CN105182752B - A kind of Fast design method of dilute acetone rectifying industrial dynamics optimal control layer output constraint - Google Patents

Abstract

The invention discloses a rapid design method for the output constraint of the dynamic optimization control layer of the distilling industry of diacetone, comprising: step 1, aiming at the distilling industry model predictive control system of the double-layer structure, calculating the stability of the model predictive controller The gain model of the state optimization layer; step 2, find the uncertain elements in the gain model and process gain matrix; step 3, obtain the upper boundary of the output constraint reachable set and the output constraint reachable set according to the uncertain elements and system disturbance variables The lower boundary of the reachable set; step 4, according to the upper boundary of the output constraint reachable set and the lower boundary of the output constraint reachable set, the output constraints of the dynamic optimization control layer are obtained. The rapid design method for the output constraints of the dynamic optimization control layer of the rectification industry of propylene ketone provided by the present invention can effectively solve the problem that it takes a long time to calculate the upper boundary of the output constraint reachable set and the lower boundary of the output constraint reachable set, and the calculation speed It is hardly affected by the dimensionality of the system.

Description

A Rapid Design of Output Constraints of Dynamic Optimal Control Layer for Distillation of Diacetone method

technical field

The invention relates to the field of industrial control, in particular to a rapid design method for dynamically optimizing the output constraints of a control layer in the rectification industry of propylene glycol.

Background technique

Acetone is one of the important basic organic raw materials, mainly used in the manufacture of cellulose acetate films, plastics and paint solvents. In different applications, acetone is required to have different purity, sometimes it is required to be of high purity, even anhydrous acetone, but this is very difficult, because acetone is extremely volatile and soluble, so, I want to It is often very difficult to obtain high-purity acetone.

In order to raise the low-purity acetone aqueous solution to high-purity, continuous distillation is generally used. The rectification operation in the chemical plant is carried out in a vertical circular rectification column, which is equipped with several layers of trays or filled with a certain height of packing.

Usually diacetone rectification column system is composed of tower body, tower reboiler, tower top cooler, tower top reflux tank and other equipment. The heat source of the distillation tower, the cooling water at the top of the tower enters the top cooler to provide a cold source for the rectification tower, and condenses and vaporizes the heavy components (water) at the top of the tower. After repeated gasification and condensation, the materials in the tower have high purity The acetone gas comes out from the top of the tower and enters the top cooler to exchange heat with the cooling water. The uncondensed gas enters the vent pipe system, and part of the acetone liquid condensed to a certain temperature is pumped back to the top of the tower by the reflux pump as cold reflux. , the other part is used as acetone product discharge device, the tower bottom liquid is water with a small amount of acetone, and the tower bottom liquid is used as sewage to directly discharge the device with a pump.

The traditional design method of the output constraints of the dynamic optimization control layer in the distillation industry of propylene glycol spends a lot of time when calculating the upper boundary of the output constraint reachable set and the lower boundary of the output constraint reachable set, especially for high-dimensional systems. It often takes several days or even longer, which greatly affects the design efficiency.

Contents of the invention

The invention provides a rapid design method for the output constraints of the dynamic optimization control layer of the rectification industry of propylene glycol, which can effectively solve the problem that the calculation of the upper boundary of the output constraint reachable set and the lower boundary of the output constraint reachable set take a long time, and obtain The retrieval speed is hardly affected by the dimensionality of the system.

A rapid design method for dynamic optimization control layer output constraints in the distillation industry of propylene glycol, including:

Step 1, aiming at the industrial model predictive control system for the distillation of propylene glycol with a double-layer structure, the gain model of the steady-state optimization layer of the model predictive controller is obtained as follows:

y=Gu+G _d d

In the formula: y is the n×1-dimensional system output, y∈SOC;

G is an n×m dimensional process gain matrix;

u is an m×1-dimensional system input variable, u∈SIC;

G _d is an interference gain matrix of n×p dimension;

d is the system disturbance variable of p×1 dimension, d∈DWC;

Step 2, find the uncertain elements in the process gain matrix in the gain model;

Step 3. Obtain the upper boundary of the output constrained reachable set and the lower bound of the output constrained reachable set according to the uncertain elements and system disturbance variables;

Step 4, according to the upper boundary of the output constraint reachable set and the lower boundary of the output constraint reachable set, the output constraints of the dynamic optimization control layer are obtained.

