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.
In spite of continuing advances in optimal solution techniques for optimization and control problems, many practical combinatorial problems remain too large or too complex to be solved by these known techniques. Thus, a heuristic approach (Neural Network Model) is often the only viable alternative. Neural Network Models offer the most unified approach to building truly intelligent systems which can provide good optimal solution for many applications. In this work we propose a hybrid (Kalkoh) neural network algorithm which is being used to model and solve the continuous stirred tank mixer (CSTM) problem. The hybrid algorithm is robust and converges fast without being trapped into a local minimal as is the case with the popular back-propagation neural network. The characteristic equations governing the dynamics of the Continuous Stirred Tank Mixer/Reactor and the controller were formulated and tested and found to be consistently stable.
The emergence of computational intelligence technology inspired by biological and human intelligence is one of the most exciting and important fields in engineering . It is expected that these technologies like fuzzy logic , will play a significant role in the development of intelligent robotic systems , machine systems , and mechatronics systems . In this work ; a Fuzzy Logic Controller is presented to control the motion of a robotic arm and avoid the obstacles existed in its mission road ,using a real platform robotic arm in combination with a vision system . This work involves constructing an integrated and autonomic MATLAB program. It could be applicable for any robotic arm . It depends on a new approach in analyzing the robotic environment videos acquired by a fixed webcam. The approach uses colors to detect and recognize the changeable locations and objects’ dimensions for each of the robot’s end-effector , the goal , and the obstacles .
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.
Petri nets are considered to be the alternative approach for analysis, design and implementation of DES. However, the prospect of uncertain and vague information still creates a problem. Hence, to handle the uncertainty, fuzzy logic is integrated into Petri nets to form Fuzzy Automation Petri nets as a modeling tool to handle these uncertainties. The use of Fuzzy Automation Petri nets (FAPN) demands a valid translation of the formalism to Ladder Logic Diagrams for implementation. This is achieved using Token Passing Methodology. This work investigates the use of Fuzzy Petri nets in supervisory control and suggests a modified and improved version called Fuzzy Automation Petri net (FAPN) as a modeling tool. It presents a systematic approach to the synthesis of Fuzzy Petri net based supervisor for the forbidden state problem using supervisory design procedure. The controlled model of the system can be constructed from this FAPN net structure. The implementation is using Flexible Manufacturing System as an example of DES.
The Imperfectly Stirred Reactor (ISR) is a model for strongly recirculating reacting flows where the conditional means of the reactive scalars are assumed to be spatially uniform and statistically stationary. This model is based on the Conditional Moment Closure (CMC) method and involves very simplified equations which can be solved at very little computational cost. The governing parameters in these equations are the averaged probability density functions (pdf), the residence time and the chemical mechanism.
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.
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.
The authors guide readers quickly and concisely through the complex topics of neural networks, fuzzy logic, mathematical modelling of electrical machines, power systems control and VHDL design. Unlike the academic monographs that have previously been published on each of these subjects, this book combines them and is based round case studies of systems analysis, control strategies, design, simulation and implementation. The result is a guide to applied control systems design that will appeal equally to students and professional design engineers. The book can also be used as a unique VHDL design aid, based on real-world power engineering applications.*Introduces cutting-edge control systems to a wide readership of engineers and students*The first book on neuro-fuzzy control systems to take a practical, applications-based approach, backed up with worked examples and case studies*Learn to use VHDL in real-world applications
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
With every year air conditioning units are increasing rapidly all over the world and air conditioning system has not only become integral part of every institution but also of our lives. The survey of this system shows that air conditioning system consumes nearly 50% of the total energy intake of a building. In air conditioning system, compressor unit consumes most of the energy. Different control techniques can be used to control the compressor speed. This book therefore provides the use of two intelligent control techniques to control the compressor speed and make air conditioning system energy efficient. The techniques used are fuzzy logic and neuro-fuzzy. This book also provides the comparison of both the techniques. This book should be helpful for professionals in artificial intelligence, or anyone else who may seek to work in Fuzzy , Neuro-fuzzy or related techniques. This book would also be helpful to students for study of these techniques.