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Abstract
This work presents a mixed integer nonlinear programming (MINLP) formulation for the design of energy-efficient multistage adsorption dryers within constraints on product temperature and moisture content. Apart from optimizing temperatures and flows, the aim is to select the most efficient adsorbent per stage and product to air flow configuration. Superstructure models consisting of commonly used adsorbents such as zeolite, alumina, and silica-gel are developed and optimized for a two-stage, low-temperature, adsorption drying system. Results show that the optimal configuration is a hybrid system with zeolite as the first-stage adsorbent and silica-gel as the second-stage adsorbent in counter-current flow between drying air and product. A specific energy consumption of 2,275 kJ/kg is achieved, which reduces to 1,730 kJ/kg with heat recovery by a heat exchanger. Compared to a conventional two-stage dryer at the same drying temperature, this represents a 59% reduction in energy consumption. The optimal system ensures the exhaust air temperature of the first-stage regenerator is high enough to regenerate the second-stage adsorbent so no utility energy is spent in the second stage. A higher second-stage adsorbent wheel speed favors energy performance as it becomes optimized for energy recovery while the first is optimized for dehumidification. Although this work considers three candidate adsorbents in a two-stage system, the same reasoning can be applied to systems with more stages and adsorbents. The developed superstructure optimization methodology can, by extension, be applied to optimize multistage hybrid drying systems in general for any objective.
ACKNOWLEDGMENTS
This work is supported by the Energy Research Program EOS of the Dutch Ministry of Economics under Project EOSLT07043.