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Optimizing methanol synthesis from CO <sub>2</sub> using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability

Ramesh Kumar, Jayato Nayak, Somnath Chowdhury, Sashikant Nayak, Shirsendu Banerjee, Bikram Basak, Masoom Raza Siddiqui, Moonis Ali Khan, Rishya Prava Chatterjee, Prashant Kumar Singh, Woojin Chung, Byong‐Hun Jeon, Sankha Chakrabortty, Suraj K. Tripathy

2024RSC Advances15 citationsDOIOpen Access PDF

Abstract

∼ 0.97). Even though both models performed well, ANN, consisting of 9 neurons in the input and 1 hidden layer, could predict optimum results closer to RSM in terms of agreement with the experimental outcome.

Topics & Concepts

MethanolGraphenePhotocatalysisParametric statisticsMaterials scienceResponse surface methodologyNanotechnologyChemical engineeringComputer scienceBiological systemChemistryMathematicsCatalysisEngineeringMachine learningOrganic chemistryStatisticsBiologyAdvanced Photocatalysis TechniquesCatalytic Processes in Materials ScienceCO2 Reduction Techniques and Catalysts
Optimizing methanol synthesis from CO <sub>2</sub> using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability | Litcius