Dynamic Water Adsorption Characteristics of Distillers Dried Grains with Solubles (DDGS)

Transcription

1 Iowa State University From the SelectedWorks of Kurt A. Rosentrater November, 2007 Dynamic Water Adsorption Characteristics of Distillers Dried Grains with Solubles (DDGS) V. Ganesan, South Dakota State University Kurt A. Rosentrater, United States Department of Agriculture Kasiviswanathan Muthukumarappan, South Dakota State University Available at:

2 Dynamic Water Adsorption Characteristics of Distillers Dried Grains with Solubles (DDGS) V. Ganesan, 1 K. A. Rosentrater, 2,3 and K. Muthukumarappan 4 ABSTRACT Cereal Chem. 84(6): Distillers dried grains with solubles (DDGS) is one of the coproducts obtained from dry-grind ethanol manufacturing. As the ethanol industry is growing exponentially, the production of DDGS has been significantly increasing as well. To optimize the use of DDGS, it has to be economically transported from one part of the country to other parts, and stored efficiently. But DDGS has some flow issues, which often makes storage and transportation very problematic. So the objective of this study was to investigate the dynamic water adsorption characteristics of DDGS with four soluble levels at four temperatures and four relative humidities. Three mathematical models were then used to fit the adsorption data (Peleg, Pilosof, and Singh-Kulshrestha). As there was no model available for describing the water adsorption characteristics of DDGS with varying soluble levels at various temperature and relative humidity conditions, a new comprehensive model was developed. The new model, Ganesan- Rosentrater-Muthu (GRM) model, encompassed soluble level, temperature, and relative humidity effects, along with time and moisture content. The GRM model (R 2 = 0.94; F = ) provided a good description of DDGS water adsorption behavior and can be used to predict the dynamic adsorption of water in DDGS for a broad range of storage conditions. Distillers dried grains with solubles (DDGS) is one of the coproducts obtained from dry-grind ethanol milling. Distillers grains are energy dense because they often contain relatively high fat contents and are nearly devoid of starch. DDGS typically contains 86 93% (db) dry matter, 26 34% (db) crude protein, and 3 13% (db) fat (Rosentrater and Muthukumarappan 2006). It is an excellent source of energy and protein for animal feeds. As the U.S. ethanol industry has been growing tremendously during the past decade, the production of DDGS has been significantly increasing as well, and it is forecasted to be over 13 million metric tons in the fiscal year (AAFC 2006). To effectively utilize DDGS in the domestic and international markets, it has to be transported greater distances. During transportation, DDGS can be subjected to varying temperature and humidity levels. DDGS is stored in bins and silos until use; sometimes this may be a relatively long time. DDGS storage and transportation can become problematic as it often hardens inside the storage structures and railcars. This can lead to severe damage when these vessels are unloaded. DDGS storage and flow behavior depend on physical and chemical properties of the material itself, as well as external conditions such as air temperature and relative humidity. At times, even small differences in these factors can cause caking and bridging (i.e., poor flowability) in DDGS. The transport and storage of DDGS, and the resulting flow behavior under the various storage conditions are important considerations, especially in the light of the growth of the industry. Most organic granular materials are hygroscopic in nature, so they will gain or lose moisture when they are exposed to various humidity conditions. This can lead to physical and chemical changes in the material itself. Each material absorbs moisture at different rates under different temperature and humidity conditions. 