CN101825870A - Method and system for controlling supply quantity of water-treatment flocculating agent - Google Patents
Method and system for controlling supply quantity of water-treatment flocculating agent Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and a system for controlling the supply quantity of a water-treatment flocculating agent. The method comprises the following steps of: selecting the primary function of an undetermined coefficient as the increment of the control input of a next moment on the basis of the control input of a current moment, and presetting the increment of the control input of each posterior moment as the product of the increment of the control input of a previous moment and a preset genetic factor; obtaining the prediction output of each moment by a prediction model and obtaining a prediction output track simultaneously; fitting the prediction output track and an expected output track to obtain the coefficient of the primary function so as to obtain the control input of the next moment; and carrying out on-line correction on the prediction model by a neural network and then entering a next cyclic process. The system disclosed by the embodiment of the invention comprises a control input module, a prediction output module, an optimization module and a correction module. By the method and the system which are disclosed by the invention, the supply quantity of the water-treatment flocculating agent can be accurately and stably controlled.
Description
Technical field
The present invention relates to water-treatment technology field, more particularly, relate to a kind of method and system of controlling supply quantity of water-treatment flocculating agent.
Background technology
Along with the progress of society and the fast development of industry, people also are faced with the severe contamination of threat, particularly water after physical environment is destroyed when enjoying high-quality life.Water is Source of life; and the assurance of life especially of the quality of water; in order to ensure people's healthy living; country goes into overdrive to carry out work such as water resource protection, wastewater treatment, water purification; quality to water has proposed requirements at the higher level; this just makes water factory must improve manufacture craft, improves the quality of output water.
Water factory's water treatment procedure generally comprises water intaking, flocculation, precipitation, filtration, sterilization several links, and flocculation process is the important step of purification of water quality, it not only has influence on the overall process of water treatment, but also be the important component part of water producing cost, to the flocculation link optimization can improve the quality of water factory's water outlet greatly.The quality of water treatment effect depends on directly whether in time flocculating agent is thrown in, accurately, threw in that I haven't seen you for ages and made and flocculated insufficiently, input too much will cause waste, thereby raises the cost.
Mostly water factory's flocculation process is to adopt artificial gravity and in-line pump directly to add flocculating agent both at home and abroad at present, and what of control injected volume this dosing mode be difficult to, thereby the waste that easily produces medicament can't guarantee effluent quality.In this case, water technology must be intelligent, finishes automatic control dispensing process by computer technology, the communication technology and control technology.The Automatic Dosage Control of Additives method of existing domestic employing mainly contains mathematical model method, simulation, streaming current method, printing opacity pulsation method and pattern-recognition method etc.Adopt the streaming current method easily the electrode of current detecting instrument to be polluted, be difficult to accurately detect streaming current, can't make correct response thereby cause; Pattern-recognition method is owing to be subjected to the influence of factors such as flow velocity, turbidity, camera lens under water when taking, and practical application effect is unsatisfactory.
What research was maximum now will count mathematical model method, but because flocculation sediment is physics, the chemical process of a complexity, the influence that how much is subjected to several factors such as time lag, environment, water quality of flocculating agent injected volume, therefore, adopting mathematical model method to set up before the filter forecast model of water turbidity also is quite to be not easy and its precision also differs and guarantees surely; And, because time stickiness, the time variation of flocculation sediment, can be because the variation of factors such as water quality, environment make the variation that the model set up can not the procedure of adaptation, thus cause the forecast model mismatch, reduce controlling performance.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of method and system of controlling supply quantity of water-treatment flocculating agent, with the precision that solves the flocculating agent injected volume, the time stickiness problem, and this control strategy can adapt to the variation of water quality, improves controlling performance.
For achieving the above object, the invention provides following technical scheme:
A kind of method of controlling supply quantity of water-treatment flocculating agent, concrete steps are: on the basis of current time control input, choose the basis function with undetermined coefficient and control the increment of input constantly as next, the increment that default each increment of controlling input constantly later on is previous moment control input multiply by gene given in advance; By forecast model to current time and following constantly filter before water turbidity predict output, obtain the track of a prediction output; Determine the track of a desired output by the expectation value of water turbidity before the prediction of current time output and the given filter; The track of described prediction output and the track of desired output are carried out match, try to achieve the coefficient of basis function, thereby draw next control input constantly according to the principle of least square; By neural network described forecast model is carried out online correction, enter next cycle control process then.
Preferably, by forecast model to current time and following constantly filter before water turbidity predict output, and error compensation is carried out in described prediction output, obtain a prediction output trajectory after the compensation.
Preferably, describedly by neural network described forecast model is carried out online correction and be specially: neural network is carried out online correction according to the residual sum water quality parameter to described forecast model; Wherein, described residual error is the difference of prediction output and actual output; Described water quality parameter is flow, turbidity, pH value or the temperature of water.
Preferably, described prediction output comprises freely responding and being forced to and responds; Wherein, described freely the response is the model response that is caused by previous moment control input, and described to be forced to respond be the model response that the increment by current time control input causes.
Preferably, described basis function is a step function.
Preferably, described gene is the number less than 1.
