Publication Date


Document Type


First Advisor

Cho, Kyu Taek

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Mechanical Engineering


Proper treatment of wastewater is crucial to maintain the health of the public and avoid pollution of the environment. Methods like filtering and chemical coagulation have been utilized to treat wastewater, but they have significant limitations such as inefficient treatment of micro-size pollutants, generation of secondary contaminants, and high operation and maintenance costs. The electro-coagulation system which generates coagulants from low-cost aluminum electrodes under the application of a current has received strong attention as a promising water treatment system. However, the EC system has not been fully utilized yet due to lack of understanding and operation guidelines.

In this study, a hybrid approach synergistically combining physics-based and data-based models was utilized to understand the effect of operating conditions on EC performance. An unsteady two-dimensional physics-based model was developed based on species transport driven by diffusion and convection coupled with electrochemical reactions at the electrode surfaces, chemical reactions in the bulk solution, and adsorption of coagulants. The model validated with experimental data was utilized to generate data for 1150 combinations of operating conditions.

The generated data points were utilized to correlate operating conditions to wastewater removal performance by utilizing eight machine learning algorithms. A regression model was developed to predict numerical values and a classification model to predict satisfactory or unsatisfactory performance. Predictions from the models were used to generate processing maps, to visually show the effect of operating conditions on removal performance. These processing maps will provide guidelines for users of the EC system, to help choose ideal operating conditions.


70 pages




Northern Illinois University

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In Copyright

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