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arXiv · 2210.01971

Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint

Alisina Bayati·Amber Srivastava·Amir Malvandi·Hao Feng·Srinivasa Salapaka
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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.

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Alisina Bayati, Amber Srivastava, Amir Malvandi, Hao Feng, Srinivasa Salapaka. 2022-10-05. Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint. https://arxiv.org/abs/2210.01971

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