The present invention also provides a kind of system that controls supply quantity of water-treatment flocculating agent, and described system comprises: control load module, prediction output module, optimal module and correcting module; Wherein: described control load module is used for choosing the basis function of undetermined coefficient and controls the increment of input constantly as next, default simultaneously after each increment of controlling input constantly be that the increment of previous moment control input multiply by gene given in advance; Described prediction output module is used for water turbidity before the following filter constantly under current time and the effect of control load module is predicted output, obtains the track of a prediction output simultaneously; Described optimal module is used for determining according to the expectation value of water turbidity before the prediction of current time output and the given filter track of a desired output, and the track that described prediction is exported and the track of desired output carry out match, thereby tries to achieve the coefficient of basis function; Described correcting module is used for by neural network described prediction output module being carried out online correction.
Preferably, described prediction output module specifically comprises prediction output sub-module and compensation submodule; Wherein: described prediction output sub-module is used for water turbidity before the following filter constantly under current time and the effect of control load module is predicted output; Described compensation submodule is used for error compensation is carried out in the prediction output that is obtained by described prediction output sub-module, obtains the track of the prediction output after the compensation simultaneously.
This shows, the technical scheme that the embodiment of the invention provided, on the basis of current time control input, choose the basis function of undetermined coefficient and control the increment of input constantly, and each increment of controlling input constantly is that the increment that previous moment control is imported multiply by gene given in advance after default as next; Obtain each prediction output constantly by forecast model, obtain a prediction output trajectory simultaneously, described prediction output trajectory and desired output track are carried out match, try to achieve the undetermined coefficient of basis function, and then the control that draws next moment is imported, by neural network forecast model is carried out online correction simultaneously, enter next cyclic process then.The embodiment of the invention has genic basis function is controlled flocculating agent as the increment of control input injected volume owing to presetting, preferably, described gene is set to the number less than 1, so make the increment of flocculating agent injected volume weaken gradually with the increase in the prediction moment, and strengthen the degree of fitting of objective function, meet the requirement that approaches of reference locus the prediction time domain.And the embodiment of the invention is carried out online correction by neural network to described forecast model, thereby makes this model can adapt to the change of water quality, environment etc., the situation of control fails can not occur, and adaptability is strong.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention, to do simple introduction to the accompanying drawing of required use among the embodiment below, apparently, accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is a kind of method flow diagram of controlling supply quantity of water-treatment flocculating agent provided by the present invention;
Fig. 2 is the method flow diagram of another kind of control supply quantity of water-treatment flocculating agent provided by the present invention;
Fig. 3 is a kind of principle schematic of controlling supply quantity of water-treatment flocculating agent provided by the present invention;
Fig. 4 is a kind of structural representation of controlling supply quantity of water-treatment flocculating agent provided by the present invention;
Fig. 5 is the structural representation after having increased a neural network on the basis of Fig. 4;
Fig. 6 is the structural representation of a kind of neural network provided by the present invention;
Fig. 7 is the system schematic of a kind of supply quantity of water-treatment flocculating agent provided by the present invention;
Fig. 8 is the system schematic of another kind of supply quantity of water-treatment flocculating agent provided by the present invention;
Fig. 9 is the general structure synoptic diagram of a kind of supply quantity of water-treatment flocculating agent provided by the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills are not making under the creative work prerequisite, and the every other embodiment that is obtained belongs to protection domain of the present invention.
In water treatment procedure, the water outlet quality that how much is directly connected to water factory of flocculating agent injected volume.The embodiment of the invention is based on the injected volume that Predictive function control (PFC) realizes intelligent control flocculating agent.Described Predictive function control (PFC) is a kind of advanced control algorithm based on model, and it decides its optimum control effect by the output of prediction controlled device and in conjunction with rolling optimization.
The PFC most important is exactly the control input to be regarded as the linear combination of one group of basis function.Among the conventional P FC, the selection of basis function depends primarily on the characteristic of reference locus and controll plant, generally can be taken as step function, ramp function and exponential function etc.These basis functions are overall situation function, and their mathematical meaning is clear and definite, it is simple to calculate, but objective function often is reduced to that optimization to prediction time domain terminal point requires or one or two match point wherein, have sacrificed the global optimization target of whole prediction time domain.Therefore, conventional P FC can only carry out match to limited several match points in the prediction time domain and approach, fail to consider whole prediction time domain global error optimization in Properties, it is not very good that the integral body of reference locus is approached performance, reduced the ability that the deviation that model mismatch causes was disturbed and eliminated in system anti-load.
Generally speaking, in control procedure, to reference locus approach requirement should with prediction constantly increase and weaken gradually, utilize these characteristics to adopt certain aggregate measure with the assembly variable replacing of original optimization variable with one group of low-dimensional, then can significantly reduce the on-line optimization calculated amount, improve the rapidity of algorithm, thereby satisfy the purpose of real-time optimization.
Existing approach thought based on Fourier and adopt sinusoidal polynomial function, the performance that can be well controlled and the scope of application as basis function.But sine function is an overall situation function, can't according to reference locus approach requirement with prediction constantly increase and the characteristics that weaken gradually are provided with flexibly, therefore need to select have the local function of tight supportive as basis function.And adopt the saturation function approximation method to be difficult to obtain analytical expression.Have, available small echo is as basis function again, and in certain prediction time domain, to the demanding period of approximation accuracy, selecting thin yardstick small echo is basis function; Approximation accuracy is required the not too high period, is basis function with thick yardstick small echo.So both guaranteed that global optimization target and current time controlled quentity controlled variable to next approximation accuracy requirement of reference locus constantly, had reduced the basis function number again as far as possible, thereby reduced weight coefficient number to be optimized, realized the assembly of optimization variable.But neither be easily to choosing of wavelet basis, selecting what wavelet basiss and which selects plant wavelet basis does not have good foundation, can only try to gather relatively to obtain the basis function of the suitable wavelet basis of a combination as PFC.
