The clean analysis process of Mn2+ for industries: a comparative study on direct determination of high-concentration Mn2+ in solution using spectrophotometry

Mn²⁺ is an essential cation extensively utilized in various industrial processes, including electrolytic manganese production, manganese dioxide manufacturing, and zinc processing. It also poses significant environmental challenges as a primary pollutant in Mn-containing wastewater and hazardous materials. Effective monitoring and control of Mn²⁺ in these processes are vital for improving resource conversion efficiency and minimizing pollutant production. However, the direct determination of high concentrations of Mn²⁺ remains challenging due to rapid reactions, which impede improvements in cleaner industrial production. Traditional detection method like potassium periodate spectrophotometry (PPS) method is limited to low concentrations and involve complex processes that contribute to secondary pollution. In this study, we evaluated the performance of four alternative methods—External Standard Calibration (EC), Area Under the Curve (AUC), Standard Addition (SA), and Multi-Energy Calibration (MEC)—for determining high-concentration Mn²⁺. The study found that the weak absorption characteristic of aqueous Mn²⁺ due to spin-forbidden transitions is advantageous for direct determination at high concentrations in its original valence state. By optimizing the optical path and wavelength, concentrations up to 50 g/L were detected, surpassing the PPS upper limit by 5000 times. Among the methods, EC demonstrated superior accuracy and precision, with a performance rate of 98.07% and a relative standard deviation of less than 1%. The EC method’s minimal time consumption and cost-effectiveness make it suitable for automation and integration into industrial systems for continuous, real-time monitoring. This research offers valuable insights into high-concentration Mn²⁺ determination using spectrophotometry, highlighting the EC method’s potential for real-time monitoring and its adaptability for large-scale industrial operations. The findings provide a substantial reference for the direct detection of other industrial components, promoting more efficient and environmentally friendly industrial practice.