Prediction Of Nitrogen Content Research Articles

Mathematical Model of Prediction of Nitrogen Pickup in Nitriding Process of Low Carbon Ferromanganese

Low carbon ferromanganese was nitrided through gas-solid reaction. The nitriding process has been carried out on lab scale at temperature range 800°C–950°C at different nitrogen pressures. Temperature, time, and partial nitrogen pressure of nitriding process of fine low carbon ferromanganese were investigated. Nitrogen content, in weight percent, was more than 9%. MATLAB software was used to derive mathematical model to predict nitrogen content as a function of temperature and nitrogen pressure. According to derived model, nitrogen content can be calculated by the following equation: N content,wt.%=(-30.8882+0.0326*T)/(1+e-((P+0.0038*T-8.4155)/(3.6374-0.0018*T))), where, T is nitriding temperature in K and P is nitrogen pressure in bar. The experimental results are in good agreement with the predicted results. The results showed that nitrogen content, at steady state, is mainly depending on temperature and pressure of nitriding process. MATLAB is a good tool to make precision mathematical model.

Soil-line vegetation indices for corn nitrogen content prediction

Soil-line vegetation indices for corn nitrogen content prediction The soil-line vegetation indices for prediction of corn canopy nitrogen content were investigated. Results indicated that the vegetation indices applied were correlated with corn canopy nitrogen content and the wavelengths between 630-860 nm are suitable for nitrogen diagnosis. The second-order polynomial equation was the best model for nitrogen content prediction among different regression types. Analyses based on both predicted and measured data were carried out to compare the performance of existing vegetation indices.

Spectral feature characterization and nitrogen content prediction in soils with different particle sizes and moisture contents.

The objective of this research is to analyze the influences of light source incidence angle, fiber height, moisture content, and particle size on loamy mixed soil spectra. Nitrogen (N) content calibration and cross-validation models at different moisture contents and particle sizes were obtained using partial least squares (PLS) analysis. Spectral data were collected using a spectrophotometer. Fiber height of 100 mm and light source angle at 45 ° were chosen to obtain the sharpest spectra without apparent scattering effect. The results show that moisture content and particle size strongly influenced the absorbance of the spectra, and a better N prediction model was obtained when the particle sizes were in the ranges of 0.5 <span style=font-size: 9.5pt; font-family:

Spectrum analysis of hyperspectral red edge position to predict rice biophysical parameters and grain weight

Rice canopy hyperspectral reflectance data were perpetually measured to analyze the red edge position (REP) corresponded to grain weight during growing season in Java Island, Indonesia. Reflectance measurements were conducted concurrently with biomass sampling of three rice cultivars and four nitrogen application levels (N level) whose were involved in this study. The potential of narrow bands in the red edge region were investigated to predict nitrogen content (N content), plant chlorophyll index (SPAD value) and grain weight with applying the inverted Gaussian (IG) and modified linear extrapolation (LE) methods. Spectral data was smoothed into five nm interval. The first derivative reflectance that derived from reflectance data subsequently used in analysis of REP. The REP moved to longer wavelength until close to heading stage and otherwise shifted to shorter wavelength after heading stage. The correlation REP-IG (R2=0.849) was stronger than REP-LE (R2=0.767) attributed to N content, while the correlation REP with SPAD value, REP-LE (R2=0.800) demonstrated slightly higher than that of REP-IG (R2=0.731) . Subsequently, REP-LE represented slightly more significant correlation with grain weight (R2= 0.884) when compared to REP-IG (R2=0.847) . The REP shift can act as quick and precise prediction for biophysical parameters and grain weight status. Its prediction capability can support crop farming management.

Infrared spectroscopic monitoring of urea addition to oriented strandboard resins

AbstractOne of the variables in phenol formaldehyde adhesive resin formulation is the addition of urea, which allows the resin manufacturer to manipulate both product functionality and cost. Nitrogen content can be used as a measure of the level of urea addition because most of the nitrogen present is derived from urea added at the end of the preparation process. Nitrogen analyses of a set of commercial oriented strandboard resins were first determined by combustion analysis. IR spectroscopic data were then collected and used along with the values for nitrogen content to generate predictive models. First, principal component analysis demonstrated the ability to separate resin mixtures from low to high nitrogen content. Partial least squares regression was then performed and gave excellent correlations between the measured and predicted nitrogen contents for a set of unrelated (test) resins and a set of mixtures prepared through combinations of resins with known nitrogen contents. Similar results were obtained on both a wet and dry mass basis. Given the flexibility of this instrumentation, such analyses could be placed in‐line for real time monitoring of resin applied during panel manufacturing. © 2007 Wiley Periodicals, Inc. JAppl Polym Sci 2007