Based on this, with reference to figure 1, be a kind of method embodiment 1 that controls supply quantity of water-treatment flocculating agent that the embodiment of the invention provided, described method specifically comprises the steps:
Step 101: on the basis of current time control input, choose the basis function with undetermined coefficient and control the increment of input constantly as next, the increment that default each increment of controlling input constantly later on is previous moment control input multiply by gene given in advance.
The embodiment of the invention is chosen undetermined coefficient on the basis of current time dosage basis function is controlled the injected volume of flocculating agent as next increment of controlling input constantly, here, described current time dosage can be given in advance one smaller dosage, perhaps is considered as zero.
Basis function described in the embodiment of the invention is a step function, and the step function that this step function is compared conventional P FC has increased a gene given in advance, after default each increment of controlling input constantly be previous moment control input increment gene doubly, even described gene becomes the common ratio that each controls the Geometric Sequence that the input increment forms constantly.
Choose gene for less than 1 number, can realize following each constantly the increment of the flocculating agent injected volume increment of comparing previous moment reducing gradually, promptly having genic basis function weakens gradually along with the increase in the prediction moment, can strengthen the degree of fitting of objective function like this, meet the requirement that approaches of reference locus the prediction time domain.
Step 102: by forecast model to current time and following constantly filter before water turbidity predict output, obtain the track of a prediction output.
Before the control flocculating agent is thrown in, at first set up a forecast model, described forecast model is used for water turbidity before the current time and the filter in the following moment is predicted output.Described prediction output is made up of two parts, is respectively freely to respond and be forced to response; Wherein, described freely the response is the model response that is caused by the control of previous moment input, and described to be forced to respond be the model response that is caused by the control input that current time increases newly, and this two-part model response stack is the prediction output that forecast model draws.In step, described forecast model all obtains a prediction output according to the control input in each step at optimal control time domain H, and these prediction outputs can be formed a curve, claim the track of this curve for prediction output.The time domain of control described in the present embodiment H step is artificial control, can determine the numerical value of H as the case may be.
Step 103: the track of determining a desired output by the expectation value of water turbidity before the prediction of current time output and the given filter.
In water treatment procedure, we can an at first given filter before the expectation value of water turbidity, by the injected volume of control flocculating agent so that before the filter behind the flocculation sediment water turbidity progressively near or reach described expectation value.But control injected volume of flocculating agent and non-once just can make that water turbidity reaches expectation value before the filter, control procedure always one make and filter preceding water turbidity slowly near the process of expectation value.Here, the expectation value of water turbidity can be determined the reference locus of a desired output before the prediction output of the current time that is drawn by forecast model and the given filter, in optimal control time domain H goes on foot, regulate the control input by having genic basis function as the increment of control input, prediction output meeting moves closer to the reference locus of desired output with prediction increase constantly under the effect of described control input.
Step 104: the track of described prediction output and the track of desired output are carried out match, try to achieve the coefficient of basis function, thereby draw next control input constantly according to the principle of least square.
The track of the prediction output that will obtain by step 102 and carry out match by the reference locus of the determined desired output of step 103, promptly choose an evaluation function, described evaluation function can be the difference of desired output and prediction output, according to the principle of least square described evaluation function is carried out the coefficient that differentiate can be tried to achieve basis function, thereby draw next control input constantly.Under the situation that next control input is constantly determined, here the increment of each control input constantly after can not determining according to gene described in the step 101, but the increment that the basis function that should choose new undetermined coefficient on next controls the basis of input is constantly again imported as following control constantly enters next cyclic process.
Step 105: by neural network described forecast model is carried out online correction, execution in step 101 then.
Before entering next cyclic process, also should carry out online correction to described forecast model by neural network.Because the complexity of flocculation sediment process changes, can make forecast model depart from the dispensing process at leisure, it is more and more lower to make forecast model meet the degree of dispensing process, at last after forecast model is offset to a boundary, the PFC control fails, therefore should in time revise forecast model in the control procedure, so that it can adapt to the variation of water quality.In the concrete control procedure, can carry out online correction to described forecast model according to the residual sum water quality parameter; Wherein, described residual error is the difference of prediction output and actual output; Described water quality parameter comprises the temperature of pH value, water of turbidity, the water of flow, the water of water or dosage etc.After described forecast model revised, execution in step 101 entered in the next cycle control.
From the foregoing description as can be seen, the present invention has adopted to have genic step function and regulates the control input as the increment of control input, by forecast model to current time and following constantly filter before water turbidity predict output, will prediction output and desired output compare the control that just can try to achieve next moment and import.The embodiment of the invention is regulated the control input owing to take to have genic step function, thereby makes the increment of flocculating agent injected volume weaken gradually with the increase in the prediction moment, meets the requirement that approaches of reference locus; And the embodiment of the invention adopts neural network that forecast model is carried out online correction, has strengthened the adaptability of forecast model to control procedure.
For the easier quilt of technical solution of the present invention is understood, describe in detail with a specific embodiment below.With reference to figure 2, be a kind of method embodiment 2 that controls supply quantity of water-treatment flocculating agent that the embodiment of the invention provided, present embodiment specifically can comprise the steps:
Step 201: on the basis of current time control input, choose the basis function with undetermined coefficient and control the increment of input constantly as next, the increment that default each increment of controlling input constantly later on is previous moment control input multiply by gene given in advance.
