TY - GEN
T1 - Towards Efficient Modularity in Industrial Drying
T2 - 2023 American Control Conference, ACC 2023
AU - Bayati, Alisina
AU - Srivastava, Amber
AU - Malvandi, Amir
AU - Feng, Hao
AU - Salapaka, Srinivasa M.
N1 - This work was supported by the U.S. Department of Energy under award DE-EE0009125 and NCCR Automation (grant number 180545) funded by the Swiss National Science Foundation.
1Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, 61801 IL, USA. [email protected],[email protected] 2Automatic Control Laboratory, Swiss Federal Institute of Technology (ETH Zurich), Physicstrasse 3, 8092 Zurich, Switzerland. [email protected] 3Department of Agriculture and Biological Engineering Sciences, University of Illinois at Urbana-Champaign, 61801 IL, USA. [email protected] 3North Carolina Agricultural and Technical State University, 27411 NC. [email protected] This work was supported by the U.S. Department of Energy under award DE-EE0009125 and NCCR Automation (grant number 180545) funded by the Swiss National Science Foundation.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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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U2 - 10.23919/ACC55779.2023.10156630
DO - 10.23919/ACC55779.2023.10156630
M3 - Conference contribution
AN - SCOPUS:85167826040
T3 - Proceedings of the American Control Conference
SP - 3827
EP - 3832
BT - 2023 American Control Conference, ACC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 31 May 2023 through 2 June 2023
ER -