Publications by authors named "Farid Amirkhani"
Article Synopsis
- Pesticide pollution poses risks to humans and ecosystems, prompting the use of photocatalysis for degradation, especially with ZnO-based photocatalysts.
- The study introduced innovative machine learning models to better estimate how well these photocatalysts degrade various pesticides, considering factors like light source, pH, and pesticide concentration.
- The radial basis function (RBF) model outperformed other models in accuracy, while sensitivity analysis revealed that light irradiation time and initial pesticide concentration are key factors affecting degradation efficiency.
Amino acid salt (AAs) aqueous solutions have recently exhibited a great potential in CO absorption from various gas mixtures. In this work, four hybrid machine learning methods were developed to evaluate 626 CO and AAs equilibrium data for different aqueous solutions of AAs (potassium sarcosinate, potassium l-asparaginate, potassium l-glutaminate, sodium l-phenylalanine, sodium glycinate, and potassium lysinate) gathered from reliable references. The models are the hybrids of the least squares support vector machine and coupled simulated annealing optimization algorithm, radial basis function neural network (RBF-NN), particle swarm optimization-adaptive neuro-fuzzy inference system, and hybrid adaptive neuro-fuzzy inference system.
View Article and Find Full Text PDF