In specific implementation process, can choose different basis functions according to water factory's actual conditions, can regulate the injected volume of flocculating agent by the linear combination of basis function as the increment of control input.Choosing basis function in the present embodiment is a step function ε, because step function is unit function, so we can think that ε is a weight coefficient to be asked, and chooses step function and can make and calculate simply, realizes quick computing.The current time dosage is given u (k) in the present embodiment, and wherein, k is a current time, and then next is that k+1 control constantly is input as constantly:
u(k+1)＝u(k)+ε (1)
Present embodiment has added gene δ given in advance on the basis of step function ε, in the PFC process, the default increment of each moment flocculating agent injected volume later on is gene δ a times of previous moment flocculating agent injected volume increment, and promptly the injected volume of k+2 moment flocculating agent is:
u(k+2)＝u(k+1)+δε＝u(k)+ε+δε (2)
The k+3 injected volume of flocculating agent constantly is:
u(k+3)＝u(k+2)+δ
2ε＝u(k)+ε+δε+δ
2ε (3)
By that analogy, then in control time domain H went on foot, the default H injected volume of flocculating agent constantly was:
u(k+H)＝u(k+H-1)+δ
H-1ε＝u(k)+ε+δε+δ
2ε+…+δ
H-1ε (4)
By top relational expression as can be seen, in the step, default each increment of flocculating agent injected volume is constantly formed a Geometric Sequence at control time domain H, and the common ratio of described Geometric Sequence promptly is gene δ.Select gene δ for less than 1 number herein, can guarantee to have genic basis function with prediction constantly increase and progressively weaken, meet the requirement that approaches of reference locus.
Step 202: set up forecast model.
Choose undetermined coefficient, have genic step function ε after, need set up a forecast model to current time and following constantly filter before water turbidity predict output.The mathematic(al) representation of the forecast model of being set up in the present embodiment is:
y
m(k+i)＝f(u(k+i)) (5)
Wherein, f (x) is the functional expression of described forecast model, and u (k+i) is illustrated in the default constantly control input of k+i, and k represents current time, and the span of i be from 1 to H, then formula (5) expression to filter constantly in future before the prediction of water turbidity export.
Step 203: by forecast model to current time and following constantly filter before water turbidity predict output.
In the control time domain H step, having provided each step in the following moment in the step 201 all has a default control input u (k+i), and forecast model is used for making the default control input u (k+i) in each step to obtain prediction output under the effect of function f (x).The prediction output in described each step includes two parts content, and a part is freely to respond, and another part is to be forced to response; Wherein, described freely respond be by the past be the model response that flocculating agent that previous step added causes constantly, it is described that to be forced to respond be to pass by the model response that the injected volume of newly-increased flocculating agent constantly causes relatively by current time, this two parts model response value and promptly be forecast model to the default control input action prediction output of water turbidity before the filter down.
Here also should import u (k) to the control of current time and predict output, to make things convenient for the reference locus of determining desired output in the step 205 by forecast model.
Step 204: error compensation is carried out in described prediction output, obtain the track of the prediction output after the compensation simultaneously.
Before the filter that obtains by described forecast model the prediction of water turbidity output always because time lag, the time change, environment, water quality etc. former thereby with the reality filter before the output of water turbidity be not inconsistent, for this reason, we carry out error compensation to described prediction output, so that its more realistic output.
By forecast model current time control input u (k) is predicted that output obtains y
m(k), consider the reason of time lag, water turbidity y before the filter that current time is recorded
m(k) carry out error compensation and obtain y
p(k), y
p(k) the formula that embodies is:
y
p(k)＝y(k)+y
m(k)-y
m(k-L) (6)
Wherein, y (k) is a water turbidity before the actual measured filter of current time, and L is the retardation time that current time is exerted an influence, y
m(k-L) be k-L prediction output constantly.
When error compensation is carried out in the prediction output in the moment in future, at first calculate the difference of the prediction output of the actual output of current time and current time, add this difference in future on the prediction output basis constantly then, can reduce each prediction output and actual error of exporting constantly so effectively, make the more approaching reality of described prediction output export, concrete mathematic(al) representation is:
y
p(k+i)＝y
m(k+i)+e(k+i) (7)
e(k+i)＝y(k)-y
m(k) (8)
Wherein, y
p(k+i) expression k+i is constantly to the prediction output after future, prediction output constantly compensated, the current k of e (k+i) the expression actual output constantly and the difference of prediction output, the span of i from 1 to H.
Prediction output by water turbidity before the filter after current time and the following compensation constantly can be drawn a curve, the prediction output trajectory after this curve is referred to as to compensate.
Step 205: the track of determining a desired output by the expectation value of water turbidity before output of the prediction after the current time compensation and the given filter.
In water treatment procedure, always preestablish an expectation value, choosing the preceding water turbidity expectation value of filter in the embodiment of the invention is Tu, can not make the prediction output y after current time compensates in the control procedure with settling at one go
p(k) be issued to expectation value Tu in next control input action constantly, As time goes on control procedure always makes filters preceding water turbidity convergence expectation value Tu gradually.Therefore by the prediction output y after the current time compensation
p(k) and expectation value Tu given in advance can determine the reference locus of a desired output, the formula that embodies of reference locus described in the present embodiment is:
y
r(k+i)＝(1-e
-i)Tu+e
-iy
p(k) (9)
In the formula, Tu is the preceding water turbidity setting value of filter, and this value is a determined value given in advance; y
p(k) be the prediction output of current k after compensating constantly, the formula of embodying is seen formula (6); The span of i from 1 to H; E is a natural logarithm.
