by Miguel J. Bagajewicz
This is the first in-depth presentation in book form of
current analytical methods for optimal design, selection and
evaluation of instrumentation for process plants. The presentation
is clear, concise and systematic-providing process engineers with
a valuable tool for improving quality, costs, safety,
loss prevention, and production accounting.
Features:
- Plant Data Management
- Instrumentation Design Goals
- Instrumentation
- Errors in Measurement
- Variable Classification
- Design and Upgrade Sensor Networks
- Data Reconciliation
- Design of Accurate Sensor Networks
- Precision Upgrade of Sensor
Networks
- Reliability of Sensor Networks
- Design of Sensors for Process Fault Diagnosis
The text is supplemented with more than 100 flow charts,
diagrams and other schematics that illustrate procedures,
systems and instrumentation. More than 70 tables provide useful
reference data.
Process Plant Instrumentation: Design and Upgrade includes:
- Guide to assessment of process plant instrumentation needs, network
design and upgrade, selection criteria, and reliability evaluation
- Improved control of quality and costs, safety, and
loss prevention
- Systematic, step-by-step presentation of methodology and
procedures
- Worked examples illustrate analytical techniques
- The first presentation in book form of recent advances in this
field
Contents:
- Plant Data Management
- Plant Information and Operations Management
- Model-Based Monitoring
- Quality of Data
- Instrumentation Design Goals
- Measured and Key Variables
- Selection of Monitoring Variables
- Selection of Key Variables in Control
- Selection of Measured Variables for Fault Diagnosis
- Instrumentation Design Goals
- Upgrading of Instrumentation
- Instrumentation
- Flow Rate Instrumentation
- Level Measurement
- Temperature Measurement
- Pressure Measurement
- Density Measurement
- On-Line Process Analyzers
- Transmission and Transformation of Signals
- Errors in Measurement
- Instrument Properties
- Measurement Quality
- Sensitivity and Speed of Response
- Hysteresis and Dead Band
- Calibration Curves
- Accuracy of Different Instruments
- Variable Classification
- Model
- Measurement Equation
- Graphs and Flowsheets
- Connectivity of Systems
- Observability
- Redundancy
- Linear Systems
- Canonical Representation of Linear Systems
- System Degree of Redundancy
- Quantification of Observability and Redundancy
- Graphs and Canonical Matrices
- Nonlinear Systems
- Design and Upgrade of Nonredundant and Redundant Sensor
Networks
- Upgrade and/or Design Goals
- Design for Estimability
- Design for Estimability Efficiency
- Compulsory Measurements and the Upgrade Case
- Sensor Networks for Bilinear Systems
- Data Reconciliation
- Data Reconciliation
- Background
- Linear Data Reconciliation
- Steady-State Linear Data Reconciliation
- Nonlinear Steady-State Data Reconciliation
- Dynamic Data Reconciliation
- Design of Accurate Sensor Networks
- Cost-Optimal Design
- Multiple Instruments and Hardware Redundancy
- Maximum Precision Models
- Generalized Maximum Precision Model
- Relation Between Sensor Network Models
- Solution Procedures for Linear Systems
- Parameter Estimation in Nonlinear Systems
- Precision Upgrade of Sensor Networks
- Upgrade Options
- Cost Benefit Analysis
- Upgrade Models Based on Addition of Sensors
- Model for Resource Reallocation
- Generalized Model for Resource Reallocation and Upgrade
- Reliability of Nonrepairable Sensor Networks
- Sensor Service Availability
- Sensor Service Reliability
- Failure Density and Failure Rate
- Markovian Model
- Mean Time to Failure
- Estimation Availability and Reliability of Variables
- Determination of Estimation of Reliability
- Estimation Reliability in Nonredundant Systems
- Availability, Reliability and Degree of Estimability
- System Availability and Reliability
- Design of Reliable Linear Nonrepairable Sensor Networks
- Nonredundant Networks Featuring Maximum Reliability
- Redundant Networks Featuring Maximum Reliability and Hardware
Redundancy
- Redundant and Restricted Networks
- Design of Reliable Bilinear Nonrepairable Sensor Networks
- Bilinear Multicomponent Systems
- Energy Networks
- Design of Reliable and Cost-Efficient Nonrepairable
Sensor Networks
- Minimum Cost Model
- Minimum Number of Sensors Model
- Solution Procedure
- Relation to Other Models
- Limitations of Previous Models
- Generalized Maximum Reliability Model
- Design of Repairable Sensor Networks
- Failure Intensity
- Repair Intensity
- Expected Number of Repairs
- Maintenance and Total Cost
- Residual Precision
- Minimum Cost Model
- Design of Robust Sensor Networks
- Origin of Gross Errors
- Gross Error Handling
- Test for Gross Error Presence
- Gross Error Detection in Dynamic Data
- Reconciliation
- Inaccuracy in Gross Error Detection
- Multiple Gross Error Identification
- Gross Error Size Estimation
- Sensor Network Error Detectability
- Sensor Network Gross Error Resilience
- Robust Sensor Networks
- Minimum Cost Model for Robust Networks
- Genetic Algorithms
- Design of Sensors for Process Fault Diagnosis
- Fault Detection, Diagnosis and Alarms
- Fault Observability
- Fault Resolution
- Sensor Network Design
- Sensor Location for Fault Observability
- Sensor Location for Fault Resolution
Index
click here 
to see books of related interest
