Zehra Zeybek

Fuzzy temperature control of industrial refineries furnaces through combined feedforward/feedback multivariable cascade systems

The purpose of this paper is to improve and apply a multivariable advanced control structure on the basis of fuzzy logic technique for two flow tubular furnace having widespread applications in petroleum refinery industries. After analyzing the dynamic properties of furnaces, it has been concluded that these furnaces are the MIMO processes which have two inputs and two outputs. There are a lot of reciprocal interactions between input and output variables. For this reason, equivalent system methods were used to investigate and develop advanced control structures for these furnaces. According to this method, symmetric MIMO system was divided into two equivalent separate systems. Finally, the fuzzy model control design of combined multivariable symmetric cascade system and its results were given.

Adaptive heuristic temperature control of a batch polymerisation reactor

The purpose of this research is to improve and apply the temperature control of a free radical solution polymerisation of styrene and to examine its performance on the basis of adaptive heuristic criticism (AHC) control. This algorithm consists of a three-layer feed forward artificial neural network (ANN) which uses supervised learning with reinforcement in a unique topology. This algorithm has two neurone-like adaptive elements and a difficult learning control problem which can be solved by means of a learning system with a single associative search element (ASE) and a single adaptive critic element (ACE). AHC uses a type of control system whose output value is either maximum or minimum. The controller will take in process data on-line and update the weights to proper ones in the control of the process. The performance results of the AHC controller are compared with the results obtained by using conventional Deadbeat algorithm. AHC control system shows satisfactory behaviour to track the reactor temperature.

Optimal temperature control in a batch polymerization reactor using fuzzy-relational models-dynamics matrix control

In this work, fuzzy-relational models-dynamics matrix control (Fuzzy-DMC) was applied to the free radical solution polymerization of styrene in a jacketted batch reactor and its performance was examined to reach the required monomer conversion, molecular weight and chain length in a minimum time. The reactor temperature was controlled by manipulating the heat-input to the reactor. The performance of the Fuzzy-DMC controller was compared with that of the nonlinear generalized predictive control (NLGPC).

Fuzzy-dynamic matrix pH control for treatment of dye wastewater plant

This paper presents an experimental application of fuzzy-dynamic matrix control (FDMC) to study the coagulation process of wastewater treatment in a dye plant. Also this study includes a series of tests in which an FDMC was used for pH control. The performance results of the FDMC controller are compared with the results obtained by using conventional PID algorithm. Experimental results show that this technique is adequate for the output tracking performance.

Optimization of electrochemical treatment of textile dye wastewater

Purpose – The purpose of this paper is to investigate the electrochemical treatment of textile dye wastewater in the presence of NaCl electrolyte by using aluminium electrodes.Design/methodology/approach – The electrochemical treatment of textile dye wastewater was optimized using response surface methodology (RSM). RSM‐based D‐optimal design was employed to construct statistical models relating turbidity and designed effective parameters known as current density, electrolyte concentration and electrolysis time. The experimental plan consists of a three‐factor (three numerical) matrix.Findings – The results show that the current density has significant effect on the reduction of turbidity. Besides, electrolysis time is the most influential factor on the turbidity. In order to enhance the electrochemical treatment performance, no coagulant addition or further physicochemical processes were employed.Originality/value – Industrial certain textile dye wastewater in Turkey is used to determine optimal values.

Evaluation of The Quality of life outcomes of Women and Man with coronary artery disease using fuzzy regression analyses

Regression is one of the efficient tools that are used in statistics. Obviously, when we want to explain the an accuracy between statistic variables caused by fuzzy quantities instead of probable structure, we should create other tools that enable us to estimate uncertain parameters under condition of new structure. Fuzzy linear regression is a method for estimation of fuzzy parameters that can be useful for estimation of relationship between variables, when the number of observations is few and the interaction of those is vague and uncertain. When speak on phrases like quality of life, intelligence quotient, etc., we will find out there is no precise and classic definition and seems that there is not any position for these concepts and tools in determining effective agents in the theorem of classic sets. A result in respect to this prospect and aim of research, we consider the effective agents on quality of life of heart diseases using fuzzy linear regression that has been developed by Tanaka.

Fuzzy Relational Pattern Dynamics of the Breast Cancer Cells

In this study, the microcalcification modeling problem has been handled to detect the microcalcification clusters. The detection of microcalcifications is carried out over the filtered mammogram image. It is observed that, the histograms of the band-pass filtered subimages are very close to the Gaussian distribution.

A Fuzzy Model Based Control of Well Mixed Reactor with Cooling Jacket

Fuzzy logic control uses linguistic rather than crisp numerical rules to control the industrial processes. Fuzzy logic control may be attractive when the process is either difficult to control or to model by conventional methods. Fuzzy control of processes is an alternative when systems cannot be well controlled by classical or modern control techniques which are based on crisp mathematical models and also reply heavily on measurements to indicate variation in process conditions. For many processes, models and measurements are very difficult to obtain correctly and they show nonlinear behavior. Chemical reactors, pH neutralization process and waste water treatment are the examples for these processes. In such a systems, control rules can be developed depending on whether process is to be controllable. If the operators knowledge and experience can be explained in words, then linguistic rules can be written easily. In such cases, fuzzy model refers to the description of the operators input/output control actions using fuzzy implications.