CN101794117B - Method for optimizing and controlling operation of membrane bioreactor based on mechanism model - Google Patents

Abstract

The invention relates to a method for optimizing and controlling operation of membrane bioreactor based on mechanism model, relating to the sewage treatment system process target control field. The invention solves the problem that self optimization control of the existing membrane bioreactor based on experience is lack of effective control, no-noise signal is input to a DPS model to obtain a predicted optimization path of control variable and control target; noise-containing signal is input to a NMPC model, and meanwhile input quantity set u and output quantity set y are obtained combining the predicted optimization path, input parameter is selected from the input quantity set u and is input to a basic control BC module, actual input parameter ubc is obtained, when the output target under the actual input parameter ubc accords with preset target y, the input parameter is selected as operation parameter of the membrane bioreactor, and optimization and control on operation of the membrane bioreactor is completed. The invention is applicable to effective control of sewage treatment system.

Description

Method for optimizing and controlling operation of membrane bioreactor based on mechanism model

Technical Field

The invention relates to the field of process target control of a sewage treatment system, in particular to a method for optimally controlling the operation of a membrane bioreactor based on a mechanism model.

Background

The Membrane Bioreactor (MBR) appears under the background of global water resource shortage, is a brand-new biological sewage treatment process, and has attracted extensive attention in the industry and hopefully since the birth of MBR for 15 years due to its excellent effluent quality, efficient sludge-water separation and small occupied space.

However, compared with the outstanding advantages of the MBR process, the disadvantages of membrane pollution are not negligible, how to realize effective regulation and control of the MBR system and slow down the occurrence of the membrane pollution phenomenon has become a popular subject in the research field of MBR, however, until now, people still know very little about the MBR membrane pollution mechanism, the complex action between biological phase and membrane filtration, etc., and at present, the methods for MBR optimal regulation and control are mostly established on the basis of the experience of the traditional biological treatment process and are realized by the univariate cycle control and the self-optimization control of the optimal input and output matching. Specifically, the method for regulating and controlling the biological phase to move the traditional activated sludge is only slightly changed in the aspects of sludge concentration and the like, indexes such as dissolved oxygen, ammonia nitrogen and the like are regulated in a PID control mode, and the operation of the membrane module is guided by parameters provided by a manufacturer or suggestions of experienced operators. The disadvantage of this approach is that it assumes that the biological processes of MBR and traditional activated sludge processes are considered to be similar, and therefore the resulting optimized parameters are difficult to really adapt to MBR processes.

Disclosure of Invention

In order to solve the problem that the self-optimization control of the existing membrane bioreactor based on experience is lack of effective regulation and control, the invention provides a method for optimally controlling the operation of a membrane bioreactor based on a mechanism model.

The invention relates to a method for optimizing and controlling the operation of a membrane bioreactor based on a mechanism model, which comprises the following specific processes:

the method comprises the following steps: obtaining an initial state x of an operating membrane bioreactor₀And initial model parameter p of activated sludge system model ASM in membrane bioreactor₀；

Step two: will be in the initial state x₀And initial model parameters p₀Inputting an extended Kalman Filter EKF model, the extended Kalman Filter EKF model corresponding to the initial state x₀And initial model parameters p₀Estimating to obtain the initial state of the membrane bioreactor without noise

And initial model parameters without noise

While obtaining a noisy initial stateAnd initial mode of noiseForm parameter

Step three: will not contain noise initial state

And initial model parameters without noise

Inputting the data into a dynamic prediction set DPS model which is set with a membrane bioreactor operation preset target y, and adopting a separation modeling thought and a mixed logic dynamic optimization MLDO model to control the membrane bioreactor operation

And membrane bioreactor operation control variable

Performing dynamic real-time optimization on the D-RTO to obtain the operation control variable of the membrane bioreactor

Membrane bioreactor operation control target

The predicted optimization trajectory of (1);

step four: in-membrane bioreactor operation control objective

Selecting control variables in the vicinity of the preset target y of the predicted optimization trajectory

