Applying Online Image Analysis to Simultaneously Evaluate the Removals of Suspended Solids and Color from Textile Wastewater in Chemical Flocculated Sedimentation

Abstract

The removal of suspended solids (SS) and color is critically important for textile wastewater treatment processes. Typically, chemical coagulation and sedimentation have been used as pretreatment processes to remove SS and color from textile wastewater. The effective removal of SS depends significantly on the particle size distribution, density, and fractal dimension. In practice, a batch settling test is used in the laboratory to evaluate the performance of chemical coagulation for the removal of SS. In this paper, we present the application of digital image analysis (DIA) for on-line and simultaneous measurement of the variations of the characteristics of particles in textile wastewater. This technology was used during a batch settling test to measure the characteristics of particles, including the mean gray value (MGV) of the captured images, particle size (i.e., equivalent diameter (ED)), total area, total volume, the fractal dimension, and the mean red/green/blue (R/G/B) values of the captured images. The on-line DIA data were used as input to regression and artificial neural network (ANN) models that predicted the efficiencies of SS removal and color removal in real textile wastewater after chemical coagulation and sedimentation. The experimental results indicated that the ANN models predicted both the SS and color removal efficiencies precisely, with correlation coefficients (R2) of 0.93 to 0.96. Thus, digital image analysis and ANN models can be used to simultaneously evaluate the removal of SS and color from textile wastewater by chemical coagulation.

Keywords: artificial neural networks, chemical coagulation, sedimentation, color removal, fractal dimension, image analysis, particle size, suspended solids