CN101963785B - On-line control method for filtering process of oxidation mother liquor in production of purified terephthalic acid - Google Patents

Abstract

The method comprises the steps of selecting technological operation parameters which have influences on the solid content of filtrate in the filtering process, normalizing the technological operation parameters, performing simulation calculation by using an improved standard BP neural network to establish a filtering process model, optimizing the filtering operation parameters by using the model, performing inverse normalization on a model real-time output value, performing online correction by using an artificial analysis value of the solid content of the filtrate, thus obtaining a soft measurement value of the solid content of the filtrate, and finally performing real-time inference control on the filtering process according to the soft measurement value and the optimized filtering operation parameters. The method for controlling the oxidation mother liquor filtration process in the purified terephthalic acid production on line intelligently controls the filtration operation process parameters of the oxidation mother liquor in the purified terephthalic acid production, and can stabilize the filtration operation, reduce the solid content of the mother liquor after the filtration operation and increase the recovery rate of the terephthalic acid.

Description

The On-Line Control Method of oxidation mother liquor filter process during the pure terephthalic acid produces

Technical field

The invention belongs to the chemical reaction On-Line Control Method; Specifically be the On-Line Control Method of oxidation mother liquor filter process during a kind of pure terephthalic acid produces, the present invention relates to adopt the On-line Control of the p xylene oxidation mother liquor filter process of P-xylene (PX) liquid phase catalytic oxidation technology in pure terephthalic acid (PTA) production technological.

Background technology

At present, PTA is the polyester industrial important source material, mainly is used for the intermedium phthalic acid glycol ester (PET) of synthesizing polyester.Polyester is widely used in processing synthetic polyester fibers, coating, engineering plastics, the very big effect of performance in production and daily life.And that the primary raw material rate of growth of three big synthon is maximum is exactly PTA, is about 3-4 times of other raw material.

The synthetic history of PTA can be traced back to the '20s in last century.After the World War II, up to the present beginning industrialization research form the ripe production technology of three kinds of PTA: BP-Amoco production technology, Invista production technology and Eastman production technology.

China has introduced the PTA process units of all maturation process, but that productive capacity still is not enough to satisfy is at present domestic to the polyester growth of requirement, and international demand is also bigger, and the existing export capability of China is also not enough.Based on above-mentioned situation, domestic each macrocyclic polyester enterprise has carried out the raising the output extending capacity reformation of PTA process units in succession on the one hand, continues on the other hand to introduce and newly-built PTA device.Along with the PTA production technology is updated, scale constantly enlarges, and is domestic the digesting and assimilating, design voluntarily and transform and obtained remarkable progress aspect the industrialized PTA process units of this technology, but still exists some need to compel the problems that solve.

During producing, PTA, is provided with the oxidation mother liquor filter element in order to realize low production cost, low environment pollution.But in actual production process, still there is the more high deficiency of terephthalic acid (TPA) (TA) loss.Wherein the thick product filtration of TA stepmother fluid solid content is higher is a major reason that causes loss; Therefore in order to improve the TA recovery, reduce production costs and reduce the pollution of solid residue to environment; Set up rational mother liquor solid content analytical model, it is very necessary to realize optimizing stable filter operation.

The present invention is the online control model that technical background is set up the mother liquor solid content with the PTA production technology.Filter operation realizes through the rotary vacuum filter of two parallel connections in the actual production process.The factor of influence filtrating solid content mainly contains in the filter operation: slurry temperature, blowback pressure, production load, inlet amount, rotating speed, spray flux and amount of filtrate etc.Because complex technical process in the actual production is carried out difficulty of operation parameter optimization, on-line operation control ratio through Analysis on Mechanism or traditional mathematical model, select here that artificial intelligence---neural network model is set up online control model.

Neural network is a kind of fuzzy mathematical model, can realize that the function data of arbitrary accuracy is approached.Wherein error back propagation (Error Back Propagation) BP neural network is set up in 1985 by people such as Rumelhart, is made up of an input layer, an output layer and some hidden layers.The BP neural network is very ripe neural network model; It is simple in structure, workable and can simulate advantages such as non-linear arbitrarily input, output relation.Fields such as pattern-recognition, Based Intelligent Control, prediction and Figure recognition have been widely used at present.The present invention promptly adopts improved standard BP neural network to set up the online control model of filter process.

