김영배(STEP Engineering & Consulting 대표, 약력)

Up-stream으로 생산된 Propane 및 Butane은 각각 500㎥/hr 유량으로 Run-down 되어 Tank Terminal로 이송된다. Run-down 된 Propane 및 Butane은 각각 -45˚C 및 -1˚C이다. 각 저장 탱크는 온도의 변화 및 기후조건에 따라 Boil Off되는 Bog Propane 및 Bog Butane을 Centrifugal Compressor로 압축하여 Condenser로 보내서, C4refrigerator 이용하여 Condensing하여 저장 Tank로 회수하여 저장한다. Propane Bog Compressor는 Centrifugal Type이며 Maximum Load때는 2대가 운전되며 1대는 Stand-by이며, Model은 동일 하다. Butane Bog Compressor는 Centrifugal Type이며 Maximum Load때는 2대가 운전되며 1대는 Stand-by이며 ,model은 3대가 서로 다르다. 대기온도는 10˚C~52˚C이다. 저장탱크는 Dome Roof Type이며 운전 압력은 0Kg/㎠G이며, 운전온도는 Propane Tank는 -45˚C이며 Bytane Tank은 0˚C이다.
저장 탱크는 각각 4대로 운전된다. 또한 저장된 Propane 및 Butane은 수출 부두로 Loading Pump 의해 4000㎥/hr로 LPG Tanker에 이송된다.

• 기존공장의 냉동기가 고장 시 Run-down된 Propane Cooling되지 않고 40˚C로 이송 되었을 때 저장 탱크에서 BOIL OFF 량.
• 저장탱크에서 기후변화와 운전MODE 변화에 따른 각 탱크에서 BOIL -OFF GAS량에 따른 CASE STUDY.
• PRopane Refrigerator의 Settle Out Pressure 및 Settle Out Pressure에서 Compressor의 Start-up시 각 운전인자에 대한 고찰.

위 공정을 Dynamic Simulation을 할 경우,

• 필요 자료
• 준비 자료
• DYNAMIC SIMULATION을 위한 절차 및 방법

을 Dynamic Simulator은 HYSIS을 기준으로 설명하고자 한다.

1. 공정설명 (PDF, 261 KB, 8 pages)
   • Introduction
   • Operating Philosophy For Propane System
2. 각 UNIT OPERATION에 대한 고찰 (PDF, 713 KB, 14 pages)
3. SYSTEM DERSIGN BASIS (PDF, 169 KB, 3 pages)
4. UNIT OPERATION (PDF, 115 KB, 2 pages)
   • Tank
   • Pump
   • Compressor
   • Safety Facilities
5. DYNAMIC MODEL DATA (PDF, 288 KB, 10 pages)
   • Unit Operation Summary
   • Dynamic Model Details
6. DYNAMIC SIMULATION (PDF, 846 KB, 51 pages)
   • Dynaic Simulation Case
   • Simulation PFD
   • Settle Out Pressure
   • Start-up Duty Compressor From Settle Out Pressure
   • Load Transfer from Duty to Stand-by Compressor
   • Case Studites for Each Load Changes
   • Case Study for All Possible Changes
   • Load Transfer at Each Cases
   • Settle Out Case With Liquid Filled Surge Drum
7. SUMMARY AND RESULTS (PDF, 222 KB, 3 pages)
   • Settle Out Pressure Results
   • Start-up Results from Settle Out Conditions
   • Load Transfer Results
   • Case Study Results

1. Basic Low-Level Control / 4
　1.1 Basic Idea of Feedback/Feedforward Control / 4
　1.2 Motivation- Why(negative) Feedback Control? / 9
　1.3 Elements of a Feedback System / 13
　1.4 Pid Formulations / 20
　　1.4.1 Integral(I) Control / 20
　　1.4.2 Proportional(P) Control / 21
　　1.4.3 Proportional Integral(PI) Control / 24
　　1.4.4 Proportional Integral Derivative(PID) Control / 25
　　1.4.5 Digital Implementation / 27
　1.5 Quantitive PID Tuning Methods / 29
　　1.5.1 Continuous Cycling Method / 29
　　1.5.2 Reaction-curve-based Method / 38
　　1.5.3 Fopdt-based Tuning Rules / 41
　　1.5.4 Direct Synthesis Method - IMC Tuning / 46
　1.6 Practical Considerations / 50

2. Multi-loop Control and Further Practical Issues / 55
　2.1 Feedforward-feedback Control / 55
　2.2 Cascade Control / 57
　2.3 Override Control / 61

