Rumela Bhadra

Document Type

Dissertation - University Access Only

Award Date

2010

Degree Name

Doctor of Philosophy (PhD)

Department / School

Agricultural and Biosystems Engineering

Abstract

Distillers Dried Grains with Solubles (DOGS) is the main coproduct from the com-based-fuel ethanol industry which operates in dry-grind process. The US Department of Energy forecast that by the end of 2010 the ethanol production will reach 13 billion gallons and will further expand in coming years. The Renewable Fuels Standards of Energy Independence and Security Act mandates that by 2015 ethanol production should be around 15 billion gallons or more. With the rapid growth of the ethanol industry, DDGS (a coproduct from the ethanol process) production will increase exponentially. It is estimated that in the fiscal year 2009-2010, DDGS production was 30.5 million metric tons. DDGS is basically the non-fermentable residue from com after ethanol extraction. Due to its high protein and fat content, it serves as an excellent energy source for cattle, swine, poultry, and in some cases for fish. Daily consumption of DDGS is limited, and hence it is dried to a certain moisture level for transportation across the country. To utilize this coproduct effectively and to increase the overall sustainability of ethanol plants, it is essential to ship DDGS to various part of the United States. The transportation and handling of DDGS is often a nuisance due to particle agglomeration and caking. DDGS particles tend to form cakes and bridges when exposed to various environmental and process conditions, and hence flowability problems occur. Caking of particles leads to insufficient unloading of DOGS from rail cars or other shipping vehicles, involves manpower to break the agglomerated cakes, and contributes to economic loss. If we assume that about 10% of DDGS is retained in a single rail car hopper due to caking, then an estimated loss of $200-220 is incured, plus additional labor cost for breaking the cakes. Thus, flowability and handling of DDGS is the third most important problem for ethanol industries, and it needs to be addressed. Before delving into flowability aspect of DDGS, various chemical and physical properties of DOGS, distillers wet grains (DWG), and distillers dried grains (DDG) from several commercial plants in South Dakota were quantified. Chemical properties of the DDGS included crude ash, Neutral Detergent Fiber (NDF, Acid Detergent Fiber (ADF), crude fiber (CF), crude protein, crude fat, and total starch. Physical properties of the DDGS included moisture content, water activity, bulk density, thermal properties (conductivity and diffusivity), color (Hunter L, Hunter a, and Hunter b), and Angle of Repose. These properties were also determined for DWG and DDG. Image analysis and size determination of the DDGS particles was also conducted. Carbon group characterization in the DDGS and DDG samples were determined using NMR spectroscopy; 0-alkyl comprised over 50% of all DDGS samples. Results from this study showed several possibilities for using DDGS in applications other than animal feed. Possibilities include harvesting residual sugars, producing additional ethanol, producing value-added compounds, using as food grade additives, or even using as inert fillers for biocomposites. As an introductory step to understanding flow issues, a complete flowability analysis of commercial DDGS samples was done. Two batches of commercial DDGS samples were obtained from five ethanol plants across South Dakota and were subjected to a variety of test: physical properties, Carr properties, and Jenike Shear properties and particle imaging. Results showed that for most of the important flow properties (Carr test and Jenike Shear test) and physical properties (particle size, moisture content, and soluble content) there were statistical significant differences present among DDGS sample obtained from various plants, and also between two batches in a single plant. Researcher showed that variability in DDGS physical and flow properties leads to inconsistency in the final product, thus, leading to particle caking and flowability issues. Chemical composition on the particle surface is believed to be an important factor for DDGS flowability problems. Therefore, cross sectional staining of DDGS particles in order to estimate the amount of protein and carbohydrate was done. Later on, protein and carbohydrate contents were correlated with Carr and Jenike Flow parameters. The results showed that samples with higher protein content in comparison to carbohydrate showed more flow problems. Finally, Confocal Scanning Laser Microscopy analysis revealed that samples with larger surface fat globules showed significant flow problems. Moving a step further, we hypothesized that the drying temperature and CDS (condensed distillers syrup) or "soluble" addition levels can significantly impact DDGS flowability. Desorption studies of distiller wet cake (DWG) mixed with varying CDS levels were done. Based on discussion with industry representatives, we selected three drying temperature for the desorption study. Out of thirteen basic models tried, a modified form Page model yielded the best results in terms of higher R2 (0.91) and least error (0.17) for predicting Moisture Ratio (MR, -) with varying time, drying temperature, and CDS levels. Drying kinetics studies were also performed simultaneously with desorption analysis, with similar drying temperature and CDS levels. Theoretical modeling using modified Chen and Douglas model yielded the best results in terms of higher R2 (0.90), lower error (21.48), and fairly random residuals. Modified Chen and Douglas model would help us to predict drying rate with varying moisture content, drying temperature, and CDS levels. This would be useful in DDGS production and handling. Based on the levels of drying temperature and CDS addition, laboratory-scale DDGS were prepared at constant moisture content and then various Carr, Jenike, and physical properties were evaluated. CDS samples had averagely 26.5 % OM (dry matter) in them, for all the cases. Results indicated that flow behavior was prominently better with increase in drying temperature but with change in CDS levels it was not much affected. Special dimensionless flow parameters and the regression models with varying drying temperature and CDS levels were