Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem

The increasing market demand for product variety forces manufacturers to design mixed-model assembly lines (MMAL) on which a variety of product models similar to product characteristics are assembled. This paper presents a method combining the new ranked based roulette wheel selection algorithm with Pareto-based population ranking algorithm, named non-dominated ranking genetic algorithm (NRGA) to a just-in-time (JIT) sequencing problem when two objectives are considered simultaneously. The two objectives are minimisation the number of setups and variation of production rates. This type of problem is NP-hard. Various operators and parameters of the proposed algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. The solutions obtained via NRGA are compared against solutions obtained via total enumeration (TE) scheme in small problems and also against four other search heuristics in small, medium and large problems. Experimental results show that the proposed algorithm is competitive with these other algorithms in terms of quality and diversity of solutions.     

Facial expression recognition by using differential geometric features

ABSTRACT

In recent years, a growing interest has been created for improvement of human interaction with computers. Hence, automatic recognition of facial expressions has become one of the active research topics. The purpose of this paper is to identify facial expressions, by using differential geometric features. In the proposed method, only the first and last images are used and differential features are extracted from these two images. Differential geometric features are extracted from changes in the important points of the face in the two images. In this method, the distance between the important points of the face and the reference point was calculated in both directions x and y, for two images, and with the difference between the distance, the differential geometric features between the two images were obtained. Based on the results, with this method, recognition accuracy of six facial expressions in the database was 96.44%, CK +.     

Statistical analysis of the performance of a bi-functional catalyst under operating conditions of LPDME process

Liquid phase direct synthesis of dimethyl ether (LPDME™) under various operating conditions (temperature, H₂/CO molar ratio of feed) was conducted in a mechanically agitated slurry reactor system. Each run was monitored for 60 h time on stream (TOS) in order to confirm the high activity and long-term stability of a bi-functional catalytic system (CuO–ZnO–Al₂O₃/H-MFI-90). Statistical experimental design was applied for determining the optimum operating conditions under which the catalytic system shows the highest performance. A significant improvement in the performance of the bi-functional catalyst was observed when the temperature and H₂/CO molar ratio of feed were increased from 200 to 240 °C and 1 to 2, respectively at a constant pressure of 35 bar and GHSV equal to 1100 mLn/(g-cat h). CO conversion was increased from 9.1 mol% at T = 200 °C and H₂/CO = 1 to 79.6 mol% at T = 240 °C and H₂/CO = 2 and the yield and selectivity of DME also increased from 7.11% to 47.05% and 41.57% to 59.96%, (molar basis) respectively. No significant deactivation has been observed during 60 h TOS at different operating conditions. Furthermore, from the main effect plots and response table results, it was concluded that the most effective factor on activity and stability of bi-functional catalytic system is temperature.     

Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach

In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.     

Low phase-noise VCO utilizing modified symmetric load and partial positive feedback for FDSM

The voltage-controlled oscillator (VCO) in frequency-based $\Updelta\Upsigma$ modulator (FDSM) systems behaves as a voltage-to-phase integrator converting an analog input voltage to phase information. Tuning range and phase noise are the most important factors of the basic design of a VCO in FDSM systems. In this paper a novel low phase-noise and wide tuning-range differential VCO based on a differential ring oscillator with modified symmetric load and a partial positive feedback in the differential delay cell is presented. The VCO is combined with a new bias circuit and implemented using 90 nm CMOS process technology. By using modified NMOS symmetric loads and a PMOS tail for delay cells, the VCO phase noise can be reduced with more than 13 dB compared to that of the conventional approach, achieving ?125 dBc/Hz at 500 kHz offset from the center frequency of 450 MHz. The wide tuning-range by using two added transistors (parallel with the active loads) increases the operating frequency range by about 22%, while the partial positive feedback provides the necessary bias condition for the circuit to oscillate. The designed VCO operating at a low power supply voltage of 0.6V can achieve low power consumption of 670???W at oscillation frequency of 800 MHz and good linearity reducing harmonic distortion in the $\Updelta\Upsigma$ modulator.     

