Conductivity meter data processing method based on sage-husa adaptive Kalman filtering

Abstract

The invention relates to the technical field of data signal processing, in particular to a conductivity meter data processing method based on sage-husa self-adaptive Kalman filtering, which comprises the following steps of: sampling conductivity and temperature original data of a measured object through a conductivity sensor probe; an improved sage-husa adaptive Kalman filtering model of the conductivity meter is established; the method comprises the following steps of: transmitting original data measured by a probe of a conductivity sensor into a sage-husa self-adaptive filter, and performing filtering and noise reduction processing on an original signal by using an improved sage-husa self-adaptive Kalman filtering algorithm; the sage-husa adaptive filter can dynamically adjust filter parameters according to the change of the system state and the difference of noise characteristics, a system dynamic model can be well reflected without setting an accurate initial value so as to obtain more accurate estimation data, the robustness of the system is enhanced through outlier judgment introduced during innovation calculation, and the robustness of the system is improved. The measurement precision is effectively improved, the risk that parameters are affected by outliers is reduced, and the real-time performance of the system is enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of data signal processing, in particular to a conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering. Background Art

[0002] Conductivity is a digital representation of the ability of a solution to conduct electric current. We often use it to indicate the purity of water. The conductivity of pure water is very small. When the water contains inorganic acids, bases, salts or organic charged colloids, the conductivity increases. Conductivity is often used to indirectly infer the total concentration of charged substances in water. The conductivity of aqueous solutions depends on the nature and concentration of charged substances, the temperature and viscosity of the solution, etc.

[0003] The Kalman filter is an optimal autoregressive data processing algorithm. It constructs the system state equation and observation equation, and uses the estimated value of the previous moment and the observed value of the current moment to quickly estimate the state of the dynamic system, thereby achieving real-time tracking and prediction of the system state. Because it is easy to implement computer programming, can update and process the data collected on site in real time, and does not need to store historical data, it is one of the most widely used filtering methods in the world. The fields involved include robot navigation, control, sensor data fusion, radar system, missile tracking, etc.

[0004] With the development of microelectronics technology, people have higher requirements for conductivity accuracy. Reducing the influence of noise and temperature during the measurement process and improving the accuracy and stability of the system have become urgent needs. When dealing with the uncertainty in the system, that is, the uncertainty of sensor noise and system model, the Kalman filter introduces the covariance matrix, so that the algorithm can quantify the uncertainty and propagate and update it to obtain a more reliable state estimate. However, the covariance matrix and various noise data in the traditional Kalman filter are preset through engineering experience, experimental data or data manuals, which makes it difficult to truly describe the system model in a constantly changing actual environment. In order to solve these problems, we propose an improved Sage-Husa adaptive Kalman filter, which adds wild value judgment and adaptive adjustment of preset parameters to the filter, and can obtain higher accuracy without precise initial values. Summary of the invention

[0005] The purpose of the present invention is to overcome the above problems or at least partially solve the above problems, and to propose a conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering.

[0006] To achieve the above object, the present invention provides the following technical solution: a conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering, comprising the following steps:

[0007] Step 1: Sample the conductivity and temperature raw data of the measurement object through the conductivity sensor probe;

[0008] Step 2, establish an improved SAGE-HUSA adaptive Kalman filter model of the conductivity meter. In this model, first set the system state equation and the measurement equation, preset the system noise and the measurement noise as mutually incoherent zero-mean Gaussian white noise, then perform a prediction process, which includes system state prediction and error covariance prediction, and then perform a correction process, which includes Kalman gain calculation, new information vector calculation, estimated value correction update and estimated error correction update. The obtained new information vector is compared with the size of the preset maximum value to determine whether the system measurement value belongs to a wild value. Finally, the SAGE-HUSA adaptive filter dynamically adjusts the measurement noise related parameters according to the measurement data by calculating the adaptive factor and the forgetting factor to achieve adaptive filtering;

[0009] Step 3: The raw data measured by the conductivity sensor probe is passed into the SAGE-HUSA adaptive filter, and the improved SAGE-HUSA adaptive Kalman filter algorithm is used to filter and reduce noise on the raw signal;

[0010] Step 4: Using the filtered data through the conductivity calculation formula to obtain the solution conductivity value at the current temperature;

[0011] Step 5: Perform temperature compensation based on the actual fitting temperature compensation formula obtained from engineering experience and calculate the solution conductivity value at the reference temperature.

