A video processing and machine vision-based automatic analyzer to determine sequentially total suspended and...

Area of Science:

• Environmental Science
• Analytical Chemistry
• Water Quality Monitoring

Background:

• Total suspended solids (TSS) and settleable solids (SetS) monitoring is vital for aquatic ecosystems, but traditional methods are time-consuming and costly.
• Pollutants transported by solids and light penetration reduction necessitate efficient monitoring techniques.

Purpose of the Study:

• To develop an automated instrument, SWAMVA, for rapid and cost-effective quantification of TSS and SetS in wastewater.
• To validate the accuracy and precision of the SWAMVA method against established standards.

Main Methods:

• Development of the Solids in Wastewater's Machine Vision-based Automatic Analyzer (SWAMVA) with an automatic sampler and real-time digital movie capture.
• Utilization of a machine vision algorithm (MVA) with the Red color plane for quantifying TSS and SetS.
• Validation using Analysis of Variance (ANOVA), t-tests, F-tests, elliptical joint confidence region (EJCR), and coefficient of variation (CV).

Main Results:

• The MVA achieved high prediction accuracy with R² values of 0.988 for TSS and 0.964 for SetS.
• Relative error of prediction (REP) was 6.133% for TSS and 9.115% for SetS.
• Analysis time reduced from 12 hours to 24 seconds per sample, with a decreased sample volume from 1.5 L to 15 mL.

Conclusions:

• SWAMVA provides an accurate, precise, automatic, fast, and low-cost method for TSS and SetS determination in wastewater.
• The instrument aligns with Green Chemistry principles and leverages Industry 4.0 technologies like machine vision and intelligent processing.
• SWAMVA presents a significant advancement over traditional methods for wastewater solids monitoring.