In the industrial model predictive control system for the distillation of propylene glycol with a double-layer structure, the output constraints of the dynamic optimization control layer need to be given by the upper steady-state optimization layer, and the traditional output constraint design method of the predictive controller can only give the output of the definite system Constraints, without considering the design method of the output constraints when the system has uncertainty, and the industrial control system of propylene glycol distillation usually has uncertainty and is non-square (the number of system input variables is less than the number of output variables), using the present invention The design method of the output constraints of the dynamic optimization control layer provided can provide the output constraints of the next layer in the steady-state optimization layer in the model predictive control system of the double-layer structure propylene glycol distillation, and ensure the model predictive control of the dynamic optimization control layer At the same time, the solution speed is short and time-consuming, and it is almost not affected by the dimension of the system.

As a preference, the output constraint reachable set LOKD is defined as follows:

LOKD(G,d)＝{y|y＝Gu+G _d d; u∈SIC, g _hk ∈Δ, G _d is a fixed value}

where g _hk is the uncertain element in the process gain matrix G, expressed as form,

The upper bound LOKDSJ of the output constrained reachable set is defined as follows:

The lower bound LOKDXJ of the output constrained reachable set is defined as follows:

In step 3, the steps to calculate the upper boundary of the output constraint reachable set are as follows:

Step 3-a-1, find and Record them as L _s,max and L _s,min respectively, and find the intersection I _s of L _s,max and L _s,min , and the midpoint CI of the intersection I _s ;

Step 3-a-2, find and Respectively recorded as L _{s, maxe} and L _{s, mine} , e is a positive real number, find the intersection I _se1 of L _{s, max} and L _{s, mine} , and the midpoint CI _e1 of the intersection I _se1 ; L _{s, min} and L _{s, the intersection I se2 of maxe ,} and the midpoint CI _e2 of the intersection I _se2 ;

Step 3-a-3, respectively calculate the distance between each vertex of L _s,max and CI, and the distance between each vertex of L _s,max and CI _e1 , if the distance between a vertex of L _s,max and CI _e1 is greater than the distance between the vertex and CI distance, then the vertex is a vertex in the upper boundary LOKDSJ of the output constraint reachable set, with Indicates the set of all vertices belonging to LOKDSJ in L _s,max ;

Step 3-a-4, respectively calculate the distance between each vertex of L _{s, min} and CI, and the distance between each vertex of L _{s, min} and CI _e2 , if the distance between a vertex of L _{s, min} and CI _e2 is greater than the distance between the vertex and CI distance, then the vertex is a vertex in the upper boundary LOKDSJ of the output constraint reachable set, with Indicates the set of all vertices belonging to LOKDSJ in L _s,min ;

Step 3-a-5, output the vertex set of the upper boundary LOKDSJ of the constrained reachable set as { I _s vertices, }, the polyhedron formed by the vertices of LOKDSJ is the upper boundary of the output constrained reachable set.

Preferably, in step 3, the steps of calculating the lower bound of the output constrained reachable set are as follows:

Step 3-b-1, find and Respectively recorded as L' _{s, max} and L' _{s, min} , find the intersection I' _s of L' _{s, max} and L' _{s, min} , and the midpoint CI' of the intersection I'_s;

Step 3-b-2, find and Respectively recorded as L' _{s, maxe} and L' _{s, mine} , e is a positive real number, find the intersection I' _se1 of L' _{s, max} and L' _{s, mine} , and the midpoint CI' _e1 of the intersection I'_se1; The intersection I' _se2 of L' _{s, min} and L' _{s, maxe} , and the midpoint CI' _e2 of the intersection I'_se2;

Step 3-b-3, respectively calculate the distance between each vertex of L' _s,max and CI', and the distance between each vertex of L' _s,max and CI' _e1 , if L' _s,max a vertex and CI' _e1 is greater than the distance between the vertex and CI', then the vertex is a vertex in the lower boundary LOKDXJ of the output constraint reachable set, with Represents the set of all vertices belonging to LOKDXJ in L' _{s, max} ;

Step 3-b-4, respectively calculate the distance between each vertex of L' _{s, min} and CI', and the distance between each vertex of L' _{s, min} and CI' _e2 , if L' _{s, min,} a certain vertex and CI' _e2 is greater than the distance between the vertex and CI', then the vertex is a vertex in the lower boundary LOKDXJ of the output constraint reachable set, with Represents the set of all vertices belonging to LOKDXJ in L'_s,min;

Step 3-b-5, output the vertex set of the lower boundary LOKDXJ of the constrained reachable set as { I- _'s apex, }, the polyhedron formed by the vertices of LOKDXJ is the lower boundary of the output constrained reachable set.