BP (Back Propagation) neural network algorithm be to utilize the error after the output estimate output layer directly before the error of conducting shell; The error of one deck before using this estimation of error more again; Anti-pass is so in layer gone down, and has just obtained the estimation of error of all other each layers.So just formed error that output layer is shown along transmitting the process that opposite direction is transmitted to the input layer of network step by step with input.Therefore, people spy is called the reverse back of error propagation algorithm with this algorithm, is called for short the BP algorithm.The multistage acyclic network that uses the BP algorithm to learn is called the BP network, belongs to the feedforward neural network type.Though the precision of this estimation of error itself can " be propagated " and constantly reduction along with error itself backward; But it provides more effective way still for the training of multitiered network; Multilayer feedforward neural network can approach any nonlinear function in addition; In science and technology field, be widely used, so this algorithm receives people and pays close attention to widely for many years always.

BP neural network algorithm ultimate principle is: the error after the utilization output is estimated the error of the directly preceding conducting shell of output layer, uses the error of the more preceding one deck of this estimation of error again, and anti-pass in layer like this is gone down, and has just obtained the estimation of error of every other each layer.

The process of BP neural network algorithm study is: neural network constantly changes the connection weights of network under the stimulation of external world's input sample, so that the output of the network output of approaching expectation constantly.The essence of study is the dynamic adjustment that each is connected weights, and learning rules are the weights regulation rules, i.e. certain regulation rule of each neuronic connection adaptability in tactics institute foundation in the network in learning process.

Summary of the invention

The present invention provides the On-Line Control Method of oxidation mother liquor filter process in a kind of pure terephthalic acid's production; It is complicated that this method has solved the PTA production process; Be unfavorable for that effectively this method of technical barrier of control utilizes improved standard BP neural network to carry out analog computation then; Set up the filter process model, finally realize the On-line Control of filter process.

Technical scheme of the present invention is:

The On-Line Control Method of oxidation mother liquor filter process during a kind of pure terephthalic acid produces may further comprise the steps:

A) utilize dcs to obtain the technological parameter of influence filtrating solid content in pure terephthalic acid's production run, comprise slurry temperature X1, blowback pressure X2, produce load X3, inlet amount X4, rotating speed X5, spray flux X6 and amount of filtrate X7, and with slurry temperature X1; Blowback pressure X2; Produce load X3, inlet amount X4, rotating speed X5; Spray flux X6, amount of filtrate X7 and current time filtrating solid content manual analysis value Y carry out normalization to be handled;

B) select steps A) in 7 parameters as the input neuron of BP neural network model; Current time filtrating solid content manual analysis value Y is as the output neuron of BP neural network model; Utilize improved standard BP neural network model to carry out analog computation and set up the filter process model, in the filter process model, the node number of input layer is 2～30; The middle layer number of plies is 1～100; The number of hidden nodes is 1～100, and output layer node number is 1～15, and transport function has limite function, linear function, sigmoid function and competitive function between the layer;

C) use the filter process model to filter operation parameter optimization and the real-time output valve of model through after the anti-normalization; Utilize dcs to pass through real-time, the continuous acquisition of data; Obtain the filtrating real-time BP neural network prediction value R of solid content, utilize filtrating solid content manual analysis value Y that BP neural network prediction value R is carried out on-line correction again:

When BP neural network prediction value and the manual analysis value relative error of filtrating solid content during greater than setting value, the coefficient of deciding that obtains through real-time analysis carries out on-line correction to the neural network prediction value, and the soft measured value of solid content obtains filtrating;

D) according to the soft measured value of filter operation rear filtrate solid content, regulate slurry temperature, blowback pressure, produce load, inlet amount, rotating speed, spray flux and amount of filtrate, realize the deduction control of filtrating solid content.

Carrying out the normalization processing in the said steps A may further comprise the steps:

Utilize formula

$x\left(i\right)=\frac{X\left(i\right)-\min \left(X\right)}{\max \left(X\right)-\min \left(X\right)}\×0.8+0.1$

$y\left(i\right)=\frac{Y\left(i\right)-\min \left(Y\right)}{\max \left(Y\right)-\min \left(Y\right)}\×0.8+0.1$

With slurry temperature X1, blowback pressure X2 produces load X3, inlet amount X4, and rotating speed X5, spray flux X6, amount of filtrate X7 and current time filtrating solid content manual analysis value Y normalize between [0.1,0.9], wherein: x, y are that the back data set is handled in normalization; X, Y are data set before the normalization; Max (X), max (Y) and min (X), min (Y) are respectively maximal value and the minimum value of data set X, Y.

