Instance-based regression with missing data applied to a photocatalytic oxidation process

In this paper, a modified nearest-neighbor regression method (kNN) is proposed to model a process with incomplete information of the measurements. This technique is based on the variation of the coefficients used to weight the distances of the instances. The case study selected for testing this algorithm was the photocatalytic degradation of Reactive Red 184 (RR184), a dye belonging to the group of azo compounds, which is widely used in manufacturing paint paper, leather and fabrics. The process is conducted with TiO₂ as catalyst (an inexpensive semiconductor material, completely inert chemically and biologically), in the presence of H₂O₂ (with the role of increasing the rate of photo-oxidation), at different pH values. The final concentration of RR184 is predicted accurately with the modified kNN regression method developed in this article. A comparison with other machine learning methods (sequential minimal optimization regression, decision table, reduced error pruning tree, M5 pruned model tree) proves the superiority and efficiency of the proposed algorithm, not only for its results, but for its simplicity and flexibility in manipulating incomplete experimental data.      

Supramolecular immobilization of glucose oxidase on gold coated with cyclodextrin-modified cysteamine core PAMAM G-4 dendron/Pt nanoparticles for mediatorless biosensor design

Cysteamine core polyamidoamine G-4 dendron branched with β-cyclodextrins was chemisorbed on the surface of Au electrodes and further coated with Pt nanoparticles. Adamantane-modified glucose oxidase was subsequently immobilized on the nanostructured electrode surface by supramolecular association. This enzyme electrode was used to construct a reagentless amperometric biosensor for glucose, making use of the electrochemical oxidation of H₂O₂ generated in the enzyme reaction. The amperometric response of the biosensor was rapid (6 s) and a linear function of glucose concentration between 5 and 705 μmol?L^?1. The biosensor had a low detection limit of 2.0 μmol?L^?1, sensitivity of 197 mA?mol^?1?L?cm^?2, and retained 94 % of its initial response after storage for nine days at 4 °C.     

Optimization methodology based on neural networks and genetic algorithms applied to electro-coagulation processes

An optimization methodology based on neural networks and genetic algorithms was developed and used to optimize a real world process — an electro-coagulation process involving three pollutants at different concentrations: kaolin (250–1000 mg L^?1), Eriochrome Black T solutions (50–200 mg L?1), and oil/water emulsion (1500–4500 mg L^?1). Feed-forward neural networks using heterogeneous combination of transfer functions were developed, leading to good results in the validation stage (relative error about 8%). The parameters of the process (concentration of pollutant, time, pH₀, conductivity and current density) were optimized handling the genetic algorithm parameters, in order to obtain a maximum removal efficiency for each pollutant. Therefore, the optimization methodology combines neural networks as modeling tools with genetic algorithms as solving method. Validation of the optimization results using supplementary experimental data reveals errors under 11%.