Developing a multi-objective, multi-item inventory model and three algorithms for its solution

We develop a multi-objective model in a multi-product inventory system.The proposed model is a joint replenishment problem (JRP) that has two objective functions.The first one is minimization of total ...     

Multiscale RBF-based central high resolution schemes for simulation of generalized thermoelasticity problems

In this study, average-interpolating radial basis functions (RBFs) are successfully integrated with central high-resolution schemes to achieve a higher-order central method. This proposed method is used for simulation of generalized coupled thermoelasticity problems including shock (singular) waves in their solutions. The thermoelasticity problems include the LS (systems with one relaxation parameter) and GN (systems without energy dissipation) theories with constant and variable coefficients. In the central high resolution formulation, RBFs lead to a reconstruction with the optimum recovery with minimized roughness on each cell: this is essential for oscillation-free reconstructions. To guarantee monotonic reconstructions at cell-edges, the nonlinear scaling limiters are used. Such reconstructions, finally, lead to the total variation bounded (TVB) feature. As RBFs work satisfactory on non-uniform cells/grids, the proposed central scheme can handle adapted cells/grids. To have cost effective and accurate simulations, the multiresolution–based grid adaptation approach is then integrated with the RBF-based central scheme. Effects of condition numbers of RBFs, computational complexity and cost of the proposed scheme are studied. Finally, different 1-D coupled thermoelasticity benchmarks are presented. There, performance of the adaptive RBF-based formulation is compared with that of the adaptive Kurganov-Tadmor (KT) second-order central high-resolution scheme with the total variation diminishing (TVD) property.     

Adaptive simulation of wave propagation problems including dislocation sources and random media

An adaptive Tikhonov regularization is integrated with an h-adaptive grid-based scheme for simulation of elastodynamic problems, involving seismic sources with discontinuous solutions and random media. The Tikhonov method is adapted by a newly-proposed detector based on the MINMOD limiters and the grids are adapted by the multiresolution analysis (MRA) via interpolation wavelets. Hence, both small and large magnitude physical waves are preserved by the adaptive estimations on non-uniform grids. Due to developing of non-dissipative spurious oscillations, numerical stability is guaranteed by the Tikhonov regularization acting as a post-processor on irregular grids. To preserve waves of small magnitudes, an adaptive regularization is utilized: using of smaller amount of smoothing for small magnitude waves. This adaptive smoothing guarantees also solution stability without over smoothing phenomenon in stochastic media. Proper distinguishing between noise and small physical waves are challenging due to existence of spurious oscillations in numerical simulations. This identification is performed in this study by the MINMOD limiter based algorithm. Finally, efficiency of the proposed concept is verified by: 1) three benchmarks of one-dimensional (1-D) wave propagation problems; 2) P-SV point sources and rupturing line-source including a bounded fault zone with stochastic material properties.     

Parameters affecting reaction rate and conversion of TiO2 chlorination in a fluidized bed reactor: Experimental and modeling approach

Pilot scale chlorination of TiO₂ was carried out with CO as reducing agent. The experimental analysis and modeling of chlorination process of TiO₂ in the presence of CO and Cl₂ in a semi-continuous fluidized bed reactor were aimed. Chlorination process was continuously monitored by measuring the amount of produced TiCl₄ with time. The effects of different operating parameters including chlorination temperature, feedstock particle size and size distribution, amount of feedstock and Cl₂ and CO flow rates on the conversion were systematically investigated. A gradual increase in chlorination temperature led to monotonous increase of conversion rate. Conversion decreased with increased particle size of feedstock. An increase in loaded feedstock led to a decrease in reaction conversion. A model was proposed to predict conversion, particle size distribution and mole fraction of components in gas phase as reaction proceeds. A good agreement between conversions predicted by the model and experimental data under various operating conditions was observed.     

Wavelet-based iterative data enhancement for implementation in purification of modal frequency for extremely noisy ambient vibration tests in Shiraz-Iran

The main purpose of the present study is to enhance high-level noisy data by a wavelet-based iterative filtering algorithm for identification of natural frequencies during ambient wind vibrational tests on a petrochemical process tower. Most of denoising methods fail to filter such noise properly. Both the signal-to-noise ratio and the peak signal-to-noise ratio are small. Multiresolution-based one-step and variational-based filtering methods fail to denoise properly with thresholds obtained by theoretical or empirical method. Due to the fact that it is impossible to completely denoise such high-level noisy data, the enhancing approach is used to improve the data quality, which is the main novelty from the application point of view here. For this iterative method, a simple computational approach is proposed to estimate the dynamic threshold values. Hence, different thresholds can be obtained for different recorded signals in one ambient test. This is in contrast to commonly used approaches recommending one global threshold estimated mainly by an empirical method. After the enhancements, modal frequencies are directly detected by the cross wavelet transform (XWT), the spectral power density and autocorrelation of wavelet coefficients. Estimated frequencies are then compared with those of an undamaged-model, simulated by the finite element method.     