Preliminary Results of IS Plasma Focus as a Breeder of Short-Lived Radioisotopes 12C(d,n)13N

Modified IS (Iranian Sun) plasma focus (10 kJ,15 kV, 94 ??F, 0.1 Hz) has been used to produce the short-lived radioisotope ¹³N (half-life of 9.97 min) through ¹²C(d,n)¹³N nuclear reaction. The filling gas was 1.5?C3 torr of hydrogen (60%) deuterium (40%) mixture. The target was solid nuclear grade graphite with 5 mm thick, 9 cm width and 13 in length. The activations of the exogenous target on average of 20 shots (only one-third acceptable) through 10?C13 kV produced the 511 keV gamma rays. Another peak found at the 570 keV gamma of which both was measured by a NaI portable gamma spectrometer calibrated by a ¹³⁷Cs 0.25 ??Ci sealed reference source with its single line at 661.65 keV and ²²Na 0.1 ??Ci at 511 keV. To measure the gamma rays, the graphite target converts to three different phases; solid graphite, powder graphite, and powder graphite in water solution. The later phase approximately has a doubled activity with respect to the solid graphite target up to 0.5 ??Ci of 511 keV and 1.1 ??Ci of 570 keV gamma lines were produced. This increment in activity was perhaps due to structural transformation of graphite powder to nano-particles characteristic in liquid water.     

Magnetic field analysis of a 9–6 without permanent magnet brushless DC motor by using 3-D finite element method

This paper presents characteristic analysis of without permanent magnet brushless DC (WPMBLDC) motor by using three-dimensional (3-D) finite element method (FEM). The paper describes the complex magnetic structure and principle of the presented WPMBLDC motor in detail. The magnetic field distribution of the presented motor is numerically computed by 3-D FEM. The primary characteristics of the motor including magnetic flux density, flux linkage and inductance profile are obtained. A prototype WPMBLDC machine has also been built and tested; additionally, the accuracy of 3-D FE analysis is verified by comparing the calculated results with the experimental measured values.     

The Design and Preparation of a Simulator for the Water Coning Phenomenon Applying in Fractured Reservoirs

Abstract

The problem of water production is one of the major technical, environmental, and economical issues associated with oil and gas production. The main goal of this study is to prepare a reservoir numerical simulator with emphasis on water coning. The study mostly involves the numerical simulation of water coning and includes proposed correlations in the literature. The computer program included four distinct modules to calculate: critical or maximum allowable oil rate, water breakthrough time, well performance after water coning take palaces, and water coning simulation. Flow equations of water and oil were discretized and numerically solved for two-dimensional coordinates. The implicit scheme was used to calculate unknown pressures of any grid block. For calculation of water saturation, explicit scheme was used. Real field data of a well in southwest Iran was put into the program and critical rate, water breakthrough time, well performance after water coning, and water coning simulation of reservoir were determined. The authors found that the results of correlations are very far from the reality. On the other hand, numerical simulation shows good agreement with real production data. In addition, it was observed that the present production rate of this well would result in rapid water coning.     

Batch and fixed‐bed column adsorption of Cu(II), Pb(II) and Cd(II) from aqueous solution onto functionalised SBA‐15 mesoporous silica

Functionalised SBA‐15 mesoporous silica with polyamidoamine groups (PAMAM‐SBA‐15) was successfully prepared with the structure characterised by X‐ray diffraction, nitrogen adsorption–desorption, Fourier transform infrared spectra and thermogravimetric analysis. PAMAM‐SBA‐15 was applied as adsorbent for Cu(II), Pb(II) and Cd(II) ions removal from aqueous solution. The effects of the solution pH, adsorbent dosage and metal ion concentration were studied under the batch mode. The Langmuir model was fitted favourably to the experimental data. The maximum sorptive capacities were determined to be 1.74 mmol g^?1 for Cu(II), 1.16 mmol g^?1 for Pb(II) and 0.97 mmol g^?1 for Cd(II). The overall sorption process was fast and its kinetics was fitted well to a pseudo‐first‐order kinetic model. The mean free energy of sorption, calculated from the Dubinin–Radushkevich isotherm, indicated that the sorption of lead and copper, with E > 16 kJ mol^?1, followed the sorption mechanism by particle diffusion. The adsorbent could be regenerated three times without significant varying its sorption capacity. A series of column tests were performed to determine the breakthrough curves with varying bed heights and flow rates. The breakthrough data gave a good fit to the Thomas model. Maximum sorption capacity of 1.6, 1.3 and 1.0 mmol g^?1 were found for Cu(II), Pb(II) and Cd(II), respectively, at flow rate of 0.4 mL min^?1 and bed height of 8 cm, which corresponds to 83%, 75% and 73% of metallic ion removal, respectively, which very close to the value determined in the batch process. Bed depth service time model could describe the breakthrough data from the column experiments properly. © 2012 Canadian Society for Chemical Engineering