An optimized framework for metal ion analysis in automobile industrial sewage

Abstract

This research paper presents an optimized approach for analyzing metal ions in automobile industrial sewage by integrating Dove Swarm Optimization (DSO) and a Recurrent Neural Network (RNN), termed DSbRNM. The DSbRNM approach is designed to detect and predict the concentration of metal ions in sewage samples. DSO is utilized to select the most relevant features, optimizing the feature set for the RNN. This optimization allows the RNN to perform classification with high accuracy, predicting metal ion concentrations effectively. The effectiveness of the DSbRNM approach was validated by analyzing real industrial sewage samples and comparing the results with those obtained using machine learning (ML) based convolutional methods. The results demonstrate that DSbRNM provides more accurate and reliable predictions of metal ion ranges. Furthermore, DSbRNM outperformed convolutional methods in terms of accuracy, recall, precision, and F1 score. The DSbRNM approach offers a rapid and effective solution for analyzing metal ions in automobile industrial sewage, enhancing the efficiency and effectiveness of wastewater treatment processes.