[20]. In many industrial applications, the stability of graphene oxide dispersions plays a crucial role for the proper solvent preparation. Therefore, many researchers have done the studies to understand the dispersion behavior and to improve the dispersion stability. Konios et al. [21] prepared GO and reduced graphene oxide (rGO) dispersions with the different solvents and they showed that the GO and rGO samples forms a stable dispersion with the deionized-water, ethylene glycol and N-methyl-2-pyrrolidone (NMP). Taha-Tijerina et al. [22] prepared a dispersion with the deionized-water, ethylene glycol, ethanol and mineral oil and they illustrated that the GO samples forms a strong stable dispersion with the deionized-water and ethylene glycol. Graphene oxide-deionized water nano-fluids are more attractive options because of the formation of fairly strong stable dispersions with graphene oxide in the deionized water and the elimination of toxic solvents such as NMP.The quality of the dispersions including "strong stability" can be represented with some criteria such as zeta potential value, thermal and electrical conductivity, pH, and particle size. Therefore, many studies have been done to analyze the GO dispersion properties. The average particle size and zeta potential value [22] [29] were analyzed without using any systematic analyzing such an experimental design approach. It has been determined that oxidants and pH of the

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Abstract:In this study, it is aimed to reduce the variability of parameters in the liquid level system controlled by PID controller for a laboratory scale device. An integrated methodology consisting of experimental design and feedback PID (proportional-integralderivative) controller was proposed to optimize and control the deviation from the average value in the offset value, variability in the offset value and the time to reach the set value in this liquid level system. The optimal valve opening levels that minimizes the average of the offset value (µ), variance (s 2 ) and the first time to reach the set value (t) were determined as 40%, 5%, 50% and 80%, respectively, using TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution)-based Taguchi method by Minitab®. A quite successful control was established in the verification test which performed with specified levels of optimal valve opening. Recovery rates in the control performance before and after optimizing the parameter were calculated as 9.53% in the deviation from the average value in the offset values, 29.37% in the variability in the offset value and 11.27% in the time to reach the set value. MATLAB/Simulink was used to simulate the liquid level system.