Celso Axelrud

Industrial application of nonlinear model predictive control technology for fuel ethanol fermentation process

There are currently 134 ethanol biorefineries in the United States with a production capacity of nearly 7.2 billion gallons per year, with an additional 6.2 billion gals per year capacity under the construction [1]. Approximately two thirds of these are dry-mill production facilities.

Plant-wide optimization of an ethanol plant using parametric hybrid models

Ethanol plants are highly integrated systems consisting of many different processing units. As a result, the optimal operation of the ethanol plant can only be achieved if the plant model properly captures the operation of the individual units and the integrated nature of the plant components. The main challenge in plant-wide optimization, therefore, lies in the need for a compromise between accuracy and computational efficiency of (a) the constituent models of the plant, and (b) the manner by which these models are integrated. This challenge is further complicated by the fact that these models must capture true operating conditions and constraints of the plant in real-time for the optimization solution to have any chance of being implementable. This paper introduces parametric hybrid modeling as a framework for achieving a workable compromise between model complexity and computational efficiency. We represent process units as parameterized shortcut models with parameters that are empirically modeled based on actual plant data. We demonstrate the viability of our approach via a simulation study in which the parametric hybrid model of an actual ethanol plant is used to determine the optimal operation set points for the ethanol plant under different economic conditions.