Effect of transition metal elements on the structural and optical properties of ZnO nanoparticles

Undoped and transition metal (TM)-doped ZnO nanoparticles (Zn_0.98X_0.02O-NPs, X = Mn, Cr, Co and Fe) were synthesized from a metal nitrate precursor and gelatin by a sol–gel method. The compounds were synthesized at calcination temperatures of 550^°C for 6 h. The synthesized undoped/doped ZnO-NPs were characterized by X-ray diffraction analysis (XRD) and scanning electron microscopy (SEM). XRD results indicated that the sample products were crystalline with a hexagonal wurtzite phase. SEM images showed the ZnO-NPs nearly spherical shapes and a non-uniform shape for doped ZnO-NPs. The crystalline development in the ZnO-NPs was investigated by X-ray peak broadening. The size–strain plot (SSP) method was used to study the individual contributions of crystallite sizes and lattice strain of the undoped and doped ZnO-NPs. The obtained results showed that strain of the NPs plays an important role in peak broadening; moreover, the mean crystalline size of the undoped and doped ZnO-NPs estimated from the SEM and the SSP method was highly inter-correlated. Finally, optical properties of the samples were studied by a UV–Vis spectrometer.     

Developing a multi-objective,multi-item inventory model and three algorithms for its solution

We develop a multi-objective model in a multi-product inventory system.The proposed model is a joint replenishment problem(JRP) that has two objective functions.The first one is minimization of total ordering and inventory holding costs,which is the same objective function as the classic JRP.To increase the applicability of the proposed model,we suppose that transportation cost is independent of time,is not a part of holding cost,and is calculated based on the maximum of stored inventory,as is the case in many real inventory problems.Thus,the second objective function is minimization of total transportation cost.To solve this problem three efficient algorithms are proposed.First,the RAND algorithm,called the best heuristic algorithm for solving the JRP,is modified to be applicable for the proposed problem.A multi-objective genetic algorithm(MOGA) is developed as the second algorithm to solve the problem.Finally,the model is solved by a new algorithm that is a combination of the RAND algorithm and MOGA.The performances of these algorithms are then compared with those of the previous approaches and with each other,and the findings imply their ability in finding Pareto optimal solutions to 3200 randomly produced problems.     

Low-temperature electrochemical synthesis and characterization of ultrafine Y（OH）_3 and Y_2O_3 nanoparticles

Ultrafine Y(OH)3 nanoparticles were successfully deposited from an additive-free 0.005 mol/L YCl3 low-temperature bath on the steel cathode at the current density of 0.5 mA/cm2 and bath temperature of 10 oC. Heat treatment of the prepared Y(OH)3 nanoparticles at 600 oC in air led to the formation of Y2O3 nanoparticles. Thermal behavior and phase transformation during the heat treatment of Y(OH)3 were investigated by differential scanning calorimetry (DSC) and thermogramimetric analysis (TGA). The morphologies, crystal structures and compositions of the prepared materials were examined by means of scanning and transmission electron microscopy (SEM and TEM) as well as X-ray diffraction (XRD) and FT-IR spectroscopy. The results showed that the prepared Y(OH)3 nanoparticles was essentially amorphous and composed of well dispersed ultrafine particles with size of 4 nm. After heat treatment, the obtained oxide product was well crystallized cubic phase of Y2O3 nanoparticles with the grain size of around 5 nm. It was concluded that low-temperature cathodic electrodeposition offered a facile and feasible way for preparation of ultrafine Y(OH)3 and Y2O3 nanoparticles.     

Optimal operation of energy at hydrothermal power plants by simultaneous minimization of pollution and costs using improved ABC algorithm

The aim of this paper is simultaneous minimization of hydrothermal units to reach the best solution by employing an improved artificial bee colony (ABC) algorithm in a multi-objective function consisting of economic dispatch (ED) considering the valve-point effect and pollution function in power systems in view of the hot water of the hydro system. In this type of optimization problem, all practical constraints of units were taken into account as much as possible in order to comply with the reality. These constraints include the maximum and minimum output power of units, the constraints caused by the balance between supply and demand, the impact of pollution, water balance, uneven production curve considering the valve-point effect and system losses. The proposed algorithm is applied on the studied system, and the obtained results indifferent operating conditions are analyzed. To investigate in various operating conditions, different load profiles in 12 h are taken into account. The obtained results are compared with those of the other methods including the genetic algorithm (GA), the Basu technique, and the improved genetic algorithm. Fast convergence is one of this improved algorithm features.