As a preference, the dynamic optimization control layer output constraint LOJX is defined as follows:

LOJX(α)＝{y|b ₁ ≤yy ₀ ≤b ₂ }

y ₀ =[y ₀₁ y ₀₂ ...y _0n ] ^T , y=[y ₁ y ₂ ...y _n ] ^T

In the formula: w ₁ W ₂ ...w _n is the weight; y ₀ is the nominal steady-state value of the process, and y is the system output;

In step 4, the steps for calculating the output constraints of the dynamic optimization control layer are as follows:

Step 4-1, using iterative algorithm to obtain α ₊₁ and α _-1 , so that LOJX(α ₊₁ ) is tangent to LOKDSJ, and the tangent point is v ₊₁ ; LOJX(α _-1 ) is tangent to LOKDXJ, and the tangent point is for v _-1 ;

Step 4-2, record v ₊₁ ＝[y ₁₊ y ₂₊ ... y _n+ ], v _-1 ＝[y _1- y _2- ... y _n- ], then the dynamic optimization control layer output constraint LOJX is:

LOJX={y|min(v ₊₁ ,v _-1 ) _≤yy0≤max (v ₊₁ ,v _-1 )}.

The rapid design method for the output constraints of the dynamic optimization control layer of the rectification industry of propylene ketone provided by the present invention can effectively solve the problem that it takes a long time to calculate the upper boundary of the output constraint reachable set and the lower boundary of the output constraint reachable set, and the calculation speed It is hardly affected by the dimensionality of the system.

Description of drawings

Fig. 1 is a schematic diagram of solving I _s and I _se1 in a two-dimensional system;

Fig. 2 is a schematic diagram of the vertices contained in the upper boundary of the reachable set of the solution output constraints in the two-dimensional system;

Fig. 3 is the schematic diagram of solving CI and CI _e2 in two-dimensional system;

Fig. 4 is a schematic diagram of the upper boundary of the reachable set of solution output constraints in a two-dimensional system;

Fig. 5 is a schematic diagram of the upper boundary of the reachable set of solution output constraints in a three-dimensional system;

Fig. 6 is a schematic diagram of the propylene rectification system in the simulation example.

Detailed ways

The following is a detailed description of the rapid design method for the dynamic optimization control layer output constraints of the propylene ketone distillation industry in the present invention in conjunction with the accompanying drawings.

A rapid design method for dynamic optimization control layer output constraints in the distillation industry of propylene glycol, including:

Step 1, aiming at the industrial model predictive control system for the distillation of propylene glycol with a double-layer structure, the gain model of the steady-state optimization layer of the model predictive controller is obtained as follows:

y=Gu+G _d d

In the formula: y is the n×1-dimensional system output, y∈SOC;

G is an n×m dimensional process gain matrix;

u is an m×1-dimensional system input variable, u∈SIC;

G _d is an interference gain matrix of n×p dimension;

d is the system disturbance variable of p×1 dimension, d∈DWC;

Step 2, find the uncertain element in the process gain matrix in the gain model, the uncertain element is denoted as g _hk , the method provided by the present invention is applicable to the case where there is only one uncertain element in the process gain matrix.

Step 3. According to the uncertain elements and system disturbance variables, the upper boundary of the output constrained reachable set and the lower bound of the output constrained reachable set are obtained.

The output constraint reachable set LOKD is defined as follows:

LOKD(G,d)＝{y|y＝Gu+G _d d; u∈SIC, g _hk ∈Δ, G _d is a fixed value}

where g _hk is the uncertain element in the process gain matrix G, expressed as form,

The upper bound LOKDSJ of the output constrained reachable set is defined as follows:

The lower bound LOKDXJ of the output constrained reachable set is defined as follows:

In step 3, the steps to calculate the upper boundary of the output constraint reachable set are as follows:

Step 3-a-1, find and Record them as L _s,max and L _s,min respectively, and find the intersection I _s of L _s,max and L _s,min , and the midpoint CI of the intersection I _s .

