Continuous stirred tank reactor is a typical chemical reactor with complex non linear dynamic characteristics. There has been considerable interest in its state estimation and real time control based on mathematical modeling. However, the lack of understanding of the dynamics of the process, the highly sensitive and non linear behavior of the reactor, has made it difficult to develop the precise mathematical modeling of the system. Efficient control of the product concentration in CSTR can be achieved only through accurate model. Here attempts are made to ease the modeling difficulties using AI techniques such as Fuzzy logic. Simulation results demonstrate the effectiveness of Fuzzy logic control technique. The system is a stirred tank reactor with two flows of liquid enter the system and another flow exits the tank with the formation of product. Simulink was employed to design a model for this system in the simulation environment. This work is aimed for utilization by researchers, industrialists’ and students. Furthermore it will be especially relevant and useful for the students of Electrical Engg. and Chemical Engg. for carrying out research in the area of simulation of CSTR.
The main objective of this book is to provide methods of designing for stable CSTR and unstable CSTR with time delay and with or without positive or negative zero. The methods consist of synthesis method and IMC method. Simulation examples on various transfer functions models model relating concentration of the product to the feed flow rate in isothermal CSTR carrying out Van de Vusse reaction, transfer function model relating the reactor temperature to jacket temperature in jacketed CSTR, transfer function model relating concentration of the cells to the dilution rate in bioreactors and transfer function relating temperature of incinerator to the inlet load rate in municipal waste incinerator are given to show the effectiveness of the proposed methods. Many recycle processes where energy and mass recycle takes place are represented by SOPTDZ transfer function model.
Over the last few decades industrial automation has become the most desirous subject to increase the productivity with quality and quantity enhancement and consider the cost management of the product. A new approach of Fuzzy Logic Time Control Discrete Event System DEV provides an opportunity to establish a control design with certain time constraint of activation under the fuzzy control of input variables. A multi-agent based approach helps the control strategy to be motivated with all internal and external factors effecting the system casually under un predetermined conditions.An approach to use the fuzzy system in local and distributed environment is explored using a simplified design algorithmic approach.Design Models of: Liquids Mixing System,Grinding and Mixing System, and Muti- Dimensional Supervisory Control System with Fuzzy Logic time control DEV strategy are explored for industrial automation.
Fuzzy controllers are used to control consumer products, such as washing machines, video cameras, and rice cookers, as well as industrial processes, such as cement kilns, underground trains, and robots. Fuzzy control is a control method based on fuzzy logic. Just as Fuzzy logic can be described simply as “computing with words rather than numbers’’ and Fuzzy control can be described simply as “control with sentences rather than equations’’. A Fuzzy controller can include empirical rules, and that is especially useful in operator controlled plants.This is the world of automation. The majority of the products allow actions to be automatically triggered by events. The Performance of a SCADA system can be much improved using a fuzzy logic controller based SCADA in industries. This book describes design of fuzzy logic based SCADA Systems using MATLAB fuzzy logic toolbox.This Book is useful for Engineers and Research Scholars in the field of Electrical & Power Engineering.
Coordinated traffic signals regulate traffic flow through urban areas and minimize delay and maximize traffic flow. In this study an attempt is made to design coordinated traffic signal using the concept of fuzzy logic. Fuzzy logic is very effective in incorporating those parameters which have an inherent fuzziness in them like the user perception. Coordinated signal is designed using fuzzy logic by formulating fuzzy rules relating the qualitative parameter (Quality of progression) and other important quantitative parameters like average stream speed, V/C ratio and average control delay .Measures of effectiveness like average control delay per vehicle per cycle length, band width and efficiency are used to determine the efficiency of signal coordination. It was found that design using Fuzzy logic concept is more efficient and can also account for the dynamic traffic flow conditions.
This study is aimed at characterization of Fuzzy logic control based system with varying span of Member Function by establishing region of stable operation for different cases using range provided by 'k' for setting the span for desired performance.The performed work may be used as reference, by designers for designing a Fuzzy logic based control system depending upon the requirement of desired application.
