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Author: search.filters.author.Hesam KamyabSubject: search.filters.subject.Artificial neural network

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    Enhanced machine learning for nanomaterial identification of photo thermal hydrogen production
    (Elsevier BV, 2024-01)
    G. Ramkumar
    ;
    M. Tamilselvi
    ;
    S. D Sundarsingh Jebaseelan
    ;
    V. Mohanavel
    ;
    Hesam Kamyab
    Instead of using temperature via an outside source, using energy created inside is the most effective method to improve the efficiency of catalysis. In this research, a novel hollowed TiO2 photothermal nano catalyst (referred to as RuO2/TiO2/Pt/Carbon) for enzymatic production of hydrogen under ultraviolet irradiation is used. It resembles a hedgehog and contains regionally dispersed Pta and RuO2 double co-catalysts. The ultraviolet (UV) thermos kinetic efficacy of the converters that were made was evaluated according to architectural characteristics and the heat influence of the carbon-based surface. There are several inherent benefits for photocatalysis with heterogeneity exist in multi-layered hollowed hetero structures having extremely thin two-dimensional (2D) nanosheet subunits of the including improved sunlight gathering, accelerated separation of charged particles and disposal, and accelerated interface oxidation reactions. The sandwich-like nanotechnology of the charcoal layer, silver tiny particles, and TiO2 surface effectively supports and protects Pt Micro particle against the accumulation and breaking down of Platinum sites that are active. Moreover, the electricity production of the reaction involving hydrogen evolution is nonetheless in its infancy, and there is tonnes of untapped potential for the use of Machine Learning (ML). The following perspective focuses on new developments in the detection of outstanding performance Hydrogen Evolution Reaction (HER) catalysts using Artificial Intelligence (AI) in an effort to stimulate more broad proposals for research. In the course of the research, an Artificial Neural Network (ANN) strategy was created and validated in order to forecast the results of the hydrogen assessment. The analysis of the dataset's test results demonstrates that the ANN technique can reliably and accurately estimate the generation of hydrogen.
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    Response surface methodology and artificial neural network modelling of palm oil decanter cake and alum sludge co-gasification for syngas (CO+H2) production
    (Elsevier BV, 2024-09)
    Kunmi Joshua Abioye
    ;
    Noorfidza Yub Harun
    ;
    Ushtar Arshad
    ;
    Suriati Sufian
    ;
    Mohammad Yusuf
    Syngas (CO + H2) production through biomass gasification offers a promising and sustainable alternative to conventional fuels. This study investigates the co-gasification of palm oil decanter cake (PODC) and Alum Sludge (AS), utilizing response surface methodology (RSM) and artificial neural network (ANN) techniques to optimize and predict syngas production. Conducted in a fixed bed horizontal reactor, the experiment investigates temperature, airflow rate, and particle size as input parameters. Results revealed that optimal condition of 900 °C temperature, 10 mL/min airflow rate, and 2 mm particle size yielded the highest syngas production at 39.48 vol%. The RSM showed an R2 value of 0.9896, whereas ANN network revealed an overall R2 value of 0.971. Both models demonstrated strong alignment with experimental data and the modelled equation. This research demonstrates the effective use of statistical modelling to enhance the efficiency and effectiveness of syngas production, thereby fostering advancements in sustainable energy production.