Standardization of optimum sieve size for maximizing seed quality in Amaranthus (Amaranthus tricolor L.)

Vegetables have a critical function in human health and nutritional security. Vegetables are considered an essential building block of any diet. Out of the leafy vegetables, Amaranthus is the most popular and salable vegetable consumed by people all over India. Seed processing experiment was undertaken in Amaranthus tricolor (CO 2) by using the sieves placed inside the mechanical seed shaker (Gyratory sieve shaker) to improve the quality of Amaranthus seeds. The seeds of Amaranthus were size graded with seed shaker attached with various sieve size of BSS 18 X18 (R), BSS 20 X 20 (R), BSS 22 X 22 (R) and BSP 22 X 22 (P). During processing, the machine could be adjusted for 2, 3, 4 and 5 minutes with an oscillating speed of 1440 rpm. The separated seeds were evaluated for seed quality characteristics such as seed germination percentage, shoot length, root length, dry matter production, 1000 seed weight and seed recovery percentage. The results revealed that the sieve size of BSS 22 X 22 (R=retained) mesh sieve had the better quality seeds with a maximum recovery of 67.41 g and when it is operated for a period of 5 minutes. The germination percentage was improved from 77 % to 95 % with 1000 seed weight of 73.21 mg, and the observed recovery was 56 per cent with the vigour index of 1145. Hence, BSS 22 X 22 retained mesh sieve with a duration of 5 minutes could be recommended as an optimum sieve size for grading Amaranthus seeds for improving the seed quality.

Thermal transitions in metastable Cu – 68.5 at. % Co alloy.

Arc melted and drop tube processed Cu – 68.5 at. % Co alloy has been subjected to differential thermal analysis (DTA). The liquidus temperature determined from the DTA curves in the arc melt sample (1664 K) was found to be close to phase diagram estimate of 1662 K. In contrast as a result of liquid phase separation in the drop tube samples, the values obtained in the powders were much lower mainly because the compositions of the demixed phases vary from that of the parent melt. The liquidus temperature of the 850 + μm powders was 1632 K while that of the < 38 μm sieve size powder was 1616 K. This variance is due to the asymmetric nature of the metastable phase diagram of the system.

Empirical Determination of Sieve Size Statistics from Grain Measurement

<p>Relationships between sieve grain size and thin section grain size have been determined empirically from the study of 72 artificially created sendstone samples. Modern sands were sieved into size fractions, which were recombined in a log normal distribution to give samples with a range of means and standard deviations, but with similar individual grain properties. Sample splits of these were impregnated with resin, and the size distribution of grain long axes selected by point counter in thin section was compared with that found by sieving the remaining sample. This method attempts to minimise the effects of factors that influence apparent size in thin section. The results have been compared with those of (1958, 1962) who studied the same size relationships in 38 natural sandstones, e.g. This work: Sieve size from Folk = 1.078(thin section mean) + 0.200 phi graphical mean 1/3(Ø16+Ø50+Ø84) Friedman (1958): Sieve size mean from = 0.903(thin section mean) + 0.381 phi combined quartile measures Ø25, Ø50,Ø75 The regression coefficients differ from those of Friedman, probably because of the range of mean sizes investigated in the present work was twice as large (5.7 phi units vs. 2.6 phi units). The correlation coefficient relating sieve to thin section analysis decreases progressively, as Friedman found, from mean (0.992) to standard deviation (0.958), skewness (O.536), and kurtosis (0.249). The correlation for skewness and kurtosis is not significant. The size range was extended to -3.5 phi by the study of the mean size of selected gravel samples measured in sawn slabs. The resulting regression line has a slope of one and an intercept of 0.4 phi and is close to that found for the sands. Grain size in grain mount is also closely related to sieve and thin section size, and a preliminary study of pebble size measured from photographs suggests that this may also be converted to an equivalent sieve size. On qualitative grounds the relationships between the various mean size statistics should involve the simple addition of a constant phi value. However the slopes of the regression equations found in the present work differ slightly from a slope of one. This difference is shown to represent a progressive shape change with size. For a constant b/a ratio of 0.73 or 0.70 conversion of thin section mean size (in phi units) to an equivalent sieve value should therefore be made by the simple addition of a 0.33 or 0.40 phi constant respectively.</p>

