End point judgment method of coulometry Karz moisture meter

Abstract

The invention belongs to the technical field of electrolytic potentiometric titration, and particularly relates to a method for judging an end point of a Coulomb-method Karz moisture meter. The method comprises the following steps: acquiring electrolytic potential dynamic data of an electrolytic system of an electrolytic tank in an electrolytic process; constructing an electrolysis potential prediction model of the electrolysis system of the electrolytic tank according to the electrolysis potential dynamic data; according to the electrolysis potential dynamic data and the electrolysis potential prediction model, constructing an optimization function for stopping electrolysis of the electrolysis system of the electrolytic tank; the electrolysis potential at the current moment is collected; according to the electrolysis potential at the current moment, an end point judgment control scheme of the electrolysis system of the electrolytic tank is generated through an optimization function, and the termination electrolysis potential of the electrolysis system of the electrolytic tank is calculated; and controlling the electrolysis system of the electrolytic tank through the termination electrolysis potential. According to the invention, the titration delay is shortened, and the measurement accuracy is improved, so that a more accurate measurement result is obtained.

Description

Technical Field

[0001] The invention belongs to the technical field of electrolytic potentiometric titration, and in particular relates to an electrolytic endpoint judgment method of a coulometric Karl Fischer moisture meter. Background Art

[0002] The detection of moisture content is one of the most common indicators in analytical testing. All industries have relevant regulations and standards, and moisture content testing is in great demand in all industries. There are many methods for determining the water content of a substance, the most commonly used of which are drying method, gas chromatography and Karl Fischer method. The Karl Fischer method tests moisture, with fast test speed, simple operation and high measurement accuracy. It is an internationally recognized classic method for trace moisture testing. At present, there is a large gap between most domestic moisture meters and imported instruments, and poor repeatability and poor measurement accuracy are the main gaps. One of the reasons for this gap is that the endpoint judgment method is relatively rough. Domestic judgment is mostly based on the titration curve. When judging the coulometric titration endpoint, the microcontroller gives a signal at point T0 to control the relay to disconnect the electrolysis circuit. At this time, the calculated power value is the total power consumed during the electrolysis process. When detecting the T0 point, the data processing method used is to examine the differential value of the sampling point i. Since the differential value is an increasing function before the endpoint and a decreasing function after the endpoint, once the differential value of a certain point is calculated to be smaller than the previous point, it is considered that the previous point has reached the titration endpoint. This method was originally used to calculate the electrolysis time in constant current electrolysis. The constant current value multiplied by the electrolysis time can be used to obtain the value of electricity. Since the above-mentioned single-chip microcomputer has lagged behind the actual end point by a △t when detecting the signal, this method has a certain error.

[0003] For special samples, a Karl Fischer furnace is required for sample injection. In addition, the Karl Fischer furnace takes a long time to inject, which makes the coulometric Karl Fischer titrator have poor repeatability when used in conjunction with a furnace. The drift value at the end of the titration is usually greater than the initial drift value because the last small amount of water is released very slowly or the sample is slowly decomposed by heat. At the same time, the evaporation of water in the final stage is also irregular. If the above endpoint judgment method is used, it will bring about a large measurement error, so that the measurement endpoint cannot be found. Summary of the invention

[0004] In view of the above technical status, the present invention proposes an endpoint determination method for a coulometric Karl Fischer moisture meter, which uses a dynamic matrix control algorithm (DMC method) to implement software endpoint determination. The technical solution adopted by the present invention is as follows:

[0005] A method for determining an endpoint of a coulometric Karl Fischer titrator, wherein the coulometric Karl Fischer titrator comprises an electrolytic cell electrolysis system, comprising the following steps:

[0006] Collecting dynamic data of electrolysis potential of the electrolytic cell electrolysis system during the electrolysis process;

[0007] Constructing an electrolysis potential prediction model of the electrolysis cell electrolysis system according to the electrolysis potential dynamic data;

[0008] Constructing an optimization function for terminating electrolysis of the electrolytic cell electrolysis system according to the electrolysis potential dynamic data and the electrolysis potential prediction model;

[0009] Collect the electrolysis potential at the current moment;

[0010] According to the electrolysis potential at the current moment, an endpoint judgment control scheme of the electrolysis system of the electrolysis cell is generated by the optimization function, and the termination electrolysis potential of the electrolysis system of the electrolysis cell is calculated;

[0011] The electrolysis cell electrolysis system is controlled by the termination electrolysis potential.

