Bounding approaches for operation assignment and capacity allocation problem in flexible manufacturing systems

This study considers an operation assignment and capacity allocation problem that arises in flexible manufacturing systems. Automated machines are assumed to have scarce time and tool magazine capacities and the tools are available in limited quantities. The aim is to select a subset of operations with maximum total weight. The weight of an operation may represent its profit, processing load, relative priority. Several upper bounding procedures have been taken into account. The results of computational tests have revealed that the proposed upper bounding procedures produce satisfactory solutions in reasonable CPU times. We suggest using some of the bounds when the quality of the solutions is more important than the speed of achieving them and some others when the speed is more important than the quality.     

The influence of heat treatment conditions on the evaporation of defect regions in structures with InGaAs quantum dots in the GaAs matrix

Structures with In(Ga)As quantum dots in the GaAs matrix obtained using molecular-beam epitaxy are investigated using photoluminescence (PL) measurements and transmission electron microscopy. The structures were subjected in situ to the procedure of the selective thermal elimination of defect regions. Based on the results of the analysis of luminescence properties, a method for evaluating the crystalline quality of structures using the measurements of PL intensity for the GaAs matrix at high temperatures (as high as 400 K) is suggested. Procedures for the elimination of defects are investigated, namely, the single-stage selective elimination of InAs defect islands at 600°C and a two-stage procedure. The latter procedure additionally includes selective overgrowth with a thin AlAs layer and high-temperature (650–700°C) heat treatment. The optimal conditions of the process, which permit the obtaining of structures with a relatively low defect density without a considerable decrease in the density of coherent quantum dots, are found.     

Effects of Temperature,Time, and pH on the Stability of Anthocyanin Extracts: Prediction of Total Anthocyanin Content Using Nonlinear Models

In this study, different anthocyanin sources including grape skin, black carrot, and red cabbage were used to determine the effect of thermal treatment, different acidity levels, and time on the anthocyanin content and degradation. The total anthocyanin contents were modeled by neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) models. The red cabbage anthocyanin stabilities were higher than others. The anthocyanins degraded more rapidly at higher temperatures. The anthocyanin contents of samples decreased with the increase of pH from 3 to 7. Comparison of the models showed that the ANFIS model performed better than the ANN model for the estimation of total anthocyanin content in all samples. The lowest root mean square error (0.0457) and highest R ² (0.9942) values were obtained for red cabbage and grape skin in the validation period with the ANFIS model, respectively. This study showed that both models can be utilized efficiently for the prediction of total anthocyanin content affected by temperature, time, and pH.     

Synchronous generator model identification and parameter estimation from operating data

A novel technique to estimate and model parameters of a 460-MVA large steam turbine generator from operating data is presented. First, data from small excitation disturbances are used to estimate linear model armature circuit and field winding parameters of the machine. Subsequently, for each set of steady state operating data, saturable inductances L/sub ds/ and L/sub qs/ are identified and modeled using nonlinear mapping functions-based estimators. Using the estimates of the armature circuit parameters, for each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). The developed nonlinear models are validated with measurements not used in the estimation procedure.     

Boron concentrations in selected foods from borate‐producing regions in Turkey

As part of a large‐scale programme to investigate the health effects of exposure to borate minerals in Turkey, boron concentrations in vegetables, fruits and some other foods were determined. From all borate‐producing regions of the country, 22 species of fruit, 17 species of vegetable, 12 species of cereal, legume and oilseed, three species of herbs and six types of other food were collected in 1999 and analysed for boron concentrations using the Azomethine H method. The results were compared with 37 types of food collected from areas well distant from the borate‐producing areas. The total number of samples evaluated was 420. Pistachio (67.0 mg kg^?1) had the highest concentration of boron, followed by grape leaf (60.48 mg kg^?1), sour cherry (57.03 mg kg^?1), quince (38.78 mg kg^?1), peach (34.49 mg kg^?1), grape (20.70 mg kg^?1), green beans (19.49 mg kg^?1), unripe peach (18.92 mg kg^?1) and parsley (10.24 mg kg^?1) from the borate‐producing regions. A large number of foods contained boron at higher concentrations than those reported in the literature, most probably due to the high boron content of these Turkish soils. According to the results of t‐tests, the area itself does not have a significant effect (p > 0.05) on boron concentration in all foods evaluated so far. Copyright © 2003 Society of Chemical Industry