[0012] In a preferred embodiment, in step 1, the conductivity sensor probe includes two sensors, a conductivity electrode and a thermocouple, which are respectively used to collect conductivity data and temperature data of the measured liquid.

[0013] In a preferred embodiment, in step 2, the state equation and the measurement equation are respectively expressed as:

[0014] X k =AX k-1 +W k (1)

[0015] Z k =HX k +V k (2)

[0016] Where: X k is the estimated value of the system state at discrete time k; A is the system state transfer matrix; W k is the system noise; Z k is the system measurement value at time k; H is the system state measurement matrix; Vk To measure noise.

[0017] In a preferred embodiment, in step 2, the system noise W is preset k and measurement noise V k is an incoherent zero-mean Gaussian white noise that satisfies:

[0018]

[0019] Where: Q k is the system noise covariance matrix; R k is the measurement noise covariance matrix; δ kj is the Kronecker function; E is the mathematical expectation.

[0020] In a preferred embodiment, in step 2, the prediction process includes system state prediction and error covariance prediction:

[0021]

[0022]

[0023] Where: is X at time k k The prior state, that is, the prediction step estimate, is obtained by combining the system state transfer matrix A and the update step estimate It is calculated that since the liquid conductivity remains constant over a period of time, the prior state is consistent with the update step estimate; is the covariance matrix of the forecast step estimates; is the update step estimated value covariance matrix; Q k-1 is the system noise covariance matrix.

[0024] In a preferred embodiment, in step 2, the Kalman gain calculation formula is:

[0025]

[0026] Where: K k is the filter gain matrix; is the covariance matrix of the forecast step estimate; R k is the measurement noise covariance matrix;

[0027] The calculation formula of innovation vector is:

[0028]

[0029] Where: Z k System measurement value, in this system, is the data directly measured by the conductivity sensor probe; kis the new information vector, which is the system measurement value Z k , system state measurement matrix H, prediction step estimate and the previous moment measurement noise mean matrix r k-1 It is calculated that the wild value is determined by presetting the new information boundary value;

[0030] The estimated value correction update formula is:

[0031]

[0032] Where: is the update step estimate; is the estimated value of the prediction step; Z k is the system measurement value at time k; K k is the filter gain matrix; r k-1 is the noise mean matrix measured at the previous moment;

[0033] The innovation vector e k Substitute the calculation formula of into formula (8) to obtain the simplified update step estimate The calculation formula of

[0034] The simplified estimate correction update formula is:

[0035]

[0036] The estimated error correction update formula is:

[0037]

[0038] Where: is the covariance matrix of the update step estimation error; is the covariance matrix of the prediction step estimation error; K k is the filter gain matrix; H is the system state measurement matrix.

[0039] In a preferred embodiment, in step 2, the Sage-Husa adaptive filter can be expressed as:

[0040] d k =(1-b)(1-b k+1 ) (11)

[0041]

[0042] Where: d k is the adaptive factor, b is the forgetting factor, the selected forgetting factor value is 0.89, r k is the mean value of the system measurement noise at time k, and the mean value matrix r of the system measurement noise at time k-1 is k-1 , system measurement value The forecast step estimate of the system With the adaptive factor d k Calculated; R k is the measurement noise covariance matrix, which is the measurement noise covariance matrix R at the previous moment. k-1 、Innovation vector e k , the covariance matrix of the forecast step estimate With the adaptive factor d k Calculated.

[0043] In a preferred embodiment, in step 4, the solution conductivity calculation formula is:

[0044]

[0045] Where: k is the solution conductivity, which is obtained by the conductivity G and the electrode constant K; G is the solution conductivity, which is calculated by the voltage V and current I in the electrolyte; K is the electrode constant, which is obtained by the distance l between the electrode plates and the effective cross-sectional area A of the electrolyte between the electrode plates.

