Industrial Control Systems: Mathematical and Statistical Models and Techniques

✍ Scribed by Adedeji Bodunde Badiru; Oye Ibidapo-Obe; Babatunde J Ayeni

• Publisher: CRC Press
• Year: 2011
• Tongue: English
• Leaves: 374
• Series: Industrial Innovation Series
• Category: Library

✦ Synopsis

''Preface This book presents the mathematical foundation for building and implementing industrial control systems. It contains mathematically rigorous models and techniques for control systems, in general, with specific orientation toward industrial systems. Industrial control encompasses several types of control systems. Some common elements of industrial control systems include supervisory control and data acquisition systems, distributed control systems, and other generic control system configurations, such as programmable logic controllers, that are often found in industrial operations and engineering infrastructure. Industrial control systems are not limited to production or manufacturing enterprises, as they are typically used in general industries such as electrical, water, oil and gas, and data acquisition devices. Based on information received from remote sensors, automated commands can be sent to remote control devices, which are referred to as field devices. Field devices are used to control local operations. These may include opening and closing valves, tripping breakers, collecting data from sensors, and monitoring local operating conditions. All of these are governed by some form of mathematical representation. Thus, this book has great importance in linking theory and practice. Distributed control systems are used to control industrial processes such as electric power generation, oil and gas refineries, water and wastewater treatment, and chemical, food, and automotive production. ''-- Read more...

✦ Table of Contents

Content: Mathematical Modeling for Product Design Introduction Literature Review Memetic Algorithm and Its Application to Collaborative Design Conclusion References Dynamic Fuzzy Systems Modeling Introduction: Decision Support Systems, Uncertainties Decision Support Systems Uncertainty Fuzziness Fuzzy Set Specifications Stochastic-Fuzzy Models Applications Conclusions References Stochastic Systems Modeling Introduction to Model Types Systems Filtering and Estimation Correlation Techniques Model Control-Model Reduction, Model Analysis References Systems Optimization Techniques Optimality Conditions Basic Structure of Local Methods Stochastic Central Problems Intelligent Heuristic Models Heuristics References Statistical Control Techniques Statistical Process Control Control Charts Process Capability Analysis Time Series Analysis and Process Estimation Time Series Analysis Example Exponentially Weighted Moving Average Cumulative Sum Chart Statistical Process Control Automatic Process Control Criticisms of SPC And APC Overcompensation, Disturbance Removal, and Information Concealing Integration Of SPC And APC Systems Approach To Process Adjustment ARIMA Modeling Of Process Data Model Identification And Estimation Minimum Variance Control Process Dynamics With Disturbance References Design of Experiment Techniques Factorial Designs Factorial Design for 3 Factors Saturated Designs Central Composite Designs Response Surface Optimization References Risk Analysis and Estimation Techniques Bayesian Estimation Procedure Computational Procedure Parameter Estimation for Hyperbolic Decline Curve Robustness of Decline Curves Mathematical Analysis Statistical Analysis Parameter Estimation Optimization Technique Iterative Procedure Residual Analysis Test Simplified Solution to the Vector Equation Integrating Neural Networks And Statistics For Process Control4 Fundamentals of Neural Network The Input Function Transfer Functions Statistics and Neural Networks Predictions Statistical Error Analysis Integration of Statistics And Neural Networks References Mathematical Modeling and Control of Multi- Constrained Projects Introduction Literature Review Methodology Representation of Resource Interdependencies and Multifunctionality Modeling of Resource Characteristics Resource Mapper Activity Scheduler Model Implementation And Graphical Illustrations Notations References Online Support Vector Regression with Varying Parameters for Time-Dependent Data Introduction Modified Gompertz Weight Function for Varying SVR Parameters Accurate Online SVR with Varying Parameters Experimental Results Conclusion References Appendix: Mathematical and Engineering References Index
Abstract: ''Preface This book presents the mathematical foundation for building and implementing industrial control systems. It contains mathematically rigorous models and techniques for control systems, in general, with specific orientation toward industrial systems. Industrial control encompasses several types of control systems. Some common elements of industrial control systems include supervisory control and data acquisition systems, distributed control systems, and other generic control system configurations, such as programmable logic controllers, that are often found in industrial operations and engineering infrastructure. Industrial control systems are not limited to production or manufacturing enterprises, as they are typically used in general industries such as electrical, water, oil and gas, and data acquisition devices. Based on information received from remote sensors, automated commands can be sent to remote control devices, which are referred to as field devices. Field devices are used to control local operations. These may include opening and closing valves, tripping breakers, collecting data from sensors, and monitoring local operating conditions. All of these are governed by some form of mathematical representation. Thus, this book has great importance in linking theory and practice. Distributed control systems are used to control industrial processes such as electric power generation, oil and gas refineries, water and wastewater treatment, and chemical, food, and automotive production. ''