- Email: [email protected]
A new design of inductive conductivity sensor for measuring electrolyte concentration in industrial field
Journal Pre-proof A New Design of Inductive Conductivity Sensor for Measuring Electrolyte Concentration in Industrial Field Kang Hui Song, Jang Hyon, Kim Gum Chol, Yu Song Chol, Kim Yong Hyok
PII:
S0924-4247(19)31591-2
DOI:
https://doi.org/10.1016/j.sna.2019.111761
Reference:
SNA 111761
To appear in:
Sensors and Actuators: A. Physical
Received Date:
3 September 2019
Revised Date:
6 November 2019
Accepted Date:
18 November 2019
Please cite this article as: Song KH, Hyon J, Chol KG, Chol YS, Hyok KY, A New Design of Inductive Conductivity Sensor for Measuring Electrolyte Concentration in Industrial Field, Sensors and Actuators: A. Physical (2019), doi: https://doi.org/10.1016/j.sna.2019.111761
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
A New Design of Inductive Conductivity Sensor for Measuring Electrolyte Concentration in Industrial Field Kang Hui Song*, Jang Hyon, Kim Gum Chol, Yu Song Chol, Kim Yong Hyok Faculty of Electronics, Kim Chaek University of Technology, 60-Gyougu, Yonggwang Street, Pyongyang, DPR of Korea
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Graphical Abstract
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Email: [email protected]
Highlights
We suggest extended model of TICS, which matches well with reality. Extended model includes equivalent loss resistance and mutual coupling factor. Virtual short of sensing amplifier eliminate the affection of permeability change. We design sensor structure, hardware and software of new sensing module. We compared new module with a commercial water quality meter F-74 (Horiba, Japan).
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Fig.1. Principle of TICS
Abstract
We have presented an extended model to analyze the characteristics of transformer type inductive conductivity sensor (TICS) and described a new sensor design based on it. The model contains equivalent loss resistances (R1 and R4) and mutual coupling factors (k12 and k34) of two transformers. With studying on this model, we have confirmed to be able to improve the sensing performance with a proper design of signal receiver and prefer virtual short method to the repeater one. Based on this virtual short method, we have designed a new sensor module and tested it with several corrosive solutions such as sulfuric acid, sodium hydroxide and sea water. And the sensing data have been
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hardly affected by the permeability change of magnetic cores. The drive voltage amplitude dynamic control method can improve the sensitivity in low measuring range and expand the measuring range. The sensitivity, response, recovery time, stability and comparison with a commercial water quality
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meter F-74 (Horiba, Japan) were evaluated.
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Keywords: conductivity sensor, contactless, toroidal, permeability, inductive
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1. Introduction
It is very important to measure the electrolyte concentration rapidly and exactly in oceanographic
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studies and in process control of the industrial fields.
solution.
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The electrolyte concentration is the most commonly estimated by the electric conductivity of the
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The traditional method for measuring the conductivity is the conductive one. In this method two or more metal electrodes are used to apply a voltage and measure the electric current flowing through the solution. These sensors are the most generally used in many laboratories because of its high sensitivity, simple structure and the ease to miniaturize [1, 2]. However, these are affected by
polarisation and the metal electrodes can be damaged by the corrosive electrolyte. So it is not recommended to use the conductive sensor in the industrial field for a long time. The inductive conductivity sensor is based on the electromagnetic induction and it has some advantages such as a robust structure and a low maintenance cost over the conductive sensor. These sensors have no bare metal electrode directly contacted with the solution, so they are not damaged by
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the chemical corrosive solutions such as sulphuric acid and sodium hydroxide. There are two kinds of the inductive conductivity sensor, eddy current type and transformer type.
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The eddy current type is based on that the magnetic permeability of the solution depends on the electrolyte concentration. Jaime et al. [3] used the eddy current type sensor (two solenoid coils) to
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measure the conductivity of underground water and they used a frequency above 100 KHz. Liao et al.
