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On-line evaluating the SS removals for chemical coagulation using digital image analysis and artificial neural networks
Yu, R.F.
Abstract
Chemical coagulation is one of the most
important processes for industrial wastewater treatment
plants to remove the suspended solids (SS), which depend
significantly on particle characteristics. A digital image
analysis system was set up in this study for the on-line
measurements of particle characteristics, including particle
size distribution, equivalent diameter, total area, total volume,
and the fractal dimension in the both coagulation and
flocculation periods in chemical coagulation. Two real
industrial wastewaters, textile wastewater and landfill
leachate, were used for conducting the coagulation and
flocculation processes with different polyaluminum chloride
dosages in a batch reactor. The artificial neural network
(ANN) models were used to construct the correlations
between the monitoring data acquired and the SS removal
efficiencies. The experimental results indicated that the
ANN models were able to precisely predict the SS removal
efficiencies and effluent SS concentration after the chemical
coagulation, with the correlation coefficient (R2) of
0.96–0.97 for real landfill leachate and R2 of 0.93–0.97 for
real textile wastewater, which provided significant benefits
for the control of chemical coagulation.
Keywords
Artificial neural network; Chemical coagulation and flocculation; Digital image analysis; Particle size distribution; Suspended solids; Fractal dimension
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