Coordinative optimization for multi-stage regeneration temperatures combination in the desiccant wheel deep dehumidification systems via model predictive control strategies

The demand for lithium-ion batteries has been steadily increasing throughout the years. In the field of lithium-ion battery manufacturing, the cascading desiccant wheel deep dehumidification (DWDD) system has been commonly employed for deep dehumidification process with supply air dew point temperature below −28 ℃. The DWDD system can be highly energy-intensive, and systematic studies on the coordinated regulation are still limited. Therefore, on-site measurements were firstly conducted to investigate the energy performance and energy saving potential of the DWDD system. It is found that energy efficiency during dehumidification decreases significantly as the dehumidification depth increases. The deep dehumidification stage handles the least proportion of the dehumidification load while having the highest energy consumption compared to conventional and low humidity dehumidification stages. A combination of high regeneration temperature in the first stage and low regeneration temperature in the second stage of the desiccant wheel improves the energy efficiency of the DWDD system. The performance predictive model is then developed using operational data to reveal the nonlinear influence of operating parameters on the deep dehumidification efficiency of the DWDD system. Model predictive control (MPC) based optimization strategies are further proposed and applied in a practical application to achieve energy savings while ensuring stable control of the supply air dew point. By properly setting the regeneration temperature range for the two stages of the desiccant wheel, approximately 8.1% of the cooling consumption and 15.6% of the total heating consumption of the DWDD system can be reduced. This study can provide a theoretical support and a feasible solution for the intelligent energy saving control of the deep dehumidification system.