Physical and Chemical Characterization of Fuel Ethanol Coproducts Relevant to Value‐Added Uses

Bhadra, Rumela; Muthukumarappan, Kasiviswanathan; Rosentrater, Kurt A.

doi:10.1094/cchem-02-10-0014

Cited by 16 publications

(15 citation statements)

References 26 publications

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“…The flowability behavior of DDGS can be influenced by any number of interrelated factors, including moisture content, fat content, particle size distribution, storage temperature, relative humidity, storage time, compaction pressure distribution, and vibrations during transport (Craik and Miller 1958;Johansson 1978;Moreyra and Peleg 1981;Teunou et al 1991;Fitzpatrick et al 2004). Various studies have been performed to determine the cause and find solutions to the flowability problem (Bhadra et al 2009a(Bhadra et al , 2009b(Bhadra et al , 2010Keierleber and Rosentrater 2010). A key factor appears to be the level of condensed distillers solubles (CDS) that is used during DDGS production.…”

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confidence: 99%

Quantification of Physical and Chemical Properties, and Identification of Potentially Valuable Components from Fuel Ethanol Process Streams

et al. 2013

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Cereal Chem. 90(1):70-79Wider exploration of ethanol coproduct uses is necessary as the ethanol industry continues to face challenges. Currently, process streams such as thin stillage and condensed distillers solubles (CDS) are processed into distillers dried grains with solubles and used as animal feeds, but other higher value opportunities may exist. The objective of this study was to identify chemical components and quantify physical properties of CDS and thin stillage. Protein, organic acid, and sugar profiles were determined. Zein protein was identified, and glycerol was determined to have a concentration of 18.8 g/L in thin stillage and 63.2 g/L in CDS. Physical properties including density, thermal conductivity, thermal diffusivity, and rheological behaviors were also examined. Thermal conductivity of thin stillage and CDS was approximately 0.54 and 0.45 W/m°C, respectively. Quantification of the physical properties and identification of the chemical constituents pave the way for exploration of new value-added uses for thin stillage and CDS.

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Quantification of Physical and Chemical Properties, and Identification of Potentially Valuable Components from Fuel Ethanol Process Streams

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“…The drying continued until the final blend moisture contents reached 8% (db). This target was based on previous research by Bhadra et al [24] and Rosentrater, [31] and upon discussions with industry representatives (unpublished), which indicated a typical average moisture content of approximately 8% (db) in the marketplace. Preparation of the dried DDGS samples (nine total treatments) was done with three replications.…”

Section: Materials and Methods Sample Collection And Preparationmentioning

confidence: 99%

“…[23] Various studies have also revealed the typical ranges of DDGS chemical, physical, and flowability properties. [3,4,24] The functionality and properties of bulk solids and granular materials are greatly influenced by drying conditions during the manufacturing process. [25][26][27][28] Two studies examined the drying rate and moisture desorption during DDGS production, using varying CDS and drying temperature levels.…”

Section: Introductionmentioning

confidence: 99%

Effects of CDS and Drying Temperature Levels on the Flowability Behavior of DDGS

2012

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Due to increasing demand for alternative fuels and theneed to reduce dependence on fossil fuels, the growth of bioethanol production has been rising. One of the problems facing this industry is transportation of the coproduct dried distillers grains with solubles (DDGS) over long distances, because caking and agglomeration between particles can lead to bulk flow problems. In this study, DDGS was prepared by combining condensed distillers solubles (CDS) and distillers wet grains (DWG) and then oven drying to achieve 8% (db) moisture content. The effects of drying temperature (100, 200, and 300 C) and CDS (10, 15, and 20%wb ) level on the resulting flowability behavior of the DDGS particles were investigated. Statistical analyses indicated significant differences (a ¼ 0.05, 95% confidence level) due to drying temperature and CDS main effects and significant interaction effects between CDS level and drying temperature for many of the flow parameters. Surface regression analysis of the ratio of total flow index/Jenike flow function as a function of CDS and drying temperature resulted in an R 2 value of 0.94. Partial least squares (PLS) regression yielded an R 2 of 0.90 for the Jenike flow function index as a function of all flow and physical properties using only two multivariate components. Understanding the effects of varying drying temperatures and CDS levels can help guide efforts to overcome DDGS flowability problems.

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“…DDGS is an excellent source of protein (32%, db), fiber (35%, db), and fat (10%, db) (Bhadra et al 2010b) and an important source of vitamins and minerals for livestock feed. With the rapid growth in the ethanol industry, the production of DDGS has also increased.…”

Section: Artificial Neural Network Modeling Of Distillers Dried Grainmentioning

confidence: 99%

Artificial Neural Network Modeling of Distillers Dried Grains with Solubles (DDGS) Flowability with Varying Process and Storage Parameters

2011

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Cereal Chem. 88(5):480-489Neural network (NN) modeling techniques were used to predict flowability behavior of distillers dried grains with solubles (DDGS) prepared with varying levels of condensed distillers solubles (10, 15, and 20%, wb), drying temperatures (100, 200, and 300°C), cooling temperatures (-12, 25, and 35°C), and storage times (0 and 1 month). Response variables were selected based on our previous research results and included aerated bulk density, Hausner ratio, angle of repose, total flowability index, and Jenike flow index. Various NN models were developed using multiple input variables in order to predict single-response and multiple-response variables simultaneously. The NN models were compared based on R 2 , mean square error, and coefficient of variation obtained. In order to achieve results with higher R 2 and lower error, the number of neurons in each hidden layer, the step size, the momentum learning rate, and the number of hidden layers were varied. Results indicate that for all the response variables, R 2 > 0.83 was obtained from NN modeling. Compared with our previous studies, NN modeling provided better results than either partial least squares modeling or regression modeling, indicating greater robustness in the NN models. Surface plots based on the predicted values from the NN models yielded process and storage conditions for favorable versus cohesive flow behavior for DDGS. Modeling of DDGS flowability using NN has not been done before, so this work will be a step toward the application of intelligent modeling procedures to this industrial challenge.

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Physical and Chemical Characterization of Fuel Ethanol Coproducts Relevant to Value‐Added Uses

Abstract: Cereal Chem. 87(5):439-447

Cited by 16 publications

References 26 publications

Quantification of Physical and Chemical Properties, and Identification of Potentially Valuable Components from Fuel Ethanol Process Streams

Quantification of Physical and Chemical Properties, and Identification of Potentially Valuable Components from Fuel Ethanol Process Streams

Effects of CDS and Drying Temperature Levels on the Flowability Behavior of DDGS

Artificial Neural Network Modeling of Distillers Dried Grains with Solubles (DDGS) Flowability with Varying Process and Storage Parameters

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