Study of Plasma Electrolysis in Water: Alternative Technologies for Hydrogen Production

Abstract

Plasma electrolysis has been studied as an alternative method for generating hydrogen. A plasma reactor is used in this electrolysis to produce glowing plasma and the reactive interface fluid layer has an asymmetrical spiral-rod electrodes configuration.  Plasma electrolysis was carried out in 2 positions at the surface of the solution and at a depth of 5 mm. On the surface, it is divided into 4 regions of I-V characteristics, namely the conventional electrolysis area, the plasma formation region, the gas formation area that covers the anode, and the glowing plasma region. While at a depth of 5 mm, the formation of plasma with a voltage of 500 V takes 120 seconds to heat the solution with a conventional electrolysis mechanism around the anode. Glowing plasma at a depth of 5 mm resembles the shape of a ball with a larger diameter as the addition of stress and the addition of the concentration of NaCl solution. The mechanism for the formation of hydrogen molecules occurs in the reactive interfacial fluid layer. The plasma ball will increase with a greater voltage. The enlargement of the glowing plasma with increasing operating voltage shows that voltage is a very important factor in the generation of plasma in water. The diameter of the glowing plasma sphere produced for electrolysis at the surface of the solution and at a depth of 5 mm in the solution always increases with increasing voltage. Furthermore, the greater the molarity of the NaCl solution in water, the larger the glowing plasmasphere. The production of hydrogen increases as the voltage increases, this is indicated by the increase in spectrum intensity H_δ and H₂.

HIGHLIGHTS

• A unique coronavirus epidemic that has swept throughout the globe is examined and analysed
• COVID-19 outbreaks in various places are analysed using machine learning models, which are visualised using charts, tables, graphs and predictions depending on the available data
• For prediction models, the time series forecasting package (PROPHET) is utilised as part of machine learning

GRAPHICAL ABSTRACT