Step 3-a-2, find and They are recorded as L _{s, maxe} and L _{s, mine} respectively, and e is a positive real number (within the acceptable precision range, e is a positive real number close to 0, usually e takes 10 ^-4 ). , Find the intersection I se1 of L _s,max and L _s,mine , and the midpoint CI _e1 of the intersection I se1; the intersection I _se2 of L _s,min and L _{s,maxe ,} and the midpoint CI _{e2 of} the intersection I _se2 ;

Step 3-a-3, respectively calculate the distance between each vertex of L _s,max and CI, and the distance between each vertex of L _s,max and CI _e1 , if the distance between a vertex of L _s,max and CI _e1 is greater than the distance between the vertex and CI distance, then the vertex is a vertex in the upper boundary LOKDSJ of the output constraint reachable set, with Indicates the set of all vertices belonging to LOKDSJ in L _s,max ;

Step 3-a-4, respectively calculate the distance between each vertex of L _{s, min} and CI, and the distance between each vertex of L _{s, min} and CI _e2 , if the distance between a vertex of L _{s, min} and CI _e2 is greater than the distance between the vertex and CI distance, then the vertex is a vertex in the upper boundary LOKDSJ of the output constraint reachable set, with Indicates the set of all vertices belonging to LOKDSJ in L _s,min ;

Step 3-a-5, output the vertex set of the upper boundary LOKDSJ of the constrained reachable set as { I _s vertices, }, the polyhedron formed by the vertices of LOKDSJ is the upper boundary of the output constrained reachable set.

Taking a two-dimensional system as an example, the calculation of the upper boundary of the reachable set with output constraints is shown in Figures 1 to 4. L _{s, max} and L _{s, min} are each a straight line, and the two ends of L _{s, max} are A and B, the two ends of L _s,min are C and D respectively, the intersection of L _s,max and L _s,min is I _s , the intersection of L _s,max and L _s,mine is I _se1 , for a two-dimensional system In terms of , I _s and I _se1 are both a point, so the middle point is itself, as shown in Figure 2, the distance from A to CI _e1 on L _s,max is greater than the distance from A to CI, so A is the vertex of LOKDSJ, And the distance from B to CI _e1 is smaller than the distance from B to CI, so B is not the vertex of LOKDSJ.

As shown in Figure 3, the distance from D to CI' _e1 on L _s,min is greater than the distance from D to CI', so D is the vertex of LOKDSJ, but D is not the vertex of LOKDSJ. As shown in Figure 4, the set of vertices of LOKDSJ is {A, I _s , D}.

Taking the three-dimensional system as an example, as shown in Fig. 5, L _s,max is a rectangular plane, the four vertices are A, B, C, D respectively, I _s and I _se1 are straight lines, and the midpoint of I _s is The midpoint of CI and I _se1 is CI _e1 , and the distances between A, B, C, D and CI, and the distances between points A, B, C, D and CI _e1 are calculated respectively, and the vertices of LOKDSJ include points A and B.

Similarly, in step 3, the steps to calculate the lower boundary of the output constraint reachable set are as follows:

Step 3-b-1, find and Respectively recorded as L' _{s, max} and L' _{s, min} , find the intersection I' _s of L' _{s, max} and L' _{s, min} , and the midpoint CI' of the intersection I'_s;

Step 3-b-2, find and Respectively recorded as L' _{s, maxe} and L' _{s, mine} , e is a positive real number, find the intersection I' _se1 of L' _{s, max} and L' _{s, mine} , and the midpoint CI' _e1 of the intersection I'_se1; The intersection I' _se2 of L' _{s, min} and L' _{s, maxe} , and the midpoint CI' _e2 of the intersection I'_se2;

Step 3-b-3, respectively calculate the distance between each vertex of L' _s,max and CI', and the distance between each vertex of L' _s,max and CI' _e1 , if L' _s,max a vertex and CI' _e1 is greater than the distance between the vertex and CI', then the vertex is a vertex in the lower boundary LOKDXJ of the output constraint reachable set, with Represents the set of all vertices belonging to LOKDXJ in L'_s,max;

Step 3-b-4, respectively calculate the distance between each vertex of L' _{s, min} and CI', and the distance between each vertex of L' _{s, min} and CI' _e2 , if L' _{s, min,} a certain vertex and CI' _e2 is greater than the distance between the vertex and CI', then the vertex is a vertex in the lower boundary LOKDXJ of the output constraint reachable set, with Represents the set of all vertices belonging to LOKDXJ in L'_s,min;

Step 3-b-5, output the vertex set of the lower boundary LOKDXJ of the constrained reachable set as { I- _'s apex, }, the polyhedron formed by the vertices of LOKDXJ is the lower boundary of the output constrained reachable set.

