Experimental Data for Performance Prediction and Inlet Air Velocity Optimization of a Condensation-Based Atmospheric Water Harvesting System Using RSM and PINN

This dataset supports the study entitled “Performance Prediction and Inlet Air Velocity Optimization of a Condensation-Based Atmospheric Water Harvesting System Using RSM and PINN.” It contains three experimental datasets from a condensation-based atmospheric water harvesting system: (1) 200 experimentally measured operating points used for Multi-task PINN modeling, including ambient temperature (T), relative humidity (RH), inlet effective air velocity (V), water yield (Y), and system power (P); (2) 20 central composite design (CCD) experimental runs used for response surface methodology (RSM) analysis; and (3) validation data from nine representative temperature–humidity conditions comparing baseline and model-recommended optimal operation. Across the nine validation conditions, optimized operation increased total water yield by 17.66% and comprehensive energy efficiency by 12.77%, with a 4.34% increase in summed system power. The dataset can be used for RSM analysis, prediction-model development, and inlet-air-velocity optimization of condensation-based atmospheric water harvesting systems.