Can find out under the situation that overhead control time H determines that by formula (9) reference locus of desired output has just had the two ends point value of determining, and then according to the variation of control time i from 1 to H, the whole piece reference locus has just been determined also.
With reference to figure 3, be a kind of principle schematic of controlling supply quantity of water-treatment flocculating agent that the embodiment of the invention provided.The control principle of PFC is exactly in H (optimization time domain) goes on foot, the controlled quentity controlled variable increment in each step is all definite, they all are the linear combination of one group of basis function, and from the first step to H (optimization time domain) step increment variation certain rule is arranged, we can say that it is to change with an acceleration under a speed; So under given reference locus, make before the filter water turbidity along or progressively arrive expectation value near reference locus, try to achieve the linear combination coefficient of basis function according to this principle, try to achieve last controlled quentity controlled variable with this.
Being the separatrix constantly with current k among the figure is divided into the The whole control time domain in the past and following two parts, and the control procedure time in future is from k to k+H.U is illustrated in the control input curve under the effect with genic step function among the figure; Ym represents that water turbidity is predicted before forecast model is to control input action filter down and exports the curve that obtains; Yp carries out estimation curve of output after the error compensation to the prediction that obtained by described forecast model output ym, and the expression formula of yp is seen formula (7), and wherein, the difference between yp and the ym is offset e, and the expression formula of described offset e is seen formula (8); Yr is the reference locus of desired output, and the formula of embodying is seen formula (9).Control procedure is exactly the prediction curve of output yp process of convergence desired output reference locus yr gradually that makes after the compensation.
Step 206: the track of the output of the prediction after the described compensation and the track of desired output are carried out match, try to achieve the coefficient of basis function, thereby draw next control input constantly according to the principle of least square.
In order to provide an evaluation function in the embodiment of the invention in the prediction output trajectory after describing compensation on the mathematics and the coincidence degree of desired output track, the formula of embodying is:
The J of evaluation function described in the present embodiment is optimizing time domain H in the step, the quadratic sum of the difference of the prediction output after each step desired output and the compensation, the value of described evaluation function J levels off to zero more, show prediction output after the compensation more near desired output, this situation is the control input that we want.Therefore according to the principle of least square described evaluation function J is carried out differentiate, gets extreme value, and then can try to achieve the weight coefficient ε of step function, thereby draw next injected volume of flocculating agent constantly exactly.
Control constantly under the definite situation of input at next, each controls the increment of input constantly after can not determining according to gene described in the step 201, but next should be controlled input constantly as known dosage, and then calculate following control input constantly by next cyclic process.
With reference to figure 4, be a kind of structural representation of controlling supply quantity of water-treatment flocculating agent that the embodiment of the invention provided.The characteristics of PFC maximum are exactly to control input structureization, select suitable basis function, just can obtain the control input that we want according to the linear combination of basis function.There is shown the cyclic process of the dosing of under the regulating action of basis function, flocculating, the amount of the flocculating agent constantly thrown in for current k of u (k) wherein, y (k) is the preceding water turbidity of the current k actual filter that records constantly, control constantly under the acting in conjunction of input at basis function and current k, forecast model is to predicting output in the following H step optimization time domain, and then draw model value, described model value carries out error compensation through actual delivery turbidity y (k) and obtains the output of process generalized object, described process generalized object output and desired output carry out match, calculate next control input u (k+1) constantly by the principle of least square, u (k+1) as the known dosage in the next cyclic process, and then is asked k+2 control input constantly.
Step 207: by neural network described forecast model is carried out online correction, execution in step 201 then.
Because flocculation sediment is physics, the chemical process of a complexity, therefore, be used for predicting that the forecast model of the preceding water turbidity of filter can slowly depart from the dispensing process along with the variation of factors such as environment, water quality, be forecast model meet the degree of dispensing process can be more and more lower, when forecast model was offset to a boundary, PFC control will be lost efficacy.
In order to make forecast model can adapt to the variation of water quality, environment etc., need carry out online correction to forecast model.By neural network forecast model is carried out online correction in the embodiment of the invention.With reference to figure 5, for having increased a kind of structural representation of controlling supply quantity of water-treatment flocculating agent after the neural network.Fig. 5 has increased a neural network on the basis of Fig. 4, this neural network can be carried out online correction to described forecast model according to the residual sum water quality parameter, the adaptability of forecast model is strengthened, thereby do not made control fails.Here, described residual error is the difference of prediction output and actual output, and described water quality parameter comprises the turbidity of pH value, water of temperature, the water of flow, the water of water or dosage etc.
The principle of utilizing neural network that forecast model is revised is: must exist certain linearity or nonlinear funtcional relationship between residual error and the model parameter, be P with function representation
t=F (e
t).But simply can't guarantee the grade of fit of model parameter, so will add that also water quality parameter guarantees with residual error correction.Suppose that residual sequence is designated as: E=(e
1, e
1..., e
n), calculate the prediction model parameters value with the value of residual sum water quality parameter, then funtcional relationship becomes: P=F (e, X).Go to approach this funtcional relationship with neural network model, can fully improve the nonlinear prediction precision of data.
