplifier for NB-IoT applications

Analog Integrated Circuits and Signal Processing - The power amplifiers (PAs) are generally the most power-consuming building blocks in Radio Frequency (RF) transceivers. This paper presents a high...     

Information properties of order statistics and spacings

We explore properties of the entropy, Kullback-Leibler information, and mutual information for order statistics. The probability integral transformation plays a pivotal role in developing our results. We provide bounds for the entropy of order statistics and some results that relate entropy ordering of order statistics to other well-known orderings of random variables. We show that the discrimination information between order statistics and data distribution, the discrimination information among the order statistics, and the mutual information between order statistics are all distribution free and are computable using the distributions of the order statistics of the samples from the uniform distribution. We also discuss information properties of spacings for uniform and exponential samples and provide a large sample distribution-free result on the entropy of spacings. The results show interesting symmetries of information orderings among order statistics.     

Brain-computer interface in multimedia communication

This article raises various issues in the design of an efficient BCI system in multimedia applications. The main focus is on one specific modality, namely an electroencephalography (EEG)-based BCI. In doing so, we provide an overview of the most recent progress achieved in this field, with an emphasis on signal processing aspects.     

Outage Probability Based Robust Distributed Beam-Forming in Multi-User Cooperative Networks with Imperfect CSI

In this paper, a new robust problem is proposed for relay beamforming in relay system with stochastic perturbation on channels of multi user and relay network. The robust problem aims to minimize the transmission power of relay nodes while the imperfect channel information (CSI) injects stochastic channel uncertainties to the parameters of optimization problem. In the power minimization framework, the relays amplification weights and phases are optimized assuming the availability of Gaussian channel distribution. The power sum of all relays is minimized while the outage probability of the instantaneous capacity (or SINR) at each link is above the outage capacity (or SINR) for each user. The robust problem is a nonconvex SDP problem with Rank constraint. Due to the nonconvexity of the original problem, three suboptimal problems are proposed. Simulation and numerical results are presented to compare the performance of the three proposed solutions with the existing worst case robust method.