1 Graduate research assistant, Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD. 2 Agricultural and Bioprocess Engineer, USDA-ARS, North Central Agricultural Research Laboratory, Brookings, SD. Mention of a trade name, propriety product or specific equipment does not constitute a guarantee or warranty by the United States Department of Agriculture and does not imply approval of a product to the exclusion of others that may be suitable. 3 Corresponding author. Phone: Fax: address: krosentr@ngirl.ars.usda.gov 4 Professor, Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD. doi: / CCHEM AACC International, Inc. Hence, there have been many studies focused on characterizing the sorption process of biomaterials such as chickpeas (Turhan et al 2002; Wood and Harden 2006), dasheen leaves (Maharaj and Sankat 2000), yellow field peas (Wang et al 2003), corn kernels (Muthukumarappan and Gunasekaran 1990), canola (Thakor et al 1995), wheat and barley (Tagawa et al 2003), African breadfruit seeds (Shittu et al 2004), pearl millet and sorghum (Badau et al 2005). Moisture sorption is coupled with increased cohesiveness, chiefly because of interparticle liquid bridge formation. Moisture content is a significant variable that affects the cohesive strength and arching ability of bulk materials (Johanson 1978). As the moisture content of granular solids increases, the adhesion (Craik and Miller 1958) and cohesion (Moreyra and Peleg 1981) tend to increase. Even a small change in moisture content will significantly affect the frictional properties (e.g., wall friction angle, internal angle of friction) of a material (Marinelli and Carson 1992). A few empirical equations are available to model the water adsorption characteristics of food and biological materials. Of those models, the Peleg model (Peleg 1988) is the simplest and it has been applied to a variety of materials by many researchers (Sopade and Obekpa 1990; Sopade et al 1992, 1994; Sopade and Okonmah 1993; Maharaj and Sankat 2000; Turhan et al 2002; Shittu et al 2004; Badau et al 2005). Pilosof et al (1985) developed a model for dehydrated food powders and Maia and Cal-Vidal (1994) evaluated its applicability to water uptake by citrus juice powder under different relative humidity conditions. To date, reports evaluating its applicability to food grains are few. Also, the Singh-Kulshrestha model has received less attention but it has been used to model water absorption data for soybean and pigeon pea (Singh and Kulshrestha 1987). Understanding water adsorption in DDGS at various temperatures and relative humidities is of practical importance to the industry to optimize storage and transportation conditions. However, to date, there are no published reports on the water adsorption characteristics of DDGS. Hence, the objectives of this study were to 1) generate water adsorption data for DDGS with four soluble levels (10, 15, 20, and 25% db) at four practical temperatures (10, 20, 30, and 40 C) and four relative humidity levels (60, 70, 80, and 90%) using appropriate saturated salt solutions; 2) evaluate the applicability of three empirical models for the prediction of moisture content of DDGS in terms of time and initial moisture content; 3) develop a new adsorption model incorporating soluble level, temperature, and relative humidity effects in addition to time and initial moisture content. 548 CEREAL CHEMISTRY