[0046] In a preferred embodiment, in step 5, the temperature coefficient calculation formula is expressed as:

[0047]

[0048] Where: a is the temperature coefficient of the solution, which is determined by the selected reference temperature T1 of the test solution, the actual temperature T2 of the test solution and the conductivity value k of the solution at the two temperatures. T1 , k T2 Calculated;

[0049] The linear temperature compensation formula is expressed as:

[0050]

[0051] Where: k Tref To measure the conductivity of the solution at the reference temperature, k T is the conductivity of the solution at the actual temperature of the solution, a is the temperature coefficient of the solution, T is the current temperature of the solution being measured, and T ref To measure the reference temperature of the solution;

[0052] The actual fitting temperature compensation calculation formula based on engineering experience is expressed as:

[0053]

[0054] Where: k R k is the conductivity value of the measured solution at a reference temperature of 25 degrees Celsius. TIt is the conductivity value of the solution measured at the actual temperature, and T is the actual temperature of the measured solution.

[0055] Compared with the prior art, the present invention has the following beneficial effects: through the conductivity meter data processing method based on Sage-HUSA adaptive Kalman filtering proposed by the present invention, compared with the traditional Kalman filtering algorithm, the Sage-HUSA adaptive filter can dynamically adjust the filter parameters according to the changes in the system state and the differences in noise characteristics, and can well reflect the system dynamic model without setting precise initial values ​​to obtain more accurate estimated data. By introducing wild value judgment when calculating new information, the robustness of the system is enhanced, the measurement accuracy is effectively improved, the risk of parameters being affected by wild values ​​is reduced, and the real-time performance of the system is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of the SAGE-HUSA adaptive Kalman filter algorithm in the present invention;

[0057] Figure 2 It is a schematic diagram of the simulation results of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0059] See also Figure 1 , Figure 2 The present invention provides a technical solution: a conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering, comprising the following steps:

[0060] Step 1: Apply an excitation signal to the conductivity sensor probe through the hardware circuit, obtain the conductivity original signal and temperature original signal fed back, and obtain the temperature data of the measured solution through linear interpolation.

[0061] Step 2: Establish an improved Sage-Husa adaptive Kalman filter model for the conductivity meter. In this model, first set the system state equation and measurement equation, which are expressed as:

[0062] X k =AX k-1 +W k (1)

[0063] Z k =HX k+V k (2)

[0064] Where: X k is the estimated value of the system state at discrete time k; A is the system state transfer matrix; W k is the system noise; Z k is the system measurement value at time k; H is the system state measurement matrix; V k To measure the noise; in this system, because the conductivity value remains constant for a certain period of time and the data is one-dimensional data, H and A are taken as 1 to simplify the model and facilitate iterative calculations.

[0065] Preset conductivity meter system noise W k and measurement noise V k is an incoherent zero-mean Gaussian white noise that satisfies the following requirements:

[0066]

[0067] Where: Q k is the system noise covariance matrix; R k is the measurement noise covariance matrix; δ kj is the Kron ecker function; E is the mathematical expectation.

[0068] The prediction process includes system state prediction and error covariance prediction:

[0069]

[0070] Where: is X at time k k The prior state, that is, the prediction step estimate, is obtained by combining the system state transfer matrix A and the update step estimate It is calculated that through the state equation, it can be known that the system state transfer matrix A is constant at 1 because of the conductivity characteristics of the solution and the dimension of the measured data, so the prior state is consistent with the update step estimate; is the covariance matrix of the forecast step estimates; is the update step estimated value covariance matrix; Q k-1 is the system noise covariance matrix.

[0071] The correction process includes Kalman gain calculation, new information vector calculation, estimation value correction update and estimation error correction update.

