Electrochemical deposition of copper using a modified electrode with polyaniline film: Experimental analysis and ANN-based prediction
| Autor: | J.E. Solís-Pérez, S. Silva-Martínez, D. E. Millán-Ocampo, J.A. Hernández-Pérez, Alberto Álvarez-Gallegos, J. Porcayo-Calderon |
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| Rok vydání: | 2021 |
| Předmět: |
Electrolysis
Materials science General Chemical Engineering Analytical chemistry chemistry.chemical_element 02 engineering and technology General Chemistry Electrolyte 010402 general chemistry 021001 nanoscience & nanotechnology Electrochemistry 01 natural sciences Copper 0104 chemical sciences law.invention Plate electrode chemistry law Electrode Deposition (phase transition) 0210 nano-technology Electroplating |
| Zdroj: | Journal of the Taiwan Institute of Chemical Engineers. |
| ISSN: | 1876-1070 |
| DOI: | 10.1016/j.jtice.2021.05.029 |
| Popis: | Wastewater from the electronics manufacturing industry contains relatively large amounts of dissolved copper in low concentrations (≤ 1000 mg L−1). Its discharge to the environment represents a danger to the bioenvironment. Among the different approaches proposed to separate toxic metal ions in low concentrations from solution, electroplating remains a practical method for removing metal ions. This work reports the potentiostatic and galvanostatic deposition of Cu(II) from dilute sulfate electrolyte containing 50–200 mg L−1 copper in a batch recycle reactor with a polyaniline-modified Pt-Ti mesh electrode. A copper plate electrode is also used for copper deposition and its performance is compared to that of the modified electrode (Pt-Ti-PANi). The experimental system is modeled by using artificial neural networks (ANN). The Pt-Ti-PANi electrode used for copper removal (99% in 2.5 h) is more efficient than the copper plate (96% in 4.5 h) in potentiostatic mode. The same is observed under the galvanostatic mode because it allows the optimization of copper deposition by applying controlled constant current steps under mass transport, thus minimizing energy consumption and process time. Six ANN configurations are tested to find the optimal architecture using process variables as inputs to the ANN models, such as initial concentration, potential, current, electrolysis time, and final concentration. The best ANN model (tansig-purelin) shows good agreement with the experimental data (ANN architecture 5:3:1 with adjusted determination coefficient 0.965 and mean square error of 1.083 × 10−10). The results show that the proposed ANN model successfully predicts the volumetric mass transport coefficient. |
| Databáze: | OpenAIRE |
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