Step 4, according to the upper boundary of the output constraint reachable set and the lower boundary of the output constraint reachable set, the output constraints of the dynamic optimization control layer are obtained.

The dynamic optimization control layer output constraint LOJX is defined as follows:

LOJX(α)＝{y|b ₁ ≤yy ₀ ≤b ₂ }

y ₀ =[y ₀₁ y ₀₂ ...y _0n ] ^T , y=[y ₁ y ₂ ...y _n ] ^T

In the formula: w ₁ w ₂ ...w _n is the weight decided by the user himself, which is 1 by default, and r=w _n :w _n-1 :...:w ₂ :w ₁ is used to set the weight, y ₀ is the nominal steady state value of the process and y is the system output.

In step 4, the steps for calculating the output constraints of the dynamic optimization control layer are as follows:

Step 4-1, using an iterative algorithm (see document Fernando V.Lima, Christos Georgakis, Design of output constraints for model-based non-square controllers using interval operability. Journal of Process Control 18(2008) 610–620) to obtain α _{+ 1} and α _-1 , so that LOJX(α ₊₁ ) is tangent to LOKDSJ at v ₊₁ ; LOJX(α _-1 ) is tangent to LOKDXJ at v _-1 ;

Step 4-2, record v ₊₁ ＝[y ₁₊ y ₂₊ ... y _n+ ], v _-1 ＝[y _1- y _2- ... y _n- ], then the dynamic optimization control layer output constraint LOJX is:

LOJX={y|min(v ₊₁ ,v _-1 ) _≤yy0≤max (v ₊₁ ,v _-1 )}.

LOJX is a polyhedron that is exactly tangent to both LOKDSJ and LOKDXJ. Tangent in the present invention means that LOJX has only one intersection with LOKDSJ and LOKDXJ respectively.

Simulation example

As shown in Figure 6, it is an acetone rectification column system, which has 4 inputs and 6 outputs, and the physical meanings represented by the inputs and outputs are shown in Table 1.

Table 1

output physical meaning enter physical meaning XD.PV The quality of the overhead product of the distillation column R.SP Distillation column top reflux XB.PV Product quality of distillation tower bottom S.SP Distillation column reboiler steam T1.PV Distillation column top temperature D.SP Extraction from the top of the distillation column T2.PV Rectification tower bottom temperature B.SP Extraction from distillation tower bottom LD.PV Distillation column reflux tank liquid level LB.PV Distillation column bottom liquid level

The gain model of the system steady-state optimization layer is:

and satisfied y ₀ =[0.390, -2.220, -0.120, 0.750, 0.197, -0.078].

Select the weight r=1:3:4:6:8:8, and the design constraints obtained by using the method shown in the present invention and the traditional method are shown in Table 2.

Table 2

output XD.PV XB.PV T1.PV T2.PV LD.PV LB.PV Constrained Lower Boundary 1.563 0.043 1.578 1.702 0.805 0.251 constrained upper bound -2.070 -4.266 -1.318 -0.273 -0.410 -0.117

Configure on the computer Under Core ^TM i7-4600UCPU@2.10GHz2.70GHZ condition, the method shown in the present invention finds LOKDSJ and LOKDXJ only used 0.831s, and traditional method then used 1224.315s, can see that the method shown in the present invention greatly improves Time is saved, and when the method of the present invention obtains the upper boundary of the output constraint reachable set and the lower boundary of the output constraint reachable set, the calculation speed is hardly affected by the system dimension, while the time spent by the traditional method is It will grow rapidly with the increase of system dimension.

