Experimental Data for Performance Prediction and Inlet Air Velocity Optimization of a Condensation-Based Atmospheric Water Harvesting System Using RSM and PINN
Description
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.
Files
Steps to reproduce
The workbook contains three experimental datasets. The 20-run CCD dataset can be used for RSM analysis, the 200-point dataset can be used for Multi-task PINN model development and evaluation, and the 9-condition validation dataset can be used to compare baseline and optimized operation. Variable definitions and summary information are provided in the README sheet.
Institutions
- Shihezi UniversityXinjiang, Shihezi