[0012] Beneficial effects of the present invention:

[0013] The dynamic matrix control algorithm proposed by the present invention can well predict the end point electrolysis current, shorten the titration delay, and improve the measurement accuracy. When the software table lookup-DMC algorithm is adopted, the user can select the difference between the electrolysis current and the set value according to the actual situation of the sample, so as to obtain a more accurate measurement result. On the basis of a large number of experiments, the program judgment end point method of the DMC algorithm is obtained, and the result obtained is more accurate and stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the electrolytic titration curve. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] In the electrode current automatic control system, Figure 1 The functional relationship between the electrolytic potential and time shows that the electrolytic current control system is a nonlinear relationship, and its potential trend is: gradually over time, and finally approaching the equilibrium state. The traditional controller cannot achieve a good control effect, while predictive control can greatly overcome the nonlinearity of the object, thereby obtaining a good end point judgment solution.

[0017] The embodiment of the present invention discloses an endpoint judgment method for a coulometric Karl Fischer titrator, and proposes to use a dynamic matrix control algorithm to judge the titration endpoint of the coulometric Karl Fischer titrator. The method requires certain experimental data as support, and the basic design concept is to use the drift value (blank value) when the moisture meter is turned on as the criterion (initial value) for the termination of the titration. The design idea is to apply a certain power supply voltage to two identical measuring electrodes, add 35Hz / 12V alternating current to both ends of the measuring electrodes, and obtain a voltage signal through a 100K current limiting resistor. The voltage signal is converted into an equivalent DC voltage through a true effective value conversion chip AD, and is converted into a digital quantity that can be recognized by a single-chip microcomputer through amplification and A / D conversion. The smaller the digital quantity, the better the conductivity of the solution, that is, the higher the concentration of elemental iodine. The digital quantity given by the measuring electrode corresponding to the most sensitive area of ​​the actual chemical reaction is determined as the equilibrium point. After water is added to the equilibrium point, the concentration of elemental iodine suddenly decreases, and the digital quantity suddenly rises. The electrolysis current corresponding to different digital quantities is given according to the actual reaction situation, and different electrolysis currents are given by the D / A conversion control transistor. When the measurement starts, the program automatically records the drift value of the starting point, and the electrolysis program starts. Different electrolysis currents are given according to the digital quantity actually measured by the measuring electrode. The measured electrolysis current is also converted into the corresponding digital quantity through A / D, so as to judge the change of electrolysis potential during the titration process. When the difference between this digital quantity and the starting point value (the digital quantity corresponding to the blank current) is less than the difference set by the user twice, the program automatically ends the electrolysis program and the integration program at the same time.

[0018] The software table looks up the endpoint to determine whether there is an over-endpoint titration phenomenon. In the embodiment of the present invention, a dynamic matrix control algorithm (DMC algorithm), which is one of the predictive control algorithms, is used to predict the endpoint electrolysis current. The algorithm first predicts the titration endpoint based on the occurrence trend of the electrolysis current, so that the system controls the titration to stop just at the endpoint.

[0019] The DMC algorithm is a control algorithm based on the non-parametric mathematical model of the controlled object. It is a predictive control algorithm based on the object step response. It uses the discrete coefficients of the object's step response as a model, avoiding the identification of the usual transfer function or state-space equation model parameters. Because it uses multi-step prediction technology, it can effectively solve the problem of time-delay process and implement control according to the quadratic performance indicator with the minimum deviation between the estimated output and the given value. It is suitable for asymptotically stable linear objects. The pure lag or non-minimum phase characteristics in the dynamic characteristics of the system do not affect the direct application of the algorithm. Therefore, it is an optimal control technology.

[0020] At the same time, the embodiment of the present invention adopts a dynamic matrix control algorithm to predict the change process of electrolysis current. The algorithm includes three parts: prediction model, rolling optimization and feedback correction, and mainly includes the following five steps:

[0021] (1) Collecting dynamic data (electrolysis potential) of the electrolytic cell electrolysis system during the electrolysis process;

[0022] (2) constructing an electrolysis potential prediction model of the electrolysis system according to the dynamic data;

[0023] (3) constructing an optimization function for terminating electrolysis of the electrolytic cell electrolysis system according to the dynamic data;

[0024] According to the actual electrolysis process, the electrolysis current signal collected during the electrolysis process is selected as the input of the predictive controller, and the electrolysis potential is selected as the output of the predictive controller. Based on the model, the future output of the system can be obtained:

[0025]

[0026] Formula (1.1) is the prediction model. is the model coefficient calculated using the least squares method. Based on formula 1.1, It can be broken down into:

[0027]

[0028] in represents the impact of future input on output, Represents the effect of past inputs on outputs.