[0072] The Kalman gain calculation formula is:

[0073]

[0074] Where: K k is the filter gain matrix; is the covariance matrix of the forecast step estimate; R k is the measurement noise covariance matrix; according to the measurement equation, H is the conductivity meter system state measurement matrix, which is constant at 1, and the filter gain matrix K k It is only affected by the covariance matrix of the forecast step estimates and the covariance matrix of the measurement noise.

[0075] The calculation formula of innovation vector is:

[0076]

[0077] Where: Z k System measurement value, in this system, is the data directly measured by the conductivity sensor probe; k is the new information vector, which is the system measurement value Z k , system state measurement matrix H, prediction step estimate and the previous moment measurement noise mean matrix r k-1 It is calculated that for the obtained new information vector, by comparing the new information vector e k Model|e k | and the preset maximum value of the innovation vector e max The size of the system measurement value Z k Whether it is an outlier, reduce the impact of outliers on the sage-husa filter and enhance the robustness of the system.

[0078] The estimated value correction update formula is:

[0079]

[0080] Where: is the update step estimate; is the estimated value of the prediction step; Z k is the system measurement value at time k; K k is the filter gain matrix; r k-1 It is the noise mean matrix measured at the previous moment.

[0081] The innovation vector e k Substituting the calculation formula into equation (8), we can get the simplified update step estimate The calculation formula for .

[0082] The simplified estimate correction update formula is:

[0083]

[0084] The estimated error correction update formula is:

[0085]

[0086] Where: is the covariance matrix of the update step estimation error; is the covariance matrix of the prediction step estimation error; K k is the filter gain matrix; H is the system state measurement matrix.

[0087] The update of the estimated value and the estimated error is completed through equations (9) and (10), which is the end of an iteration in the Kalman filter. The updated estimated value is the output filtering result. Since the filtering effect of the traditional Kalman filter will tend to be stable after a period of iteration, it can only use empirical estimation for external uncertain noise. The filtering accuracy is not ideal, and when the data fluctuates violently, it will seriously affect the stability of the system. In order to solve the above shortcomings, the Sage-Husa adaptive filter with wild value judgment is introduced here. The wild value judgment is made when the new information is calculated, which can reduce the amount of system calculation after the wild value is detected.

[0088] The Sage-Husa adaptive filter can be expressed as:

[0089] d k =(1-b)(1-b k+1 ) (11)

[0090]

[0091] Where: d k is the adaptive factor; b is the forgetting factor. In engineering applications, it is generally taken as [0.85, 0.99]. In this scheme, the forgetting factor value is selected as 0.89 based on a large number of tests; r k is the mean value of the system measurement noise at time k, and the mean value matrix r of the system measurement noise at time k-1 is k-1 , system measurement value The forecast step estimate of the system With the adaptive factor d k Calculated; R k is the measurement noise covariance matrix, which is the measurement noise covariance matrix R at the previous moment. k-1 、Innovation vector e k , the covariance matrix of the forecast step estimate With the adaptive factor d k The purpose of self-adaptation is achieved by dynamically adjusting the influence of measurement noise through actual measurement values, estimated values ​​and continuously updated adaptive factors.

[0092] Step 3: The raw data measured by the conductivity sensor probe is passed into the Sage-HUSA adaptive filter, and the improved Sage-HUSA adaptive Kalman filter algorithm is used to perform noise reduction and smoothing on the raw signal. After stabilization, the conductivity data after filtering and noise reduction can be obtained.

[0093] Step 4: For the calculation of the conductivity value, the data processed by the filter is calculated using the solution conductivity calculation formula.

[0094] The solution conductivity calculation formula is:

[0095]

[0096] In the formula: k is the conductivity of the solution, which is obtained by the conductivity G and the electrode constant K; G is the conductivity of the solution, which is calculated by the voltage V and current I in the electrolyte; K is the electrode constant, which is obtained by the distance l between the electrode plates and the effective cross-sectional area A of the electrolyte between the electrode plates. Generally, the electrode manufacturers used will mark the electrode constant and can be used directly.