Empirical Determination of Sieve Size Statistics from Grain Measurement

<p>Relationships between sieve grain size and thin section grain size have been determined empirically from the study of 72 artificially created sendstone samples. Modern sands were sieved into size fractions, which were recombined in a log normal distribution to give samples with a range of means and standard deviations, but with similar individual grain properties. Sample splits of these were impregnated with resin, and the size distribution of grain long axes selected by point counter in thin section was compared with that found by sieving the remaining sample. This method attempts to minimise the effects of factors that influence apparent size in thin section. The results have been compared with those of (1958, 1962) who studied the same size relationships in 38 natural sandstones, e.g. This work: Sieve size from Folk = 1.078(thin section mean) + 0.200 phi graphical mean 1/3(Ø16+Ø50+Ø84) Friedman (1958): Sieve size mean from = 0.903(thin section mean) + 0.381 phi combined quartile measures Ø25, Ø50,Ø75 The regression coefficients differ from those of Friedman, probably because of the range of mean sizes investigated in the present work was twice as large (5.7 phi units vs. 2.6 phi units). The correlation coefficient relating sieve to thin section analysis decreases progressively, as Friedman found, from mean (0.992) to standard deviation (0.958), skewness (O.536), and kurtosis (0.249). The correlation for skewness and kurtosis is not significant. The size range was extended to -3.5 phi by the study of the mean size of selected gravel samples measured in sawn slabs. The resulting regression line has a slope of one and an intercept of 0.4 phi and is close to that found for the sands. Grain size in grain mount is also closely related to sieve and thin section size, and a preliminary study of pebble size measured from photographs suggests that this may also be converted to an equivalent sieve size. On qualitative grounds the relationships between the various mean size statistics should involve the simple addition of a constant phi value. However the slopes of the regression equations found in the present work differ slightly from a slope of one. This difference is shown to represent a progressive shape change with size. For a constant b/a ratio of 0.73 or 0.70 conversion of thin section mean size (in phi units) to an equivalent sieve value should therefore be made by the simple addition of a 0.33 or 0.40 phi constant respectively.</p>

Determination of work index of Filin Kokuwa Gold Deposit in Toro Local Government, Bauchi State Nigeria

This report shows the work index of Filin Kokuwa Gold ore sample in Bauchi State, Nigeria. The “reference sample (granites)” were sourced from outcrops of granites around Toro town. The samples were crushed, ground, and pulverized using appropriate laboratory milling machine. 80% passing size for the gold ore and granites samples were obtained at 100μm sieve size for the ball mill feeds and products respectively. The work indexes of reference samples i.e. granites were used to calculate the work index of the Filin Kokuwa gold ore sample. The values of 13.277kWh/ton and 15.192kWh/ton were obtained respectively for the two different reference granites samples used and 14.21kWh/ton as their average which is the value of the work index of the Filin Kokuwa gold ore deposit. The energy required for grinding the ore was found to be 3.581kW.

Effect of Particle Size on the Aerobic and Anaerobic Digestion Characteristics of Whole Rice Straw

The effect of reducing particle size on physical properties, the methane yield and energy flow were investigated through the biochemical methane potential (BMP) experiment of aerobic-anaerobic digestion (AAD) of rice straw (RS). The whole straw was crushed through four sieves of different aperture sizes (1, 3, 5, and 7 mm) to obtain the actual and non-uniform particle size distribution (PSD). The results indicated that the actual particle sizes were normally or logarithmic normally distributed. Reducing particle size could significantly promote the aerobic hydrolysis and acidification process, increase the content of volatile fatty acids (VFAs) from 4408.78 to 6225.15 mg/L and the degradation of volatile solids (VS) from 40.56% to 50.49%. The results of path analysis suggested that particle size reduction played an important role in improving lignocellulosic degradability, which was the main factor affecting methane production with the comprehensive decision of 0.4616. The maximum methane production obtained at 1 mm sieve size was 176.47 mLCH4g−1 VS. The phyla of Firmicutes (61.5%), Proteobacteria (9.3%), Chloroflexi (8.3%), Bacteroidetes (4.1%), Cyanobacteria/Chloroplast (4.6%) were mainly responsible for VFAs production and lignocellulose degradation. However, the net negative energy balance was observed at the 1 mm sieve size due to the increased energy input. Therefore, the optimum sieve size for AAD was 3 mm.

An Intelligent System for Multi-Label Classification Based on Particle Size and Shape Features using a Cascade Approach

Intelligent systems are becoming more and more popular in the petroleum industry. Particle Size Distribution (PSD) based on sieve size is a key signature of the unconsolidated/weakly consolidated sandstone formations and is commonly the main parameter in the sand control design. With available extensive PSD measurement techniques and a large number of measurements, especially for horizontal wells, it is necessary to classify the PSDs prior to further analysis for the sand control design. On the other hand, PSD analysis is not enough for sand control design, and particle shapes need to be taken into account as well. A successful clustering algorithm for the mentioned purposes needs to be a cascade, multi-label, unsupervised and self-adaptive approach since the particles can be assigned to more than one group and there is no prior idea on how many clusters should be formed after the clustering process. Besides, due to the differences between sieve size and shape features, they should be used separately for clustering the particles. In the current study, a cascade approach is used for clustering the particles. In the first level of the cascade, an unsupervised and self-adaptive algorithm is introduced based on the sieve size features. The algorithm optimizes the number of clusters through a self-adaptive and incremental approach. The proposed clustering method uses a minimum similarity threshold (δ) as the only input parameter to start the clustering and tries to minimize the number of clusters during the clustering. In the second level of the cascade, the similarity between all particles in each cluster with their corresponding cluster-center is measured, and those particles that do not respect the δ in terms of the shape similarity, are moved out of the cluster. The novelty of the proposed method is in three folds. The first one is to provide a particle clustering algorithm, which works based on the whole range of the sizes and shape descriptors rather than focusing on certain points in the size graph (D-values). The second one is the dynamic nature of the clustering, which tends to optimize the number of clusters during the clustering process. The third one is that we have used a cascade approach for involving both size and shape parameters for the clustering. Our proposed method can be applied in field application for downhole monitoring and sand screen design.

