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.     

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...     

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.     

Analysis of cooperative and competitive spectrum sharing for heterogeneous networks based on differential dynamics model

The heterogeneous networks belonging to different service providers form a coalition system for maximizing the profit, where they may either compete or cooperate with each other. In this paper, we introduce the Lokta–Volterra model, a differential dynamics model, to build the competitive and cooperative mechanisms of heterogeneous networks. It considers the natural growth rate of the network itself and competitive and cooperative effects among networks. Then, according to the ordinary differential principle, the stability of the proposed model and its equilibrium points are analyzed. And finally, system performances are evaluated by Vensim that is used for developing, analyzing, and packaging dynamic feedback models. Analysis and simulation results show that the natural growth rate of the network cannot increase its profit, but effective cooperative mechanism among heterogeneous networks can increase the profit of each network. Copyright © 2013 John Wiley & Sons, Ltd.     

Photonic crystal-based permutation switch for optical networks

We present, for the first time, the design of a low-cross talk scalable permutation switch employing photonic crystal ring resonators in an optical network. Through this novel approach, the transition between different states of the \(2 \times 2\) optical switch, as the basic element, is achieved by applying different operating wavelengths. Subsequently, the shuffling mechanisms in \(3 \times 3\) and \(4 \times 4\) optical networks are realized by controlling the position of photonics crystal ring resonators. Lowest cross talk levels of 6 and 5% are obtained for “bar” and “cross” switching states, respectively.     

Delay constrained throughput optimisation with imperfect CSI using discrete adaptive power

Link adaptation is an effective tool to overcome fading effects in wireless links. However, time-varying adaptive transmission rate leads to queueing delay, and moreover, in practise, imperfect channel state information (CSI) is available for the transmitter to adapt its transmission rate and power. This article aims to consider practical constraints to enhance the link adaptation scheme. We reformulate and optimise buffer delay constrained throughput of a wireless link based on outdated noisy CSI. Discrete power adaptation is proposed, in which a limited number of feedback bits (just the index of transmission power level) is required, while the performance is improved compared to the constant power and is close to continuous adaptive power. A unified scheme is set-up, where constant, discrete or continuous adaptive power transmission is utilised considering average or instantaneous bit error rate constraints based on imperfect CSI. The effectiveness of our designs is evaluated by numerical evaluations.