Modeling and Simulation of a Packed Column Batch Still for Fruit Wine Distillations

Abstract

Batch distillations are extensively used worldwide to produce fruit wine spirits with distinctive aromas. These processes are typically operated manually, based on the experience of the distiller. However, dynamic optimization and automatic control strategies could be valuable for ensuring a more consistent product and quickly adapting to new market tendencies. Consequently, reliable process models are required for the application of these methods. The novelty of this study is developing a highly efficient method to reliably simulate and calibrate a rigorous first-principles model of a packed batch column still. We applied the method of lines (MOL) to transform the partial differential equations describing the packed column dynamics into an ordinary differential equation (ODE) system. The nonlinear phase equilibrium equations were pre-solved, and several polynomials were fitted to get explicit algebraic equations. The final differential-algebraic system (DAE) comprises 31 ODEs, 113 explicit algebraic equations, and two implicit algebraic equations. Variables scaling, smoothing of discontinuities, and applying an algebraic loop within Simulink to solve the implicit algebraic equations resulted in 600 times faster simulations than a naïve approach. The model was regressed using pilot-scale experimental data and subjected to extensive validation using independent data. Sensitivity and residual analysis established that a better heat loss model could improve ethanol concentration prediction. This new heat loss model reduced the average ethanol concentration error by more than five times. The developed model can be applied to design reliable control systems and optimal operating strategies for packed column batch distillations.

Keywords

• Differential algebraic systems
• model calibration
• algebraic loops
• first-principles models