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Advanced Process Control 1st Edition by Cecil L Smith ISBN 0470381973 9780470381977

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Advanced Process Control Cecil L. Smith Digital Instant Download

Author(s): Cecil L. Smith
ISBN(s): 9780470381977, 0470381973
Edition: Kindle
File Details: PDF, 3.84 MB
Year: 2010
Language: english
SKU: EB-1991136 Category: Tag:

Advanced Process Control 1st Edition by Cecil L Smith – Ebook PDF Instant Download/Delivery: 0470381973, 9780470381977
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ISBN 10: 0470381973 
ISBN 13: 9780470381977
Author: Cecil L Smith

This book fills the gap between basic control configurations (Practical Process Control) and model predictive control (MPC). For those loops whose performance has a direct impact on plant economics or product quality, going beyond simple feedback or cascade can improve control performance, or specifically, reduce the variance about the target. However, the effort required to implement such control technology must be offset by increased economic returns from production operations. The economic aspects of the application of the various advanced control technologies are stressed throughout the book.

Advanced Process Control 1st Table of contents:

Part I: Foundations and Review of Advanced Control Concepts

  • Chapter 1: Review of Process Dynamics and Basic Control
    • Dynamic Models: First-Order, Second-Order, Time Delays
    • Transfer Functions and Block Diagrams
    • Frequency Response Analysis
    • PID Control: Tuning Methods and Performance Criteria
    • Limitations of Basic Feedback Control
  • Chapter 2: Advanced Single-Loop Control Strategies
    • Feedforward Control: Design and Tuning
    • Cascade Control: Improving Disturbance Rejection
    • Ratio Control and Its Applications
    • Selector Control and Override Control
    • Adaptive Control: Basic Concepts and Self-Tuning Regulators
  • Chapter 3: Introduction to Multivariable Systems
    • Process Interactions and Pairing of Inputs/Outputs
    • Relative Gain Array (RGA) Analysis: A Tool for Pairing
    • Singular Value Decomposition (SVD) for System Analysis
    • Degrees of Freedom in Multivariable Control

Part II: Multivariable Control Techniques

  • Chapter 4: Decoupling and Interaction Compensation
    • Static Decoupling: Design and Limitations
    • Dynamic Decoupling: Ideal and Practical Decouplers
    • Multivariable PID Control Strategies
  • Chapter 5: State-Space Representation and Control Design
    • State-Space Models from First Principles and Linearization
    • Controllability and Observability
    • State Feedback Control and Pole Placement
    • LQR (Linear Quadratic Regulator) Control
    • Kalman Filtering for State Estimation

Part III: Model Predictive Control (MPC)

  • Chapter 6: Principles of Model Predictive Control
    • The MPC Concept: Prediction, Optimization, Receding Horizon
    • Dynamic Models for MPC: Impulse Response, Step Response, State-Space
    • Prediction Horizon and Control Horizon
    • Handling Constraints: Input and Output Constraints
  • Chapter 7: Design and Implementation of MPC
    • Quadratic Programming (QP) for MPC Optimization
    • Tuning MPC Controllers: Weighting Factors, Horizons
    • Robustness and Stability Considerations in MPC
    • Dealing with Model Mismatch and Disturbances
  • Chapter 8: Variations and Extensions of MPC
    • Dynamic Matrix Control (DMC)
    • Generalized Predictive Control (GPC)
    • Nonlinear Model Predictive Control (NMPC)
    • Hybrid MPC for Discrete and Continuous Systems

Part IV: Optimization and Advanced Applications

  • Chapter 9: Inferential Control and Soft Sensors
    • Estimation of Unmeasured Variables
    • Statistical Models for Inferential Control
    • Kalman Filters and Luenberger Observers for Process Estimation
    • Applications in Quality Control
  • Chapter 10: Real-Time Optimization (RTO)
    • The Hierarchical Control Structure
    • Formulation of the RTO Problem: Economic Objectives
    • Optimization Algorithms for RTO
    • Integration of RTO with MPC and Regulatory Control
    • Data Reconciliation and Gross Error Detection
  • Chapter 11: Fault Detection and Diagnosis (FDD) in Process Control
    • Types of Faults in Process Systems
    • Statistical Process Control (SPC) for Monitoring
    • Model-Based FDD Techniques (e.g., Residual Analysis)
    • Data-Driven FDD: Principal Component Analysis (PCA), Partial Least Squares (PLS)
  • Chapter 12: Distributed Control Systems (DCS) and Advanced Control Implementation
    • Architecture of Modern Control Systems
    • Integration of Advanced Control Algorithms within DCS
    • Human-Machine Interface (HMI) Design for Advanced Control
    • Cybersecurity Considerations in Industrial Control Systems

Part V: Emerging Topics and Case Studies

  • Chapter 13: Advanced Control in Specific Industries
    • Process Control in Chemical and Petrochemical Plants
    • Control Applications in Pulp and Paper Industry
    • Batch Process Control
    • Bioreactor Control
    • Energy Systems and Smart Grids
  • Chapter 14: Machine Learning and Artificial Intelligence in Process Control
    • Overview of AI/ML Techniques for Process Control
    • Reinforcement Learning for Control
    • Neural Networks in Process Modeling and Control
    • Challenges and Opportunities
  • Chapter 15: Future Directions in Process Control
    • Digital Twins and Model Integration
    • Autonomous Operations
    • Sustainability and Energy Efficiency through Control
    • Addressing the Skills Gap in Advanced Control

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Tags: Cecil L Smith, Advanced, Process