A CNN-Based Method for Heavy-Metal Ion Detection

Data processing is an essential component of heavy-metal ion detection. Most the research now uses a conventional data-processing approach, which inefficient and time-consuming. The development efficient accurate automatic measurement method for ions has practical implications. This paper proposes CNN-based detection system, can automatically, accurately, efficiently detect type concentration ions. First, we used square-wave voltammetry to collect data from solutions. For this purpose, portable electrochemical constant potential instrument was designed acquisition. Next, dataset 1200 samples created after preprocessing expansion. Finally, network, called HMID-NET. HMID-NET consists backbone two branch networks that simultaneously in solution. results assay on 12 sets solutions with different ionic species concentrations showed proposed algorithm ultimately obtained classification accuracy 99.99% mean relative error 8.85% terms concentration.