HARVEST AND STORAGE OF WET CORN STOVER BIOMASS

2004 ◽  
Author(s):  
Kevin J. Shinners ◽  
Ben N. Binversie
Keyword(s):  
Corn Stover ◽  
And Storage

HARVEST AND STORAGE OF WET AND DRY CORN STOVER AS A BIOMASS FEEDSTOCK

2013 ◽  
Author(s):  
Kevin J. Shinners ◽  
Ben N. Binversie ◽  
Philippe Savoie
Keyword(s):  
Corn Stover ◽  
Biomass Feedstock ◽  
And Storage

Comparison of wet and dry corn stover harvest and storage

2007 ◽  
Vol 31 (4) ◽  
pp. 211-221 ◽  
Author(s):  
K SHINNERS ◽  
B BINVERSIE ◽  
R MUCK ◽  
P WEIMER
Keyword(s):  
Corn Stover ◽  
And Storage

scholarly journals Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Le Gao ◽  
Shulin Chen ◽  
Dongyuan Zhang
Keyword(s):  
Cell Wall ◽  
Corn Stover ◽  
Large Population ◽  
Structural Features ◽  
Coefficient Of Determination ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Relationship Of ◽  
And Storage

The classic assay for a large population biomass is time-consuming, labor intensive, and chemically expensive. This paper would find out a rapid assay for predicting biomass digestibility from biomass structural features without hydrolysis. We examined the 62 representative corn stover accessions that displayed a diverse cell-wall composition and varied biomass digestibility. Correlation analysis was firstly to detect effects of cell-wall compositions and wall polymer features on corn stover digestibility. Based on the dependable relationship of structural features and digestibility, a neural networks model has been developed and successfully predicted the corn stover saccharification based on the features without enzymatic hydrolysis. The actual measured and net-simulated predicted corn stover saccharification had good results as mean square error of 1.80E-05, coefficient of determination of 0.942 and average relative deviation of 3.95. The trained networks satisfactorily predicted the saccharification results based on the features of corn stover. Predicting the corn stover saccharification without hydrolysis will reduce capital and operational costs for corn stover purchasing and storage.

scholarly journals Physiochemical Characterization of Briquettes Made from Different Feedstocks

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
C. Karunanithy ◽  
Y. Wang ◽  
K. Muthukumarappan ◽  
S. Pugalendhi
Keyword(s):  
Bulk Density ◽  
Corn Stover ◽  
Storage Systems ◽  
Compositional Analysis ◽  
Pigeon Pea ◽  
Cotton Stalk ◽  
Geometrical Mean ◽  
Loading And Unloading ◽  
And Storage

Densification of biomass can address handling, transportation, and storage problems and also lend itself to an automated loading and unloading of transport vehicles and storage systems. The purpose of this study is to compare the physicochemical properties of briquettes made from different feedstocks. Feedstocks such as corn stover, switchgrass, prairie cord grass, sawdust, pigeon pea grass, and cotton stalk were densified using a briquetting system. Physical characterization includes particle size distribution, geometrical mean diameter (GMD), densities (bulk and true), porosity, and glass transition temperature. The compositional analysis of control and briquettes was also performed. Statistical analyses confirmed the existence of significant differences in these physical properties and chemical composition of control and briquettes. Correlation analysis confirms the contribution of lignin to bulk density and durability. Among the feedstocks tested, cotton stalk had the highest bulk density of 964 kg/m3 which is an elevenfold increase compared to control cotton stalk. Corn stover and pigeon pea grass had the highest (96.6%) and lowest (61%) durability.

An Overview of Digital Image Processing and Recognition (Invited Paper)

1982 ◽  
Vol 40 ◽  
pp. 700-702
Author(s):  
R. C. Gonzalez
Keyword(s):  
Image Processing ◽  
New York ◽  
Digital Image Processing ◽  
Digital Image ◽  
Practical Implementation ◽  
Submarine Cable ◽  
Vigorous Growth ◽  
Digitized Pictures ◽  
Processing Techniques ◽  
And Storage

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

Control of Secretory Production in the Isopod Hepatopancreas

1972 ◽  
Vol 30 ◽  
pp. 166-167
Author(s):  
John W. Roberts ◽  
E. R. Witkus
Keyword(s):  
B Cells ◽  
Morphological Data ◽  
Secretory Product ◽  
Secretory Function ◽  
Secretory Material ◽  
Blind Ending ◽  
Secretory Production ◽  
Regenerative Cells ◽  
Immature Cells ◽  
And Storage

The isopod hepatopancreas, as exemplified by Oniscus ascellus. is comprised of four blind-ending diverticula. The regenerative cells at the tip of each diverticula differentiate into either club-shaped B-cells, which serve a secretory function, or into conoid S-cells, which serve in the absorption and storage of nutrients.The glandular B-cells begin producing secretory material with the development of rough endoplasmic reticulum during their process of maturation from the undifferentiated regenerative cells. Cytochemical and morphological data indicate that the hepatopancreas sequentially produces two types of secretory material within the large club-shaped cells. The production of the carbohydrate-like secretory product in immature cells seems to be phased out as the production of the osmiophilic secretion was phased in as the cell matured.

Quantitative Analysis Of X-Ray Images From Geological Materials

1990 ◽  
Vol 48 (2) ◽  
pp. 244-245
Author(s):  
J. M. Paque ◽  
R. Browning ◽  
P. L. King ◽  
P. Pianetta
Keyword(s):  
Quantitative Analysis ◽  
Bulk Composition ◽  
Principal Component ◽  
Analytical Description ◽  
Bulk Sample ◽  
Geological Samples ◽  
X Ray ◽  
Software And Hardware ◽  
And Storage ◽  
Insight Into

Geological samples typically contain many minerals (phases) with multiple element compositions. A complete analytical description should give the number of phases present, the volume occupied by each phase in the bulk sample, the average and range of composition of each phase, and the bulk composition of the sample. A practical approach to providing such a complete description is from quantitative analysis of multi-elemental x-ray images.With the advances in recent years in the speed and storage capabilities of laboratory computers, large quantities of data can be efficiently manipulated. Commercial software and hardware presently available allow simultaneous collection of multiple x-ray images from a sample (up to 16 for the Kevex Delta system). Thus, high resolution x-ray images of the majority of the detectable elements in a sample can be collected. The use of statistical techniques, including principal component analysis (PCA), can provide insight into mineral phase composition and the distribution of minerals within a sample.

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