And will include the initial state of noise

And initial model parameters including noise

Adding the model into a nonlinear model predictive control NMPC model, and obtaining the operation input parameters of the membrane bioreactor by adopting a model predictive control MPC model

Step five: inputting the operation parameters of the membrane bioreactor

Inputting the operation parameters into a basic control BC module which inputs the operation input parameters of the membrane bioreactorThe real-time transmission of the real-time parameter u to the membrane bioreactor is carried out without difference, and the actual parameter u is simultaneously transmitted to the membrane bioreactor_reFeedback to the basic control BC module

At the moment, the membrane bioreactor outputs the operation output target of the membrane bioreactor

Step six: judging the operation output target of the membrane bioreactor

If yes, executing step eight, otherwise executing step seven;

step seven: operating the membrane bioreactor at the current input parameters

The state x and model parameters p under the condition are respectively defined as a new initial state x₀And new initial model parameters p₀Go back to executionStep two;

step eight: outputting the operation of the membrane bioreactor to a target

Corresponding membrane bioreactor operation input parameter

Is defined as the operation parameter of the membrane bioreactor, and completes the optimization control of the operation of the membrane bioreactor.

The invention has the beneficial effects that: the method optimizes and controls the operation parameters of the membrane bioreactor in real time by setting a DPS model, a nonlinear model predictive control NMPC model and an extended Kalman filtering EKF model through dynamic prediction, and is more suitable for the membrane bioreactor process than the existing free control established on the basis of experience; in the invention, a user can automatically set the operation preset target of the membrane bioreactor in the dynamic prediction set DPS model according to the requirement.

Drawings

Fig. 1 is a flow chart of a method for optimizing and controlling the operation of a membrane bioreactor based on a mechanism model, fig. 2 is a schematic diagram of a setting result of an operation scheme in a dynamic prediction setting DPS model, fig. 3 is a schematic diagram of discretization characterization of a nonlinear model predictive control NMPC model, and fig. 4 is a schematic diagram of a working principle of a fourth embodiment.

Detailed Description

The first embodiment is as follows: the embodiment is specifically illustrated in the attached drawing 1 of the specification, and the method for optimizing and controlling the operation of the membrane bioreactor based on the mechanism model comprises the following specific processes:

the method comprises the following steps: obtaining an initial state x of an operating membrane bioreactor₀And initial model parameter p of activated sludge system model ASM in membrane bioreactor₀；

Step two: will be in the initial state x₀And initial model parameters p₀Inputting an extended Kalman Filter EKF model, the extended Kalman Filter EKF model corresponding to the initial state x₀And initial model parameters p₀Estimating to obtain the initial state of the membrane bioreactor without noise

And initial model parameters without noise

While obtaining a noisy initial stateAnd initial model parameters including noise

Step three: will not contain noise initial state

And initial model parameters without noise

Inputting the data into a dynamic prediction set DPS model which is set with a membrane bioreactor operation preset target y, and adopting a separation modeling thought and a mixed logic dynamic optimization MLDO model to control the membrane bioreactor operation

And membrane bioreactor operation control variable

Performing dynamic real-time optimization on the D-RTO to obtain the operation control variable of the membrane bioreactor

Membrane bioreactor operation control targetThe predicted optimization trajectory of (1);

step four: in-membrane bioreactor operation control objective

Selecting control variables in the vicinity of the preset target y of the predicted optimization trajectoryAnd will include the initial state of noise

And initial model parameters including noise

Adding the model into a nonlinear model predictive control NMPC model, and obtaining the operation input parameters of the membrane bioreactor by adopting a model predictive control MPC model

Step five: inputting the operation parameters of the membrane bioreactor

Inputting the operation parameters into a basic control BC module which inputs the operation input parameters of the membrane bioreactor