3. Control of Multi-input Multi-output(MIMO) Processes / 62
　3.1 MIMO Process? / 62
　3.2 Interaction and I/O Pairing / 64
　　3.2.1 Interaction / 64
　　3.2.2. I/O Pairing / 67
　3.3 Decoupling / 70
　3.4 Current Trends / 74
　　3.4.1 Computer Integrated Process Management / 74
　　3.4.2 Model-based Approaches / 75
　　3.4.3 Computing Environment / 77
　　3.4.4 Computer Control System / 78
　　3.4.5 Smart Instrument and Field Bus / 79

1. Introduction To Model Predictive Control / 5
　1.1 Background for MPC Development / 5
　1.2 What's MPC? / 6
　1.3 Why MPC? / 8
　　1.3.1 Some Examples / 9
　　1.3.2 Summary / 24
　1.4 Industrial Use of MPC: Overview / 25
　　1.4.1 Motivation / 25
　　1.4.2 Survey of MPC Use / 31
　1.5 Historical Perspective / 32
　1.6 Challenges / 34
　　1.6.1 Modeling & Identification / 34
　　1.6.2 Incorporation of Statistical Concepts / 41
　　1.6.3 Nonlinear Control / 48
　　1.6.4 Other Issues / 49

2. Dynamic Matrix Control / 50
　2.1 Finite Impulse and Step Response Model / 50
　　2.1.1 Overview of Computer Control / 50
　　2.1.2 Impulse Response and Impulse Response Model / 52
　　2.1.3 Step Response and Step Response Model / 54
　2.2 Multi-step Prediction / 57
　　2.2.1 Overview / 57
　　2.2.2 Recursive Multi-step Prediction for an Fir System / 58
　　2.2.3 Recursive Multi-step Prediction for an Fir System with Differenced Input / 62
　　2.2.4 Multivariable Generation / 66
　2.3 Dynamic Matrix Control Algorithm / 67
　　2.3.1 Major Constituents / 67
　　2.3.2 Basic Problem Setup / 68
　　2.3.3 Definition and Update of Memory / 69
　　2.3.4 Prediction Equation / 70
　　2.3.5 Quadratic Criterion / 73
　　2.3.6 Constraints / 75
　　2.3.7 Quadratic Programming / 79
　　2.3.8 Summary of Real-time Implementation / 83
　2.4 Additional Issues / 84
　　2.4.1 Feasibility Issue and Constraint Relaxation / 84
　　2.4.2 Guidelines for Choosing the Horizon Size / 85
　　2.4.3 Bi-level Formulation / 86
　　2.4.4 Property Estimation / 89
　　2.4.5 System Decomposition / 91
　　2.4.6 Model Conditioning / 98
　　2.4.7 Blocking / 102

3. System Identification / 107
　3.1 Dynamic Matrix Identification / 107
　　3.1.1 Step Testing / 107
　　3.1.2 Pulse Testing / 111
　　3.1.3 Random Input Testing / 112
　　3.1.4 Data Pretreatment / 118
　3.2 Basic Concepts of Identification / 120
　3.3 Model Description / 124
　　3.3.1 Nonparametric Model / 124
　　3.3.2 Parametric Method / 125
　3.4 Experimental Conditions / 128
　　3.4.1 Sampling Interval / 128
　　3.4.2 Open-loop Vs. Closed-loop Experiments / 129
　　3.4.3 Input Design / 130
　3.5 Identification Methods / 132
　　3.5.1 Prediction Error Method / 132
　　3.5.2 Subspace Identification / 137
　3.6 Identification of a Process with Strong Directionality / 138

1. Basics of Linear Algebra / 5
　1.1 Vectors / 5
　1.2 Matrices / 11
　1.3 Singular Value Decomposition / 23

2. Basic of Linear Systems / 30
　2.1 State Space Description / 30
　2.2 Finite Impulse Response Model / 38
　2.3 Truncated Step Response Model / 41
　2.4 Reachability and Observability / 44
　2.5 Static State Feedback Controller and State Estimator / 47

3. Basics of Optimization / 52
　3.1 Introduction / 52
　3.2 Unconstrained Optimization Problems / 55
　3.3 Necessary Condition of Optimality for Constrained Optimization Problems / 59
　3.4 Convex Optimization / 67
　3.5 Algorithm for Constrained Optimization Problems / 71

4. Ramdom Variables / 79
　4.1 Introduction / 79
　4.2 Basic Probability Concepts / 81
　　4.2.1 Probability Distribution, Density:Scalar Case / 81
　　4.2.2 Probability Distribution, Density:Vector Case / 83
　　4.2.3 Expectation of Random Variables and Random Variable functions:Scalar Case / 86
　　4.2.4 Expectation of Random Variables and Random Variable functions:Vector Case / 87
　　4.2.5 Conditional Probability Density:Scalar Case / 92
　　4.2.6 Conditional Probability Density:Vector Case / 97
　4.3 Statistics / 99
　　4.3.1 Prediction / 99
　　4.3.2 Sample Mean and Covariance, Probabilistic Model / 100