3 MATERIALS AND METHODS Sample Collection and Preparation Samples of condensed distillers solubles (CDS) and distillers dried grains (DDG) were obtained from a commercial ethanol plant (Dakota Ethanol, Wentworth, SD) and were stored in sealed plastic buckets (the DDG at room temperature and the CDS at 4 ± 1 C) until needed. The soluble content of both the DDG and the CDS were determined, and the DDGS with four soluble levels (10, 15, 20, and 25% db) were prepared using the methodology developed by Ganesan et al (2006a). Additionally, the moisture content of the DDG and CDS were determined using Approved Method (AACC International 2000). Initially DDG had 10% (db) solubles and 6.1% (db) moisture content. To obtain the water adsorption data over a wide range of temperatures and relative humidities, it was essential that experimental samples had constant initial moisture content. So, before experimentation, the DDGS samples that had been prepared with the various soluble contents were dried at 100 C to a moisture content of 6.1% (db) using an air oven. The time for drying DDGS was min. Water adsorption characteristics and the equilibrium moisture contents of the DDGS were determined at four constant temperatures (10, 20, 30, and 40 C) and four humidity levels (60, 70, 80, and 90%) using the static gravimetric method. Saturated salt solutions were used to maintain a constant relative humidity in each environment. The equilibrium relative humidity (ERH) values of the saturated salt solutions (Table I) were obtained from the reported data (Spencer 1926; Hodgman 1954, 1955; Greenspan 1977) and were verified using thermo hygrometers (model , Sper Scientific). In-depth details about the experimental set up and procedures are found in Ganesan et al (2006b). The DDGS samples were exposed to different temperature and humidity conditions and weighed every 1 hr during an 8-hr period, and then at constant intervals (every 24 hr in the first week; every 48 hr in the second week; every 72 hr in the third week; and every 168 hr in rest of the weeks) for a period of five to six weeks. The experiment was stopped when the difference between successive weighings was g. Triplicates were measured for each treatment. Water Adsorption Models In this study, the applicability of three common empirical models that describe moisture adsorption over time were evaluated for DDGS: Peleg model, Pilosof model, and Singh-Kulshrestha model. Peleg model (Peleg 1988) M t M + A + Bt = 0 where M is the moisture content after time t (% db), M 0 is the initial moisture content (% db), A is the water absorption rate constant (hr/%db), and B is the characteristic water absorption constant (1/% db). Pilosof model (Pilosof et al 1985) (1) At M = (2) B + t where M is the total moisture uptake at time t (g/100 g of solid), A is the water absorption capacity (g/g of dry solid), and B is the half life (hr). Singh-Kulshrestha model (Singh and Kulshrestha 1987) M M 0 Bt = (3) A M Bt where M is the moisture content at any time t (g/g of dry solid), M 0 is the initial moisture content (g/g, db), A is the water absorption capacity (g/g of dry solid), and B is the water absorption rate constant (1/hr). Model constants were determined by iterative nonlinear regression on the collected data using PROC NLIN of SAS v.9.1 (SAS Institute, Cary, NC). The nonlinear regression procedure used the Gauss-Newton method and minimized the sum of squares of deviation between the observed and predicted moisture adsorption data to resolve the models. The goodness-of-fit between the experimentally observed and predicted amount of water absorbed was evaluated and compared using the standard error of the mean (SEM), the root mean square deviation (RMSD), the coefficient of determination (R 2 ), and F-statistic values where M i and ˆM i are the experimentally observed and predicted amount of absorbed water respectively; n is the number of data points; and DF is the degrees of freedom (number of data points minus the number of coefficients in each model). PROC NLIN directly provides the F-statistic and R 2 values (model sum of squares/total sum of squares). Many researchers have used other criteria to test model performance. Some have used Chi square, R 2, and RMSD (Sopade et al 1992; Sacchetti et al 2003; Shittu et al 2004; Badau et al 2005), and some have used mean relative error (MRE), R 2, and P values (Maharaj and Sankat 2000; Turhan et al 2002; Wang et al 2003) for model performance analysis. In this study, a model was considered good, when it produced relatively small SEM, RMSD, and high F-statistic and R 2 values. RESULTS AND DISCUSSION Water Adsorption Behavior The water adsorption behavior of DDGS with four soluble (10, 15, 20, and 25% db) levels at four temperatures (10, 20, 30, and 40 C) and four relative humidity (60, 70, 80, and 90%) levels are shown in Figs. 1 and 2. The water adsorption trends followed an asymptotic growth pattern for all treatment combinations. Similar patterns were reported for cereals, legumes, and seeds (Sopade et al 1992, 1994; Hung et al 1993; Turhan et al 2002; Shittu et al 2004; Badau et al 2005). The water adsorption of DDGS for all the temperature and humidity combinations increased rapidly in the first 24 hr, and then slowly approached steady-state, which was typically reached at hr. It was observed that the moisture adsorption rate was very high initially and became slower in the later stages. Similar responses were found for cereals grains and legumes (Engels et al 1986, 1987; Sopade and Obekpa 1990; Sopade et al 1992, 1994; Sopade and Okonmah 1993). The water adsorption capacity of DDGS with a soluble level of 25% (db) at 40 C and 90% RH was greater compared with the other treatment TABLE I Saturated Salt Solutions Used for Producing Control Environments (actual measured ERH a ) RH (decimal) 10 C 20 C 30 C 40 C 0.60 Sodium bromide (0.622) Sodium bromide (0.591) Sodium bromide (0.560) Sodium bromide (0.570) 0.70 Lithium acetate dihydrate (0.720) Lithium acetate dihydrate (0.700) Strontium chloride (0.691) Strontium chloride (0.680) 0.80 Ammonium sulfate (0.821) Ammonium chloride (0.792) Potassium bromide (0.803) Potassium bromide (0.794) 0.90 Strontium nitrate (0.906) Barium chloride (0.910) Barium chloride (0.890) Potassium nitrate (0.890) a Equilibrium relative humidity values. Vol. 84, No. 6,