The real-time transmission of the real-time parameter u to the membrane bioreactor is carried out without difference, and the actual parameter u is simultaneously transmitted to the membrane bioreactor_reFeedback to the basic control BC module

At this time, the filmBioreactor output membrane bioreactor operation output target

Step six: judging the operation output target of the membrane bioreactorIf yes, executing step eight, otherwise executing step seven;

step seven: operating the membrane bioreactor at the current input parametersThe state x and model parameters p under the condition are respectively defined as a new initial state x₀And new initial model parameters p₀Returning to execute the step two;

step eight: outputting the operation of the membrane bioreactor to a target

Corresponding membrane bioreactor operation input parameter

Is defined as the operation parameter of the membrane bioreactor, and completes the optimization control of the operation of the membrane bioreactor.

The second embodiment is as follows: in the first step, the initial state x of the operating membrane bioreactor is obtained by performing online monitoring on the membrane bioreactor, performing offline monitoring on the membrane bioreactor or estimating by using an activated sludge system model (ASM)₀And initial model parameter p of activated sludge system model ASM in membrane bioreactor₀。

The third concrete implementation mode: the present embodiment is a modification of the first embodimentOne step illustrates that in the first specific embodiment, in the third step, the membrane bioreactor operation control target is optimized by adopting the separation modeling idea and the mixed logic dynamic optimization MLDO model

And membrane bioreactor operation control variable

The method for dynamically optimizing the D-RTO in real time comprises the following steps:

step three, firstly: selecting an optimal time range t₀≤t≤t_fAnd dividing the optimal time range into M stages, wherein the start time of the jth stage is

And is provided with

The end time of the jth stage is

And is provided with

The duration of the jth phase is

j is in the range of { 1.,....,. M }, and has

j∈{1，......，M-1}；

Step three: each stage has i operating schemes, i ∈ { 0., N }, each of which satisfies the following four conditions at each stage:

the conditions are as follows, <math> <mrow> <mn>0</mn> <mo>=</mo> <msubsup> <mi>f</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mrow> <mo>(</mo> <msup> <mover> <mi>x</mi> <mo>&CenterDot;</mo> </mover> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>x</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>y</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>u</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>d</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>p</mi> <mi>j</mi> </msup> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </math>

The second condition, <math> <mrow> <mn>0</mn> <mo>&GreaterEqual;</mo> <msubsup> <mi>g</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>y</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>u</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>d</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>p</mi> <mi>j</mi> </msup> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </math>

The third condition, $0=h_{\mathrm{eq},i}^j(x^j\left(t_f\right),y^j\left(t_f\right),u^j\left(t_f\right),d^j\left(t_f\right),p^j\left(t_f\right)),$

The condition IV, <math> <mrow> <mn>0</mn> <mo>&GreaterEqual;</mo> <msubsup> <mi>h</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>y</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>u</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>d</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>p</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </math>

According to the four conditions, the initial state without noise is introduced

And initial model parameters without noise

Obtaining the objective function of each scheme in the j stage <math> <mrow> <msubsup> <mi>&phi;</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>=</mo> <msubsup> <mi>&phi;</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>y</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>u</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>d</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>p</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </mrow> </math> Wherein,representing the equation of state of the ith solution at the jth stage,representing the differential state quantity, x, of the j-th stage^jRepresents the state quantity of the j stage, y^jOutput quantity, u, representing the j-th stage^jRepresenting the input of the j stage, d^jRepresenting the noise of the j stage, p^jThe model parameters representing the jth stage are,

the inequality path constraint equation representing the jth stage,

the equation node representing the ith scheme at the jth stage constrains the equations,

representing an inequality node constraint equation of the ith scheme in the jth stage;

step three: introducing Boolean parameter Y_i ^jE { True, False }, and is in accordance with

