AI Article Synopsis
- The study evaluates how well different regression methods estimate the performance of TiO/Cu catalysts for converting CO to methanol, specifically using hydrogen exfoliated graphene (HEG).
- The research found that 30 wt% HEG provided the best conversion efficiency in producing methanol, with key factors such as HEG dosing and CO inflow rate significantly influencing the results.
- Nonlinear regression via artificial neural networks (ANN) outperformed linear regression methods, achieving a higher determination coefficient and more accurate predictions in line with experimental data.
Article Abstract
Assessment of the performance of linear and nonlinear regression-based methods for estimating catalytic CO transformations employing TiO/Cu coupled with hydrogen exfoliation graphene (HEG) has been investigated. The yield of methanol was thoroughly optimized and predicted using response surface methodology (RSM) and artificial neural network (ANN) model after rigorous experimentation and comparison. Amongst the different types of HEG loading from 10 to 40 wt%, the 30 wt% in the HEG-TiO/Cu assisted photosynthetic catalyst was found to be successful in providing the highest conversion efficiency of methanol from CO. The most influencing parameters, HEG dosing and inflow rate of CO were found to affect the conversion rate in the acidic reaction regime (at pH of 3). According to RSM and ANN, the optimum methanol yields were 36.3 mg g of catalyst and 37.3 mg g of catalyst, respectively. Through the comparison of performances using the least squared error analysis, the nonlinear regression-based ANN showed a better determination coefficient (overall > 0.985) than the linear regression-based RSM model (overall ∼ 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11022185 | PMC |
| http://dx.doi.org/10.1039/d4ra00578c | DOI Listing |
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