Estimation of Leaf Nitrogen Content using Artificial Neural Network with Cross-Learning Scheme and Significant Wavelengths

Reflectance from crops provides spectral information for non-destructive monitoring of their nutrition status. In order to develop a multi-spectral imaging system for remote sensing of the nitrogen content of crops, the significant wavelengths and calibration models were carefully evaluated in this study. The significant wavelengths in full band (400-2500 nm) and a selected band (450-950 nm), which is suitable for silicon CCD cameras, were investigated. In this article, significant wavelengths for estimating nitrogen content of cabbage seedling leaves were first determined by SMLR (stepwise multi-linear regression) analysis. A proposed ANN (artificial neural network) model with cross-learning scheme (ANN-CL) was further developed to increase the prediction accuracy. To comply with the design of a practical multi-spectral imaging system using silicon CCD cameras and commercially available bandpass filters, an ANN-CL model with four inputs of spectral absorbance at 490, 570, 600, and 680 nm was developed. The calibration results (rc = 0.93, SEC = 0.873%, and SEV = 0.960%) reduced the SEV about 15% when compared with the SMLR method with four wavelengths (SEV = 1.099%). In addition, the results were comparable to that of SMLR with seven wavelengths (rc = 0.94, SEC = 0.806%, and SEV = 0.993%) in the full band. These results indicated that the ANN model with cross-learning using spectral information at 490, 570, 600, and 680 nm could be used to develop a practical remote sensing system to predict nitrogen content of cabbage seedlings.

Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species

The main focus of recent studies relating vegetation leaf chemistry with remotely sensed data is the prediction of chlorophyll and nitrogen content using indices based on a combination of bands from the red and infrared wavelengths. The use of high spectral resolution data offers the opportunity to select the optimal wavebands for predicting plant chemical properties. In order to test the optimal band combinations for predicting nitrogen content, normalized ratio indices were calculated for all wavebands between 350 and 2200 nm for five different species. The correlation between these indices and the nitrogen content of the samples was calculated and compared between species. The results show a strong correlation between individual normalized ratio indices and the nitrogen content for different species. The spectral regions that are most effective for predicting nitrogen content are, for each individual species, different from the normalized difference vegetation index (NDVI) spectral region. By combining the areas of maximum correlation it was possible to determine the optimal spectral regions for predicting leaf nitrogen content across species. In a cross‐species situation, normalized ratio indices using the combination of reflectance at 1770 nm and at 693 nm may give the best relation to nitrogen content for individual species.

Predicting Nitrogen Content in the Northern Mixed-Grass Prairie

Forage quality and quantity are important factors affecting livestock production from grazing lands. ‘‘Greenness’’ has been proposed as an indicator of herbage quality in semiarid environments, particularly nitrogen (N) content. The objective of this study was to assess the potential of estimating N content of forage using dead:green ratios and accumulated growing-degreedays (AGDD). Standing crop samples were collected April through October over 3 years from each of 3 replicated grazing regimes on a silty range site in eastern Montana. Samples were sorted into live, current dead, and old dead components, then dried, ground, and analyzed for N content. The AGDD for base temperature 458F (7.28C) was calculated from National Oceanic and Atmospheric Administration (NOAA) reported monthly average temperatures for Miles City. An equation to predict percent N in the total standing crop from percent dead forage and AGDD was developed using multiple linear regression. This equation accounted for 75.9% of variation in percent N, and prediction error variance was 0.026. To validate this equation, data were obtained from samples collected from April through September in an independent study of 8 areas on silty and claypan range sites grazed during autumn and winter. Samples from these sites were treated and analyzed in the same manner as those used to develop the equation. The developed equation was used to predict percent N for the validation data. The resulting correlation between predicted and actual values was 0.79. The regression coefficient for actual values on predicted values was 0.995 6 0.125. The intercept did not differ from 0.0 (P ¼ 0.22), and prediction error variance was 0.042. This equation has utility for predicting N level of forage from Northern Great Plains rangelands.