[0029] According to the DMC algorithm, the objective function of the system is selected as:

[0030]

[0031] The control goal is to minimize the objective function. In formula (1.3), m is the control step length, p is the prediction step length, r(k) is the output setting value, q j and r i is the weighting coefficient. The subsequent model parameter selection mainly focuses on the selection of m, p, q, and r.

[0032] The error is defined as follows:

[0033]

[0034] In the formula You can and e(k+j) is expressed as in:

[0035]

[0036] because make:

[0037]

[0038] You can get:

[0039] Δu=Gu-bu(k-1) (1.5)

[0040] Assume that the two weight matrices are as follows:

[0041] Q=diag(q1…q p ),R=diag(r1…r m ),

[0042] Bring in Get the least squares solution

[0043]

[0044] According to the actual situation, the first item of the input sequence participates in the control, that is:

[0045]

[0046] Among them, k e =b Τ (H Τ QH+G Τ RG) -1 H Τ Q,

[0047] k u =b Τ (H Τ QH+G Τ RG) -1 G Τ Rb.

[0048] (4) collecting the electrolytic potential of the electrolytic cell at the current moment;

[0049] According to the electrolytic potential of the electrolytic cell at the current moment, the endpoint judgment control scheme of the electrolytic cell electrolysis system is generated by the optimization function, and the termination electrolysis potential of the electrolytic cell electrolysis system is calculated (Formula 2.2).

[0050] (5) Controlling the electrolysis system of the electrolytic cell by means of the termination electrolysis potential.

[0051] The electrolysis process is a dynamic process. It is initially in a state of equilibrium. When water is added to the equilibrium point, the concentration of elemental iodine will suddenly decrease. The digital quantity will rise suddenly. The electrolysis current corresponding to different digital quantities is given according to the actual reaction situation. Different electrolysis currents are given by controlling the transistor through D / A conversion. When the measurement starts, the program automatically records the drift value of the starting point, and the electrolysis program starts. Different electrolysis currents are given according to the digital quantity measured by the measuring electrode. The measured electrolysis current is also converted into the corresponding digital quantity through A / D. When the difference between this digital quantity and the starting value (the digital quantity corresponding to the blank current) is less than the difference set by the user twice, the program automatically ends the electrolysis program and the integration program at the same time.

[0052] The method for determining the endpoint of a coulometric Karl Fischer titrator includes three stages: prediction model, rolling optimization and feedback correction. During the electrolysis process, when approaching the endpoint, the electrolysis current tends to be stable and has a certain hysteresis.

[0053] The DMC algorithm mainly uses step response as the system model.

[0054] (1) Prediction model.

[0055] By adding a control increment at the input end of the system, a series of output values ​​can be obtained at the output end at each sampling time t = T, 2T, 3T...NT, which can be expressed by parameters a1, a2, ...a N It means, (a1, a2, ...a N ) is the model parameter of the system, and N is the order of the model.

[0056] The unit step response of the potential of the electrolytic current of the controlled object at the sampling time is a1, a2, ...a N , for a convergent system, its unit step response is N Then it tends to the steady-state value a N =a(∞), according to the proportional and superposition principle of linear systems, the potential output value of the entire electrolysis system at a certain moment in the future can be predicted by the unit step response and control increment of the electrolysis potential of the given object. When the electrolysis current at time k has a control value Δu(k), the output value at the future moment is:

[0057] y1(k+i / k)=y0(k+i / k)+a i Δu(k) (2.1)

[0058] Where i = 1, 2, ..., N, y0(k+i / k) is the initial prediction value. Under the action of M consecutive control increments Δu(k), ..., △u(k+M-1), the output value of the electrolysis potential at each moment in the future is:

[0059]

[0060] Wherein, i = 1, 2, ... N. This model is a mathematical estimation model for the electrolysis potential to stabilize at the end point.

[0061] (2) Rolling optimization.

