Modeling and dynamic optimization of fuel-grade ethanol fermentation using fed-batch process

This paper investigates optimization of operational strategies of an industrial ethanol fermentation process. One of the challenges associated with this type or process is that most of the measurements are only taken sporadically, thereby complicating process monitoring and optimization. The one exception to this rule involves temperature measurements, which are readily available. However, an existing model of the plant investigated in this paper does not include an energy balance and, accordingly, the temperature measurements cannot be used to estimate model parameters. This paper addresses these deficiencies and proposes modifications to an existing ethanol fermentation model. The proposed changes include the derivation of an energy balance, modification of the reaction kinetics to include additional inhibition terms, and also estimation of model parameters from industrial data. The new model is validated against plant data and then used for optimization of the process operations. It is shown that modifications of the input profiles for the cooling rate and the glucoamylase addition can lead to an approximately 10% increase in ethanol yield. These are promising results, even though these findings will ultimately need to be validated during real plant operations.