Predicting Nitrogen Content in the Northern Mixed-Grass Prairie

Forage quality and quantity are important factors affecting livestock production from grazing lands. ‘‘Greenness’’ has been proposed as an indicator of herbage quality in semiarid environments, particularly nitrogen (N) content. The objective of this study was to assess the potential of estimating N content of forage using dead:green ratios and accumulated growing-degree- days (AGDD). Standing crop samples were collected April through October over 3 years from each of 3 replicated grazing regimes on a silty range site in eastern Montana. Samples were sorted into live, current dead, and old dead components, then dried, ground, and analyzed for N content. The AGDD for base temperature 45 degreesF (7.2 degreesC) was calculated from National Oceanic and Atmospheric Administration (NOAA) reported monthly average temperatures for Miles City. An equation to predict percent N in the total standing crop from percent dead forage and AGDD was developed using multiple linear regression. This equation accounted for 75.9% of variation in percent N, and prediction error variance was 0.026. To validate this equation, data were obtained from samples collected from April through September in an independent study of 8 areas on silty and clay- pan range sites grazed during autumn and winter. Samples from these sites were treated and analyzed in the same manner as those used to develop the equation. The developed equation was used to predict percent N for the validation data. The resulting correlation between predicted and actual values was 0.79. The regression coefficient for actual values on predicted values was 0.995 +/- 0.125. The intercept did not differ from 0.0 (P = 0.22), and prediction error variance was 0.042. This equation has utility for predicting N level of forage from Northern Great Plains rangelands.

CropSyst, a cropping systems simulation model: Water/nitrogen budgets and crop yield

In agriculture, water and nitrogen are two critical resources for growing a crop. However, their management cannot be analyzed independently of weather, soil characteristics, field hydrology, crop characteristics, crop rotation, and management factors. This paper describes the water, nitrogen, and crop growth components of CropSyst, a comprehensive cropping systems simulation model, and provides preliminary verification of these components. The water budget of the model properly describes crop water use. Predicted nitrogen contents throughout the soil profile did not exactly match the measured values from leaching experiments, but they did follow the general trends of the data. The agreement between simulated and observed biomass and yield of corn, winter wheat and spring wheat grown in two locations with a total of 77 data points was good as shown by several statistical indicators. Based on this preliminary validation, CropSyst appears promising as a tool to analyze management practices for water and nitrogen. Additional validation of model components, including a wide range of crops and conditions, should be conducted in the future.

Predicting the evolution of fuel nitrogen from various coals

Nitrogen release from most coal types occurs by three mechanisms: tar shuttling, HCN production from char-N, and combustion of char-N. This modeling study extends FLASHCHAIN to characterize the first two N-release mechanisms for heating rates from 5 to 10 4 K/s, temperatures to 1550 K, pressures from vacuum to several atmospheres, and 13 coals of rank from sub-bituminous through low volatile bituminous. Based primarily on the accuracy of predicted tar yields, the agreement among predicted and observed evolution histories of tar-N, HCN, and char-N and the nitrogen contents of tar is within experimental uncertainty throught this domain. Under conditions of rapid heating, tar shuttling is the only mechanism for nitrogen release as long as tar is being expelled, but for slower heating conditions, it is overlapped by HCN production from char-N. The predictions correctly depict the rank dependence of nitrogen evolution, exhibiting a broad maximum for hv bituminous coals before falling off for low volatility coals. Tar shuttling is almost entirely responsible for this trend. Also, the predicted nitrogen contents of tar increase throughout primary devolatilization as cumulative tar yields grow, in accord with measured values. HCN production from char-N has an extremely broad thermal response that is well represented with a very broad distribution of activation energies. Predicted char-N and HCN evolution histories for numerous coals based on the same energy distribution are within experimental uncertainty of observed levels for several coal types, although indications that HCN production becomes slower for coals of progressively higher rank are also considered.

Primordial retention of nitrogen by terrestrial planets and meteorites

Thermodynamic calculations of the amount of dissolved nitrogen in Fe‐Ni alloy in a solar composition gas were done over a wide range of temperatures and pressures. The stabilities of the nitride minerals found in meteorites, a large number of other nitrides, and ammonium aluminosilicates were also calculated in a solar gas. This thorough study indicates that equilibrium mechanisms cannot account for the nitrogen contents of the terrestrial planets and meteorites. The best available data indicate that the observed nitrogen contents of planets and meteorites are several orders of magnitude greater than the predicted nitrogen contents of condensed material in a solar gas. It is suggested that the nitrogen in meteorites was originally retained as organic compounds produced by disequilibrium mechanisms. Nitrogen retention by the terrestrial planets could be due to homogeneous accretion or to accretion of a volatile‐rich veneer. However the actual mode of nitrogen accretion cannot yet be determined.

An evaluation of near infra-red reflectance analysis in some plant breeding programmes

SUMMARYSome applications of near infra-red reflectance (NIR) analysis in plant breeding have been tested. Prediction of the nitrogen content of cereals, kale and beans, and nitrogen and oil in rapeseed was sufficiently accurate for screening purposes although wheat straw gave a less accurate result. The prediction of nitrogen content of wheat grain could be supplemented by the simultaneous estimation of grinding resistance and bran cleanliness, characters of importance for bread-making quality. Hagberg falling number, sodium dodecyl sulphate (SDS) sedimentation volume and loaf volume could not be accurately predicted by NIR. Encouraging results were obtained for predicting malting quality of barley and the digestibility of kale samples.