Experimental results for solar melting of zinc metal using multi-facet parabolic dish and a cavity receiver

The dataset contains the experimental data collected from thermocouples positioned inside a dual cavity solar receiver, used to demonstrate and evaluate the melting of zinc metal using only concentrated solar power as heat input. More information pertaining to the thermocouple positioning and the receiver design can be found in the thesis titled "Testing and development of a solar-dish cavity receiver for the melting of zinc metal".

Experiments were conducted, each with a unique set of environmental conditions:

1. Experiment 1 – 26^th of July 2022 = “Exp 1_26072022”
2. Experiment 2 – 04t of August 2022= “Exp 2_04082022”
3. Experiment 3 – 16^th of August 2022= “Exp 3_16082022”
4. Experiment 4 – 21^st of August 2022= “Exp 4_21082022”
5. Experiment 5 – 5^th of September 2022= “Exp 5_05092022”

Also included in the dataset are the original weather data collected on the respective experimental test work days as well as the weather data in the processed form after correcting the weather data to serve as input for the numerical model developed in the Python coding language.

Raw weather data:

1. Exp 1_Weather data_Original_26072022
2. Exp 2_Weather data_Original_04082022
3. Exp 3_Weather data_Original_16082022
4. Exp 4_Weather data_Original_21082022
5. Exp 5_Weather data_Original_05092022

Processed weather data:

1. Exp 1_Weather data_Post-process_26072022
2. Exp 2_Weather data_Post-process_04082022
3. Exp 3_Weather data_Post-process_16082022
4. Exp 4_Weather data_Post-process_21082022
5. Exp 5_Weather data_Post-process_05092022

In addition to all the weather data and the experimental results collected on the five experimental runs, the dataset also contains the Python code used to predict the zinc temperature in the cavity receiver. The code was compiled in Jupyter Notebook and the files consist of the heat loss calculations and zinc temperature prediction for each experimental run. The code contained has been validated against the experimental data and has been demonstrated to have a mean absolute percentage error (MAPE) of 2.7%. The code can thus be used to within 2.7% accuracy predict the zinc temperature inside a cavity receiver, by making use of actual weather data as input.

Python code for each experiment, with heat transfer factor validated using experimental data mentioned above:

1. Experiment 1.ipynb
2. Experiment 2.ipynb
3. Experiment 3.ipynb
4. Experiment 4.ipynb
5. Experiment 5.ipynb