Claims (2)

1. a kind of quick design method of industry dynamic optimization control layer output constraint of distilling acetyl ketone, it is characterized in that, comprises: Step 1, aiming at the industrial model predictive control system for the distillation of propylene glycol with a double-layer structure, the gain model of the steady-state optimization layer of the model predictive controller is obtained as follows: y=Gu+G _d d In the formula: y is the n×1-dimensional system output, y∈SOC; G is an n×m dimensional process gain matrix; u is an m×1-dimensional system input variable, u∈SIC; Gd is an interference gain matrix of n×p dimension; d is the system disturbance variable of p×1 dimension, d∈DWC; <mrow><mi>S</mi><mi>I</mi><mi>C</mi><mo>=</mo><mo>{</mo><mi>u</mi><mo>|</mo><msubsup><mi>u</mi><mi>i</mi><mi>min</mi></msubsup><mo>&amp;le;</mo><msub><mi>u</mi><mi>i</mi></msub><mo>&amp;le;</mo><msubsup><mi>u</mi><mi>i</mi><mi>max</mi></msubsup><mo>;</mo><mn>1</mn><mo>&amp;le;</mo><mi>i</mi><mo>&amp;le;</mo><mi>m</mi><mo>}</mo><mo>;</mo></mrow> <mrow><mi>S</mi><mi>O</mi><mi>C</mi><mo>=</mo><mo>{</mo><mi>y</mi>><mo>|</mo><msubsup><mi>y</mi><mi>i</mi><mi>min</mi></msubsup><mo>&amp;le;</mo><msub><mi>y</mi><mi>i</mi></msub><mo>&amp;le;</mo><msubsup><mi>y</mi><mi>i</mi><mi>max</mi></msubsup><mo>;</mo><mn>1</mn><mo>&amp;le;</mo><mi>i</mi><mo>&amp;le;</mo><mi>n</mi><mo>}</mo><mo>;</mo></mrow> <mrow><mi>D</mi><mi>W</mi><mi>C</mi><mo>=</mo><mo>{</mo><mi>d</mi><mo>|</mo><msubsup><mi>d</mi><mi>i</mi><mi>min</mi></msubsup><mo>&amp;le;</mo><msub><mi>d</mi><mi>i</mi></msub><mo>&amp;le;</mo><msubsup><mi>d</mi><mi>i</mi><mi>max</mi></msubsup><mo>;</mo><mn>1</mn><mo>&amp;le;</mo><mi>i</mi><mo>&amp;le;</mo><mi>p</mi><mo>}</mo><mo>;</mo></mrow> Step 2, find the uncertain elements in the process gain matrix in the gain model; Step 3. Obtain the upper boundary of the output constrained reachable set and the lower bound of the output constrained reachable set according to the uncertain elements and system disturbance variables; The output constraint reachable set LOKD is defined as follows: LOKD(G,d)＝{y|y＝Gu+Gdd; u∈SIC, g _hk ∈Δ, G _d is a fixed value} <mrow><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mo>=</mo><munder><mrow><mi></mi><mo>&amp;cup;</mo></mrow><mrow><msub><mi>g</mi><mrow><mi>h</mi><mi>k</mi></mrow></msub><mo>&amp;Element;</mo><mi>&amp;Delta;</mi></mrow></munder><munder><mrow><mi></mi><mo>&amp;cup;</mo></mrow><mrow><mi>d</mi><mo>&amp;Element;</mo><mi>D</mi><mi>W</mi><mi>C</mi></mrow></munder><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mrow><mo>(</mo><mi>G</mi><mo>,</mo><mi>d</mi><mo>)</mo></mrow></mrow> where g _hk is the uncertain element in the process gain matrix G, expressed as form, The upper bound LOKDSJ of the output constrained reachable set is defined as follows: <mfenced open = "" close = ""><mtable><mtr><mtd><mrow><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mi>S</mi><mi>J</mi><mo>=</mo><msub><mi>C</mi><mrow><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mrow><mo>(</mo><mrow><mo>&amp;ForAll;</mo><msub><mi>g</mi><mrow><mi>h</mi><mi>k</mi></mrow></msub><mo>&amp;Element;</mo><mi>&amp;Delta;</mi><mo>,</mo><mi>d</mi><mo>=</mo><msup><mi>d</mi><mi>max</mi></msup></mrow><mo>)</mo></mrow></mrow></msub><mo>(</mo><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mo>(</mo><mo>&amp;ForAll;</mo><msub><mi>g</mi><mrow><mi>h</mi><mi>k</mi></mrow></msub><mo>&amp;Element;</mo><mi>&amp;Delta;</mi><mo>,</mo><mi>d</mi></mrow></mtd></mtr><mtr><mtd><mrow><mo>=</mo><msup><mi>d</mi><mi>max</mi></msup><mo>)</mo><mo>&amp;cap;</mo><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mo>(</mo><mrow><mo>&amp;ForAll;</mo><msub><mi>g</mi><mrow><mi>h</mi><mi>k</mi></mrow></msub><mo>&amp;Element;</mo><mi>&amp;Delta;</mi><mo>,</mo><mi>d</mi><mo>&amp;NotEqual;</mo><msup><mi>d</mi><mi>max</mi></msup></mrow><mo>)</mo><mo>)</mo></mrow></mtd></mtr></mtable></mfenced> The