The method of on-line correction is among the said step C:

Utilize formula

Y ^*＝(1+γ)*Y

Carry out on-line correction, if

then

otherwise γ=0

Wherein Y representes the manual analysis value, and R representes neural network prediction value, Y ^*Be corrected value,

The real-time BP neural network prediction value R of filtrating solid content is through the soft measured value of the solid content that obtains behind the on-line correction filtrating; If its value is then regulated filter operation with reference to the filter operation controlled variable greater than>=1.1%, its controlled variable scope is following: slurry temperature: [90.47; 94.25], ℃; Blowback pressure [13.7,15], MPa; Produce load [26.71,30.93], %; Inlet amount [251.86,271.7], m ³/ hr; Rotating speed [5.52,7.5], r/min; Spray flux [15.72,16.95], m ³/ hr; Amount of filtrate [98.41,121.96], m ³/ hr.

After using the filter process model to filter operation parameter optimization and the anti-normalization of the real-time output valve process of model among the said step C; Utilize dcs pass through data in real time, continuous acquisition, the method for the real-time BP neural network prediction value R of the solid content that obtains filtrating is:

Arrive some groups of service datas at the commercial production collection in worksite, as the training sample of filter process model.Standard BP neural network after selecting to improve is carried out analyses and prediction to the filtrating solid content, and input neuron corresponds to slurry temperature, blowback pressure, production load, inlet amount, rotating speed, spray flux and amount of filtrate: the x1 after normalization is handled, x2 respectively; X3, x4, x5; X6, x7; Output neuron corresponds to the filtrating solid content manual analysis value y after normalization is handled;

In above-mentioned training sample, select partial data as the neural network learning sample; Remaining data detects Stability in Neural Networks and generalization ability as test sample book; Get one group of all less weights of predicted value and the manual analysis value relative error of learning sample and test sample book and threshold values at last as the neural network model parameter, set up the filter process model;

After the filter process modelling; Bringing neural network into after can handling the data of field real-time acquisition (model input variable desired data) normalization calculates; Then the neural network output valve is handled the neural network prediction value of the solid content that obtains filtrating through anti-normalization.

The invention has the beneficial effects as follows:

The On-Line Control Method of oxidation mother liquor filter process was implemented Based Intelligent Control through using this to invent described method to the filter operation technological parameter of oxidation mother liquor in pure terephthalic acid's production during pure terephthalic acid of the present invention produced, and can stablize filter operation, reduces filter operation stepmother fluid solid content, increase terephthaldehyde's acid recovering rate.

The present invention carries out on-line correction to the Neural Network model predictive value; Make this neural network model can adapt to the variability and the continuity of industrial processes; Finally obtain the filtrating soft measured value of solid content has overcome the deviation that the manual analysis value of Neural Network model predictive value and the commercial plant of filtrating solid content unavoidably can produce.

The present invention has adopted additional momentum method and adaptive learning rate method to combine the improved standard BP neural network in back.Overcome because traditional BP neural metwork training process uncertain.

The present invention gets one group of less weights of predicted value and the manual analysis value relative error of learning sample and test sample book and threshold values as the neural network model parameter, sets up the filter process model.After the filter process modelling, can utilize the model optimization operating parameter and set up the filter process online control model.

Description of drawings

Fig. 1 is a BP neural network structure block diagram.

Fig. 2 is a mother liquor filtrator neural network soft sensor model structural drawing, and this soft-sensing model adopts improved standard BP neural network.

Fig. 3 is a mother liquor solid content on-line control system block diagram.

Fig. 4 is a solid content soft-sensing model flow chart.

Embodiment

Below in conjunction with accompanying drawing the present invention is further described:

The present invention chooses the influential process operation parameter of filtrating solid content in the filter process; And with its normalization, utilize improved standard BP neural network to carry out analog computation then and set up the filter process model, use a model to after filter operation parameter optimization and the anti-normalization of the real-time output valve process of model; Utilize filtrating solid content manual analysis value to carry out on-line correction again; Thereby the soft measured value of the solid content that obtains filtrating is inferred control according to the filter operation parameter of soft measured value and optimization to filter process at last, in real time promptly according to the soft measured value of filter operation rear filtrate solid content; Regulate the filter process operating parameter, realize the deduction control of filtrating solid content.

