Modeling and Optimization of Electrodeposition Process for Copper Nanoparticle Synthesis Using ANN and Nature-Inspired Algorithms

Due to its outstanding physical, chemical, and thermal properties, an increasing consideration has been paid produce copper (Cu) nanoparticles (NPs). Various methods are accessible for producing Cu NPs by conceiving the top–down bottom–up approaches. Electrodeposition is a method synthesize high-quality at low cost. The attributes of rely on their way deduction electrochemical process parameters. This work aims deduce mean size NPs. Artificial neural networks (ANN) nature-inspired algorithms, namely genetic algorithm (GA), firefly (FA), cuckoo search (CS) were used predict optimize results obtained from ANN prediction agreed with data electrodeposition process. All algorithms reveal similar operating conditions as optimal minimum NP 20 nm was parameters 4 g·l−1 CuSO4 concentration, electrode distance 3 cm, potential difference 27 V. synthesized in line anticipated size. scanning electron microscope X-ray diffractometer (XRD) performed analyze nanoparticle morphology.