CN101963785A - 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 technology of the p xylene oxidation mother liquor filter process of P-xylene (PX) liquid phase catalytic oxidation technology in pure terephthalic acid (PTA) production.

Background technology

At present, PTA is the polyester industrial important source material, is mainly used to the intermediate 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 maximum of three big synthon 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 volume increase 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 by the rotary vacuum filter of two parallel connections in the actual production process.The factor that influences the filtrate solid content in the filter operation mainly contains: 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 by 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, use the error of the more preceding one deck of this estimation of error again, so anti-pass is 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 oppositely back propagation algorithm of error 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 backward along with error itself, 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 is subjected to 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 so anti-pass is in layer 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, promptly in learning process in the network each neuronic connection weight change certain regulation rule of institute's foundation.

Summary of the invention

The invention provides the On-Line Control Method of oxidation mother liquor filter process in a kind of pure terephthalic acid's production, this method has solved PTA production process complexity, 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 is obtained influences the filtrate solid content in pure terephthalic acid's production run technological parameter, 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 filtrate solid content manual analysis value Y carry out normalized;

B) select steps A) in 7 parameters as the input neuron of BP neural network model, current time filtrate 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, 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 real-time BP neural network prediction value R of filtrate solid content, utilize filtrate solid content manual analysis value Y that BP neural network prediction value R is carried out on-line correction again:

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

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 filtrate solid content.

Carrying out normalized in the described 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 filtrate solid content manual analysis value Y normalize between [0.1,0.9], wherein: x, y are data set after the normalized; 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 described step C:

Utilize formula

Y ^*＝(1+γ)*Y

Carry out on-line correction, if

Then

Otherwise γ=0

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

The real-time BP neural network prediction value R of filtrate solid content is through obtaining the soft measured value of filtrate solid content behind the on-line correction, 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 as follows: 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 described step C, utilize dcs to pass through real-time, the continuous acquisition of data, the method that obtains the real-time BP neural network prediction value R of filtrate solid content 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 filtrate 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 the normalized, x2 respectively, x3, x4, x5, x6, x7; Output neuron corresponds to the filtrate solid content manual analysis value y after the normalized;

The selection portion divided data is as the neural network learning sample in above-mentioned training sample, remaining data detects Stability in Neural Networks and generalization ability as test sample book, get one group of all less weights of the predicted value of learning sample and test sample book and manual analysis value relative error and threshold values at last as the neural network model parameter, set up the filter process model;

After the filter process modelling, can calculate bringing neural network into after the data of field real-time acquisition (the model input variable desired data) normalized, then with the neural network output valve through anti-normalized, obtain the neural network prediction value of filtrate solid content.

The invention has the beneficial effects as follows:

The On-Line Control Method of oxidation mother liquor filter process was implemented Based Intelligent Control by 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 soft measured value of filtrate solid content, overcome the deviation that the manual analysis value of the Neural Network model predictive value of filtrate solid content and commercial plant unavoidably can produce.

The present invention has adopted additional momentum method and adaptive learning rate method in conjunction with 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 the predicted value of learning sample and test sample book and manual analysis value relative error 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 filtrate 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 filtrate solid content manual analysis value to carry out on-line correction again, thereby obtain the soft measured value of filtrate solid content, at last filter process is inferred control in real time according to the filter operation parameter of soft measured value and optimization, 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 filtrate 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 in conjunction with the improved standard BP neural network in back.The specific algorithm rule is 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 is multiplied 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 as same wave filter; Can stablize the higher pace of learning of use under the prerequisite keeping algorithm; After track enters certain consistent gradient direction, can accelerating convergence.Here adopt improved standard BP neural network model to set up the filter process model for this reason, its structure as shown in Figure 1, 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 filtrate 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 filtrate 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 above-mentioned technological parameter that influences the filtrate 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 filtrate 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 filtrate 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 filtrate solid content manual analysis value Y carry out normalized, 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 data set after the normalized; 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), the middle layer number of plies is L (L=1～100), 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 invention process: the node number of input layer is 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 filtrate 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 the normalized, x2 respectively, x3, x4, x5, x6, x7; Output neuron corresponds to the filtrate solid content manual analysis value y after the normalized.

