Understanding and modeling of flowability of DDGS with varying process and storage variables

Distillers Dried Grains with Solubles (DDGS) is the main coproduct from the corn-based-fuel ethanol industry which operates in dry-grind process. Flowability and handling of DDGS needs to be addressed.;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.;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 % DM (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.;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. (Abstract shortened by UMI.)...