developed using response surface methodology. Results indicate that drying temperature higher ~220 to 235°C showed more favorable values of the dimensionless flow parameters in order to have better DDGS flowability. For prediction of flowability (with varying drying temperature and CDS levels), the above mentioned dimensionless flow parameters (along with regression models) are recommended. After flowability analysis with only varying process variables, we implemented the effect of storage or cooling temperature on flowability modeling. Laboratory-scale DDGS samples are prepared with varying drying temperature, cooling temperature, and CDS levels were analyzed. Multivariate analysis techniques like Partial Least Squares (PLS) modeling and Principal Component Analysis (PCA) yielded a predictive modeling for angle of repose (AoR) and Jenike flow index as a function of drying temperature, cooling temperature, and CDS levels. The moisture content of the samples was constant throughout the analyses. Artifical Neural Network (ANN) modeling procedure was used to predict flowability of DDGS prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100,200, and 300°C), cooling temperature (-12, 25, and 35°C), and cooling time (0 and 1 month) levels. Results showed that for all response variables (as selected) R2 was greater than 0.83. Our results also showed that ANN modeling technique provided better models than PLS modeling procedure, implemented in our previous study. ANN models fitted fairly (R2 >0.63) with the flowability dataset obtained by Ganesan et al (2007), indicating that projected ANN architecture and the corresponding model is very robust. Finally, based on the predicted ANN output, surface plots were generated to project the range of process and storage variables required for "good," "fair," and "poor" flow. An overall comprehensive modeling and simulation experimental design with conical hoppers were done. This study included varying drying temperature, CDS addition, cooling (consolidation) temperature, cooling type, consolidation pressure, consolidation time, and drying time as independent variables for the overall flowability study, using Taguchi's experimental design. Results showed high correlation among measured dependent variables. A comprehensive regression model for mass flow rate as a function of Hausner ratio and Angle of Repose was developed, which provided predicted optimum ranges of Hausner ratio and Angle of Repose for "good," "fair," and "poor" flow. Based on this result, predicted optimum ranges of moisture content for "good," "fair," and "poor" flow was determined. Drying temperature, cooling type, and consolidation temperature were most significant variables in predicting mass flow rate from hopper. Johanson model (theoretical model) was calibrated and modified for predicting mass flow rate as a function of packed bulk density, for conical hoppers. Finally, combining empirical and theoretical models we proposed three models for predicting overall mass flow rate, as a function of significant independent variables. Glass transition temperature is found to be related with powder caking and structural collapse. Hence, we took a second perspective for understanding and modeling flowability in DDGS. We characterized and compared glass transition temperature (Tg) and sticky point temperature (Ts) of laboratory-scale DDGS sample with varying drying temperature, CDS, and moisture contents. Modified Gordon-Taylor model was developed to predict Tg as a function of drying temperature, CDS, and moisture contents. Regression model correlating Tg and Ts was found for DDGS samples. Finally, Global comprehensive model was developed for Ts as a function of drying temperature, CDS, and moisture contents. Results indicated that higher the moisture content, lower is Tg and Ts values, indicating flow problems. Also, drying temperature and CDS affecte significantly Tg and Ts. The second step for this approach was to study the Tg behavior of laboratory scale prepared DDGS with varying drying temperature, CDS levels, and cooling temperature. At this time the moisture content was kept constant. Results showed that DDGS stored or cooled at -l2°C had lower Tg, hence more tendency to have flow problems, than samples cooled at 35°C. Overall regression model for predicted Tg as a function of drying temperature, cooling temperature, and CDS was obtained with fair R2 values. Finally, based on the above regression model, optimum ranges for drying and cooling temperature required in DOGS (with 20% CDS levels) in order to prevent caking problems, was determined. Overall, results from this study show that laboratory-scale prepared DDGS had better flow behavior with higher drying temperature ranges. CDS levels did not affect flowability properties to a greater extent (sub-main effect), unlike drying temperature (main effect). Drying temperature, cooling type, and consolidation (cooling) temperature, and drying time were significant for determining flowability behavior in DDGS through hoppers. However, this study was based on a narrow range of CDS addition levels. Perhaps future studies with wider CDS ranges can be implemented to prove this point. Also, the consolidation pressure ranges are not sufficient to adequately simulate the pressure in a real rail car scenario. Hence, further studies with higher CDS addition levels and more consolidation pressure values are highly recommended. From glass transition temperature perspective, it was concluded that higher drying temperature and lower CDS levels predicted higher Tg, indicating better flow in the DDGS samples. Moreover, cooling at sub-zero temperatures yielded slightly lower Tg, indicating possible flow problems in DDGS. To date, there is no literature available that relates drying and cooling conditions with DDGS flowability. Thus, this series of information and predictive models about DDGS flowability is vital importance for optimization of process variables and ambient temperature conditions during DSGS production, transportation, and handling. This project is a step towards understanding effective handling and storage of DDGS.

Library of Congress Subject Headings

Distillers feeds -- Storage

Distillers feeds -- Handling

Format

application/pdf

Number of Pages

587

Publisher

South Dakota State University

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