Solar energy potential in Turkey

The solar energy potential of Turkey (36° and 42° north latitudes) is investigated based on measurements of a complete year's data. Global solar radiation and sunshine duration for various stations in Turkey are presented. Consumption and demand projections of solar energy in Turkey are also introduced.This study was supported as Research Project 07/2003/47 of Gazi University. The authors are grateful to the Turkish State Meteorological Service for providing the data

Utilization of waste-heat recovery in textile drying

The growing cost of energy has required its more effective use. However, many industrial heating processes generate waste energy. Use of the waste-heat recovery systems decreases energy consumption. Drying is often one of the most energy-intensive operations in textile processes and such dryers exhaust large amounts of warm and moist air. The heat-recovery systems utilize the heat produced for the drying process. © 2004 Elsevier Ltd. All rights reserved

Potential of renewable energies in Turkey

Turkey has a large economy and, thus, it expects a very large growth in energy demand. Today, Turkey's economy is mainly dependent on oil, natural gas, and electricity. On the other hand, Turkey's energy production meets nearly 35% of its total primary energy consumption. Turkey is an energy importing country. Turkey presently has considerable renewable energy sources. The most important renewable sources are hydropower, wind, solar, geothermal, and biomass. Turkey has about 1% of the total world hydroelectric potential and its significant potential for geothermal power production is ranked seventh in the world. This paper presents the energy situation and potential of the renewable energy sources in Turkey. © 2007 ASCE

Sectoral energy consumption in Turkey

Turkey expects a very large growth in energy demand, especially for electricity and natural gas. Today, Turkey's energy production meets nearly 48% of the total primary energy demand. Total primary energy demand will reach 98 Mtoe in 2001 and 308 Mtoe in 2020. Import of primary energy will reach 226 Mtoe and production of primary energy will increase 81 Mtoe in 2020. As seen, Turkey is an importer country for primary energy. Turkey's indigenous energy sources are limited, and the country is heavily dependent on the import of primary energy from abroad. The growth of Turkey's industry is giving rise to a substantial increase in energy demand. In this paper, the primary energy production and sectoral consumption in Turkey is investigated. Further, a sectoral energy demand projection in Turkey is given until 2020. © 2002 Elsevier Science Ltd. All rights reserved

The effect of thermal insulation of clothing on human thermal comfort

The human body is under various continuous external conditions, some of which make wearers uncomfortable. Usually, human thermal comfort depends on combinations of clothing, climate, and physical activity. Specifically thermal insulation in clothing is an important parameter of thermal comfort. Therefore, this paper discusses theoretically the basic physical principles of the body's mechanism for heat transfer with the environment. In this study, the body's heat balance was examined, and the effects of clothes and various climatic conditions on thermal comfort were investigated for different physical activities

Air permeability of woven fabrics

Air permeability is an important property for wovens and it depends on many parameters of the fabric. Thus, a theoretical determination is highly complex and difficult in relating the parameters to the air permeability. Therefore, establish of the air permeability is usually made experimentally. In this study, it has been attempted to establish a simple theoretical model for the air permeability of woven fabrics. For the purpose, a capillary model of porous systems on D'Arcy's law was used, and theoretical values were investigated

Comparison of the properties of a cotton package made of vortex (MVS) and open-end rotor yarns

In this study, the effects of the different structures and properties of yarns produced with vortex and open-end (rotor) spinning systems on the colour efficiency of package dyeing were investigated. For this purpose, 100% combed open-end and vortex yarns with different linear densities were produced from the same raw material blend and wound on perforated plastic dye tubes (cones) according to the principle of loose winding (package density: 370 g/dm3), and the physical properties of the yarns were tested. The bobbins were dyed with reactive dye at three different dyebath concentrations in a universal package dyeing machine and then dried with a high frequency drier. The colour strengths (K/S) and differences (?E) were measured by a spectrophotometer. The test results were analysed using statistical methods. In conclusion, the unevenness, total imperfections and hairiness values of the vortex yarns gave better results compared to the rotor spun yarns. It was determined that vortex spun yarns have darker shades than rotor spun yarns, and the effect of yarn physical properties on finishing was found to be significant

Prediction of CIELab data and wash fastness of nylon 6,6 using artificial neural network and linear regression model

We tried to predict the CIELab data and wash fastness values of scoured nylon 6.6 knitted fabric dyed with 1:2 metal-complex acid dyes and aftertreated using three different methods named as syntan, syntan/cation and full backtan by artificial neural network (ANN) with Levenberg-Marquardt algorithm and regression models. Afterward, the predicting performance of these models was tested and compared with each other using unseen data sets. We were able to achieve to predict the all colorimetric data satisfactorily such as L*, a*, b*, C, ho and wash fastness performance using both models. The statistical findings indicated that the regression models provide more accurate prediction for all colour data with an average error of 1 % contrast to previous study. In terms of prediction of fastness, artificial neural network is a bit more useful than regression models for prediction of staining value on the nylon part of adjacent multifiber

Optimization of air permeability of knitted fabrics with the Taguchi approach

Air permeability is often used in evaluating and comparing the "breathability" of various fabrics for end uses such as raincoats, tents, and uniform shirtings. This paper focuses on the optimization of yarn and fabric factors affecting the air permeability of knitted fabrics. For this purpose, the Taguchi experimental design technique, analysis of variance (ANOVA), and signal-to-noise (S/N) ratio were used. Instead of 27 different fabric configurations, with respect to full factorial design, only nine fabric configurations with respect to Taguchi's L9 orthogonal design were knitted and tested. As a result of these experiments, the fabric configuration satisfying the maximum air permeability value was determined, which showed considerable improvement in the S/N ratio as compared with the initial design. © 2011 The Textile Institute

The prediction of elongation and recovery of woven bi-stretch fabric using artificial neural network and linear regression models

Stretch woven fabrics are widely used because of their good elongation and recovery (residual extension) properties. Several parameters and test method are used to measure the properties of these fabrics. Each different set of test parameters means a different test application. Sometimes, repeating tests for different test involves wasting time and labour. In this study, the test results were used to try and predict elongation and recovery using neural network and linear regression models. Certain test parameters such as rate of extension, gauge length (jaw separation), and maximum load were selected as input variables. The accuracies of predictions of elongation in the direction of warp and weft by both models were found to be similar and satisfactory. The predictions for the recovery test showed differences as to fabric warp and weft direction. All the statistical results indicate that predicting the fabrics' test results from an unseen data set is very good for both models