Thermal and mechanical properties of polyurethane rigid foam/modified nanosilica composite

Surface modification of fumed nanosilica was performed by using n‐(2‐aminoethyl)‐3‐aminopropyltrimethoxysilane as a coupling agent. Then, modified nanosilica was utilized in the preparation of polyurethane rigid foam. The characterization and the study of properties were done by some techniques, such as Fourier transform infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy, dynamic mechanical analysis, and thermomechanical analysis. Also, tensile test was examined to evaluate the static mechanical properties. With the increasing of modified nanosilica, thermal and static mechanical properties were enhanced, but dynamic mechanical behavior was different from static mechanical behavior because of the different properties of interfacial domain and bulk matrix. The presence of functional groups on the nanosilica surface affected stoichiometry and reduced hard phase formation in bulk polymer. The decrease in glass transition temperature (T_g) confirmed this statement. POLYM. ENG. SCI., 2010. © 2009 Society of Plastics Engineers     

PM10 speciation and determination of air quality target levels. A case study in a highly industrialized area of Spain

The paper shows how PM speciation studies allow the evaluation of the strategies to be followed to diminish PM pollution in highly industrialized areas with a large number of potential pollution sources. Evolution of levels and speciation of PM10 in the ceramic producing area of Castelló (East Spain) was studied from April 2002 until December 2005. PM10 levels were measured at one rural (Borriana-rural), two suburban (Almassora and Onda) and three urban (Borriana-urban, L'Alcora and Vila-real) sites, all influenced by the ceramics industry. Average PM10 levels varied between 27 and 36 microg/m3 for the study period. Evaluation of 1996-2005 PM data from Onda shows a clear decrease of PM levels since the beginning of 2002. Summer peak levels and winter minima occurred at both rural and suburban sites, whereas urban sites had no clear seasonal trend, with high PM10 episodes being due variously to local, regional, and African dust intrusion events. PM10 chemical analysis at four of the sites showed the dominant constituent to be mineral matter, exceeding by 5-12 microg/m3 the usual ranges of annual mineral loadings in PM10 at comparable Spanish urban or regional background sites with no industrial influence. Given current PM10 loadings, we recommend a lowering target of 3-5 microg/m3 of the annual mean at the urban sites, which should be achievable given available emission abatement techniques.     

Gas leak locating in steel pipe using wavelet transform and cross-correlation method

Pipelines leakage in power plant, petrochemical complexes, and refineries can lead to explosion, pollution, and severe physical damages, so precise and on time leak locating is very important. There are many techniques for detecting and locating the leakage. In this research, we represent the leak locating principle in pressurized gas pipelines by using acoustic emission theory. An algorithm for finding the location of continuous acoustic waves resulted from leakage is provided by MATLAB software. The used leak locating technique is a combination of wavelet transform, filtering, and cross-correlation methods. The resulted acoustic emission signals were analyzed into high and low frequencies by wavelet transform and available noises on them were omitted completely by filtering. Then de-noised acoustic emission signals were reconstructed. Time differences of de-noised waves were calculated precisely by using cross-correlation function. For studying the accuracy of used method, acoustic emission testing was done by continuous leakage source. The resulted signals of leakage were recorded by two acoustic sensors in two sides of leakage source, and time difference and leak location were calculated by using the algorithm. Several tests were done by changing sensor distance from leakage source and error percent of less than 3 % was resulted in leak locating that indicated high precision of used algorithm.     

Modeling Acoustic Emission Signals Caused by Leakage in Pressurized Gas Pipe

Leakage in high pressure pipes creates stress waves which transmitted through the pipe wall. These waves can be recorded by using acoustic sensor or accelerometer installed on the pipe wall. Knowing how these waves vibrate pipe is very important in continuous leak source locating process. In this paper the pipe radial displacement caused by acoustic emission due to leakage is modeled analytically. The standard form of Donnell’s nonlinear cylindrical shell theory is used to derive the motion equation of the pipe for simply supported boundary condition. Using Galerkin method, the motion equation has been solved and a system of nonlinear equations with 7 degrees of freedom is obtained. A MATLAB code according to Runge-Kutta numerical method is generated to solve these equations and derive the pipe radial displacement. To check the theoretical results, acoustic emission testing with continuous leak source and linear array of two sensors positioned on two sides of the leakage source were carried out. The major noise of recorded signals was removed through the wavelet transform and filtering technique. For better analysis, fast Fourier transform (FFT) was taken from theoretical and de-noised experimental results. Comparing the results showed that the frequency which carried the most amount of energy is the same that expresses excellent agreement between the theoretical and experimental results validating the analytical model.     