With reference to figure 6, be the structural representation of a kind of neural network that the embodiment of the invention provided.This figure is a BP three-layer neural network structural drawing, comprises input layer, hidden layer and output layer; Wherein, input layer and hidden layer are necessary layers, and the input layer number is the input number of samples, and the output layer neuron number is the output sample number; Hidden layer is a variable layer, and its neuron number is determined by particular problem, needs could determine after constantly adjustment reaches precision.
In the embodiment of the invention, neural network is revised forecast model according to the residual sum water quality parameter; Described water quality parameter comprises the flow of water, the temperature of water, the pH value of water, turbidity and 5 factors of dosage of water, adds the residual error factor, so the input layer number is 6, certainly, different according to circumstances input layer numbers also can be different; The output layer neuron number is the number of parameters of forecast model.Wherein, the input sample is the residual sum water quality parameter, chooses n group input sample data; The output sample sequence is a prediction model parameters, also chooses the n group, and herein, model parameter is the model parameter by the forecast model of the water quality parameter input sample data foundation of correspondence.Constantly adjust weights W through the BP neural network BP training algorithm
IjWith threshold value V
j, and manual adjustments hidden layer neuron number reaches the final weights W of determining after the precision of forecasting model
Ij, threshold value V
iWith the hidden layer neuron number.
When using neural metwork training, at first whole samples are made normalized, one group of sample of picked at random offers network then, calculates the neuronic weights W of each hidden layer and output layer
IjWith threshold value V
j, next group sample of picked at random carries out same training then, finishes up to n sample training.From n sample, choose one group of sample training again, up to network global error E less than predetermined minimal value, i.e. network convergence; If frequency of training is greater than predefined value, then network can't be restrained.After training finishes, obtain a model parameter sequence:
The number of prediction model parameters is 14 in the embodiment of the invention, and described model parameter number also can be different according to circumstances and different.The model parameter sequence that obtains with training replaces original model parameter, can obtain model more accurately, and be applied among the PFC this moment, makes the control better effects if.
Certainly, step 207 is not limited thereto, as long as factors such as environment, water quality change, can revise forecast model in real time.Before the next cyclic process of beginning, forecast model is carried out online correction in the present embodiment, execution in step 201 after revising.
Present embodiment has been set forth technical scheme of the present invention by comparatively detailed example, relative the foregoing description, present embodiment can not only satisfy requirement weakens and described forecast model is carried out online correction with prediction increase constantly the requirement that approaches to reference locus, and, therefore can control the injected volume of flocculating agent more exactly owing to error compensation has been carried out in the prediction output that forecast model is recorded.
The embodiment of the invention also discloses a kind of system that controls supply quantity of water-treatment flocculating agent, with reference to figure 7, be a kind of system embodiment 1 of controlling supply quantity of water-treatment flocculating agent, system specifically comprises described in the present embodiment: control load module 701, prediction output module 702, optimal module 703 and correcting module 704.
Wherein, described control load module 701 is used for choosing the basis function of undetermined coefficient and controls the increment of input constantly as next, default simultaneously after each increment of controlling input constantly be that the increment of previous moment control input multiply by gene given in advance.
In the specific implementation process, choose and have genic basis function is regulated flocculating agent as the increment of control input injected volume.Choose gene in the present embodiment for less than 1 number, make like this flocculating agent injected volume that basis function is controlled increment in time increase and successively decrease, the requirement of satisfying reference locus that approaches weakens with prediction increase constantly, and can strengthen the degree of fitting of objective function to the prediction time domain.
Described prediction output module 702 is used for water turbidity before the following filter constantly under current time and 701 effects of control load module is predicted output, obtains the track of a prediction output simultaneously.
Under the effect of the constantly given control input u (k) of current k, obtain the prediction output that current k filters preceding water turbidity constantly by prediction output module 702; Simultaneously, import under the effect of u (k+i) in the 701 default following controls constantly of control load module, obtain following prediction output of filtering preceding water turbidity constantly by prediction output module 702, these prediction output composition curves constantly in future, this curve is referred to as to predict the track of output.
Described optimal module 703 is used for determining according to the expectation value of water turbidity before the prediction of current time output and the given filter track of a desired output, and the track that described prediction is exported and the track of desired output carry out match, thereby tries to achieve the coefficient of basis function.
In water treatment procedure, always preestablish an expectation value, described expectation value is the desired output of the preceding water turbidity of filter behind the flocculation sediment, but because input flocculating agent and non-once just can make the preceding water turbidity of filter reach expectation value, input makes it move closer to expectation value but need repeatedly.Therefore, can determine the track of a desired output, so that make passing that water turbidity before the filter of throwing in after the flocculating agent can be in time progressively approaching or reach expectation value according to the expectation value of water turbidity before the prediction of current time output and the given filter.
Described optimal module 703 is at first determined the reference locus of a desired output according to the expectation value of water turbidity before the prediction of current time output and the given filter, to carry out match by prediction output trajectory and the desired output track that described prediction output module 702 obtains then, try to achieve the undetermined coefficient of basis function according to the principle of least square, and then can draw next injected volume of flocculating agent constantly.The injected volume of next moment flocculating agent is imported as known control, entered cyclic process next time.
Described correcting module 704 is used for by neural network described prediction output module 702 being carried out online correction.