The foundation of filter process neural network model:

Feed-forward type neural network (BP model) is the network model that present field of neural networks research is maximum, application is maximum.Its non-linear approximation capability is the main cause that it gains in favor.But standard BP algorithm also has some defectives, mainly is because its training process uncertain.The present invention has adopted additional momentum method and adaptive learning rate method to combine the improved standard BP neural network in back.Specific algorithm is regular as follows: adaptive learning speed:

1. if square error (on whole training set) weights have increased after renewal, and have surpassed the percentage δ of certain setting, then right value update cancellation, learning rate multiply by a factor ρ (0＜ρ＜1), and momentum factor γ is set to 0;

2. if square error reduces behind right value update, then right value update is accepted, and pace of learning multiply by factor η＞1.If γ is set to 0, value before then returning to;

3. if the growth of square error is less than δ, then right value update is accepted, but pace of learning remains unchanged.If γ is set to 0, value before then returning to.

Adaptive learning speed can solve long problem of standard BP algorithm training time.

The additional momentum method:

$\mathrm{\Δ}{\mathrm{\ω}}^m\left(k\right)=\mathrm{\γ\Δ}{\mathrm{\ω}}^m(k-1)-(1-\mathrm{\γ}){\∂s}^m{\left(a^{m-1}\right)}^T$

$\mathrm{\Δ}{b}^m\left(k\right)=\mathrm{\γ\Δ}{b}^m(k-1)-(1-\mathrm{\γ}){\∂s}^m$

ω in the formula, b are weights and threshold values, and γ is a momentum term.

Use momentum term γ to have several respects to improve:, can avoid neural network to be absorbed in local smallest point like same wave filter; Can stablize the higher pace of learning of use under the prerequisite keeping algorithm; After track gets into certain consistent gradient direction, can accelerating convergence.Here adopt improved standard BP neural network model to set up the filter process model, its structure is shown in accompanying drawing 1 for this reason, and wherein w1, b1 represent weights, the threshold values between input layer and the hidden layer respectively; W2, b2 represent weights, the threshold values between hidden layer and the output layer respectively.

In filter process, a lot of to the influential factor of filtrating solid content, for example: filter slurry composition, filter operation condition and other operating modes etc.Here according to practical production experience, extracted operation factors that the filtrating solid content is had a main influence as model variable: slurry temperature, blowback pressure, produce load, inlet amount, rotating speed, spray flux and amount of filtrate.

In dcs (DCS), obtain the technological parameter of above-mentioned influence filtrating solid content, comprise slurry temperature (℃, X1), blowback pressure (MPa; X2), produce load (%, X3), inlet amount (t/h, X4), rotating speed (r/min; X5), spray flux (t/h, X6) and amount of filtrate (t/h, X7).The filter process model has here been considered the influence of above-mentioned 7 parameters to the filtrating solid content, so select above-mentioned 7 parameters as BP neural network input neuron, (% is Y) as the neural network output neuron for current time filtrating solid content manual analysis value.

With slurry temperature X1, blowback pressure X2 produces load X3, inlet amount X4; Rotating speed X5, spray flux X6, amount of filtrate X7 and current time filtrating solid content manual analysis value Y carry out normalization to be handled, and the normalization scope can be chosen for [0; 1], [1,1], [0.5; 0.5] etc., here it is normalized between [0.1,0.9].Method for normalizing is:

$x\left(i\right)=\frac{X\left(i\right)-\min \left(X\right)}{\max \left(X\right)-\min \left(X\right)}\×0.8+0.1$

$y\left(i\right)=\frac{Y\left(i\right)-\min \left(Y\right)}{\max \left(Y\right)-\min \left(Y\right)}\×0.8+0.1$

Wherein: x, y are that the back data set is handled in normalization; X, Y are data set before the normalization; Max (X), max (Y) and min (X), min (Y) are respectively maximal value and the minimum value of data set X, Y.

In neural network model; The node number of input layer is i (i=2～30), and the middle layer number of plies is L (L=1～100), and the number of hidden nodes is j (j=1～100); Output layer node number is k (k=1～15), and transport function has limite function, linear function, sigmoid function and competitive function etc. between the layer.When the present invention implemented: the node number of input layer was i (i=7); The hidden layer number of plies is L (L=1); The number of hidden nodes is j (j=14); Output layer node number is k (k=1), and transport function is that tanh sigmoid function, hidden layer and output layer transport function are logarithm-sigmoid function between input layer and the hidden layer.

