The State of the Department of Grain Science & Industry at Kansas State University and Its Current Priorities

Maier, Dirk E. Dr. The State of the Department of Grain Science & Industry at Kansas State University and Its Current Priorities. 2009. Kansas State University

Department of Grain Science & Industry Department Responsibilities, Accomplishments and Goals

Maier, Dirk E. Dr. Department of Grain Science & Industry Department Responsibilities, Accomplishments and Goals. 2013. Kansas State University

Quantification of biomass feedstock availability to a biorefinery based on multi-crop rotation cropping systems using a GIS-based method

The feasibility of utilizing cellulosic biomass as an energy feedstock is dominated by factors such as facility location, feedstock availability, and transportation cost. Our previous case study showed improvements in quantification of feedstock availability for a biorefinery by introducing the effect of field-level yield variance and variable residue removal rates as improvement parameters into the GIS-based analysis. Even though the improved GIS-based method enhanced quantification of feedstock availability with the addition of the improvement parameters, a biorefinery would most likely procure more than one feedstock type. In this case study, quantification of feedstock availability based on multi-crop rotation cropping systems was done using the previously improved GIS-based variable residue removal (VRR) method. We observed on average a 3,793 ±5,733 DT per service area difference when increasing the number of crops used to estimate feedstock quantification. The supplementary use of crop-specific VRR rates affected residue availability, given that a crop’s residue removal rate is influenced by crop yield, crop rotation, soil characteristics, as well as field location and management. It was also observed that the amount of available hectares of the three main crops analyzed in this case study affected residue availability. Corn represented 26.2% (440,636 ha; 1,101,591 acres), sorghum represented 12.9% (217,432 ha; 543,579 acres), and wheat represented 60.9% (1,024,607 ha; 2,561,518 acres) of the hectares in the study area. The validation study showed the importance of taking into account the seasonal availability of crop residue when estimating procurement service areas, given that in some cases feedstock requirements were not met

Simulated Performance of Conventional High-Temperature Drying, Dryeration, and Combination Drying of Shelled Corn with Automatic Conditioning

Combination drying, based on computer simulation, was evaluated as an alternative drying technique to traditional high-temperature drying and dryeration. Simulation models of high-temperature crossflow drying and in-bin drying and conditioning were used to evaluate the performance of conventional crossflow drying and full-heat crossflow drying followed by dryeration or natural-air drying for Indianapolis, Indiana, and Des Moines, Iowa. Energy costs from propane, electricity, moisture shrink below the market moisture content, and dry matter loss were estimated to find the total average drying cost over 29 years. Dryeration and combination drying reduced the total drying cost by approximately 10% compared to conventional drying and cooling within the dryer at current economic conditions. The greatest benefit was an increase of 72 and 159% in drying capacity when dryeration and combination drying were used instead of conventional drying and cooling within the dryer, respectively. However, the economic return of combination drying could be improved by the development of natural-air drying techniques or controllers that would limit the predicted moisture shrink loss

Reconditioning Corn and Soybeans to Optimal Processing Moisture Contents

Experimental trials were carried out to evaluate the technical feasibility of reconditioning overly dry corn and soybeans to optimal market and processing moisture contents. Data obtained from experimental trials were used to validate an aeration simulation model. This model was used to evaluate the feasibility of reconditioning soybeans and corn. Reconditioning of grain was feasible at low airflow rates (0.11 m3 min–1 t–1) over a six-month period when an automatic aeration controller was used. Using downflow aeration and monthly unloading of the bin allowed for the greatest net economic gain. Predicted reconditioning in Des Moines, Iowa, had a lower net economic gain than in Indianapolis, Indiana, based on 29 years of historic weather records

Three-dimensional transient heat, mass, momentum, and species transfer in the stored grain ecosystem: Part I. Model development and evaluation

A 3D transient heat, mass, momentum, and species transfer model for the stored grain ecosystem was developed using the finite element method. Hourly weather data such as ambient temperature and relative humidity, solar radiation, and wind speed were used as input in the model. The 3D model has different components that predict grain temperature and moisture content, dry matter loss, insect population, and species (CO2 and fumigant) concentration. The 3D model was evaluated using linear elements with three different numbers of nodes and quadratic elements with three different numbers of nodes. The accuracy of prediction for each category was evaluated using the observed and predicted temperature values. The linear model with 384 nodes and the quadratic model with 415 nodes were found to be the best based on the lowest standard error compared to other combinations. Four different time discretization schemes were used to evaluate model accuracy over time. The Crank-Nicolson time discretization scheme was found to be the best of the four

Validation of a Finite-Element Stored Grain Ecosystem Model

An axisymmetric finite–element model was validated with respect to predicting the heat, mass, and momentum transfer that occurred in upright corrugated–steel storage bins due to conduction, diffusion, and natural convection using realistic boundary conditions. Hourly weather data that included hourly total solar radiation, wind speed, ambient temperature, and relative humidity were used to model the corn temperature and moisture content during storage with no aeration, and with ambient and chilled aeration. Periods of aeration were simulated assuming a uniform airflow rate through the grain mass. Sixteen bins with a capacity of 11.7 t each and instrumented with temperature cables were available to validate the model using two years of measured corn temperatures and moisture contents during summer storage. The average standard error between the experimental and predicted temperatures was 2.4° C (1.1° C to 5.7° C range), and the standard error between experimental and predicted moisture contents was 0.7 percentage points. The average standard error was 1.5° C in three non–aerated bins with sealed plenums when corn temperature was predicted as a function of the natural convection equation. The predicted natural convection effect was not applicable unless the plenum was assumed sealed

Development of a Finite-Element Stored Grain Ecosystem Model

An axisymmetric finite–element model was developed that predicts the heat, mass, and momentum transfer that occurred in upright corrugated steel storage structures due to conduction, diffusion, and natural convection using realistic boundary conditions. Weather data that included hourly total solar radiation, wind speed, ambient temperature, and relative humidity were used to model the temperature, moisture content, dry matter loss, and maize weevil development during storage with no aeration, and with ambient and chilled aeration. Periods of aeration were simulated assuming a uniform airflow rate through the grain mass. Heat and mass balances were used to calculate the temperature and absolute humidity in the headspace and plenum based on solar radiation, wind speed, ambient conditions, air infiltration, convective heat and mass transfer from the grain surface, and permeable boundaries that allowed natural convection currents to cross grain surfaces. A heat balance was used to estimate the wall temperature. The type of weather data in terms of solar radiation and frequency of data appear to be important when predicting the grain temperature, moisture content, dry matter loss, and maize weevil development