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[4] studied the sensing performance of different coplanar dual-coil geometries and they used 12 MHz
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frequency. Li et al. [5] studied a novel inspection method that uses dual-coil inductance to evaluate the quality of raw milk and they used 9MHz. Ding et al. [6] proposed a new inductive salt solution
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concentration sensor with digital frequency output and a planar structure sensing coil which is small
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in size and easy to integrate and they used a frequency about 10MHz. The eddy current type inductive sensor has several advantages such as an ease to miniaturize and a good stability. But this method uses a high frequency up to 10MHz, so the sensing circuitry is relatively complicated. The transformer type sensor has a robust structure and a good linearity. It is easy to install and convenient to use in the industrial field. Striggow et al. [7] presented the results of a general
theoretical investigation of three commonly used types of inductive conductivity sensors, i.e., the single transformer, the double transformer and the double transformer with an additional loop. A.J. Fougere et al. [8. 9] presented improved transformer type inductive sensor for long-term deployment in biologically active ocean regions and their sensor was not affected by the external field. Ribeiro et al. [10] presented an inductive sensor constructed as a double transformer, to be utilized to measure
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the water salinity in the sea and estuaries. Linda et al. [11, 12] presented the design, development and testing of a salinity sensor system for estuary studies and the sensor was based on the double
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transformer principle. Wu et al. [13] reported the principle of the transformer type inductive conductivity sensor and presented a mathematical model to show the characteristics of the sensor.
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Wu et al. reported that a soft magnetic material with high permeability is required as inductive core
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to improve the sensor resolution and the appropriate change of excited current and frequency can be
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also useful for the optimization of sensor sensitivity.
The transformer type inductive conductivity sensor has one or more magnetic cores. And the
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permeability values of them are changed by temperature and pressure. It is very important to
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compensate or eliminate the affection of magnetic core permeability change for improving the sensing performance.
In this paper we have suggested the extended model of TICS including the magnetic loss resistance and mutual coupling factor. Then we have compared the output signal of the model in the cases of repeater and virtual short. From the investigation, it becomes found that the output signal of
the model simulation using virtual short amplifier configuration is independent of the permeability values of magnetic cores and is proportional to the conductivity of the solution. We designed a new module based on the virtual short configuration to measure conductivity and concentration of the electrolyte with TICS. The inductive conductivity sensors are widely applied in many oceanographic research and hydro-meteorological measurement. However, they are very
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expensive and their salinity measuring range is below 5%. In the module design we kept simple circuitry, high resolution, low power consumption, low cost, and low maintenance cost in mind. So
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newly designed sensor module has only about 30$ cost. And its conductivity measuring range is up
This paper consists of 5 sections.
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to 350mS/cm, which is higher than the maximum conductivity of sodium chloride solution at 25℃.
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In section 2, we present the extended model of TICS and study the affection of the input
magnetic core.
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impedance of signal amplifier to the output signal dependence on the permeability change of the
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In section 3, the module design is presented and it includes the sensor structure design, hardware
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design and software design.
In section 4, the permeability change compensating ability, sensitivity, linearity, response, recovery time, stability and the comparison with a commercial water quality meter are evaluated. Section 5 concludes the paper and describes our future work.
2. Sensing model of TICS
Fig.1. Principle of TICS
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Fig. 1 shows the principle of the transformer type inductive conductivity sensor.
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TICS consists of drive coil, sense coil and temperature sensor. Applying an alternating voltage to the drive coil induces an ionic electric current in the solution around the sensor. This ionic electric
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current also induces an electric current in the sense coil which is proportional to the conductivity of
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the solution. This electric current induced in the sense coil is converted to the conductivity or the
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concentration of the solution. Here two coils are encapsulated by chemical-resistant plastic material, so they are not attacked by the corrosive electrolytes.
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The double transformer type inductive conductivity sensor can be equalized as shown in Fig.2.
Fig.2. Sensor model of TICS: P.C. is for Primary Coil and S.C. is for Secondary Coil
R1, L1 and N1 are the magnetic loss resistance, self-inductance and turn number of drive coil of the sensor or primary coil of the first transformer respectively. RS is the resistance of the electrolyte under test. L2 and N2 are the self-inductance and turn number of secondary coil of the first transformer respectively and N2 = 1. L3 and N3 are the self-inductance and turn number of primary coil of the second transformer respectively and N3 = 1.
