Multitarget Wastewater Quality Assessment in a Smart Industry Context

This study addresses the need for a rapid and accurate process monitoring by developing an innovative approach for wastewater quality assessment to enhance the Industry 4.0’s vision. By integrating Ultraviolet-Visible (UV-Vis) spectroscopy with Machine Learning (ML), we focus on accurately determine key indicators such as Chemical Oxygen Demand, Total Suspended Solids (TSS), chlorides, and conductivity. Our findings demonstrate the efficacy of ML models in accurately predicting water quality from UV-Vis spectral data, underscoring their potential for real-time monitoring and analysis in industrial settings. The study also revealed the potential for both single and multitarget predictions. Additionally, the feature importance analysis provided valuable insights into the spectral regions most relevant for predicting each water quality indicator. This approach aligns with the goals of Industry 4.0, offering a smart, efficient solution for environmental monitoring and sustainable resource management.