Because flocculation sediment is physics, the chemical process of a complexity, therefore As time goes on, the variation of factor such as environment, water quality can make prediction output module 702 slowly depart from the dispensing process, and when prediction output module 702 departed from the dispensing process and reaches a boundary, PFC control will be lost efficacy.Therefore in order to make prediction output module 702 can adapt to the variation of water quality, environment etc., need revise it.
By neural network prediction output module 702 is carried out online correction by correcting module 704 in the present embodiment.In control procedure, it is a lot of to make prediction output module 702 depart from the reason of dispensing process, and wherein topmost is the residual sum water quality parameter, so neural network is carried out online correction according to the residual sum water quality parameter to described prediction output module 702.Wherein, described residual error is the difference of prediction output and actual output, and described water quality parameter comprises the turbidity of pH value, water of temperature, the water of flow, the water of water or dosage etc.Revised prediction output module 702 continues water turbidity before the current time and the filter in the following moment is predicted output.
The system that present embodiment provided has genic basis function is regulated flocculating agent as the increment of control input injected volume owing to having adopted in described control load module 701, and described gene is set to the number less than 1, make the increment of flocculating agent injected volume weaken like this, thereby satisfy the requirement that approaches reference locus with prediction increase constantly; And the system that present embodiment provided has adopted 704 pairs of prediction output modules of correcting module 702 to carry out online correction, has strengthened the adaptability of 702 pairs of control procedures of described prediction output module.
With reference to figure 8, be a kind of system embodiment 2 of controlling supply quantity of water-treatment flocculating agent that the embodiment of the invention provided, system specifically comprises described in the present embodiment: control load module 701, prediction output sub-module 801, compensation submodule 802, optimal module 703 and correcting module 704.The relative the foregoing description of present embodiment, adopt described prediction output sub-module 801 and 802 two submodules of compensation submodule to finish the function of prediction output module 702, roughly the same in other module and the foregoing description, below simple prediction output sub-module 801 and the compensation submodule 802 introduced.
Described prediction output sub-module 801 is used for water turbidity before the following filter constantly under current time and 701 effects of control load module is predicted output.
Described compensation submodule 802 is used for error compensation is carried out in the prediction output that is obtained by described prediction output sub-module 801, obtains the track of the prediction output after the compensation simultaneously.
The prediction output that obtains by described prediction output sub-module 801 always because time lag, the time change, environment, water quality etc. former thereby with the reality filter before the output of water turbidity be not inconsistent, therefore, need carry out error compensation to the prediction output that prediction output sub-module 801 obtains, so that its more realistic output.
Prediction output to current time compensates, and needs to consider the reason of time lag.By current k is brought constantly influence retardation time L control input predict and output obtain y
m(k-L).Actual output and prediction output sum by current time deduct the prediction output that is obtained by the time lag reason again, are the prediction output after the current time compensation.
Prediction output constantly in future is compensated the difference of the prediction output of the actual output that needs to calculate earlier current time and current time, all add this difference in the prediction in each step output afterwards then, so just can reduce the error between each step prediction output and the reality output effectively, make following prediction constantly export more approaching actual output.
Obtain a prediction output trajectory after the compensation according to the output of the prediction after the compensation.
Need to prove, optimal module described in the present embodiment 703 is used for determining according to the expectation value of water turbidity before output of the prediction after the current time compensation and the given filter track of a desired output, and prediction output trajectory after the described compensation and desired output track carried out match, thereby try to achieve the coefficient of basis function; Described correcting module 704 is used for described compensation submodule 801 is carried out online correction.
The system that present embodiment provided requires and prediction output sub-module 801 is carried out the online correction approaching of reference locus except satisfying, also owing to the prediction output of having adopted 802 pairs of described prediction output sub-modules 801 of compensation submodule to obtain compensates, therefore, can control the injected volume of flocculating agent more exactly.
With reference to figure 9, be a kind of general structure synoptic diagram of controlling supply quantity of water-treatment flocculating agent that the embodiment of the invention provided.There is shown water turbidity forecast model and genetic algorithm PFC-control; Wherein, comprise soft measuring process and pure lag model in the water turbidity forecast model; Genetic algorithm PFC-control is to have genic basis function is regulated flocculating agent as the increment of control input injected volume.Under the effect of the determined controlled quentity controlled variable u of current time, finish flocculation sediment at reaction tank, settling basin on the one hand, and measure actual delivery turbidity; On the other hand water turbidity before the filter of current time is predicted output, at first controlled quentity controlled variable u is carried out the adjustment of data, to eliminate because the appreciable error that flow, turbidity, pH value or the temperature etc. of reaction tank Central Plains water cause, controlled quentity controlled variable u after calibrated is calculated the prediction output of current time by soft measuring process, the prediction output of described current time is further eliminated hysteresis error by pure lag model, and the prediction output of the current time after the elimination hysteresis error and the difference of actual delivery turbidity are the error of current time.Genetic algorithm PFC-control is regulated following control input constantly by having genic basis function, and described following control input constantly calculates following prediction output constantly through the soft measuring process of water turbidity forecast model.Described following prediction output is constantly compensated by the error of current time again, and then the following turbidity predicted value constantly after being compensated.Described following turbidity predicted value and turbidity setting value constantly compares, match, can calculate next controlled quentity controlled variable constantly according to least square method, thereby enter next cyclic process.
The control system that the embodiment of the invention provided is a kind of control system based on the PFC control strategy, forecast model in the PFC control is according to the difference of water factory's process condition and difference to some extent, and the difference of model structure makes the forecast model of foundation also can be different, so can both use at this as long as meet the model that control requires.
