AI Article Synopsis

  • The use of hydrogen in daily applications is increasing, with electrolysis of water being a cost-effective method for its production.
  • A novel hydrogen production system using a Photovoltaic-Thermal (PVT) solar collector was developed, analyzing how factors like collector tilt angle and thermal design affect its efficiency.
  • The Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to predict thermal and electrical efficiency, showing that tilt angle and spiral-shaped thermal collectors significantly enhance hydrogen production rates.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7569347PMC
http://dx.doi.org/10.1016/j.heliyon.2020.e05271DOI Listing

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