Potential Use of Groundnut Shell Ash as Soils Strength Enhancer

This research investigates the potential use of groundnut shell ash (GSA) as soils strength enhancer. The GSA was used as admixture on selected soil samples from four different locations and samples were named T1, T2, T3 & T4. The tests carried out on the samples include Atterberg limit, sieve size analysis, soil hydrometer, compaction and California bearing ratio (CBR), X-ray fluorescence (XRF). Sieve size analysis, soil hydrometer test, Atterberg limit test were used to classify soil samples’ properties and classification was done as per AASHTO soil classification system. Sample T1 was classified as A-6, samples T2, T3 and T4 were classified as A-4. GSA was added to the soil samples; 2, 4, 6, 8, 10 and 15% of GSA by weight of soil samples. Compaction test and California bearing ratio (CBR) were carried out on soil samples with added GSA. Results from XRF showed that SiO2 + Al2O3+ Fe2O3 = 25.61%. For GSA to be classified as pozzolan, SiO2 + Al2O3+ Fe2O3 ≥ 70% as per ASTM C618 – 08. Therefore, GSA cannot be classified as pozzolan as it does not meet requirement stipulated in ASTM C618 but rather as inert pores filler. Based on the results from compaction and CBR, the study showed that 4-8% of GSA was found to have improved and enhanced the strength of the soil samples.

Optimization of hammer grinder for white lupine “Dega” processing

Исследования проведены в 2019 году в лаборатории «Технология и техника мукомольно-крупяного производства» ВНИИ зерна и продуктов его переработки – филиала ФГБНУ «ФНЦ пищевых систем им. В. М. Горбатова» РАН. В качестве объекта исследований использовали зерно белого люпина сорта Дега. Эксперименты по изучению условий измельчения зерна проводились на лабораторной молотковой дробилке с регулируемой частотой вращения ротора. Рассматривалось влияние влажности зерна, скорости молотков и диаметра отверстий рабочего сита дробилки на выход крупки и содержание в ней недоруша (зёрен и частиц с остатками оболочки, крупных частей неотвеянной оболочки). Использование простой технологической схемы обрушения зерна белого люпина на базе молотковой дробилки позволило получить до 70% крупки с повышенным содержанием белка и низким содержанием клетчатки. С ростом скорости молотков и уменьшением диаметра отверстий рабочего сита дробилки выход крупки снижался, что объяснимо ростом доли мелкой фракции, которая отвеивается при пневмосепарации. Крупка представляет из себя частицы ядра с недорушем, а относы являются смесью дроблёной оболочки с мучкой — мелкой фракцией дробленого ядра. Основная доля мучки ядра была сосредоточена во фракции меньше 1,5–2,0 мм, в которой содержится больше белка. Данная фракция составляла около половины массы относов или около 15% от массы зерна. Наличие недоруша в крупке повышало содержание в ней клетчатки и снижало содержание белка. С ростом влажности и диаметра отверстий рабочего сита дробилки содержание недоруша возрастало, а при росте скорости — падало. При фиксированной влажности содержание недоруша можно снизить за счёт повышения скорости удара (увеличения числа оборотов) или уменьшения диаметра отверстий рабочего сита дробилки, но и тот и другой метод повышают энергозатраты. При возрастании скорости удара росла и производительность. The research was conducted in 2019. Hammer grinder with adjustable rotor speed was used to crush the grain of white lupine “Dega”. The effects of grain water content, crushing speed and sieve size were analyzed on grain crushing and hulling as well as the proportion of unhulled grain. This technology resulted in up to 70% of crushed hulled grain rich in protein but poor in fiber. Increase in hammer speed and decrease in sieve size negatively affected the proportion of crushed grain due to the high content of fine fractions discarded further via pneumatic separation. Crushed hulled grain is normally combined with unhulled grain. The mixture of crushed hulls and fine particles of crushed grain are to be separated. Most of the bran was found in the fraction of 1.5–2.0 mm containing more protein. This fraction amounted to 15% of grain mass and 50% of the mass to be discarded. Unhulled grain mixed with crushed hulled grain increased fiber content in the mixture but reduced protein concentration. Higher grain water content and larger sieve size increased the amount of unhulled grain, while higher hammer speed decreased its fraction. Higher hammer speed as well as smaller sieve size reduce the content of unhulled grain but increased energy costs under constant grain water content. Increase in hammer speed improved the capacity of the grinder.