Obtaining the optimal scheme i selected from the i schemes in the j stage^*Is an objective function of

i ∈ { 0.,. N }, where,

indicating whether the ith scheme is selected in the jth stage, if soIf the value is True, selecting the ith scheme in the jth stage, otherwise, not selecting the ith scheme in the jth stage, wherein V represents exclusive operation;

step three and four: obtaining a global optimization objective function

And satisfy algebraic expressions within an optimal time range <math> <mrow> <msubsup> <mi>q</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>q</mi> <mn>2</mn> <mi>j</mi> </msubsup> <mo>+</mo> <mi>K</mi> <mo>+</mo> <msubsup> <mi>q</mi> <mi>N</mi> <mi>j</mi> </msubsup> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>&Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mi>K</mi> <mo>,</mo> <mi>M</mi> <mo>}</mo> <mo>,</mo> </mrow> </math> Solving an objective function simultaneously

Obtaining the operation control target of the membrane bioreactor

And membrane bioreactor operation control variable

Completing the control of the target

And a control variableIn real time, wherein,

representation and Boolean parameters

A related binary variable, if

If the value is True, the corresponding binary variable is obtained

Is 1, otherwise, the corresponding binary variableThe value of (d) is 0.

In the embodiment, the abstract relationship between the control target and the control parameter is explored by adopting a separate modeling idea and a hybrid logic dynamic optimization (MLDO) model. All control parameters are sequenced in sequence and optimized by a programmed control target. Because different optimization schemes can produce different results, comparison needs to be carried out on an optimization platform, for this reason, each optimization process is divided into a plurality of stages, and the length of each stage can be fixed or can have certain floating.

The result of setting the operation recipe in the DPS model by dynamic prediction in this embodiment is shown in fig. 2.

The fourth concrete implementation mode: the first embodiment is further explained, in the fourth embodiment, the model predictive control MPC model is adopted to obtain the operation input parameters of the membrane bioreactor

The method comprises the following steps:

step four, firstly: introducing the amount of time interval Δ t_cDiscretizing the optimization time of the non-linear model predictive control NMPC model, wherein the optimization time of the non-linear model predictive control NMPC model is the selected control variableCorresponding time, and dividing the optimized time of the non-linear model predictive control NMPC model into N time intervals of delta t_cThe discrete time index is:

t_k＝t_N，0+k·Δt_c，

wherein k is in accordance with { 0.,. An, N }, t_N，0The starting time of the optimization time of the non-linear model predictive control NMPC model is represented;

step four and step two: introducing noisy initial states

And initial model parameters including noise

And according to the expression of model predictive control MPC model under discrete condition

0＝f(x_k+1，y_k，u_k，p_k，d_k)，

Obtaining at each discrete time index t_kInput amount u of_kAnd an output y_k，

Step four and step three: according to the obtained index t at each discrete time_kInput amount u of_kAnd an output y_kObtaining a set of inputs over an optimization time of a non-linear model predictive control (NMPC) modelu＝[(u₀)^T，K(u_k)^T，K，(u_N)^T]^TAnd output quantity sety＝[(y₀)^T，Λ(y_k)^T，(y_N)^T]^T；

Step four: setting dynamic predictions to control variables on a predictive optimization trajectory of a DPS model over an optimization time of a non-linear model predictive control NMPC model

And control target

Are respectively defined as discrete control variables

And discrete control targets

And obtaining the difference value of the input parameters of the nonlinear model predictive control NMPC model and the dynamic prediction setting DPS model in the optimization time of the nonlinear model predictive control NMPC model

