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Enhanced hydrogen generation in a combined hybrid cycle using aluminum and cooper oxide nanomaterial based on biomass and vanadium chloride cycle: Optimization based on deep learning techniques and Environmental appraisal

  • Jincheng Zhou
  • , Masood Ashraf Ali
  • , Tao Hai
  • , Kamal Sharma
  • , Kosar Hama Aziz
  • , Farah Qasim Ahmed Alyousuf
  • , Khaled Twfiq Almoalimi
  • , Sattam Fahad Almojil
  • , Abdulaziz Ibrahim Almohana
  • , Abdulrhman Fahmi Alali
  • Qiannan Normal College for Nationalities
  • Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province
  • Prince Sattam Bin Abdulaziz University
  • Universiti Teknologi MARA
  • GLA University
  • University of Technology- Iraq
  • Lebanese French University
  • King Saud University

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The usage of Nanofluid to enhance heat transfer has been investigated by many researchers in small-scale heat transfers; however, in large-scale power plants, it has not been taken care of. In this research paper, a newly proposed energy system for hydrogen generation based on renewable sources is proposed and analyzed in detail. Unlike other power input systems, the vanadium chloride system generates hydrogen because it uses waste heat. In this regard, the nanoparticles of Al2O3 and CuO are utilized in the main heat exchanger to enhance the heat transfer to the reactor of VCLC to generate more hydrogen. Also, the compound system has the gasifier-based internally fired gas turbine as the main system and can generate hydrogen in a more green way. The optimization based on deep learning methods is applied to seek the highest point of operation by the system. The results exhibit that Al2O3 causes more heat transfer and efficiency enhancement, thus in hydrogen production, causing an increase of 18.5% compared to base fluid and 8.3% compared to CuO in H2 Generation In circumstances when it is optimal, the values for exergetic efficiency and total hydrogen production are 64.5% and 4.5 kg/s respectively. In addition, using a nanofluid heat exchanger and biomass energy reduces CO2 emissions to 0.91 kg/kWh.

Original languageEnglish
Pages (from-to)104-114
Number of pages11
JournalInternational Journal of Hydrogen Energy
Volume52
DOIs
StatePublished - 2 Jan 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • AlO
  • Deep learning
  • Environmental impacts
  • Heat transfer enhancement
  • Nanoparticles
  • Optimization

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