Arrive some groups of service datas at the commercial production collection in worksite, as the training sample of filter process model.Standard BP neural network after selecting to improve is carried out analyses and prediction to the filtrating solid content, and input neuron corresponds to slurry temperature, blowback pressure, production load, inlet amount, rotating speed, spray flux and amount of filtrate: the x1 after normalization is handled, x2 respectively; X3, x4, x5; X6, x7; Output neuron corresponds to the filtrating solid content manual analysis value y after normalization is handled.

In above-mentioned training sample, select partial data as the neural network learning sample; Remaining data detects Stability in Neural Networks and generalization ability as test sample book; Get one group of all less weights of predicted value and the manual analysis value relative error of learning sample and test sample book and threshold values at last as the neural network model parameter, set up the filter process model.After the filter process modelling, can utilize the model optimization operating parameter and set up the filter process online control model.

The filter operation Parameter Optimization is to adopt data statistical analysis method: according to the empirical factor level, generate the experimental design calendar, coding is converted into experimental level numerical value experimentizes, and import model predication value in the EE table as experimental result; Utilize the analysis of different statistic analytical model to obtain a series of statistic analysis result such as experimental variance table, Pareto figure, factor affecting table, response surface figure, thus analyses and prediction optimum experimental condition.The present invention adopts the non-factorial Response Design experiment of Central Composite to carry out the filter process operation parameter optimization.

After the filter process modelling; Bringing neural network into after can handling the data of field real-time acquisition (model input variable desired data) normalization calculates; Then the neural network output valve is handled through anti-normalization, the neural network prediction value of the solid content that obtains filtrating, unit is %.

Model tuning:

Because have multiple disturbing factor in the actual production process, the Neural Network model predictive value of above-mentioned filtrating solid content and the manual analysis value of commercial plant unavoidably can produce certain deviation.Therefore; Must be at set intervals; With manual analysis value (usually every day analyze once) the Neural Network model predictive value is carried out on-line correction, make this neural network model can adapt to the variability and the continuity of industrial processes, the soft measured value of the solid content that finally obtains filtrating.Model tuning method:, then predicted value is proofreaied and correct the soft measured value of the solid content that obtains filtrating if relative error exceeds neural network model permissible error scope between neural network prediction value and the manual analysis value through certain coefficient.The result is as shown in Figure 2 for filtrating solid content soft-sensing model.

The foundation of mother liquor solid content on-line analysis model:

Soft measured value through the analysis and filter process model; Two filter filter processes to parallel connection are inferred control in real time: when filtrating solid content during greater than expectation value; With reference to filtration parameter and filtrating solid content variation relation and filter operation parameter optimization value, regulate slurry temperature, blowback pressure, produce load, inlet amount, rotating speed, spray flux and amount of filtrate, realize control to the filtrating solid content; Promptly realize the deduction control of filter process, as shown in Figure 3.

On the application module of dcs (DCS) or advanced process administration module, realize the programming of control language by program circuit shown in Figure 4.Through data in real time, continuous acquisition, the real-time neural network prediction value of the solid content that obtains filtrating, again through model tuning, the soft measured value of solid content that obtains filtrating is realized further that filter process is inferred to control.

Like Fig. 1, Fig. 2, Fig. 3, in dcs (DCS), obtain influence PTA produce in the main technologic parameters of oxidation mother liquor filter operation filtrating solid content: the slurry temperature of filtrator (℃, X1), blowback pressure (MPa; X2), produce load (%, X3), inlet amount (t/h, X4), rotating speed (r/min; X5), spray flux (t/h, X6) and amount of filtrate (t/h, X7); And the manual analysis value of current time filtrating solid content (%, Y).With the scope of these data normalizations to [0.1,0.9], method for normalizing is following then:

$xi=\frac{Xi-\min \left(\mathrm{Xi}\right)}{\max \left(\mathrm{Xi}\right)-\min \left(\mathrm{Xi}\right)}\×0.8+0.1$

$y=\frac{Y-\min \left(Y\right)}{\max \left(Y\right)-\min \left(Y\right)}\×0.8+0.1$

Wherein: x, y are that the back data set is handled in normalization; X, Y are data set before the normalization; Max (X), max (Y) and min (X), min (Y) are respectively maximal value and the minimum value of data set X, Y.The slurry temperature variation range is taken as [84.16,94.25], ℃; The blowback pressure range is taken as [8.5,15.0], MPa; Production load variations scope is [22.5,55.7], %; The inlet amount variation range is taken as [218.8,271.7], t/h; The rotation speed change scope is [2 .22,7.5], r/min; The spray flux scope is [5.98,16.95], t/h; The amount of filtrate variation range is [59.15,121.96], t/h; Corresponding filtrating solid content manual analysis value variation range is [1.0,1.9], %.