Multi-criteria expertness based cooperative Q-learning

One of the most influential points in cooperative learning is the type of exchanging information. If the content of exchanging information among agents is rich, cooperation gives rise to better results. To extract proper knowledge of agents during the cooperation process, some expertness measures that assign expertness levels to the other agents are used. In this paper, a new method named Multi-Criteria Expertness based cooperative Q-learning (MCE) is proposed that utilizes all of the expertness measures and attempts to enrich the exchanging information more efficiently. In MCE, all expertness measures are considered simultaneously and collective knowledge is equal to the combination of learned knowledge by each of expertness measures. The experimental results confirm outstanding performance of the proposed method on a sample maze world and a hunter-prey problem.     

Modeling and solving the bi-objective capacitated location-routing problem with probabilistic travel times

The location-routing problem (LRP) is a relatively new research area within location analysis that concerns simultaneously both the problems of location of facilities and routing of vehicles among the established facilities and the demand points. In this work, we address the capacitated LRP with probabilistic travel times, which may arise in many practical contexts in logistics and supply chain management, and present some bi-objective mathematical programming formulations to model the problem using different stochastic programming approaches. The first objective is to minimize the overall system-wide costs, while the second objective concerns minimization of the maximum delivery time to the customers. In all the cases, the deterministic equivalents of the stochastic models have been extracted. To solve the resulted models, a variable neighborhood descent-based heuristic is proposed and finally computational study is performed and numerical results are reported.     

A clustering approach to identify the time of a step change in Shewhart control charts

Control charts are the most popular statistical process control tools used to monitor process changes. When a control chart indicates an out‐of‐control signal it means that the process has changed. However, control chart signals do not indicate the real time of process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the change is known as the change‐point estimation problem. Most of the traditional methods of estimating the process change point are developed based on the assumption that the process follows a normal distribution with known parameters, which is seldom true. In this paper, we propose clustering techniques to estimate Shewhart control chart change points. The proposed approach does not depend on the true values of the parameters and even the distribution of the process variables. Accordingly, it is applicable to both phase‐I and phase‐II of normal and non‐normal processes. At the end, we discuss the performance of the proposed method in comparison with the traditional procedures through extensive simulation studies. Copyright © 2008 John Wiley & Sons, Ltd.     

Fault-tolerant switched reluctance motor drive using adaptive fuzzy logic controller

An adaptive fuzzy controller has been designed to develop a high-performance fault-tolerant switched reluctance motor (SRM) drive. The fuzzy controller continuously adapts its properties to regulate the machine torque as desired by the drive system even under fault conditions. The adaptation of the fuzzy membership functions results in extended conduction period and increased peak current of the healthy phases to deliver the commanded torque, as much as possible. The adaptive fuzzy controller provides smooth torque output with minimum ripple, even under fault conditions, yielding a high-performance SRM drive with fault-tolerant capability.     

PI and fuzzy estimators for tuning the stator resistance in directtorque control of induction machines

Direct torque control (DTC) of induction machines uses the stator resistance of the machine for estimation of the stator flux. Variations of stator resistance due to changes in temperature or frequency make the operation of DTC difficult at low speeds. A method for the estimation of changes in stator resistance during the operation of the machine is presented. The estimation method is implemented using proportional-integral (PI) control and fuzzy logic control schemes. The estimators observe the machine stator current vector to detect the changes in stator resistance. The performance of the two methods are compared using simulation and experimental results. Results obtained have shown improvement in DTC at low speeds