{"product_id":"process-control-isbn-9781119157748","title":"Process Control","description":"\u003cp\u003eThis expanded new edition is specifically designed to meet the needs of the process industry, and  closes the gap between theory and practice.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eBack-to-basics approach, with a focus on techniques that have an immediate practical application, and heavy maths relegated to the end of the book\u003c\/li\u003e \u003cli\u003eWritten by an experienced practitioner, highly regarded by major corporations, with 25 years of teaching industry courses\u003c\/li\u003e \u003cli\u003eSupports the increasing expectations for Universities to teach more practical process control (supported by IChemE)\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePreface x\u003c\/p\u003e \u003cp\u003eAbout the Author xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Process Dynamics 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Definition 3\u003c\/p\u003e \u003cp\u003e2.2 Cascade Control 10\u003c\/p\u003e \u003cp\u003e2.3 Model Identification 12\u003c\/p\u003e \u003cp\u003e2.4 Integrating Processes 26\u003c\/p\u003e \u003cp\u003e2.5 Other Types of Process 29\u003c\/p\u003e \u003cp\u003e2.6 Robustness 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. PID Algorithm 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Definitions 35\u003c\/p\u003e \u003cp\u003e3.2 Proportional Action 36\u003c\/p\u003e \u003cp\u003e3.3 Integral Action 41\u003c\/p\u003e \u003cp\u003e3.4 Derivative Action 43\u003c\/p\u003e \u003cp\u003e3.5 Versions of Control Algorithm 49\u003c\/p\u003e \u003cp\u003e3.6 Interactive PID Controller 51\u003c\/p\u003e \u003cp\u003e3.7 Proportional‐on‐PV Controller 56\u003c\/p\u003e \u003cp\u003e3.8 Nonstandard Algorithms 64\u003c\/p\u003e \u003cp\u003e3.9 Tuning 65\u003c\/p\u003e \u003cp\u003e3.10 Ziegler‐Nichols Tuning Method 66\u003c\/p\u003e \u003cp\u003e3.11 Cohen‐Coon Tuning Method 72\u003c\/p\u003e \u003cp\u003e3.12 Tuning Based on Penalty Functions 73\u003c\/p\u003e \u003cp\u003e3.13 Manipulated Variable Overshoot 77\u003c\/p\u003e \u003cp\u003e3.14 Lambda Tuning Method 80\u003c\/p\u003e \u003cp\u003e3.15 IMC Tuning Method 80\u003c\/p\u003e \u003cp\u003e3.16 Choice of Tuning Method 83\u003c\/p\u003e \u003cp\u003e3.17 Suggested Tuning Method for Self‐Regulating Processes 84\u003c\/p\u003e \u003cp\u003e3.18 Tuning for Load Changes 87\u003c\/p\u003e \u003cp\u003e3.19 Tuning for SP Ramps 89\u003c\/p\u003e \u003cp\u003e3.20 Tuning for Unconstrained MV Overshoot 91\u003c\/p\u003e \u003cp\u003e3.21 PI Tuning Compared to PID Tuning 92\u003c\/p\u003e \u003cp\u003e3.22 Tuning for Large Scan Interval 94\u003c\/p\u003e \u003cp\u003e3.23 Suggested Tuning Method for Integrating Processes 97\u003c\/p\u003e \u003cp\u003e3.24 Measure of Robustness 99\u003c\/p\u003e \u003cp\u003e3.25 Implementation of Tuning 100\u003c\/p\u003e \u003cp\u003e3.26 Tuning Cascades 101\u003c\/p\u003e \u003cp\u003e3.27 Loop Gain 104\u003c\/p\u003e \u003cp\u003e3.28 Adaptive Tuning 105\u003c\/p\u003e \u003cp\u003e3.29 Initialisation 106\u003c\/p\u003e \u003cp\u003e3.30 Anti‐Reset Windup 108\u003c\/p\u003e \u003cp\u003e3.31 On‐Off Control 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Level Control 112\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Use of Cascade Control 112\u003c\/p\u003e \u003cp\u003e4.2 Parameters Required for Tuning Calculations 