Browsing by Author "Hapoglu, Hale"

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Combined Statistical Analysis of Water Quality for Determination of Relationships Between Parameters: Case Study of Akkopru Stream, Van/Turkey
(Elsevier Science inc, 2022) Turan, Aysenur; Aldemir, Adnan; Hapoglu, Hale
Today, water is affected negatively in terms of its characteristics and quality due to many factors. Necessary measures should be taken to control environmental pollution by analyzing the conditions and parameters that cause pollution and monitoring the water quality in water bodies such as lakes, streams, rivers and ponds. Akkopru Stream is one of the rivers flowing into Lake Van and it has great importance within the borders of Van/Turkey. The data in this study were used to perform water quality analysis based on the physicochemical parameters obtained from Akkopru Stream. A total of 22 water quality parameters were used to determine water quality at the discharge into Lake Van. Evaluation of these parameters was made according to the regulation about water pollution control (Turkey). As a result, Akkopru Stream has Class I water quality in terms of seasonal conditions and water parameters. Among the parametric analysis methods, trend distribution, normality, correlation, matrix table, regression and normal distribution of the data set were examined and the relationships between parameters were interpreted statistically. According to the results, most of the parameters were within the normal range, solid matter and hardness effects were correlated, and matrix relations and regression equations were related to other parameters.
Comparison of Wireless Temperature Profiles With Generalized Predictive Control
(Gazi Univ, 2016) Aldemir, Adnan; Hapoglu, Hale
In this study, Generalized Predictive Control (GPC) algorithm is applied to a process simulator which wireless temperature experiments were achieved and the results of the experiments were compared under the same conditions obtained. To achieve the data transfer between computer in Process Control Laboratory and the process simulator in Unit Operations Laboratory, wireless communication system was established and wireless experiments were performed on-line by means of MATLAB/Simulink program. Wireless data transfers during the experiments were carried out by using radio waves at a frequency of 2.4 GHz. In wireless temperature control experiments which are conducted using with algorithm of GPC, all L (control weighting) values which are bigger than 1.0 are not suitable for temperature control because the heater made very big oscillatory behaviour and consequently temperatures also made very big oscillatories at any Nu (control costing horizon), N1 (minimum costing horizon) and N, (maximum costing horizon) values. Therefore GPC experiments were carried out in L values smaller than 1.0. Changes of temperature profiles were observed with time as a result of experiments carried under the same conditions, using the Nu, N-1 ve L parameters in the GPC algoritm to the N-u=1.0; N-1=1.0; N-2=2.0; N-U =1.0; N-1=1.0; N-2=4.0; N-U=2.0; N-1=1.0; N-2=2.0 ve N-U=2.0; N-1 1.0; N-2 =4.0 values for the L=0.001; 0.005; 0.01; 0.05; 0.1 and 0.5 values. According to the experimental results analyzing the temperature profiles, the temperatures the N-1=1.0 and N-2=2.0 values of control was closer to the set point and oscillations were found to be less than according to the values of N-U=1.0; N-1=1.0; N-2=4.0; N-U=2.0; N-1=1.0; N-2=2.0 ve N-U=2.0; N-U=1.0; N-2=4.0. Also by increasing the value of Nu and N, temperatures are begin to move away from the set point and the oscillations are increased. Wireless experiments conducted under the same conditions for Tset=50 degrees C and Tset 60 degrees C which using the best temperature control parameters with the L=0.001; 0.005; 0.01; 0.05; 0.1 and 0.5 values were determined with the integral of the square of the error (ISE) and the integral of the absoluteof the error (IAE) values. According to the experiments carried out at the lowest values of ISE and IAE were determined that the value L 0.01. In addition, lower and higher values of L= 0.01 calculated ISE and IAE values were higher and that can be seen the temperature away from set point. In conclusion, the best temperature control is obtained N-U=1.0; N-1=1.0; N-2=2.0 and L=0.01 values.
Distributed Wireless Liquid Level Control of a Process Simulator Over a Network
(Springer, 2018) Bayram, Ismail; Hapoglu, Hale; Aldemir, Adnan
In a process simulator, the distributed wireless liquid level control experiments have been performed by using the generalized predictive control algorithm. The wireless local area network was established with antennas between process simulator in Unit Operations Laboratory and the computer in Process Control Laboratory. We performed the online wireless experiments with MATLAB/Simulink program. Data transfer during the wireless experiments were carried out using radio waves at a frequency of 2.4 GHz. Experiments on the same conditions were achieved by three certain values of control weighting factor in the controller algorithm, whilst the changes in valve opening and liquid level with time were monitored. In all experiments performed with the distributed control system, the control valve opening is fixed with very small oscillations after a sudden increase in the beginning. We obtained the best liquid level control response to different set point changes for the same set of controller tuning values. The Integrated Square of the Error and the Integrated Absolute of the Error values were chosen as performance criteria. The distributed wireless liquid level control was effectively realized to the process simulator and it is recommended for industrial applications.
Impact of Robust Error Control on Fluid Level by Wireless Network Applications
(Gazi Univ, 2018) Bayram, Ismail; Hapoglu, Hale; Aldemir, Adnan
The paper presents a process simulator developed with wireless process control purpose and antennas and modules for wireless communication applications. The impact of robust error control code onwireless liquid level control experiments is investigated by means of three tuning coefficients of the proportional integral derivative actionswhich are initially determined by using nonparametric methods. The initial controller tuning coefficients were determined using the process reaction curve which sketched the data obtained in response to a step change by wireless communication. The well-tuned control parameters were assessed by means of a MATLAB graphical tool (SISO). To determine the bias values of the process simulator, an initial steady state was obtained and the system output was monitored at the constant control valve openness (10%) for 100s. At the end of 100 seconds, the control key in MATLAB/ Simulink block diagram was changed and the control algorithm was activated and different set point changes were given to the system at the same time and the effect of the parameters was observed. It was seen that the liquid level tended to level off around the desired set values in the wireless control experiments performed to follow different set points by using the wireless robust error control with well-tuned parameters. The proposed wireless control and communication networkperformances were compared with the integral of squared error (ISE) and the integral of absolute error (IAE) criteria at various fluid levels.