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R4, L4 and N4 are the magnetic loss resistance, self-inductance and turn number of sense coil of the sensor or secondary coil of the second transformer respectively. M12 and M34 are the mutual
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inductances of the two transformers respectively.
M14 is the mutual inductance between drive coil and sense coil, although it is not illustrated in fig.2.
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M14 is not discussed in this paper because the high permeability magnetic core and the shielding
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plate between the drive coil and sense coil can reduce M14 to nearly zero. As for M13 and M24,
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although they are not illustrated in fig.2, M13 is same as M12 and M24 is same as M34 because L2 and L3 are made by the identical electrical current loop of the solution under test.
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By Kirchhoff’s law and Ohm’s law of closed circuit, we can get the following equations: jω𝑀12 𝐼2 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1
(1)
jω𝑀12 𝐼1 + jω𝑀34 𝐼4 + 𝐼2 (𝑅𝑆 + jω𝐿2 + jω𝐿3 ) = 0
(2)
jω𝑀34 𝐼2 + 𝐼4 (𝑅4 + 𝑅𝑟 + jω𝐿4 ) = 0
From the three equations as above, we can get four formulas as following:
(3)
𝐴
𝐼1 =
2 𝐴(𝑅1 +𝑗𝜔𝐿1 )+𝜔2 𝑀12
𝐼2 = − 𝐼4 =
A=
2 𝜔2 𝑀34
𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4
𝑗𝜔𝑀12 𝐴
𝑈1
(4)
𝐼1
(5)
−𝑗𝜔𝑀34 𝐼 𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4 2
(6)
+ (𝑅𝑆 + jω𝐿2 + jω𝐿3 )
(7)
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(The equations induction process is attached in appendix A.) M12 and M34 are mutual inductances of the transformers and they are as following:
(8)
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𝑀12 = 𝑘12 √𝐿1 𝐿2 , 𝑀34 = 𝑘34 √𝐿3 𝐿4
Eq.4-7 is the extended model including magnetic loss resistance and mutual coupling factor of
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TICS and we can analyze the characteristics of the sensor.
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In the model, L1, L2, L3 and L4 are self-inductances of the coils and they are dependent on the permeability of the magnetic core materials.
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Here, k12 and k34 are the mutual coupling factors of the transformers and they are also dependent
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on the permeability of the magnetic core.
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We simulated the relationship between U1 and I4 using MATLAB software. (The MATLAB m files are attached in appendix B.)
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Fig.3 Calculation results based on the extended model: The signal voltage of the sensor with
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different magnetic cores to several conductance in the case of repeater configuration circuit (a); and
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virtual short configuration circuit (b); (c) The signal voltage change and error when the permeability changes from 0 to 20000 in the case of virtual short; (d) The signal voltage change depending on
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drive frequency and permeability in the case of virtual short.
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Fig.3 is the illustration of calculation results based on the new model. The relationships of conductance versus output signal are shown in the case of 𝑅𝑟 → ∞ (Fig.3a) and in the case of 𝑅𝑟 = 0 (Fig.3b). When Rr→ ∞, the output signal is changed remarkably by the change of the permeability of the magnetic core. However when Rr = 0, the output signal is scarcely changed by the change of the permeability. This result gives us a
possibility to design a new sensor independent of the permeability change of the magnetic core. Let’s suppose some relational expressions as following are true. (9)
𝑅4 ≪ 𝑁42 𝑅𝑆 , 𝑅4 ≪ ω𝐿4
(10)
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𝑅1 ≪ 𝑁12 𝑅𝑆 , 𝑅1 ≪ ω𝐿1
𝑘12 = 𝑘34 = 1
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𝑅𝑟 = 0
(11)
(12)
Then we can get equation 13 from the equations 4-7.
𝑈1
=
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𝐼4 =
𝑁1 𝑁4 𝑅𝑆
𝑈1
𝑁1 𝑁4
𝐺
(13)
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Equation 13 shows that when Rr = 0, output signal I4 is proportional to the conductance of the
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solution, G, and is nearly independent of the permeability of the magnetic core. Eq.13 is equal to the equation illustrated in literature [7].
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This means that our new model is more universal than before.