lower bound LOKDXJ of the output constrained reachable set is defined as follows: <mrow><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mi>X</mi><mi>J</mi>><mo>=</mo><msub><mi>C</mi><mrow><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mrow><mo>(</mo><mo>&amp;ForAll;</mo><msub><mi>g</mi><mrow><mi>h</mi><mi>k</mi></mrow></msub><mo>&amp;Element;</mo><mi>&amp;Delta;</mi><mo>,</mo><mi>d</mi><mo>=</mo><msup><mi>d</mi><mrow><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msup><mo>)</mo></mrow></mrow></msub><mo>(</mo><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mo>(</mo><mo>&amp;ForAll;</mo><msub><mi>g</mi><mrow><mi>h</mi><mi>k</mi></mrow></msub><mo>&amp;Element;</mo><mi>&amp;Delta;</mi><mo>,</mo><mi>d</mi></mrow> <mrow><mo>=</mo><msup><mi>d</mi><mrow><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msup><mo>)</mo><mo>&amp;cap;</mo><mi>L</mi><mi>O</mi><mi>K</mi><mi>D</mi><mo>(</mo><mrow><mo>&amp;ForAll;</mo><msub><mi>g</mi><mrow><mi>h</mi><mi>k</mi></mrow></msub><mo>&amp;Element;</mo><mi>&amp;Delta;</mi><mo>,</mo><mi>d</mi><mo>&amp;NotEqual;</mo><msup><mi>d</mi><mrow><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msup></mrow><mo>)</mo><mo>)</mo></mrow> In step 3, the steps to calculate the upper boundary of the output constraint reachable set are as follows: Step 3-a-1, find and Record them as L _s,max and L _s,min respectively, and find the intersection I _s of L _s,max and L _s,min , and the midpoint CI of the intersection I _s ; Step 3-a-2, find and Respectively recorded as L _{s, maxe} and L _{s, mine} , e is a positive real number, find the intersection I _se1 of L _{s, max} and L _{s, mine} , and the midpoint CI _e1 of the intersection I _se1 ; L _{s, min} and L _{s, the intersection I se2 of maxe ,} and the midpoint CI _e2 of the intersection I _se2 ; Step 3-a-3, respectively calculate the distance between each vertex of L _s,max and CI, and the distance between each vertex of L _s,max and CI _e1 , if the distance between a vertex of L _s,max and CI _e1 is greater than the distance between the vertex and CI distance, then the vertex is a vertex in the upper boundary LOKDSJ of the output constraint reachable set, with Indicates the set of all vertices belonging to LOKDSJ in L _s,max ; Step 3-a-4, respectively calculate the distance between each vertex of L _{s, min} and CI, and the distance between each vertex of L _{s, min} and CI _e2 , if the distance between a vertex of L _{s, min} and CI _e2 is greater than the distance between the vertex and CI distance, then the vertex is a vertex in the upper boundary LOKDSJ of the output constraint reachable set, with Indicates the set of all vertices belonging to LOKDSJ in L _s,min ; Step 3-a-5, output the vertex set of the upper boundary LOKDSJ of the constrained reachable set as The polyhedron formed by the vertices of LOKDSJ is the upper boundary of the reachable set of output constraints; The steps to calculate the lower bound of the output constrained reachable set are as follows: Step 3-b-1, find and Respectively recorded as L' _{s, max} and L' _{s, min} , find the intersection I' _s of L' _{s, max} and L' _{s, min} , and