And difference of output parameters

And there is:

y _min≤y≤y _max，

u _min≤u≤u _max，

<math> <mrow> <mi>&Delta;</mi> <msubsup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>min</mi> <mi>d</mi> </msubsup> <mo>&le;</mo> <mi>&Delta;</mi> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>&le;</mo> <mi>&Delta;</mi> <msubsup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>max</mi> <mi>d</mi> </msubsup> <mo>,</mo> </mrow> </math>

wherein, Deltau ^dRepresenting input parameter difference u obtained by non-linear model predictive control NMPC model in two times^dA difference of (d);

step four and five: calculating a second order objective function

<math> <mrow> <msub> <mi>&phi;</mi> <mn>2</mn> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mrow> <mo>(</mo> <msup> <munder> <mi>y</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mi>Q</mi> <msup> <munder> <mi>y</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mrow> <mo>(</mo> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mi>R</mi> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mrow> <mo>(</mo> <mi>&Delta;</mi> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mi>S&Delta;</mi> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>,</mo> </mrow> </math> And calculateWill make phi₂Obtained at a minimumuSelected as the operation input parameter of the membrane bioreactorWhere Q represents the output weight matrix, R represents the input weight matrix, and S represents the input difference weight matrix.

The operation principle of the present embodiment is shown in fig. 4.

In the present embodiment, the main purpose is to reduce the input parameters

And a control variable

Difference between and control target

And output the target

The difference between them.

In this embodiment, the NMPC model may be controlled using a linear time variable LTV model.

In this embodiment, the state x includes various components in the first row of the ASM model matrix, such as:

S_Sreadily biodegradable organic substrates, S_NH-Ammonia Nitrogen, S_NO-nitrate nitrogen content;

controlled variable

The method comprises the following parameters: temperature, dissolved oxygen, hydraulic retention time, sludge retention time (and sludge age), and adjusting these parameters to achieve output goals

Control of (2);

output targetThe method comprises the following steps: economic goals, ecological goals, and MBR system impact load resistance goals,

1) the economic target is as follows: the aeration rate and the membrane passing pressure value are controlled within a certain range, so that the energy consumption and the cost for washing and replacing the membrane are reduced;

2) ecological goals: the quality of the membrane filtration effluent reaches the national sewage discharge standard;

3) MBR system impact load resistance target: when the water inlet quantity and the water quality are greatly changed, the stable growth of the activated sludge in the reactor is not influenced as much as possible. Here, the F/M value, i.e., the ratio of nutrients to microorganisms, is controlled to be within a certain range. Nutrients here refer to the content of organic matter, ammonia nitrogen, nitrate nitrogen, etc.

Claims (3)

1. A method for optimizing and controlling the operation of a membrane bioreactor based on a mechanism model is characterized by comprising the following specific processes:

the method comprises the following steps: obtaining an initial state x of an operating membrane bioreactor₀And initial model parameter p of activated sludge system model ASM in membrane bioreactor₀；

Step two: will be in the initial state x₀And initial model parameters p₀Inputting an extended Kalman Filter EKF model, the extended Kalman Filter EKF model corresponding to the initial state x₀And (c) aStarting model parameter p₀Estimating to obtain the initial state of the membrane bioreactor without noise

And initial model parameters without noise

While obtaining a noisy initial state

And initial model parameters including noise

Step three: will not contain noise initial state

And initial model parameters without noise

Inputting the data into a dynamic prediction set DPS model which is set with a membrane bioreactor operation preset target y, and adopting a separation modeling thought and a mixed logic dynamic optimization MLDO model to control the membrane bioreactor operation

And membrane bioreactor operation control variable

Performing dynamic real-time optimization on the D-RTO to obtain the operation control variable of the membrane bioreactor

Membrane bioreactor operation control target

The predicted optimization trajectory of (1);

step four: in-membrane bioreactor operation control objective

Selecting control variables in the vicinity of the preset target y of the predicted optimization trajectory

And will include the initial state of noise

And initial model parameters including noise

Adding the model into a nonlinear model predictive control NMPC model, and obtaining the operation input parameters of the membrane bioreactor by adopting a model predictive control MPC model

Step five: inputting the operation parameters of the membrane bioreactor

Inputting the operation parameters into a basic control BC module which inputs the operation input parameters of the membrane bioreactor

The real-time transmission of the real-time parameter u to the membrane bioreactor is carried out without difference, and the actual parameter u is simultaneously transmitted to the membrane bioreactor_reFeedback to the basic control BC module