[0062] The electrolytic potential predictive control of the electrolytic process is a control strategy that determines the control quantity according to certain optimization criteria.

[0063] where y M The vector form y of (k+i / k) PM (k) is:

[0064] y PM (k) = y PO (k)+AΔu M (k) (2.3)

[0065]

[0066]

[0067] Here A is the dynamic matrix, M is the control time domain, and P is the optimization time domain. According to the definition, M≤P≤N.

[0068] According to the situation that the control increment Δu cannot change drastically, the optimization performance index of the system is selected as:

[0069]

[0070] In formula (2.4), q i and r j is the weight coefficient, coefficient q i Represents the suppression of tracking error, coefficient r j It is to suppress the change of the control quantity. Change the formula (2.4) into the form of vector:

[0071]

[0072] where ω p (k) = [ω(k+1)…ω(k+P)] Τ , Q=diag(q1…q P ), R = diag (r1…r M ), Q is the error weight matrix and R is the control weight matrix. Substituting equation (3) into equation (5), we get:

[0073]

[0074] Through the extreme value necessary condition dJ(k) / dΔu M (k) = 0 to obtain:

[0075]

[0076] Due to the existence of model errors in the actual system and the influence of factors such as nonlinearity, the control quantity calculated by equation (2.4) cannot closely track the expected value and effectively suppress disturbances. Therefore, only the first calculated control increment is implemented, and this operation is repeated in the next step to achieve the rolling optimization of DMC.

[0077] (3) Feedback correction.

[0078] Since the model is easily affected by environmental interference, which leads to reduced model accuracy, there is a certain error between the calculated predicted value and the actual value. Feedback correction of the error is required. The calculated model prediction output needs to be Compare the difference with the actual output value y(k+1) to get the output error:

[0079]

[0080] The output prediction is corrected in a weighted manner, as shown in formula (2.9):

[0081]

[0082] Where:

[0083]

[0084] is the corrected prediction vector, which is an N-dimensional vector h = [h1…h N ] Τ .

[0085] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for determining an endpoint of a coulometric Karl Fischer titrator, the coulometric Karl Fischer titrator comprising an electrolytic cell electrolysis system, characterized in that: The following steps are involved: Collecting dynamic data of electrolysis potential of the electrolytic cell electrolysis system during the electrolysis process; Constructing an electrolysis potential prediction model of the electrolysis cell electrolysis system according to the electrolysis potential dynamic data; Constructing an optimization function for terminating electrolysis of the electrolytic cell electrolysis system according to the electrolysis potential dynamic data and the electrolysis potential prediction model; Collect the electrolysis potential at the current moment; According to the electrolysis potential at the current moment, an endpoint judgment control scheme of the electrolysis system of the electrolysis cell is generated by the optimization function, and the termination electrolysis potential of the electrolysis system of the electrolysis cell is calculated; The electrolysis cell electrolysis system is controlled by the termination electrolysis potential.

2. The endpoint determination method of a coulometric Karl Fischer titrator according to claim 1, characterized in that: According to the actual electrolysis process, the electrolysis current signal collected during the electrolysis process is selected as the input of the predictive controller, and the electrolysis potential is selected as the output of the predictive controller. The future output is obtained based on the electrolysis potential prediction model: Formula (1.1) is the prediction model. are the model coefficients calculated using the least squares method.

3. The endpoint determination method of a coulometric Karl Fischer titrator according to claim 2, characterized in that: Based on formula (1.1), This breaks down into: in, represents the impact of future input on output, Indicates the impact of past inputs on outputs; According to the DMC algorithm, the objective function is selected as: The control objective is to minimize the objective function. In formula (1.3), m is the control step size, p is the prediction step size, r(k) is the output setting value, and q j and r i is the weighting coefficient; The error is defined as follows: In the formula Will and e(k+j) is expressed as in: Since Δu(k+i-1)=u(k+i-1)-u(k+i-2), make: get: Δu=Gu-bu(k-1)(1.5) Assume that the two weight matrices are as follows: Q=diag(q1…q p ),R=diag(r1…r m ), Bring in Get the least squares solution: According to the actual situation, the first item of the input sequence participates in the control, that is: in, k e =b Τ (H Τ QH+G Τ RG) -1 H Τ Q, k u =b Τ (H Τ QH+G Τ RG) -1 G Τ Rb。

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