| Title | Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network |
|---|---|
| ID_Doc | 20500 |
| Authors | Ardebili, AA; Sáez, PV; Cortina, MG; Cruz, DMT; Sáiz, AR; Atanes-Sánchez, E |
| Title | Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network |
| Year | 2023 |
| Published | |
| Abstract | The construction sector is one of the main industries generating greater environmental impacts. In this sense, the European Commission is forcing the sector to implement alternative measurement and strategies to tackle this situation and bring the sector to a circular economy. One of the adopted measures is the use of recycled materials to produce construction materials and products. In this sense, many scientific works have been conducted analyzing the incorporation of different waste categories in gypsum products. In this sense, the main objective of this research is to characterize new gypsum-based materials that incorporate waste from the automotive sector. For this, mixed waste (containing polyurethane, cardboard and fiberglass) obtained during the production of automobiles' backboards was used. A total of 171 specimens were produced incorporating different percentage and size of mixed waste. These specimens were tested according to the bulk density, superficial hardness, and flexural, compressive and bonding strengths. Results show that it is possible to incorporate up to 11% of mixed waste overpassing the minimum strength values established by the regulations. In addition, the lightness of the material and its compression and flexion behavior improved considerably compared to the reference specimens without any waste addition. |
| https://doi.org/10.1016/j.conbuildmat.2023.130675 |
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