At the moment, the membrane bioreactor outputs the operation output target of the membrane bioreactor

Step six: judging the operation output target of the membrane bioreactor

If yes, executing step eight, otherwise executing step seven;

step seven: operating the membrane bioreactor at the current input parametersThe state x and model parameters p under the condition are respectively defined as a new initial state x₀And new initial model parameters p₀Returning to execute the step two;

step eight: outputting the operation of the membrane bioreactor to a targetCorresponding membrane bioreactor operation input parameter

Is defined as the operation parameter of the membrane bioreactor, and completes the optimization control of the operation of the membrane bioreactor.

2. The method for optimizing the operation of a membrane bioreactor based on a mechanism model according to claim 1, wherein in step three, the MLDO model is dynamically optimized to control the operation of the membrane bioreactor by adopting a separation modeling idea and a mixed logic

And membrane bioreactor operation control variable

The method for dynamically optimizing the D-RTO in real time comprises the following steps:

step three, firstly: selecting an optimal time range t₀≤t≤t_fAnd dividing the optimal time range into M stages, wherein the start time of the jth stage is

And is provided with

The end time of the jth stage is

And is provided with

The duration of the jth phase is

j is in the range of {1, KK, M }, and has

j∈{1，KK，M-1}；

Step three: each stage has i operating schemes, i ∈ { 0., N }, each of which satisfies the following four conditions at each stage:

the conditions are as follows,

The second condition, <math> <mrow> <mn>0</mn> <mo>&GreaterEqual;</mo> <msubsup> <mi>g</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>y</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>u</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>d</mi> <mi>j</mi> </msup> <mo>,</mo> <msup> <mi>p</mi> <mi>j</mi> </msup> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </math>

The third condition, $0=h_{\mathrm{eq},i}^j(x^j\left(t_f\right),y^j\left(t_f\right),u^j\left(t_f\right),d^j\left(t_f\right),p^j\left(t_f\right)),$

The condition IV, <math> <mrow> <mn>0</mn> <mo>&GreaterEqual;</mo> <msubsup> <mi>h</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>y</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>u</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>d</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>p</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </math>

According to the four conditions, the initial state without noise is introduced

And initial model parameters without noise

Obtaining the objective function of each scheme in the j stage <math> <mrow> <msubsup> <mi>&phi;</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mo>=</mo> <msubsup> <mi>&phi;</mi> <mi>i</mi> <mi>j</mi> </msubsup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>y</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>u</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>d</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>,</mo> <msup> <mi>p</mi> <mi>j</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </mrow> </math> Wherein,

representing the equation of state of the ith solution at the jth stage,representing the differential state quantity, x, of the j-th stage^jRepresents the state quantity of the j stage, y^jOutput quantity, u, representing the j-th stage^jRepresenting the input of the j stage, d^jRepresenting the noise of the j stage, p^jThe model parameters representing the jth stage are,

the inequality path constraint equation representing the jth stage,

the equation node representing the ith scheme at the jth stage constrains the equations,

representing an inequality node constraint equation of the ith scheme in the jth stage;

step three: introducing Boolean parameter Y_i ^jE { True, False }, and is in accordance with

Obtaining the optimal scheme i selected from the i schemes in the j stage^*Is an objective function of

i ∈ { 0.,. N }, where,

indicating whether the ith scheme is selected in the jth stage, if so

If the value is True, selecting the ith scheme in the jth stage, otherwise, not selecting the ith scheme in the jth stage, wherein V represents exclusive operation;

step three and four: obtaining a global optimization objective function

And satisfy algebraic expressions within an optimal time range <math> <mrow> <msubsup> <mi>q</mi> <mn>1</mn> <mi>j</mi> </msubsup> <mo>+</mo> <msubsup> <mi>q</mi> <mn>2</mn> <mi>j</mi> </msubsup> <mo>+</mo> <mi>K</mi> <mo>+</mo> <msubsup> <mi>q</mi> <mi>N</mi> <mi>j</mi> </msubsup> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>&Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mi>K</mi> <mo>,</mo> <mi>M</mi> <mo>}</mo> <mo>,</mo> </mrow> </math> Solving an objective function simultaneously

