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

Three dimensional transient heat, mass, momentum and species transfer stored grain ecosystem model using the finite element method

The objective of this research was to develop a three-dimensional transient heat, mass, momentum and species transfer model using the finite element method to predict grain temperature, moisture content, interstitial air velocity and gas (fumigant, CO2) concentration in the stored grain mass. The physical, chemical and biological processes for stored grain ecosystems were represented as partial differential governing equations (PDE). Effects of boundary conditions including solar radiation, internal heat generated by insects and molds, and wind speed and direction on the ecosystems are three dimensional. Thus, two-dimensional ecosystem models developed by various researches are not adequate. Dry matter loss (DML) and insect population were calculated as post-processing using the predicted grain temperature and moisture content. The developed 3D stored grain ecosystem model was validated using data collected in two locations: PHERC Bin12 at Purdue University, West Lafayette, IN for corn and the SPREC bin at Oklahoma State University, Stillwater, OK for wheat. Different combinations of models such as conduction, convection and internal heat generation were studied along with linear and quadratic elements. The conduction plus convection model predicted grain temperatures that closely followed the observed grain temperatures during the non-aeration period. The standard error of prediction was in the range of 0.9-3.6°C for wheat and 1.0-3.1°C for corn. The predicted and observed grain moisture contents varied with an error of 0.1-1.28%. Validation of the 3D model required formulation of improved headspace, plenum and wall models into systems of ordinary differential equations (ODEs) using energy and mass balance principles which were solved by the Fourth Order Runga Kutta Method. There were nine headspace air temperatures and relative humidities, nine plenum air temperatures and relative humidities and forty eight wall temperatures formulated which is unique compared to published literature. The predicted headspace air, wall and roof temperatures, the plenum air and wall temperatures and the grain mass wall temperatures closely followed the trend of observed values with acceptable standard error of prediction. The developed 3D stored grain ecosystem model was applied to study the effect of non-uniform airflow in cored, leveled and peaked grain mass configurations. Non-uniform airflow distribution was evaluated using the finite volume method for constant and variable porosities and airflow rates of 1.1 m3/min.t (natural air drying air) and 0.11 m3/min.t (aeration). For cored, leveled and peaked grain mass configurations, a variable porosity range of 0.34 in the core and 0.38 towards the bin wall predicted air velocity with reasonable accuracy. The 3D heat and momentum transfer in cored, leveled and peaked grain mass configurations was evaluated for an airflow rate of 0.11 m3/min.t. The change in grain temperature in the cored grain mass configuration was the fastest followed by the leveled and peaked configurations. For the peaked cone region, the model predicted a delay in the cooling front of 59 hours versus the cored configuration. The developed 3D stored grain ecosystem model was also applied to study aeration strategies for wheat bulk storage in the sub-tropical climate of North India. Five years of weather data were analyzed using the Modified Chung-Pfost equation for wheat. Six aeration strategies were formulated based on the sub-tropical weather conditions of North India and combinations of the following parameters: temperature control; EMC control; 1h, 2h or 4h morning and/or evening aeration; and 0.11 m3/min/t airflow rate. The initial grain temperature was assumed as 27°C and the initial grain moisture as 12 % (w.b.) based on typical harvest conditions. Dry matter loss, insect development, fan run hours, average grain temperature and moisture content for these aeration strategies were quantified based on weather data from 2000-01 to 2004-05. Strategy 13 was selected as the best aeration strategy based on optimum combination of low values of DML, insect development, grain temperature, moisture content, and fan run hours. This strategy consisted of aerating wheat with EMC-based fan control independent of ambient temperature, an airflow rate of 0.11 m 3/min/t and allowable operating windows of 4 hours in the morning and evening during the maintenance period, and with EMC and temperature-based fan control during the cooling period at any time during the day or night. The results of both 2D and 3D models were compared for different strategies. Both models predicted Strategy 13 as the optimum strategy for North Indian conditions. As a result of this dissertation research the 3D PHAST-FEM (Post-Harvest Aeration Simulation Tool - Finite Element Method) model was developed, validated and applied. The model is sufficiently accurate and robust that it could be used as a decision support tool for effective stored grain quality management. (Abstract shortened by ProQuest.

The Economics of Integrated Insect Pest Management in Stored Corn

Replaced with a revised version of the paper 12/09/09.Production Economics,