113\u003c\/p\u003e \u003cp\u003e4.3 Tight Level Control 120\u003c\/p\u003e \u003cp\u003e4.4 Averaging Level Control 122\u003c\/p\u003e \u003cp\u003e4.5 Error‐Squared Controller 129\u003c\/p\u003e \u003cp\u003e4.6 Gap Controller 132\u003c\/p\u003e \u003cp\u003e4.7 Impact of Noise on Averaging Control 134\u003c\/p\u003e \u003cp\u003e4.8 Potential Disadvantage of Averaging Level Control 136\u003c\/p\u003e \u003cp\u003e4.9 General Approach to Tuning 137\u003c\/p\u003e \u003cp\u003e4.10 Three‐Element Level Control 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Signal Conditioning 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Instrument Linearisation 143\u003c\/p\u003e \u003cp\u003e5.2 Process Linearisation 145\u003c\/p\u003e \u003cp\u003e5.3 Control of pH 147\u003c\/p\u003e \u003cp\u003e5.4 Constraint Conditioning 151\u003c\/p\u003e \u003cp\u003e5.5 Pressure Compensation of Distillation Tray Temperature 153\u003c\/p\u003e \u003cp\u003e5.6 Compensation of Gas Flow Measurement 153\u003c\/p\u003e \u003cp\u003e5.7 Filtering 155\u003c\/p\u003e \u003cp\u003e5.8 Exponential Filter 157\u003c\/p\u003e \u003cp\u003e5.9 Nonlinear Exponential Filter 161\u003c\/p\u003e \u003cp\u003e5.10 Moving Average Filter 161\u003c\/p\u003e \u003cp\u003e5.11 Least Squares Filter 163\u003c\/p\u003e \u003cp\u003e5.12 Tuning the Filter 169\u003c\/p\u003e \u003cp\u003e5.13 Control Valve Characterisation 170\u003c\/p\u003e \u003cp\u003e5.14 Equal Percentage Valve 172\u003c\/p\u003e \u003cp\u003e5.15 Split‐Range Valves 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Feedforward Control 184\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Ratio Algorithm 185\u003c\/p\u003e \u003cp\u003e6.2 Bias Algorithm 188\u003c\/p\u003e \u003cp\u003e6.3 Deadtime and Lead‐Lag Algorithms 190\u003c\/p\u003e \u003cp\u003e6.4 Tuning 194\u003c\/p\u003e \u003cp\u003e6.5 Laplace Derivation of Dynamic Compensation 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Deadtime Compensation 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Smith Predictor 201\u003c\/p\u003e \u003cp\u003e7.2 Internal Model Control 206\u003c\/p\u003e \u003cp\u003e7.3 Dahlin Algorithm 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Multivariable Control 210\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Constraint Control 210\u003c\/p\u003e \u003cp\u003e8.2 SISO Constraint Control 211\u003c\/p\u003e \u003cp\u003e8.3 Signal Selectors 213\u003c\/p\u003e \u003cp\u003e8.4 Relative Gain Analysis 217\u003c\/p\u003e \u003cp\u003e8.5 Niederlinski Index 226\u003c\/p\u003e \u003cp\u003e8.6 Condition Number 227\u003c\/p\u003e \u003cp\u003e8.7 Steady State Decoupling 229\u003c\/p\u003e \u003cp\u003e8.8 Dynamic Decoupling 231\u003c\/p\u003e \u003cp\u003e8.9 MPC Principles 237\u003c\/p\u003e \u003cp\u003e8.10 Parallel Coordinates 239\u003c\/p\u003e \u003cp\u003e8.11 Enhanced Operator Displays 240\u003c\/p\u003e \u003cp\u003e8.12 MPC Performance Monitoring 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Inferentials and Analysers 248\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Inferential Properties 248\u003c\/p\u003e \u003cp\u003e9.2 Assessing Accuracy 256\u003c\/p\u003e \u003cp\u003e9.3 Laboratory Update of Inferential 262\u003c\/p\u003e \u003cp\u003e9.4 Analyser Update of Inferential 266\u003c\/p\u003e \u003cp\u003e9.5 Monitoring On‐Stream Analysers 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Combustion