4 combinations. Sopade et al (1992) observed that the water absorption of cereal grains increased with increasing temperature. Large differences were found at higher equilibrium relative humidity (ERH) values, often because of the inherent difficulty in determining equilibrium moisture content (EMC) at these elevated humidity levels. Similar issues were reported by Maroulis et al (1988) and Yu et al (1999). Water Adsorption Modeling The Peleg model, Pilosof model, and Singh-Kulshrestha model were used to fit the experimentally observed water adsorption data. The Singh-Kulshrestha model did not fit the experimental data well, as the model coefficients did not converge with the iterative nonlinear regression procedure. So the model was not considered for further evaluation. Tables II and III show parameters obtained Fig. 1. Water adsorption characteristics of DDGS with various soluble levels at 10 and 20 C, and 60, 70, 80, 90% RH conditions (± 1 SD). 550 CEREAL CHEMISTRY

5 from nonlinear regression analysis using the Peleg and Pilosof models. The R 2 values varied from 0.71 to 0.99 and 0.95 to 0.99 for the Peleg and Pilosof models, respectively. Both models produced high F-statistic and R 2 values and small error terms (SEM, RMSD). Overall, the Peleg model produced higher F- statistic values and smaller error terms than did the Pilosof model, except for few treatment combinations. The DDGS did show some instability at 40 C, and a similar effect was reported for African breadfruit seeds at higher temperatures (Shittu et al 2004). Both the Peleg and Pilosof models performed and fit the DDGS water adsorption data well. In commercial ethanol plants, CDS (often referred to as syrup ) is added back to the wet-distillers grains, the combination of which is then dried to produce DDGS. Fig. 2. Water adsorption characteristics of DDGS with various soluble levels at 30 and 40 C, and 60, 70, 80, 90% RH conditions (± 1 SD). Vol. 84, No. 6,

6 The amount of CDS added will vary for each production cycle and is often not a systematic process. Considering this, the soluble level was subsequently incorporated into the study as a key parameter in the Peleg equation, and this modified equation was then evaluated for performance. Modified Peleg Equation (MPE1) where M is moisture content after time t (% db), M 0 is the initial (6) Soluble Level (% db) Temp ( C) TABLE II Regression Parameters Obtained for DDGS with 10 to 25% (db) Solubles at Various Temperature and Relative Humidity Conditions Using Peleg s Equation Relative Humidity (%) Constant A (hr/%db) Constant B (1/%db) F-Statistic 10 4 R 2 SEM RMSD CEREAL CHEMISTRY

7 moisture content (% db), S is soluble level (% db), A is time constant (hr), B is dimensionless constant, and C is water adsorption rate constant (hr/% db) The parameters obtained for this model are shown in Table IV. The model produced higher F-statistic and R 2 values and smaller error terms (SEM, RMSD). The R 2 obtained varied from 0.96 to The R 2 and F-statistic values were smaller and error terms were higher when compared with the parameters obtained from the Peleg model (Eq. 1). But these values were not significantly different from the Peleg model. This was expected, however, as a Soluble Level (% db) Temp ( C) TABLE III Regression Parameters Obtained for DDGS with 10 to 25% (db) Solubles at Various Temperature and Relative Humidity Conditions Using Pilosof s Equation Relative Humidity (%) Constant A (g/g of dry solids) Constant B (hr) F-Statistic 10 4 R 2 SEM RMSD Vol. 84, No. 6,