the midpoint CI' of the intersection I'_s; Step 3-b-2, find and Respectively recorded as L' _{s, maxe} and L' _{s, mine} , e is a positive real number, find the intersection I' _se1 of L' _{s, max} and L' _{s, mine} , and the midpoint CI' _e1 of the intersection I'_se1; The intersection I' _se2 of L' _{s, min} and L' _{s, maxe} , and the midpoint CI' _e2 of the intersection I'_se2; Step 3-b-3, respectively calculate the distance between each vertex of L' _s,max and CI', and the distance between each vertex of L' _s,max and CI' _e1 , if L' _s,max a vertex and CI' _e1 is greater than the distance between the vertex and CI', then the vertex is a vertex in the lower boundary LOKDXJ of the output constraint reachable set, with Represents the set of all vertices belonging to LOKDXJ in L' _{s, max} ; Step 3-b-4, respectively calculate the distance between each vertex of L' _{s, min} and CI', and the distance between each vertex of L' _{s, min} and CI' _e2 , if L' _{s, min,} a certain vertex and CI' _e2 is greater than the distance between the vertex and CI', then the vertex is a vertex in the lower boundary LOKDXJ of the output constraint reachable set, with Represents the set of all vertices belonging to LOKDXJ in L'_s,min; Step 3-b-5, output the vertex set of the lower boundary LOKDXJ of the constrained reachable set as The polyhedron formed by the vertices of LOKDXJ is the lower boundary of the reachable set of output constraints; Step 4, according to the upper boundary of the output constraint reachable set and the lower boundary of the output constraint reachable set, the output constraints of the dynamic optimization control layer are obtained. 2. the fast design method of industry dynamic optimization control layer output constraint of distilling ketone as claimed in claim 1, it is characterized in that, dynamic optimization control layer output constraint LOJX is defined as follows: LOJX(α)＝{y|b ₁ ≤yy ₀ ≤b ₂ } <mrow><msub><mi>b</mi><mn>1</mn></msub><mo>=</mo><msup><mfenced open = "[" close = "]"><mtable><mtr><mtd><mfrac><mrow><mo>-</mo><mi>&amp;alpha;</mi></mrow><msub><mi>w</mi><mn>1</mn></msub></mfrac></mtd><mtd><mfrac><mrow><mo>-</mo><mi>&amp;alpha;</mi></mrow><msub><mi>w</mi><mn>2</mn></msub></mfrac></mtd><mtd><mn>...</mn></mtd><mtd><mfrac><mrow><mo>-</mo><mi>&amp;alpha;</mi></mrow><msub><mi>w</mi><mi>n</mi></msub></mfrac></mtd></mtr></mtable></mfenced><mi>T</mi></msup><mo>,</mo><msub><mi>b</mi><mn>2</mn></msub><mo>=</mo><msup><mfenced open = "[" close = "]"><mtable><mtr><mtd><mfrac><mi>&amp;alpha;</mi><msub><mi>w</mi><mn>1</mn></msub></mfrac></mtd><mtd><mfrac><mi>&amp;alpha;</mi><msub><mi>w</mi><mn>2</mn></msub></mfrac></mtd><mtd><mn>...</mn></mtd><mtd><mfrac><mi>&amp;alpha;</mi><msub><mi>w</mi><mi>n</mi></msub></mfrac></mtd></mtr></mtable></mfenced><mi>T</mi></msup></mrow> y ₀ =[y ₀₁ y ₀₂ ...y _0n ] ^T , y=[y ₁ y ₂ ...y _n ] ^T In the formula: w ₁ w ₂ ...w _n is the weight; y ₀ is the nominal steady-state value of the process, and y is the system output; In step 4, the steps for calculating the output constraints of the dynamic optimization control layer are as follows: Step 4-1, using iterative algorithm to obtain α ₊₁ and α _-1 , so that LOJX(α ₊₁ ) is tangent to LOKDSJ, and the tangent point is v ₊₁ ; LOJX(α _-1 ) is tangent to LOKDXJ, and the tangent point is for v _-1 ; Step 4-2, record v ₊₁ ＝[y ₁₊ y ₂₊ ... y _n+ ], v _-1 ＝[y _1- y _2- ... y _n- ], then the dynamic optimization control layer output constraint LOJX is: LOJX={y|min(v ₊₁ ,v _-1 ) _≤yy0≤max (v ₊₁ ,v _-1 )}.