The selection portion divided data is as the neural network learning sample in above-mentioned training sample, remaining data detects Stability in Neural Networks and generalization ability as test sample book, get one group of all less weights of the predicted value of learning sample and test sample book and manual analysis value relative error 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 model predication value is imported in the experimental establishment table as experimental result; Utilize different Statistic analysis models analyses 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 of Central Composite contrived experiment to carry out the filter process operation parameter optimization.

After the filter process modelling, can calculate bringing neural network into after the data of field real-time acquisition (the model input variable desired data) normalized, then with the neural network output valve through anti-normalized, obtain the neural network prediction value of filtrate solid content, unit is %.

Model tuning:

Because have multiple disturbing factor in the actual production process, the Neural Network model predictive value of above-mentioned filtrate 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, finally obtain the soft measured value of filtrate solid content.Model tuning method: if relative error exceeds neural network model permissible error scope between neural network prediction value and the manual analysis value, then predicted value is proofreaied and correct, obtain the soft measured value of filtrate solid content by certain coefficient.Filtrate solid content soft-sensing model result as shown in Figure 2.

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

Soft measured value by the analysis and filter process model, two filter filter processes to parallel connection are inferred control in real time: when filtrate solid content during greater than expectation value, with reference to filtration parameter and filtrate 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, realization is to the control of filtrate 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.By real-time, the continuous acquisition of data, obtain the real-time neural network prediction value of filtrate solid content, again by model tuning, obtain the soft measured value of filtrate solid content, further realize filter process deduction control.

As Fig. 1, Fig. 2, Fig. 3, in dcs (DCS), obtain the main technologic parameters that influences oxidation mother liquor filter operation filtrate solid content in the PTA production: 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 filtrate solid content (%, Y).With the scope of these data normalizations to [0.1,0.9], method for normalizing is as follows 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 data set after the normalized; 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 filtrate 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 filtrate 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.By 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 filtrate solid content that above-mentioned neural network model analysis obtains and the manual analysis value, mean absolute relative error is 2.23%.This shows that within the industrial permissible error scope model of setting up can realize the online deduction control of 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.By 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 filtrate solid content is 1.1% to the maximum, and mean value is 1%.This shows the purpose that can realize stable filter operation, reduction filtrate solid content under above-mentioned filter operation condition, improve the TA recovery.

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

Y ^*＝(1+γ)*Y

If

Then Otherwise γ=0

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

Predicted value is through obtaining the soft measured value of filtrate solid content behind the on-line correction, 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 as follows: 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 (4)

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 is obtained influences the filtrate solid content in pure terephthalic acid's production run technological parameter, 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 filtrate solid content manual analysis value Y carry out normalized;

B) select steps A) in 7 parameters as the input neuron of BP neural network model, current time filtrate 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, 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 real-time BP neural network prediction value R of filtrate solid content, utilize filtrate solid content manual analysis value Y that BP neural network prediction value R is carried out on-line correction again:

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

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 filtrate 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 described steps A normalized and may further comprise the steps:

Utilize formula

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

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

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 filtrate solid content manual analysis value Y normalize between [0.1,0.9], wherein: x, y are data set after the normalized; 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 is characterized in that the method for on-line correction among the described step C is:

Utilize formula

Y ^*＝(1+γ)*Y

Carry out on-line correction, if

Then

Otherwise γ=0

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

The real-time BP neural network prediction value R of filtrate solid content is through obtaining the soft measured value of filtrate solid content behind the on-line correction, 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 as follows: 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.

4. 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 described 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 to pass through real-time, the continuous acquisition of data, the method that obtains the real-time BP neural network prediction value R of filtrate solid content 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 filtrate 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 the normalized, x2 respectively, x3, x4, x5, x6, x7; Output neuron corresponds to the filtrate solid content manual analysis value y after the normalized;

The selection portion divided data is as the neural network learning sample in above-mentioned training sample, remaining data detects Stability in Neural Networks and generalization ability as test sample book, get one group of all less weights of the predicted value of learning sample and test sample book and manual analysis value relative error and threshold values at last as the neural network model parameter, set up the filter process model;

After the filter process modelling, can calculate bringing neural network into after the data of field real-time acquisition (the model input variable desired data) normalized, then with the neural network output valve through anti-normalized, obtain the neural network prediction value of filtrate solid content.

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