8 greater number of varying data was included in the model equation. As discussed above, temperature and relative humidity can play a major role in the rate and amount of water absorbed by DDGS during transport and storage. Hence, the model (Eq. 6) was further modified to include the effects of temperature and relative humidity individually, as well as in combination. Thirty different combinations were iteratively tried; only the three models that performed well are discussed below. Modified Peleg Equation 2 (MPE 2) where M is the moisture content after time t (% db), M 0 is the initial moisture content (% db), S is the soluble level (% db), T is the temperature level ( C), A is an empirical constant (% db), B is the time constant (hr), and C is an empirical constant ( C). Modified Peleg Equation 3 (MPE 3) where M is the moisture content after time t (% db), M 0 is the initial moisture content (% db), S is the soluble level (% db), R is the relative humidity level (%), A is an empirical constant (% db), B is a time constant (hr), and C is a dimensionless constant. Modified Peleg Equation 4 (MPE 4) where M is the moisture content after time t (% db), M 0 is the initial moisture content (% db), S is the soluble level (% db), T is the temperature level ( C), R is the relative humidity level (%), A is a constant (% db), B is a time constant (hr), C is an empirical constant ( C), and D is a dimensionless constant. Table V shows the parameters obtained through modified Peleg equations (Eqs. 7 9). These models produced smaller R 2 (0.94) and F-statistic ( ) values and higher error terms (SEM (7) (8) (9) 6.00, RMSD 0.10) than the original Peleg model (Eq. 1). Out of these three models (MPE 2, MPE 3, and MPE 4), the MPE 4 model performed the best and is called the Ganesan-Rosentrater- Muthu (GRM) model. The GRM model produced comparatively high R 2 and F-statistic values (Table V). The F-statistic value produced by this model is nearly twice the F-statistic values produced by other models (MPE 2 and MPE 3). Also, the error terms are comparatively less than those of the MPE 2 and MPE 3 models. Moreover, the GRM model encompassed the effect of all soluble levels, temperatures, and relative humidity levels. The GRM model was superior to other models and is applicable over a wide range of environmental combinations. Thus the GRM model is recommended for characterizing dynamic DDGS water adsorption and should be further examined with DDGS from other ethanol plants. CONCLUSIONS Dynamic water adsorption characteristics of DDGS with four soluble levels (10, 15, 20, and 25% db) at four temperatures (10, 20, 30, and 40 C) and four relative humidities (60, 70, 80, and 90%) were studied. This was the first step taken toward quantifying the water adsorption characteristics of DDGS. DDGS water adsorption data followed an asymptotical growth pattern and showed higher experimental error at higher temperatures and relative humidity levels. The obtained water adsorption data were mathematically modeled using the three common empirical equations (Peleg, Pilosof, and Singh-Kulshrestha). The Peleg and Pilosof models gave better descriptions of DDGS water adsorption data. The Singh-Kulshrestha model did not perform well because the model parameters did not converge. The addition of CDS greatly affects the water adsorption characteristics of DDGS at various temperature and humidity conditions. So the Peleg equation was iteratively modified by including soluble, temperature, and relative humidity effects. The optimal model, the GRM model (R ; F ), encompassed soluble, temperature, and relative humidity effects, in addition to time and moisture content effects and is thus applicable over a variety of environmental conditions. Temp. ( C) TABLE IV Regression Parameters Obtained for DDGS at Various Temperature and Relative Humidity Conditions Using Modified Peleg Equation Relative Humidity (%) Constant A (-) Constant B (1/hr) Constant C (hr/% db) F-Statistic 10 4 R 2 SEM RMSD TABLE V Regression Parameters Obtained for DDGS Using Modified Peleg Equations Constants F-Statistic ID A (% db) B (hr) C ( C) D (-) 10 4 R 2 SEM RMSD MPE MPE MPE 4 a a MPE 4 model has been termed the Ganesan-Rosentrater-Muthu or GRM model in this study. 554 CEREAL CHEMISTRY

9 The proposed GRM model can be used by ethanol producers to plausibly predict water adsorption of DDGS at any given time, specific temperature, and relative humidity conditions. Future work should investigate the sorption behavior of DDGS at lower relative humidity levels as well. The flowability of DDGS was not investigated in this study after exposing DDGS to various temperature and humidity levels. Thus, it is suggested to examine the flowability of DDGS at various levels of temperature and humidity. This would allow us to determine the optimum level of solubles, temperature, and RH for better flowability of DDGS during storage and transport. ACKNOWLEDGMENTS We would like to thank Dakota Ethanol (Wentworth, SD) which contributed DDG and CDS for this study. We would also like to thank the South Dakota Corn Utilization Council (SDCUC), the South Dakota Agricultural Experimental Station (AES), and the USDA-ARS for financial support. 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