Performance assessment of a solar powered hydrogen production system and its ANFIS model

• Published in: Heliyon Vol. 6; no. 10; p. e05271
• Main Authors: Senthilraja, S., Gangadevi, R., Köten, Hasan, Marimuthu, R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-10-2020; Elsevier
• Subjects: Adaptive Neuro Fuzzy Inference System (ANFIS); Electrical efficiency; Electrical engineering; Energy; Energy conservation; Heat transfer; Hydrogen energy; Hydrogen production system; Photovoltaic; Photovoltaics; Solar energy; Thermal collector; Electrical engineering; Adaptive Neuro Fuzzy Inference System (ANFIS); Hydrogen energy; Photovoltaic; Energy; Energy conservation; Solar energy; Hydrogen production system; Electrical efficiency; Heat transfer; Thermal collector; Photovoltaics
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2020.e05271

Apart from many limitations, the usage of hydrogen in different day-to-day applications have been increasing drastically in recent years. However, numerous techniques available to produce hydrogen, electrolysis of water is one of the simplest and cost-effective hydrogen production techniques. In this method, water is split into hydrogen and oxygen by using external electric current. In this research, a novel hydrogen production system incorporated with Photovoltaic – Thermal (PVT) solar collector is developed. The influence of different parameters like solar collector tilt angle, thermal collector design and type of heat transfer fluid on the performance of PVT system and hydrogen production system are also discussed. Finally, thermal efficiency, electrical efficiency, and hydrogen production rate have been predicted by using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique. Based on this study results, it can be inferred that the solar collector tilt angle plays a significant role to improve the performance of the electrical and thermal performance of PVT solar system and Hydrogen yield rate. On the other side, the spiral-shaped thermal collector with water exhibited better end result than the other hydrogen production systems. The predicted results ANFIS techniques represent an excellent agreement with the experimental results. In consequence, it is suggested that the developed ANFIS model can be adopted for further studies to predict the performance of the hydrogen production system.
Electrical engineering; Energy; Hydrogen energy; Solar energy; Photovoltaics; Energy conservation; Heat transfer; Photovoltaic - Thermal collector; Hydrogen production system; Adaptive Neuro Fuzzy Inference System (ANFIS); Electrical efficiency.