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The mutual coupling factors k12 and k34 converge to 1 as the permeability becomes larger. In fact they are nearly 1 when the permeability of the magnetic core is over 10000. The signal voltage of the sensor with the magnetic core of permeability over 10000 is hardly affected by the permeability change (Fig.3c). The equivalent loss resistances R1 and R4 depend on the turn number of drive coil and sense
coil. They are also dependent on the frequency. From the investigation with our new model it becomes found that the rational turn numbers of drive coil and sense coil are 10 respectively. The signal voltage is hardly affected when the drive frequency over 10kHz is used (Fig.3d). A frequency higher than 10KHz may increase the magnetic loss, as a drive frequency 10KHz is selected.
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3. Design and Implementation
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3.1. Sensor Structure Design
Characteristic parameters of magnetic core
Value
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Parameter
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Table 1
HS10
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Model
Mn-Zn ferrite 10000±25%
Relative loss coefficient, tanδ/μi, (×10-6)
30 (100kHz)
Saturation flux density, Bs, mT
380 (H=1194A/m, 25℃)
Residual flux density, Br, mT
120 (H=1194A/m, 25℃)
Coercive field, Hc, A/m
5
Curie temperature, Tc, ℃
120
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Initial Permeability, μi
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Material
Density, db, kg/m3
4.9
Resistivity, ρv, Ωm
0.2
According to eq.13, the signal current is independent on permeability of magnetic core. However there are some preconditions for eq.13. They are the conditions 9 – 12. In the condition 11, k is mutual coupling factor. This becomes closer to 1 when the permeability of magnetic core becomes
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larger. From the conditions 9 and 10 the magnetic loss must be so small that it may be neglected. In a word we have to use magnetic cores which have high permeability and small magnetic loss. HS10
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(TDK company) is selected as the most suitable magnetic core for our purpose. The properties of HS10 are given in Table 1.
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The sensor structure and its fabrication steps are given in Fig.4.
Fig.4 Sensor structure design and assembling steps: (a) step1; (b) step 2; (c) step 3; (d) completed model and the photo The drive coil and sense coil are fixed on the PCB and welded to the lead wire (Fig.4a). The main body encapsulates the two coils and PCB (Fig.4b). The seal ring, fixing cap and gland are assembled each other (Fig.4c).
In fig.4d the completed structure and the photo are given.
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The seal ring and gland are for water resistant structure and the fixing cap is for setup in tank.
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Each part is fabricated by using 3D printer and its material is ABS plastic.
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The thickness of the main body is 2mm.
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ABS plastic material has a high chemical resistance, a high water resistance and low cost.
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3.2. Hardware Design
The electronic circuit diagram is shown in Fig.5.
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The circuit consists of MCU, power supply, communication part, drive power amplifier, signal
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amplifier and temperature sensing circuit. We used STM32F103VET6 as the MCU. The input voltage of module is 12V and we used LM2576, LM1117 and TL431 as voltage regulators. The communication is done by MAX485.
The drive power amplifier and signal amplifier are for TICS. The drive power amplifier is to apply stable sine wave to drive coil and the sine wave is made by DAC (Digital to Analog Converter) of
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MCU.
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Fig.5 Electronic circuit diagram
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The signal amplifier is configured as virtual short as discussed in section 2. The virtual short
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configuration of the signal amplifier is shown in Fig.6.
Fig.6 Virtual short configuration
From the basic property of operation amplifier the relational expression as following is established. 𝑈𝑁 = 𝑈𝑃 = 𝑈𝑉𝐺
(14)
𝑈𝑉𝐺 is the virtual ground voltage.
So it is assumed that two terminals of sense coil are short-circuited. Because the negative input of operation amplifier has very large impedance, the electric current of sense coil flows through Rf. So there is an alternating voltage proportional to the intensity of the electric current flowing through the sense coil. The intensity of the electric current flowing through
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the sense coil is proportional to the conductivity of the solution.
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Fig.7 shows the photograph of sensor module.
Fig.7 Photograph of the sensor module
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The pressure sensor on the board is to measure precisely the level of salty water in a salt works
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and it is not associated with TICS being discussed in this paper. The power supply is designed for 12V input voltage and it takes area as large as 30% of all the board and it can be minimized as small as the situation may need.