5. Stochastic Processes / 102
　5.1 Basic Probability Concepts / 102
　　5.1.1 Distribution Function / 102
　　5.1.2 Mean and Covariance / 103
　　5.1.3 Stationary Stochastic Processes / 103
　　5.1.4 Spectra of Stationary Stochastic Processes / 104
　　5.1.5 Discrete-time White Noise / 106
　　5.1.6 Colored Noise / 106
　　5.1.7 Integrated White Noise and Nonstationary processes / 108
　　5.1.8 Stochastic Differences Equation / 109
　5.2 Stochastic System Models / 111
　　5.2.1 State-space Model / 111
　　5.2.2 Input-output Models / 114

6. State Estimation / 116
　6.1 Linear Observer Structure / 117
　6.2 Pole Placement / 119
　6.3 Kalman Filter / 120
　　6.3.1 Kalman Filter as the Optimal Linear Observer / 121
　　6.3.2 Kalman Filter as the Optimal Estimator for Gaussian Systems / 123

7. System Identification / 127
　7.1 Problem Overview / 127
　7.2 Parametric Identification Methods / 128
　　7.2.1 Model Structures / 129
　　7.2.2 Parameter Estimation via Prediction Error minimization / 134
　　7.2.3 Parameter Estimation via Statistical Methods / 143
　　7.2.4 Other Methods / 150
　7.3 Nonparametric Identification Methods / 151
　　7.3.1 Frequency Response Identification / 152
　　7.3.2 Impulse Response Identification / 156
　　7.3.3 Subspace Identification / 158

1. State-space Model Predictive Control / 3
　1.1 Shortcomings of Current Industrial MPC Practice / 3
　1.2 State Space MPC / 5
　1.3 Disturbance Estimation via State Estimation / 7
　1.4 MPC Formulation Using State-space Model / 12

2. Nonlinear and Adaptive Model Predictive Control / 13
　2.1 Motivation / 13
　2.2 Issues in Nonlinear MPC / 15
　2.3 Linearization Based Nonlinear MPC / 17
　2.4 Example: Paper Machine Headbox Control / 24
　2.5 Additional Issues / 27
　2.6 Recursive Parameters Estimation / 28
　2.7 Adaptive MPC Formulation / 29
　2.8 Example: Binary Distillation Column / 30
　2.9 Potential Improvements in System Identification / 33

1. Overview and Fundamentals of SPC / 5
　1.1 Introduction / 6
　　1.1.1 Motivation for SPC / 6
　　1.1.2 Main Points / 7
　1.2 Traditional SPC Techniques / 8
　　1.2.1 Milestones / Key Players of SPC / 8
　　1.2.2 Shewart Chart / 9
　　1.2.3 Cusum Chart / 12
　　1.2.4 Ewma Chart / 14
　1.3 Multivariate Analysis / 15
　　1.3.1 Motivation / 15
　　1.3.2 Basics of Multivariable Statistics and Chi-square monitoring / 17
　　1.3.3 Principal Component Analysis / 21
　　1.3.4 Examples: Multivariate Analysis Vs. Single Variate analysis / 25
　1.4 Time Series Modeling / 25
　　1.4.1 Limitations of Rhe Traditional SPC Methods / 25
　　1.4.2 Motivating Example / 27
　　1.4.3 Time-series Models / 30
　　1.4.4 Computation of Prediction Error / 31
　　1.4.5 Including the Determinstic Inputs Into the Model / 32
　　1.4.6 Modeling Drifting Behavior Using a Nonstationary Sequence / 33
　　1.4.7 Multivariable Time-series Model / 36
　1.5 Regression / 38
　　1.5.1 Problem Definition / 38
　　1.5.2 The Method of Least Squares / 39
　　1.5.3 Limitations of Least Squares / 41
　　1.5.4 Principal Component Regression / 42
　　1.5.5 Partial Least Squares (PLS) / 44
　　1.5.6 Nonlinear Extentions / 46
　　1.5.7 Extensions to the Dynamic Case / 49

2. Application and Case Studies / 50
　2.1 Pca Monitoring of an Sbr Semi-batch Reactor System / 51
　　2.1.1 Introduction / 51
　　2.1.2 Problem Description / 52
　　2.1.3 Results / 53
　2.2 Data-based Inferential Quality Control in Batch Reactors / 61
　　2.2.1 Introduction / 61
　　2.2.2 Case Study in Details / 62
　2.3 Inferential Quality Control of Continuous Pulp Digester / 63
　　2.3.1 Introduction / 63
　　2.3.2 Case Study in Details / 63