In 280 groups of real time datas of commercial production collection in worksite, utilize filtrating solid content manual analysis value to carry out neural metwork training as desired value.Wherein preceding 240 groups of data are as training sample, and the 40 groups of data in back are as forecast sample.Through neural network model is trained, the standard BP neural network structure, weights and the threshold values that are improved.The transport function that adopts between improved standard BP neural net layer and the layer is followed successively by tanh sigmoid function and logarithm sigmoid function.

Through test, utilize that maximum absolute relative error is 4.97% between filtrating solid content that above-mentioned neural network model analysis obtains and the manual analysis value, mean absolute relative error is 2.23%.This is illustrated in the online deduction control that the model of setting up within the industrial permissible error scope can be realized filter process.

Adopt the non-factorial response of Central Composite statistical analysis technique to design the totally 87 groups of experiments of 7 factors, 3 levels, and utilize the filter process neural network model to draw experimental result.Through to interpretation, optimized the filter operation parameter: slurry temperature: [90.47,94.25], ℃; Blowback pressure [13.7,15], MPa; Produce load [26.71,30.93], %; Inlet amount [251.86,271.7], m ³/ hr; Rotating speed [5.52,7.5], r/min; Spray flux [15.72,16.95], m ³/ hr; Amount of filtrate [98.41,121.96], m ³/ hr.Under the aforesaid operations condition, the filtrating solid content is 1.1% to the maximum, and mean value is 1%.This is illustrated in the purpose that can realize stable filter operation, reduction filtrating solid content under the above-mentioned filter operation condition, improve the TA recovery.

On the application module of dcs (DCS) or advanced process control control module, realize the programming of control language according to the program of Fig. 4; Real-time, continuous acquisition through data; Bringing the weights that train and threshold values into neural network calculates; The filtrating solid content that obtain this moment is between [0.1,0.9]; This neural network calculated value is carried out anti-normalization, and the solid content predicted value obtains filtrating; At last, the neural network prediction value of the manual analysis value of utilizing the solid content of constantly filtrating recently after to anti-normalization carried out on-line correction, and concrete grammar is:

Y ^*＝(1+γ)*Y

If

then

or γ = 0

Wherein Y representes the manual analysis value, and R representes neural network prediction value, Y ^*Be corrected value.

Predicted value is through the soft measured value of the solid content that obtains behind the on-line correction filtrating, if its value is then regulated filter operation with reference to the filter operation controlled variable greater than>=1.1%, its controlled variable scope is following: slurry temperature: [90.47,94.25], ℃; Blowback pressure [13.7,15], MPa; Produce load [26.71,30.93], %; Inlet amount [251.86,271.7], m ³/ hr; Rotating speed [5.52,7.5], r/min; Spray flux [15.72,16.95], m ³/ hr; Amount of filtrate [98.41,121.96], m ³/ hr.

Claims (3)

1. the On-Line Control Method of oxidation mother liquor filter process during a pure terephthalic acid produces is characterized in that may further comprise the steps:

A) utilize dcs to obtain the technological parameter of influence filtrating solid content in pure terephthalic acid's production run, comprise slurry temperature X1, blowback pressure X2, produce load X3, inlet amount X4, rotating speed X5, spray flux X6 and amount of filtrate X7, and with slurry temperature X1; Blowback pressure X2; Produce load X3, inlet amount X4, rotating speed X5; Spray flux X6, amount of filtrate X7 and current time filtrating solid content manual analysis value Y carry out normalization to be handled;

