This presentation was made at the 2019 NAFEMS World Congress in Quebec Canada
Toasting is a key process step for tortilla chip making. It determines the characteristic texture and blister formation in finished chip. The key functionality also includes crust development, creating rigid structure enabling handling downstream, and reducing moisture content of chips. The process is accomplished by running the chips through controlled conditions in a baking oven where burners are used to provide the heat and drive off the moisture. The business has great value in controlling and optimizing the process conditions in the ovens in order to achieve a set degree of ‘crusting’. However, this is extremely difficult to quantify and predict analytically.
During the baking process, the masa chips heat up and lose moisture due to infrared and convective heating. The moisture and heat change profile over the time drives the change of micro-structure change generating the texture characteristics. Traditionally physical testing is the only way to approach this in order to understand this complex physical-chemical process. However, experimental testing at full scale is challenging due to many relevant variables that need to be investigated. Some of these include burner heights, burner profile, bake times, and burner firing rates. Hardware limitations make the burner configurations not adjustable and controls limitations make it difficult to independently control burner firing rates. A virtual modeling approach to offer insights in the process and predict blister performance with respect to hardware and conditions change is of great value.
In this study, computational Fluid Dynamics (CFD) modeling is used to investigate the temperature and moisture field inside the oven. By coupling the CFD results with a product level heat and mass transfer model, predictions of the complete thermal and mass change time profile throughout the process are made available. Further step is made in statistically evaluating blister profile (key texture attribute) correlations with the thermal/mass profile and the ability to predict texture based on process operating conditions is achieved.
Experimental validations of the models are carried out through field testing in the production ovens to measure temperatures at various locations. Acceptable agreement is achieved. The model results are also compared with the moisture level measurement at the oven outlet. The model is then further employed to virtually test new designs and lock in the optimal set-points, control scheme, and hardware design.
|Date||18th June 2019|