Be owing between prediction model parameters and the residual error corresponding relation is arranged to forecast model correction by neural network in the embodiment of the invention, so according to this principle, can both be applied to this for some other model, fuzzy model, expert system etc., as long as can reach the purpose of revising forecast model and correction effect than good can both the substituting of neural network.
Be understandable that the present invention can be used in numerous general or special purpose computingasystem environment or the configuration.For example: personal computer, server computer, handheld device or portable set, plate equipment, multicomputer system, the system based on microprocessor, set top box, programmable consumer-elcetronics devices, network PC, small-size computer, mainframe computer, comprise distributed computing environment of above any system or equipment or the like.
The present invention can describe in the general context of the computer executable instructions of being carried out by computing machine, for example program module.Usually, program module comprises the routine carrying out particular task or realize particular abstract, program, object, assembly, data structure or the like.Also can in distributed computing environment, put into practice the present invention, in these distributed computing environment, by by communication network connected teleprocessing equipment execute the task.In distributed computing environment, program module can be arranged in the local and remote computer-readable storage medium that comprises memory device.
Need to prove, in this article, relational terms such as first and second grades only is used for an entity or operation are made a distinction with another entity or operation, and not necessarily requires or hint and have the relation of any this reality or in proper order between these entities or the operation.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thereby make and comprise that process, method, article or the equipment of a series of key elements not only comprise those key elements, but also comprise other key elements of clearly not listing, or also be included as this process, method, article or equipment intrinsic key element.Do not having under the situation of more restrictions, the key element that limits by statement " comprising ... ", and be not precluded within process, method, article or the equipment that comprises described key element and also have other identical element.
For system embodiment, because it corresponds essentially to method embodiment, so relevant part gets final product referring to the part explanation of method embodiment.System embodiment described above only is schematic, wherein said unit as the separating component explanation can or can not be physically to separate also, the parts that show as the unit can be or can not be physical locations also, promptly can be positioned at a place, perhaps also can be distributed on a plurality of network element.Can select wherein some or all of module to realize the purpose of present embodiment scheme according to the actual needs.Those of ordinary skills promptly can understand and implement under the situation of not paying creative work.
To the above-mentioned explanation of the disclosed embodiments, make this area professional and technical personnel can realize or use the present invention.Multiple modification to these embodiment will be conspicuous concerning those skilled in the art, and defined herein General Principle can realize under the situation that does not break away from the spirit or scope of the present invention in other embodiments.Therefore, the present invention will can not be restricted to these embodiment shown in this article, but will meet and principle disclosed herein and features of novelty the wideest corresponding to scope.
Claims (8)
1. a method of controlling supply quantity of water-treatment flocculating agent is characterized in that, comprising:
A, on the basis of current time control input, choose basis function and control the increment of input constantly as next with undetermined coefficient, default after each increment of controlling input constantly be that the increment of previous moment control input multiply by gene given in advance;
B, by forecast model to current time and following constantly filter before water turbidity predict output, obtain the track of a prediction output;
C, determine the track of a desired output by the expectation value of water turbidity before the prediction of current time output and the given filter;
D, the track of described prediction output and the track of desired output are carried out match, try to achieve the coefficient of basis function according to the principle of least square, thereby draw next control input constantly;
E, described forecast model is carried out online correction, then execution in step a by neural network.
2. method according to claim 1 is characterized in that, described step b further comprises, error compensation is carried out in described prediction output.
3. method according to claim 1 is characterized in that, described step e is specially: neural network is carried out online correction according to the residual sum water quality parameter to described forecast model, then execution in step a;
Wherein:
Described residual error is the difference of prediction output and actual output;
Described water quality parameter is flow, turbidity, pH value or the temperature of water.
4. according to each described method of claim 1～3, it is characterized in that described prediction output comprises freely responding and being forced to and responds; Wherein, described freely the response is the model response that is caused by previous moment control input, and described to be forced to respond be the model response that the increment by current time control input causes.
5. according to each described method of claim 1～3, it is characterized in that described basis function is a step function.
6. according to each described method of claim 1～3, it is characterized in that described gene is the number less than 1.
7. a system that controls supply quantity of water-treatment flocculating agent is characterized in that, comprising: control load module, prediction output module, optimal module and correcting module;
Wherein:
Described control load module is used for choosing the basis function of undetermined coefficient and controls the increment of input constantly as next, default simultaneously after each increment of controlling input constantly be that the increment of previous moment control input multiply by gene given in advance;
Described prediction output module is used for water turbidity before the following filter constantly under current time and the effect of control load module is predicted output, obtains the track of a prediction output simultaneously;
Described optimal module is used for determining according to the expectation value of water turbidity before the prediction of current time output and the given filter track of a desired output, and the track that described prediction is exported and the track of desired output carry out match, thereby tries to achieve the coefficient of basis function;
Described correcting module is used for by neural network described prediction output module being carried out online correction.
8. system according to claim 9 is characterized in that, described prediction output module specifically comprises prediction output sub-module and compensation submodule;
Wherein:
Described prediction output sub-module is used for water turbidity before the following filter constantly under current time and the effect of control load module is predicted output;
Described compensation submodule is used for error compensation is carried out in the prediction output that is obtained by described prediction output sub-module, obtains the track of the prediction output after the compensation simultaneously.
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