In this module the signal generation and process are conducted in MCU and the complicated analog parts of TICS are simplified. In fact the analog circuit of our sensor module consists of two operation amplifiers for power amplification and virtual short configuration.
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3.3 Software Design
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The internal operation mechanism of MCU is illustrated in Fig.8.
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Fig.8 Internal operation mechanism of MCU
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The sine wave to drive the sensor is made by DAC1 which is controlled by DMA3 (Direct Memory Access). The signal from the virtual short circuit is converted to digital in ADC1 which is
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controlled by DMA4.
DMA3 and DMA4 are synchronized by TIM1 (Timer). The sensor signal is sampled in ADC 60 times every period and the sampled data is processed digitally by using FFT (Fast Fourier Transform).
Fig.9 Digital signal processing in MCU Fig.9 shows the DSP (Digital Signal Processing) in MCU. By using DSP unnecessary frequency
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components of signal are eliminated. The MCU calculated out the conductivity of the solution with only the main frequency 10 KHz component.
From the conductivity and the temperature of the solution the MCU gets out the concentration of
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Fig.10 shows the software algorithm of the MCU.
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the electrolyte.
Fig.10 Software algorithm and time consumption 4. Experiment Result and Discussion
4.1.
Response Characteristics
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4.1.1. Experiments with several resistors
An experimental investigation with several resistors was carried out to assess the response characteristics of the sensor module. The experiment conditions are given in Table 2.
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The experiment conditions to test the response characteristics.
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Table 2
Drive Coil
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Sense Coil
Turns
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Parameter
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Ambient Temperature
Permeability Inductance
10
10500
751μH
10
11500
822μH
25℃
Rf, Feedback Resistor
10kΩ
Frequency
10kHz
U1, Drive Voltage Amplitude
300mV
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Self-
The permeability, self-inductance and other magnetic properties were measured by a LCR meter (UT612, UNI-T) and Hysteresis Curves Test System (FE-2100SA).
The resistance values of resistors were measured exactly by Precision Multi-meter (8846A, Fluke).
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Schematic diagram and photograph of the experimental setup is shown in Fig. 11.
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Fig.11. Schematic diagram and photograph of experimental setup: (a) Schematic diagram; (b) a photograph.
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The resistor RS stands for the solution loop resistor (Fig.11a).
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The experiment conditions in Table 2 were used to simulate the sensor model by using MATLAB software.
(MATLAB m file for simulation is given in appendix B.)
The simulation data by using the sensor model and the experimental data by using several resistors were compared and the results are given in Fig.12.
Fig.12. Comparison the experimental data with the sensor model simulation data
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The experimental data curve is nearly coincident with the sensor model simulation curve. This shows that the sensor model suggested in this paper is consistent well with the real measuring and it
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can be used to investigate the TICS.
reference voltage of microprocessor.
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For a resistor with the conductance over 50mS the sensor signal voltage becomes over the ADC
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A lower amplitude drive voltage can be used to measure the conductance over 50mS.
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The drive voltage is made by DAC of microprocessor and it can be controlled easily. By using this method so-called drive voltage amplitude dynamic control method this sensor
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module can measure the wide range conductance. In this paper the amplitude of the drive voltage is
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fixed at 300mV and this drive voltage amplitude dynamic control method is not used. Sequentially the influence of ferrite permeability change by temperature to the measuring was studied. The sensor module was designed by using virtual short circuit in order to reduce the influence of permeability change by temperature and other environmental conditions to the measuring. Firstly the permeability of the magnetic core was measured heating the sensor. Then the
sensing value was recorded heating the sensor with 100Ω precision resistor as a solution loop resistor (Fig.11a). The temperature coefficient of the resistor is about 0.005%/℃ and the resistance change of the resistor by temperature can be neglected. By using a constant temperature bath the sensor was heated from 20℃ to 70℃ with 5℃ interval and at each temperature keeping point the measuring
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was done several times for 20 minutes. The test results are given in Fig.13.
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Fig.13. The test result of heating the sensor with 100Ω precision resistor.
