过程控制工程Lecture-17(CP17)_outline

17.4.3 Complex control applications F FY
V
17.4.3 Control of column pressure Vapor pressure control: Most distillation column control systems assume that the tower operates at a constant pressure. When using temperature control as a substitute for composition control, pressure compensation is essential to maintain desired compositions. Typically, distillation may operate at negative pressure, normal pressure and positive pressure, respectively.
17.1 Introduction 17.2 Fundamental Models 17.3 Candidate Controlled Variables 17.4 Overall Control Schemes 17.5 Novel Control Strategies
17.1 Introduction
xD = F ( z f − xB ) + xB D z f − xB
xB = F (z f − xD ) + xD B
L D,xD F,zf V
Binary Distillation Fig.17-1 Heat carrier
LB
Bottoms, B,xB
D = F xD − xB
B F
=
x x
D D
● Direct variables — composition of products On-line composition analyzer is vert variables — operating temperature In the presence of unchanged pressure, one-to-one correspondence between the composition and the temperature is available.
KpO P T Kp T z=T-Kp+KpO (4) Inferential control
Fig.17-3
Chapter
17
17.4 Overall Control Schemes Mass balance based schemes Without quality (temperature) control P332 Fig.7-10, Fig.17-11
17.4.3 Complex control applications (1) Cascade control Temperature-steam flow cascade control Fig. 17-20 (A), (B) (2) Averaging control Level averaging control (3) Ratio control Control of ratio of reflux to feed flow, D/F (4) Feed-forward control Feed-forward control in terms of feed flow
17.2 Fundamental Models
Chapter
17
Control of Distillation Columns
j+1
L j +1 − L j + V j −1 − V j =
dM j dt
17.1 Introduction 17.2 Fundamental Models 17.3 Candidate Controlled Variables 17.4 Overall Control Schemes 17.5 Novel Control Strategies
− z − x
f B
Mass balance equations：
D/F ↑ ：xB xD ↓
B/F ↑： xB xD ↑
1
17.1.1 Basic principles (2) Energy balance Degree of separation
x (1 − x B ) V = β ln D F x B (1 − x D )
17.3 Candidate Controlled Variables Pressure Compensation (1) Direct compensating
17.3 Candidate Controlled Variables (2) Temperature difference control (3) Dual temperature difference control
Distillation has extensive applications in a variety of industrial sectors, such as petrochemical, chemical and food processing plants.
17.1 Introduction Distillation is a method of separating mixtures based on differences in their volatilities in a boiling liquid mixture. Continuous distillation, Batch distillation Industrial distillation is typically performed in large, vertical cylindrical columns known as distillation towers or distillation columns with diameters ranging from about 65 centimeters to 16 meters and heights ranging from about 6 meters to 90 meters or more. MIMO systems, highly correlated variables, complexity in mechanism, slow dynamic responses, energy-intensive processes Difficult to Control
D
1. Manipulated by D Temperature increase → D Decrease → Level Increase → Temperature Decrease
F V Q B
→LIncrease
Demerits: Large lag 2. Manipulated by L
Chapter
17
Process Control Engineering
Chapter 17
2011-17 Li Hongguang Automation Department Beijing University of Chemical Technology
Control of Distillation Columns
FC
Control of Distillation Columns
17.1 Introduction 17.2 Fundamental Models 17.3 Candidate Controlled Variables 17.4 Overall Control Schemes 17.5 Novel Control Strategies
j j-1
L j +1 x j +1 − L j x j + V j −1 y j −1 − V j y j =
d[M j x j ] dt
Detailed analysis of the dynamics is given in P325-326
2
17.3 Candidate Controlled Variables Direct or indirect variables
Energy balance equations
⎛ V ⎞ s ≈ f ⎜ ⎟ ⎝ F ⎠
Level (3) Energy balance Pressure (4) Safety
Regarding specific columns, the compositions of bottoms and distillates depend on D/F and V/F .
17.1 Introduction
Coolant
17.1.1 Basic principles LD Mass balance; Energy balance (1) Mass balance F=B+D B, D: Bottom products ( bottoms) and distillate Fzf=DxD+BxB xD, xB : Light composition of bottoms and distillate
17.1.3 Disturbances
Chapter
Feed relevance ( flow, composition, temperature) Operating temperature Operating pressure
17
Control of Distillation Columns
17.1 Introduction 17.2 Fundamental Models 17.3 Candidate Controlled Variables 17.4 Overall Control Schemes 17.5 Novel Control Strategies