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Three distinct sol-gel synthesis methodologies were employed to fabricate niobium catalysts supported on stainless steel meshes via the dip-coating technique, with the objective of their utilization in layered configurations for water purification applications. The structured catalysts were subjected to calcination at three varying temperatures: no calcination, calcination at 823 K, and calcination at 1173 K. Ibuprofen, selected as the model pollutant due to its emerging contaminant profile, was utilized for evaluation. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) images, in conjunction with adhesion assessments, elucidated that thermally treated catalysts exhibited nearly 80% retention of Nb2O5 nanoparticles adhered uniformly to the support surface. In batch reactor experiments, the catalyst derived from the NbCl5 precursor and polyacrylonitrile (PAN) exhibited the most promising performance, achieving a 90% degradation of the contaminant within a 120 min timeframe. This catalyst, exhibiting superior efficacy, was subsequently employed in tests utilizing photocatalytic bench reactors, where it demonstrated analogous performance to that observed in the batch reactor setup. Upon upscaling to a high-capacity photocatalytic prototype, the degradation efficiency was sustained above that observed in photolytic testing, even after four utilization cycles. [ABSTRACT FROM AUTHOR]

This work aims to analyze two metaheuristics optimization techniques, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with six variations each, and compare them regarding their convergence, quality, and dispersion of solutions. The optimization target is the Gaussian Adaptive PID control (GAPID) to find the best parameters to achieve enhanced performance and robustness to load variations related to the traditional PID. The adaptive rule of GAPID is based on a Gaussian function that has as adjustment parameters its concavity and the lower and upper bound of the gains. It is a smooth function with smooth derivatives. As a result, it helps avoid problems related to abrupt increases transition, commonly found in other adaptive methods. Because there is no mathematical methodology to set these parameters, this work used bio-inspired optimization algorithms. The test plant is a DC motor with a beam with a variable load. Results obtained by load and gain sweep tests prove the GAPID presents fast responses with very low overshoot and good robustness to load changes, with minimal variations, which is impossible to achieve when using the linear PID. [ABSTRACT FROM AUTHOR]