[0097] Step 5. The temperature compensation method of the conductivity meter is usually to first calculate the temperature coefficient a of the solution through the temperature coefficient calculation formula, and then obtain the conductivity value compensated to the reference temperature through the linear temperature compensation formula (16). However, in practical applications, due to the unstable factors such as the cumbersome and time-consuming measurement process caused by this method, the reference temperature is difficult to maintain stable, and the calculation amount is large, this solution uses the actual fitting temperature compensation algorithm obtained from a large amount of engineering experience to perform temperature compensation calculations. Through the actual fitting of a large amount of experimental data, the temperature compensation curve can be approximately obtained, and because the actual fitting curve has a large amount of data support, it can more accurately obtain the compensated conductivity value in practical applications.

[0098] The temperature coefficient calculation formula is expressed as:

[0099]

[0100] Where: a is the temperature coefficient of the solution, which is determined by the reference temperature T1 of the selected test solution, the actual temperature T2 of the test solution and the conductivity value k of the test solution at the two temperatures. T1 , k T2 Calculated.

[0101] The linear temperature compensation formula is expressed as:

[0102]

[0103] Where: k Tref To measure the conductivity of a solution at a reference temperature, k T To measure the conductivity of the solution at the actual temperature, a is the temperature coefficient of the solution, T is the current temperature of the measured solution, and Tref is the reference temperature of the measured solution.

[0104] The actual fitting temperature compensation calculation formula based on engineering experience is expressed as:

[0105]

[0106] Where: k R is the conductivity of the solution at a reference temperature of 25 degrees Celsius, k T It is the conductivity value of the solution measured at the actual temperature, and T is the actual temperature of the measured solution.

[0107] Furthermore, in order to verify the feasibility of this application, a set of comparative experiments was set up, such as Figure 2 As shown in the figure, there are conventional Kalman filtering and Kalman filtering with improved Sage-HUSA adaptive filter added. The conventional Kalman filtering model and the improved Sage-HUSA adaptive Kalman filtering model in step 2 are built in the simulation software and the initial parameters are preset. Then, similar data signals and noise disturbances are simulated in the simulation software according to the actual conductivity data, and the simulated data are simulated and filtered. By comparing the original data with the data after filtering by the two filters, it can be intuitively seen that the performance of this scheme has been significantly improved in real-time, accuracy and robustness compared with the traditional Kalman filtering.

[0108] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering, characterized in that: The following steps are involved: Step 1: Sample the conductivity and temperature raw data of the measurement object through the conductivity sensor probe; Step 2, establish an improved SAGE-HUSA adaptive Kalman filter model of the conductivity meter. In this model, first set the system state equation and the measurement equation, preset the system noise and the measurement noise as mutually incoherent zero-mean Gaussian white noise, then perform a prediction process, which includes system state prediction and error covariance prediction, and then perform a correction process, which includes Kalman gain calculation, new information vector calculation, estimated value correction update and estimated error correction update. The obtained new information vector is compared with the size of the preset maximum value to determine whether the system measurement value belongs to a wild value. Finally, the SAGE-HUSA adaptive filter dynamically adjusts the measurement noise related parameters according to the measurement data by calculating the adaptive factor and the forgetting factor to achieve adaptive filtering; Step 3: The raw data measured by the conductivity sensor probe is passed into the SAGE-HUSA adaptive filter, and the improved SAGE-HUSA adaptive Kalman filter algorithm is used to filter and reduce noise on the raw signal; Step 4: Using the filtered data through the conductivity calculation formula to obtain the solution conductivity value at the current temperature; Step 5: Perform temperature compensation based on the actual fitting temperature compensation formula obtained from engineering experience and calculate the solution conductivity value at the reference temperature.

2. The conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering according to claim 1, characterized in that: In the step 1, the conductivity sensor probe includes two sensors, a conductivity electrode and a thermocouple, which are respectively used to collect conductivity data and temperature data of the measured liquid.

3. The conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering according to claim 1 is characterized in that: In the step 2, the state equation and the measurement equation are respectively expressed as: X k =AX k-1 +W k (1) Z k =HX k +V k (2) Where: X k is the estimated value of the system state at discrete time k; A is the system state transfer matrix; W k is the system noise; Z k is the system measurement value at time k; H is the system state measurement matrix; V k To measure noise.