The design of controllers for non- linear systems in industry is a complex and difficult task. The development of non-linear control techniques has been approaches in many different ways with varied results. One approach, which has shown promise for solving nonlinear control problems, is the use of fuzzy logic control. This book will discuss the Magnetic Levitation (Maglev) models as an example of nonlinear systems. It will also show the design of fuzzy logic controllers for this model to prove that the fuzzy controller can work properly with nonlinear system. Genetic Algorithm (GA) is used in this book as optimization method that optimizes the membership, output gain and inputs gain of the fuzzy controllers. Finally, fuzzy controller will be implemented using FPGA chip. The design will use a high-level programming language HDL for implementing the fuzzy logic controller using the Xfuzzy CAD tools to implement the fuzzy logic controller into HDL code. This book is designed for the professional and academic audience interested primarily in applications of fuzzy logic in engineering and technology.
The main part of the book is a comprehensive overview of the development of fuzzy logic and its applications in various areas of human affair since its genesis in the mid 1960s. This overview is then employed for assessing the significance of fuzzy logic and mathematics based on fuzzy logic.
This book presents the details theory and applications of Fuzzy sets,fuzzy systems,membership functions & controller designed. A logic based on the two truth values True and False is sometimes inadequate when describing human reasoning. Fuzzy logic uses the whole interval between 0 (False) and 1 (True ) to describe human reasoning. As a result, fuzzy logic is being applied in rule based automatic controllers. A fuzzy control method for automatic steering and a method for line tracking are conveyed in this article. The principal for fuzzy control steering and the construction of fuzzy controller are described in detail.For Example,A vehicle is refitted with a storage battery car. Navigation control system is developed based on digital compass and other sensors. The vehicle acquires the location information using the fuzzy control steering and line tracking method. Test results indicated that the navigation lateral error was less than 0.3m when the robot ran following the predefined line route. Finally, a power system network with UPFC has been considered & A POD controller has designed by using Fuzzy Logic to improve the damping oscillation of power system network.
Fuzzy Logic Control (FLC) is an important alternative method to the conventional Proportional, Integral, and Derivative (PID) control method for use in nonlinear systems. This book, therefore, highlight the feasibility and effectiveness of fuzzy logic control in application to mathematical models of two basic types of steam turbines; straight expansion and single-automatic extraction turbines. The derived performance of the developed mathematical models, in terms of input/output duty variables without mean of control, is found to be in a good agreement with the actual performance of typical steam turbines with practical technical data and operating conditions. Model components exhibit nonlinear behavior. A comparison is made between the efficiency of Fuzzy Logic Control and the conventional PID control for the dynamic responses of the closed loop drive system. In case of straight expansion steam turbines, the control task is either speed or backpressure control. In case of single extraction steam turbines, the control task is to maintain both speed and extraction pressure of the turbine constants. This is done in presence of severe changes in load and/or steam demand conditions.
The optimization of chemical processes has attracted attention because, in the face of growing competition, it is a natural choice for reducing production costs, improving product quality, and meeting safety requirements and environmental regulations. From an industrial perspective, the main objective is often economic and is stated in terms such as return, profitability or payback time of an investment.In this book, modeling, simulation and optimization of fluid catalytic cracking unit, (FCCU)was carried out. The heterogeneous catalytic cracking reactor was modeled as plug flow reactor (PFR) with the gas oil being the fluid mixed with the Magnesiev-370 catalyst, while the coke combustion reactor was modeled as continuous stirred tank reactor (CSTR). The model however was lumped as gas oil, fuel gas, gasoline, and bottom oil; thus the model was built in HSYSY simulation environment.The book focuses on providing processing engineering, modeling supports and optimization using a computer based simulator ‘HYSYS’ to help numerous refiners around the globe and researchers to develop a process optimization strategy for the FCCU
This book considers an isothermal CSTR and models it using state space technique. The primary goal of the controller is to control the product concentration of isothermal CSTR while manipulating the input flow rate. This book designs a PID controller, a fuzzy controller and tunes the PID controller using particle swarm optimization technique. The gain values and transient responses obtained through PID controller using particle swarm optimization, provides best output results than other controller such as PID controller, PID controller with disturbance, PID controller with (disturbance and delay) and fuzzy based controller.