Yücel Tekin

The Engine Tests of Biodiesel from Used Frying Oil

Biodiesel is an environmentally friendly and a renewable alternative diesel fuel that can be used in diesel engines with little or no modification. Used frying oil is one of the raw materials which can be used for biodiesel production. The objective of this study was to investigate the effects of used frying oil originated from biodiesel on engine performance and emissions in a Fiat Doblo 1.9 DS, four-cylinder, four-stroke, 46 kW power capacity diesel engine. Comparative measurements with no. 2 diesel fuel were conducted on both engine power and emission characteristics of each of the fuel used. Biodiesel, when compared to no. 2 diesel fuel, showed reduction in wheel force over 3.35% and it also reduced the wheel power by over 2.03%. In the acceleration tests, 40–100 km/h and 60–100 km/h acceleration periods were measured and a reduction of 7.32% and 8.78% were observed, respectively. According to emission tests, as a result of biodiesel consumption, a reduction of 8.59% in CO emission and an increase of 2.62% were observed in CO2 emission. Also, NOx emissions increased by 5.03% as a result of biodiesel consumption. HC emissions and particulate emissions have a significant effect on air pollution. As a result of biodiesel usage, HC and particulate emissions decreased by 30.66% and 63.33%, respectively. When the fuel consumption amounts are compared, it was observed that biodiesel consumption was 2.43% less than that of no. 2 diesel fuel. These emission engine test results show that biodiesel is a more environment friendly fuel than no. 2 diesel fuel.

Geometric and Mechanical Properties of Mung Bean (Vigna Radiata L.) Grain: Effect of Moisture

In this research, selected geometric and mechanical properties of mung bean grain were evaluated as a function of moisture content. Five levels of moisture content ranging from 7.28 to 17.77% d.b. (dry basis) were used. The average length, width, thickness, arithmetic and geometric mean diameters, sphericity, thousand grain mass and angle of repose ranged from 5.145 to 6.199 mm, 3.760 to 4.474 mm, 3.537 to 4.223 mm, 4.147 to 4.965 mm, 4.090 to 4.893 mm, 0.795 to 0.789, 52.3 to 64.6 g, and 25.87 to 29.38° as the moisture content increased from 7.28 to 17.77% d.b., respectively. The bulk density was found to be decreased from 821.3 to 745.2 kg/m3, whereas the grain volume, true density, porosity, terminal velocity, and projected area were found to be increased from 27.88 to 47.33 mm3, 1230.0 to 1456.7 kg/m3, 30.43 to 46.57%, 4.86 to 5.29 m/s, and 17.48 to 19.26 mm2, respectively. There is a 43% increase in surface area from grain moisture content of 7.28 to 17.77% d.b. The static coefficient of friction on various surfaces increased linearly with the increase in moisture content. The rubber as a surface for sliding offered the maximum friction followed by galvanised iron, medium density fibreboard, stainless steel, aluminium and glass sheet. As moisture content increased from 7.28 to 17.77%, the rupture forces values ranged from 67.39 to 39.44 N; 63.86 to 42.18 N, and 53.96 to 41.79 N for thickness (Z axis), length (Y-axis) and width (X-axis), respectively.

Potential of on-line visible and near infrared spectroscopy for measurement of pH for deriving variable rate lime recommendations.

This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate.

Sensing and 3D Mapping of Soil Compaction

Soil compaction is an important physical limiting factor for the root growth and plant emergence and is one of the major causes for reduced crop yield worldwide. The objective of this study was to generate 2D/3D soil compaction maps for different depth layers of the soil. To do so, a soil penetrometer was designed, which was mounted on the three-point hitch of an agricultural tractor, consisting of a mechanical system, data acquisition system (DAS), and 2D/3D imaging and analysis software. The system was successfully tested in field conditions, measuring soil penetration resistances as a function of depth from 0 to 40 cm at 1 cm intervals. The software allows user to either tabulate the measured quantities or generate maps as soon as data collection has been terminated. The system may also incorporate GPS data to create geo-referenced soil maps. The software enables the user to graph penetration resistances at a specified coordinate. Alternately, soil compaction maps could be generated using data collected from multiple coordinates. The data could be automatically stratified to determine soil compaction distribution at different layers of 5, 10,.…, 40 cm depths. It was concluded that the system tested in this study could be used to assess the soil compaction at topsoil and the randomly distributed hardpan formations just below the common tillage depths, enabling visualization of spatial variability through the imaging software.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

