Regulating humidification-dehumidification systems via machine learning based on quasi-digital twin

Solar humidification-dehumidification technology is advantageous due to its ease of maintenance and clean operational profile. Integrating a digital twin into these systems enables timely and precisely control capabilities for enhanced system optimization. This work implements a quasi-digital twin optimization framework driven by machine learning models into the solar humidification-dehumidification system. The machine learning model was trained and validated using experimental data, achieving R² values of 0.96 for freshwater production predictions and 0.93 for temperature forecasts. Leveraging the machine learning-assisted quasi-digital twin, this study explores the real-time optimization of operational parameters across four distinct modes: production-maximized, energy-saving, efficiency-optimized, and balanced. Genetic algorithms were employed to determine optimal configurations under varying weather conditions. Results indicate significant performance improvements: under the production-maximized mode, hourly freshwater output increased by up to 25% during a sunny clear day and 49 % during a cloudy day. In energy-saving mode, daily energy consumption was reduced by 58 % on a clear day and 52 % on a cloudy day, respectively, relative to baseline operations. This research not only elevates the performance of solar humidification-dehumidification systems but also underscores the broader applicability of the optimization framework by using digital twin to diverse engineering challenges.