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In Fig.13 the permeability of the magnetic core has the maximum value at about 25℃ and the
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minimum value at about 60℃. The maximum value is 12000 and the minimum value is 10000. At the time the signal voltage is 289.3mV at 25℃ and 289.0mV at 60℃. This shows that the
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permeability change of magnetic core hardly affects the signal voltage and the virtual short circuit is
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effective to reduce the influence of the permeability change to the sensing value.
4.1.2. Experiments with solutions
An experiment was performed to test the sensitivity and linearity of the sensor module.
The sulphuric acid solution of which the molar conductivity is high relatively was used because the high conductivity over 350mS/cm should be measured. And the other reason why to select the sulphuric acid is to test the ability of the sensor to stand in the chemical corrosive solution such as the strong acid and alkali. 4 kinds of sulphuric acid solutions which had 1%, 3%, 5%, 8% concentrations respectively were
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prepared. The high purity sulphuric acid and distilled water was used for preparation of solutions. A constant temperature bath was used to keep the temperature constantly at 25℃.
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Each solution was measured 5 times at 25℃ and the measured values were averaged and
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The test results are presented in Fig.14
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recorded.
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Fig.14. Experimental data points and linear fitting result
The result of the experiment to measure the real electrolyte shows that our sensor module has a good linearity. The sensitivity is about 4mV per 1mS/cm and the measuring range is up to 350mS/cm.
If the drive voltage amplitude dynamic control method mentioned above is used, the sensitivity and the measuring range can be improved remarkably. And the sensor is not damaged by the sulphuric acid. So this sensor can be used to measure the concentration and conductivity of the sulphuric acid.
4.2. Response, recovery time and stability
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An experiment was performed to test the response and recovery time of the sensor module.
For the experiment 0.15M sodium hydroxide solution was used. For the solution preparation high
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purity sodium hydroxide and distilled water was used. The temperature of solution was kept
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constantly at 25℃.
Fig.15. (a): Response and recovery of the sensor module in case of empty container and 0.15M sodium hydroxide solution; (b): Stability of the sensing value in 0.15M sodium hydroxide solution at 25℃. In this experiment TICS was firstly immersed into the prepared sodium hydroxide solution for 3 minutes. The measurement was done once per 300 milliseconds. Then the sensor was exposed to
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atmosphere of laboratory for 3 minutes. Then the above steps were repeated several times. Fig.15a shows the response and recovery characteristics of the sensor module.
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As shown in Fig. 15a the measured response and recovery time were within one second.
Fig.15b shows the stability of sensing value in 0.15M sodium hydroxide solution at 25℃.
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Tiny measured value fluctuations (about 1.8mV) were observed and the uncertainty of the sensor
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module was calculated to be about 0.12% in the full scale.
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And the sensor is not damaged by the sodium hydroxide solution. So this sensor can be used to
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measure the concentration and conductivity of the sodium hydroxide.
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4.3. Characteristics Comparison
A Comparison test between the present sensor module and a commercial meter was performed. Water Quality Meter F-74 (Horiba, Japan) was used as the commercial meter. The conductivity electrode 9382-10D (Horiba, Japan) was used and its measuring range is below 100mS/cm.
Sea water concentrated 2.2% was used as a test solution and its conductivity measured by commercial water quality meter F-74 was 44.5mS/cm at 25℃. The signal voltage of the TICS module was 185.6mV and its conductance was 6.36mS. The conductivity and the conductance are related with each other as following: κ = G × 𝑘𝑐
(15)
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κ is the electrical conductivity, G is the conductance and kc is the cell constant. The cell constant is related with geometry of the sensor.
In the case of the conductive electrode the cell constant is calculated as following: 𝐿 𝑆
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𝑘𝑐 =
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L is the distance between electrodes and S is the area of the electrodes.
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In the case of the inductive sensor the cell constant calculation is relatively complicated. The cell constant is 6.9968 in our case.
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The sea water was heated from 15℃ to 47℃ slowly and the conductivity value of the sensor module and the commercial meter was recorded at several temperature points together (Fig.11b).
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The temperature control magnetic stirrer was used to heat and stir the solution.
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Fig.16 shows the results.