A Modular Training Project for Vocational Education and Improvement in Turkey

Various vocational training methods, most of which overlap with lifelong learning programmes, are being used to address the problem of unemployment – an issue of vital importance, especially for developing countries. This article examines the introduction of a modular certification project in Turkey supported by EU training funds. The objective of the project is to enable people who are unemployed to find work and people in employment to improve their professional skills and competences, and therefore their opportunities. The researchers observed the employment status and development of the 245 people who received a certificate. They found that 89% either had found work in a sector consistent with their training, thanks to their participation in the project, or had to taken up a different position in their current field of employment.

Investigation of performance and emission characteristics of waste cooking oil as biodiesel in a diesel engine

Biodiesel is one of the most popular prospective alternative fuels and can be obtained from a variety of sources. Waste frying oil is one such source along with the various raw vegetable oils. However, some specific technical treatments are required to improve certain fuel properties such as viscosity and calorific value of the biodiesel being obtained from waste cooking oil methyl ester (WCOME). Various treatments are applied depending on the source and therefore the composition of the cooking oil. This research investigated the performance of WCOME as an alternative biofuel in a four-stroke direct injection diesel engine. An 8-mode test was undertaken with diesel fuel and five WCOME blends. The best compromise blend in terms of performance and emissions was identified. Results showed that energy utilization factors of the blends were similar within the range of the operational parameters (speed, load and WCOME content). Increasing biodiesel content produced slightly more smoke and NOx for a great majority of test points, while the CO and THC emissions were lower.

On-line Vis-Nir sensor determination of soil variations of sodium, potassium and magnesium

Among proximal measurement methods, visible and near infrared (Vis-Nir) spectroscopy probably has the greatest potential for determining the physico-chemical properties of different natural resources, including soils. This study was conducted to determine the sodium, potassium and magnesium variations in a 10. Ha field located in Karacabey district (Bursa Province, Turkey) using an on-line Vis-Nir sensor. A total of 92 soil samples were collected from the field. The performance and accuracy of the Na, K and Mg calibration models was evaluated in cross-validation and independent validation. Three categories of maps were developed: 1) reference laboratory analyses maps based on 92 points 2) Full-data point maps based on all 6486 on-line points Vis-Nir predicted in 2013 and 3) full- data point maps based on all 2496 on-line points Vis-Nir predicted in 2015. Results showed that the prediction performance in the validation set was successful, with average R2 values of 0.82 for Na, 0.70 for K, and 0.79 for Mg, average root mean square error of prediction (RMSEP) values of 0.02% (Na), 0.20% (K), and 1.32% (Mg) and average residual prediction deviation (RPD) values of 2.13 (Na), 0.97 (K), and 2.20 (Mg). On-line field measurement was also proven to be successful with validation results showing average R2 values of 0.78 (Na), 0.64 (K), and 0.60 (Mg), average RMSEP values of 0.04% (Na), 0.13% (K), and 2.19% (Mg) and average RPD values of 1.57 (Na) 1.68 (K) and 1.56 (Mg). Based on 3297 points, maps of Na, K and Mg were produced after N, P, K and organic fertilizer applications, and these maps were then compared to the corresponding maps from the previous year. The comparison showed a variation in soil properties that was attributed to the variable rate of fertilization implemented in the preceding year.

Online Measurement of Soil Organic Carbon as Correlated with Wheat Normalised Difference Vegetation Index in a Vertisol Field

This study explores the potential of visible and near infrared (vis-NIR) spectroscopy for online measurement of soil organic carbon (SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized differential vegetation index (NDVI) of a wheat crop. The online measurement was carried out in a clay vertisol field covering 10 ha of area in Karacabey, Bursa, Turkey. Kappa statistics were carried out between different SOC and NDVI data to investigate potential similarities. Calibration model of SOC in full cross-validationresulted in a good accuracy (R 2 = 0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy (R 2 = 0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra (R 2 = 0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity indicated by a low Kappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all points measured in the field, e.g., 6486 points) map of SOC and NDVI map.