B) select steps A) in 7 parameters as the input neuron of BP neural network model, said steps A) in 7 parameters be: slurry temperature X1, blowback pressure X2 produces load X3, inlet amount X4, rotating speed X5, spray flux X6 and amount of filtrate X7; Current time filtrating solid content manual analysis value Y is as the output neuron of BP neural network model; Utilize improved standard BP neural network model to carry out analog computation and set up the filter process model, in the filter process model, the node number of input layer is 2～30; The middle layer number of plies is 1～100; The number of hidden nodes is 1～100, and output layer node number is 1～15, and transport function has limite function, linear function, sigmoid function and competitive function between the layer;

C) use the filter process model to filter operation parameter optimization and the real-time output valve of model through after the anti-normalization; Utilize dcs to pass through real-time, the continuous acquisition of data; Obtain the filtrating real-time BP neural network prediction value R of solid content, utilize filtrating solid content manual analysis value Y that BP neural network prediction value R is carried out on-line correction again:

When BP neural network prediction value and the manual analysis value relative error of filtrating solid content during greater than setting value, the coefficient of deciding that obtains through real-time analysis carries out on-line correction to the neural network prediction value, and the soft measured value of solid content obtains filtrating;

The method of on-line correction is among the said step C:

Utilize formula

Y ^*＝(1+γ)*Y

Carry out on-line correction, if

then

otherwise γ=0

Wherein Y representes the manual analysis value, and R representes neural network prediction value, Y ^*Be corrected value,

The real-time BP neural network prediction value R of filtrating solid content is through the soft measured value of the solid content that obtains behind the on-line correction filtrating; If its value>=1.1% is then regulated filter operation with reference to the filter operation controlled variable, its controlled variable scope is following: slurry temperature: [90.47; 94.25], ℃; Blowback pressure [13.7,15], MPa; Produce load [26.71,30.93], %; Inlet amount [251.86,271.7], m ³/ hr; Rotating speed [5.52,7.5], r/min; Spray flux [15.72,16.95], m ³/ hr; Amount of filtrate [98.41,121.96], m ³/ hr;

D) according to the soft measured value of filter operation rear filtrate solid content, regulate slurry temperature, blowback pressure, produce load, inlet amount, rotating speed, spray flux and amount of filtrate, realize the deduction control of filtrating solid content.

2. the On-Line Control Method of oxidation mother liquor filter process during pure terephthalic acid according to claim 1 produces is characterized in that carrying out in the said steps A normalization processing and may further comprise the steps:

Utilize formula

$x\left(i\right)=\frac{X\left(i\right)-\min \left(X\right)}{\max \left(X\right)-\min \left(X\right)}\×0.8+0.1$

$y\left(i\right)=\frac{Y\left(i\right)\text{-min}\left(Y\right)}{\max \left(Y\right)-\min \left(Y\right)}\×0.8+0.1$

With slurry temperature X1, blowback pressure X2 produces load X3, inlet amount X4, and rotating speed X5, spray flux X6, amount of filtrate X7 and current time filtrating solid content manual analysis value Y normalize between [0.1,0.9], wherein: x, y are that the back data set is handled in normalization; X, Y are data set before the normalization; Max (X), max (Y) and min (X), min (Y) are respectively maximal value and the minimum value of data set X, Y.

3. the On-Line Control Method of oxidation mother liquor filter process during pure terephthalic acid according to claim 1 produces; After it is characterized in that using among the said step C filter process model to filter operation parameter optimization and the anti-normalization of the real-time output valve process of model; Utilize dcs pass through data in real time, continuous acquisition, the method for the real-time BP neural network prediction value R of the solid content that obtains filtrating is:

Arrive some groups of service datas at the commercial production collection in worksite, as the training sample of filter process model; Standard BP neural network after selecting to improve is carried out analyses and prediction to the filtrating solid content, and input neuron corresponds to slurry temperature, blowback pressure, production load, inlet amount, rotating speed, spray flux and amount of filtrate: the x1 after normalization is handled, x2 respectively; X3, x4, x5; X6, x7; Output neuron corresponds to the filtrating solid content manual analysis value y after normalization is handled;

In above-mentioned training sample, select partial data as the neural network learning sample; Remaining data detects Stability in Neural Networks and generalization ability as test sample book; Get one group of all less weights of predicted value and the manual analysis value relative error of learning sample and test sample book and threshold values at last as the neural network model parameter, set up the filter process model;

After the filter process modelling, bring neural network into after handling the data normalization of field real-time acquisition and calculate, then the neural network output valve is handled through anti-normalization, the neural network prediction value of the solid content that obtains filtrating.

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