Experimental optimization and modeling of organic matter removal from water by electrochemical method

In this master's thesis, the experimental optimization and modeling of the electrochemical removal of organic matter from waters are described using an approach that combines practical experiments with modeling techniques. Initially, a series of designed experiments were conducted to optimize the effective parameters in the electrochemical removal of organic matter process. This study identifies the most effective conditions for removing organic matter from water. An experimental setup using aluminum plate electrodes was prepared for the electrochemical removal of organic pollutants from water. The experiments examined the effects of pH, voltage, sodium sulfate quantity, and exposure time on the removal of dye substances as well as energy consumption, determining the optimum conditions. The study, complementing its experimental aspect, integrates advanced modeling techniques to provide a comprehensive understanding of the underlying mechanisms. The experiments are analyzed using Response Surface Methodology (RSM) to generate 30 sets of experimental data. The obtained data are compared using MATLAB's artificial neural network module to assess R², RSM (mean square error), and error differences. The artificial neural network modeling tool in MATLAB R2023a is employed for modeling, utilizing two different algorithms (Levenberg-Marquardt and Bayesian-Regularization). The study concludes that the best modeling is achieved with the Bayesian-Regularization algorithm. This study focuses on simulating and modeling the behavior of electrochemical removal of substances from water under various conditions, showcasing the techniques used. The combination of experimental optimization and theoretical modeling with electrochemical methods provides a comprehensive perspective on the dynamics of organic matter removal from water. Significant results have been presented regarding effective modeling techniques through this study. Keywords: Organic matter, experimental optimization, modeling, algorithm, electrochemical treatment, artificial neural network.