4. The conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering according to claim 3 is characterized in that: In step 2, the system noise W is preset k and measurement noise V k is an incoherent zero-mean Gaussian white noise that satisfies: Where: Q k is the system noise covariance matrix; R k is the measurement noise covariance matrix; δ kj is the Kronecker function; E is the mathematical expectation.

5. The conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering according to claim 4 is characterized in that: In step 2, the prediction process includes system state prediction and error covariance prediction: Where: is X at time k k The prior state, that is, the prediction step estimate, is obtained by combining the system state transfer matrix A and the update step estimate It is calculated that since the liquid conductivity remains constant over a period of time, the prior state is consistent with the update step estimate; is the covariance matrix of the forecast step estimates; is the update step estimated value covariance matrix; Q k-1 is the system noise covariance matrix.

6. The conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering according to claim 5 is characterized in that: In step 2, the Kalman gain calculation formula is: Where: K k is the filter gain matrix; is the covariance matrix of the forecast step estimate; R k is the measurement noise covariance matrix; The calculation formula of innovation vector is: Where: Z k System measurement value, in this system, is the data directly measured by the conductivity sensor probe; k is the new information vector, which is the system measurement value Z k , system state measurement matrix H, prediction step estimate and the previous moment measurement noise mean matrix r k-1 It is calculated that the wild value is determined by presetting the new information boundary value; The estimated value correction update formula is: Where: X k + is the update step estimate; is the estimated value of the prediction step; Z k is the system measurement value at time k; K k is the filter gain matrix; r k-1 is the noise mean matrix measured at the previous moment; The innovation vector e k Substitute the calculation formula of into formula (8) to obtain the simplified update step estimate The calculation formula of The simplified estimate correction update formula is: The estimated error correction update formula is: Where: P k + is the covariance matrix of the update step estimation error; is the covariance matrix of the prediction step estimation error; K k is the filter gain matrix; H is the system state measurement matrix.

7. The conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering according to claim 6 is characterized in that: In the step 2, the Sage-Husa adaptive filter can be expressed as: d k =(1-b)(1-b k+1 ) (11) Where: d k is the adaptive factor, b is the forgetting factor, the selected forgetting factor value is 0.89, r k is the mean value of the system measurement noise at time k, and the mean value matrix r of the system measurement noise at time k-1 is k-1 , system measurement value The forecast step estimate of the system With the adaptive factor d k Calculated; R k is the measurement noise covariance matrix, which is the measurement noise covariance matrix R at the previous moment. k-1 、Innovation vector e k , the covariance matrix of the forecast step estimate With the adaptive factor d k Calculated.

8. A conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering according to any one of claims 3 to 7, characterized in that: In step 4, the solution conductivity is calculated as: Where: k is the solution conductivity, which is obtained by the conductivity G and the electrode constant K; G is the solution conductivity, which is calculated by the voltage V and current I in the electrolyte; K is the electrode constant, which is obtained by the distance l between the electrode plates and the effective cross-sectional area A of the electrolyte between the electrode plates.

9. The conductivity meter data processing method based on Sage-Husa adaptive Kalman filtering according to claim 8, characterized in that: In step 5, the temperature coefficient calculation formula is expressed as: Where: a is the temperature coefficient of the solution, which is determined by the selected reference temperature T1 of the test solution, the actual temperature T2 of the test solution and the conductivity value k of the solution at the two temperatures. T1 , k T2 Calculated; The linear temperature compensation formula is expressed as: Where: k Tref To measure the conductivity of the solution at the reference temperature, k T is the conductivity of the solution at the actual temperature of the solution, a is the temperature coefficient of the solution, T is the current temperature of the solution being measured, and T ref To measure the reference temperature of the solution; The actual fitting temperature compensation calculation formula based on engineering experience is expressed as: Where: k R k is the conductivity value of the measured solution at a reference temperature of 25 degrees Celsius. T It is the conductivity value of the solution measured at the actual temperature, and T is the actual temperature of the measured solution.

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