  The industrial drying process consumes approximately 12% of the total energy
used in manufacturing, with the potential for a 40% reduction in energy usage
through improved process controls and the development of new drying
technologies. To achieve cost-efficient and high-performing drying, multiple
drying technologies can be combined in a modular fashion with optimal
sequencing and control parameters for each. This paper presents a mathematical
formulation of this optimization problem and proposes a framework based on the
Maximum Entropy Principle (MEP) to simultaneously solve for both optimal values
of control parameters and optimal sequence. The proposed algorithm addresses
the combinatorial optimization problem with a non-convex cost function riddled
with multiple poor local minima. Simulation results on drying distillers dried
grain (DDG) products show up to 12% improvement in energy consumption compared
to the most efficient single-stage drying process. The proposed algorithm
converges to local minima and is designed heuristically to reach the global
minimum.