Control 270\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Fuel Gas Flow Correction 270\u003c\/p\u003e \u003cp\u003e10.2 Measuring NHV 278\u003c\/p\u003e \u003cp\u003e10.3 Dual Firing 280\u003c\/p\u003e \u003cp\u003e10.4 Heater Inlet Temperature Feedforward 281\u003c\/p\u003e \u003cp\u003e10.5 Fuel Pressure Control 284\u003c\/p\u003e \u003cp\u003e10.6 Firebox Pressure 287\u003c\/p\u003e \u003cp\u003e10.7 Combustion Air Control 288\u003c\/p\u003e \u003cp\u003e10.8 Boiler Control 299\u003c\/p\u003e \u003cp\u003e10.9 Fired Heater Pass Balancing 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Compressor Control 306\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Polytropic Head 306\u003c\/p\u003e \u003cp\u003e11.2 Load Control (Turbo‐Machines) 310\u003c\/p\u003e \u003cp\u003e11.3 Load Control (Reciprocating Machines) 314\u003c\/p\u003e \u003cp\u003e11.4 Anti‐Surge Control 315\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Distillation Control 322\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Key Components 325\u003c\/p\u003e \u003cp\u003e12.2 Relative Volatility 325\u003c\/p\u003e \u003cp\u003e12.3 McCabe‐Thiele Diagram 328\u003c\/p\u003e \u003cp\u003e12.4 Cut and Separation 333\u003c\/p\u003e \u003cp\u003e12.5 Effect of Process Design 345\u003c\/p\u003e \u003cp\u003e12.6 Basic Controls 350\u003c\/p\u003e \u003cp\u003e12.7 Pressure Control 350\u003c\/p\u003e \u003cp\u003e12.8 Level Control 364\u003c\/p\u003e \u003cp\u003e12.9 Tray Temperature Control 382\u003c\/p\u003e \u003cp\u003e12.10 Pressure Compensated Temperature 393\u003c\/p\u003e \u003cp\u003e12.11 Inferentials 402\u003c\/p\u003e \u003cp\u003e12.12 First‐Principle Inferentials 411\u003c\/p\u003e \u003cp\u003e12.13 Feedforward on Feed Rate 413\u003c\/p\u003e \u003cp\u003e12.14 Feed Composition Feedforward 416\u003c\/p\u003e \u003cp\u003e12.15 Feed Enthalpy Feedforward 418\u003c\/p\u003e \u003cp\u003e12.16 Decoupling 419\u003c\/p\u003e \u003cp\u003e12.17 Multivariable Control 424\u003c\/p\u003e \u003cp\u003e12.18 On‐Stream Analysers 433\u003c\/p\u003e \u003cp\u003e12.19 Towers with Sidestreams 433\u003c\/p\u003e \u003cp\u003e12.20 Column Optimisation 435\u003c\/p\u003e \u003cp\u003e12.21 Optimisation of Column Pressure 438\u003c\/p\u003e \u003cp\u003e12.22 Energy\/Yield Optimisation 441\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. APC Project Execution 444\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Benefits Study 444\u003c\/p\u003e \u003cp\u003e13.2 Benefit Estimation for Improved Regulatory Control 445\u003c\/p\u003e \u003cp\u003e13.3 Benefits of Closed‐Loop Real‐Time Optimisation 455\u003c\/p\u003e \u003cp\u003e13.4 Basic Controls 458\u003c\/p\u003e \u003cp\u003e13.5 Basic Control Monitoring 459\u003c\/p\u003e \u003cp\u003e13.6 Inferential Properties 464\u003c\/p\u003e \u003cp\u003e13.7 Organisation 464\u003c\/p\u003e \u003cp\u003e13.8 Vendor Selection 468\u003c\/p\u003e \u003cp\u003e13.9 Safety in APC Design 471\u003c\/p\u003e \u003cp\u003e13.10 Alarms 471\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Statistical Methods 473\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Central Limit Theorem 473\u003c\/p\u003e \u003cp\u003e14.2 Generating a Normal Distribution 475\u003c\/p\u003e \u003cp\u003e14.3 Quantile Plots 477\u003c\/p\u003e \u003cp\u003e14.4 Calculating Standard Deviation 478\u003c\/p\u003e \u003cp\u003e14.5 Skewness and Kurtosis 480\u003c\/p\u003e \u003cp\u003e14.6 Correlation 480\u003c\/p\u003e \u003cp\u003e14.7 Confidence Interval 481\u003c\/p\u003e \u003cp\u003e14.8 Westinghouse Electric Company Rules 484\u003c\/p\u003e \u003cp\u003e14.9 Gamma Function 485\u003c\/p\u003e \u003cp\u003e14.10 Student t Distribution 486\u003c\/p\u003e \u003cp\u003e14.11 χ2 Distribution 489\u003c\/p\u003e \u003cp\u003e14.12 \u003ci\u003eF\u003c\/i\u003e Distribution 492\u003c\/p\u003e \u003cp\u003e14.13 Akaike Information Criterion 497\u003c\/p\u003e \u003cp\u003e14.14 Adjusted \u003ci\u003eR\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e 499\u003c\/p\u003e \u003cp\u003e14.15 Levene’s Test 500\u003c\/p\u003e \u003cp\u003e14.16 Box‐Wetz Ratio 501\u003c\/p\u003e \u003cp\u003e14.17 Regression Analysis 502\u003c\/p\u003e \u003cp\u003e14.18 Outliers 513\u003c\/p\u003e \u003cp\u003e14.19 Model Identification 514\u003c\/p\u003e \u003cp\u003e14.20 Autocorrelation and Autocovariance 518\u003c\/p\u003e \u003cp\u003e14.21 Artificial Neural Networks 527\u003c\/p\u003e \u003cp\u003e14.22 Repeatability 533\u003c\/p\u003e \u003cp\u003e14.23 Reproducibility 533\u003c\/p\u003e \u003cp\u003e14.24 Six‐Sigma 535\u003c\/p\u003e \u003cp\u003e14.25 Data Reconciliation 535\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Mathematical Techniques 540\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Fourier Transform 540\u003c\/p\u003e \u003cp\u003e15.2 Recursive Filters 548\u003c\/p\u003e \u003cp\u003e15.3 Lagrangian Interpolation 553\u003c\/p\u003e \u003cp\u003e15.4 Padé Approximation 557\u003c\/p\u003e \u003cp\u003e15.5 Laplace Transform Derivations 560\u003c\/p\u003e \u003cp\u003e15.6 Laplace Transforms for Processes 563\u003c\/p\u003e \u003cp\u003e15.7 Laplace Transforms for Controllers 569\u003c\/p\u003e \u003cp\u003e15.8 I‐PD versus PI‐D Algorithm 572\u003c\/p\u003e \u003cp\u003e15.9 Direct Synthesis 573\u003c\/p\u003e \u003cp\u003e15.10 Predicting Filter Attenuation 578\u003c\/p\u003e \u003cp\u003e15.11 Stability Limit for PID Control 579\u003c\/p\u003e \u003cp\u003e15.12 Ziegler‐Nichols Tuning from Process Dynamics 583\u003c\/p\u003e \u003cp\u003e15.13 Partial Fractions 586\u003c\/p\u003e \u003cp\u003e15.14 \u003ci\u003ez\u003c\/i\u003e‐Transforms and Finite Difference Equations 588\u003c\/p\u003e \u003cp\u003eReferences 594\u003c\/p\u003e \u003cp\u003eIndex 596\u003c\/p\u003e \u003cb\u003eMyke King\u003c\/b\u003e is Director of Whitehouse Consulting which provides process control consulting and training services. He has been running courses for industry covering all aspects of process control for the past 30 years (over 150 courses to over 1,500 delegates).\u003cbr\u003eMyke graduated from Cambridge University in 1974 with a master’s degree in Chemical Engineering. After University he joined Exxon to work as control engineer in their oil refinery in the UK, later managing the process control section. In 1983 he co-founded the consulting company KBC Process Automation, which was later sold to Honeywell. He thus has about 40 years of relevant experience - working in over 30 countries providing services to over 100 companies.","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989860696293,"sku":"NP9781119157748","price":164.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119157748.jpg?v=1761785708","url":"https:\/\/k12savings.com\/products\/process-control-isbn-9781119157748","provider":"K12savings","version":"1.0","type":"link"}