Obtaining the operation control target of the membrane bioreactor

And membrane bioreactor operation control variableCompleting the control of the target

And a control variable

In real time, wherein,

representation and Boolean parameters

A related binary variable, if

If the value is True, the corresponding binary variable is obtained

Is 1, otherwise, the corresponding binary variable

The value of (d) is 0.

3. The method for optimizing the operation of a membrane bioreactor based on a mechanism model as claimed in claim 1, wherein in step four, the model predictive control MPC model is used to obtain the operation input parameters of the membrane bioreactor

The method comprises the following steps:

step four, firstly: introducing the amount of time interval Δ t_cDiscretizing the optimization time of the non-linear model predictive control NMPC model, wherein the optimization time of the non-linear model predictive control NMPC model is the selected control variableCorresponding time, and dividing the optimized time of the non-linear model predictive control NMPC model into N time intervals of delta t_cThe discrete time index is:

t_k＝t_N，0+k·Δt_c，

wherein k is in accordance with { 0.,. An, N }, t_N，0The starting time of the optimization time of the non-linear model predictive control NMPC model is represented;

step four and step two: introducing noisy initial states

And initial model parameters including noise

And according to the expression of model predictive control MPC model under discrete condition

0＝f(x_k+1，y_k，u_k，p_k，d_k)，

Obtaining at each discrete time index t_kInput amount u of_kAnd an output y_k，

Step four and step three: according to the obtained index t at each discrete time_kInput amount u of_kAnd an output y_kObtaining a set of inputs over an optimization time of a non-linear model predictive control (NMPC) modelu＝[(u₀)^T，K(u_k)^T，K，(u_N)^T]^TAnd the set of output quantities y ═ y [ ("y₀)^T，Λ(y_k)^T，(y_N)^T]^T；

Step four: setting dynamic predictions to control variables on a predictive optimization trajectory of a DPS model over an optimization time of a non-linear model predictive control NMPC model

And control target

Are respectively defined as discrete control variables

And discrete control targets

And obtaining the difference value of the input parameters of the nonlinear model predictive control NMPC model and the dynamic prediction setting DPS model in the optimization time of the nonlinear model predictive control NMPC modelAnd difference of output parameters

And there is:

y _min≤y≤y _max，

u _min≤u≤u _max，

<math> <mrow> <mi>&Delta;</mi> <msubsup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>min</mi> <mi>d</mi> </msubsup> <mo>&le;</mo> <mi>&Delta;</mi> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>&le;</mo> <mi>&Delta;</mi> <msubsup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>max</mi> <mi>d</mi> </msubsup> <mo>,</mo> </mrow> </math>

wherein, Deltau ^dRepresenting the difference value of input parameters obtained by a nonlinear model predictive control NMPC model in two timesu ^dA difference of (d);

step four and five: calculating a second order objective function

<math> <mrow> <msub> <mi>&phi;</mi> <mn>2</mn> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mrow> <mo>(</mo> <msup> <munder> <mi>y</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mi>Q</mi> <msup> <munder> <mi>y</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mrow> <mo>(</mo> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mi>R</mi> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>+</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msup> <mrow> <mo>(</mo> <mi>&Delta;</mi> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mi>S&Delta;</mi> <msup> <munder> <mi>u</mi> <mo>&OverBar;</mo> </munder> <mi>d</mi> </msup> <mo>,</mo> </mrow> </math> And calculate

Will make phi₂Obtained at a minimumuSelected as the operation input parameter of the membrane bioreactor

Where Q represents the output weight matrix, R represents the input weight matrix, and S represents the input difference weight matrix.

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