Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint

TL;DR

This paper introduces a mathematical framework using the Maximum Entropy Principle to optimize the sequence and control parameters of modular industrial drying processes, significantly reducing energy consumption.

Contribution

It presents a novel combinatorial optimization approach for modular drying technology, addressing non-convexity and local minima issues.

Findings

Up to 12% energy savings in drying DDG products.

The algorithm effectively converges to local minima and heuristically reaches the global minimum.

Demonstrates the potential for cost-efficient, high-performance drying processes.

Abstract

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.