(16)
Fig.16 The result of comparison test with the commercial water quality meter F-74 (Horiba, Japan) The result shows that the maximum error between the sensor module and the commercial meter is 0.6mS/cm. The relative error of the sensor module was calculated to be 0.17% in the full scale. 5. Conclusion The simulation results using our new model of TICS have shown a good similarity to the experiment results. The higher permeability of magnetic core is, the more reliable this model is.
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The receiving signal is hardly affected when the impedance of signal receiver is zero.
The smart sensing device has been developed by using synchronization of DAC and ADC, digital signal processing by Fast Fourier Transform and discontinuous wave generation. This sensing device
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has low cost, low power consumption, simple circuitry, good linearity, high sensitivity and wide measuring range. An important problem in this sensing device is the contact resistance between the
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sensor and the PCB board. As time goes by, this resistance may become larger than the initial value
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because of the environmental condition. And this change may affect the sensing value. In the future we will investigate the frequency modulating method which uses the different 3 or more frequencies
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to eliminate the influence of the contact resistance. Next time the mathematical model to calculate the cell constant of TICS will be discussed in detail and this can be helpful to get the rational
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geometry of the sensor.
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Conflict of Interest No Interest.
Acknowledgement We thank Prof. Choe Yong Nam, the director of Mirae Centre of Science and Technology, Kim chaek University of Technology for providing a good experimental condition. We thank Prof. Kang Il Yong, the director of the School of Science and Engineering, Kim chaek University of Technology
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for his great theoretical support.
References
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[1] P.A. Ramon, L.G. Javier, Constant-phase element identification in conductivity sensors using a single square wave, Sens. Actuators A-Phys. 132 (2006) 122-128.
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[2] X. Huang, R.W. Pascal, K. Chamberlain, C.J. Banks, M. Mowlem, F. Morgan, A miniature,
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2011, 11, 3246–3252.
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high precision conductivity and temperature sensor system for ocean monitoring, IEEE Sens. J.
[3] http://dx.doi.org/doi:10.3390/s150920990
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[4] http://dx.doi.org/doi:10.1016/j.sna.2017.07.022
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[5] http://dx.doi.org/DOI: 10.1016/j.sna.2016.01.035 [6] http://dx.doi.org/doi:10.1016/j.sna.2017.05.043 [7] K. Striggow, R. Dankert, The exact theory of inductive conductivity sensors for oceanographic application, IEEE J. Ocean. Eng. 1985, 10, 175–179.
[8] A. J. Fougere, New non-external field inductive conductivity sensor (NXIC) for long term deployments in biologically active regions, OCEANS 2000 MTS/IEEE Conference and Exhibition, Vol. 1, pp. 623-630, Sept. 2000. [9] A. Fougere, M. St Germain, F.J. Kelly, Field evaluation of a revolutionary CTD design, OCEANS 2003 Proceedings, Vol. 4, pp. 2249-2253, Sept. 2003.
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[10] A.L. Ribeiro, H.M.G. Ramos, P.M. Ramos, J.M.D. Pereira, Inductive Conductivity Cell for Water Salinity Monitoring. Metrology for a Sustainable Development September, 17 – 22,
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2006, Rio de Janeiro, Brazil
[11] T.T. Pham, T. Green, J. Chen, P.Truong, A. Vaidya and L. Bushnell, A Salinity Sensor System
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for Estuary Studies, Applied Math Journal: Networks and Heterogeneous Media, vol. 4, no. 2,
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pp. 381-392, 2009.
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[12] R.T. Wood, A. Bannazadeh, N.Q. Nguyen, L.G. Bushnell, A salinity sensor for long-term data collection in estuary studies, In Proceedings of the IEEE OCEANS, Sydney, Australia, 24–27
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May 2010; pp. 1–6.
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[13] S. Wu, H. Lan, J.J. Liang, Y. Tian, Y. Deng, H.Z. Li, N. Liu, Investigation of the performance of an inductive seawater conductivity sensor, Sens. Trans. 186 (2015) 43-48.
Kang Hui Song was born in Sinuiju, DPRK, in 1989. He works on the Ph.D. degree in Kim Chaek University of Technology, Pyongyang, DPRK. His current research is the water quality sensing devices.
Appendix A jω𝑀12 𝐼2 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1
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(A.1)
jω𝑀12 𝐼1 + jω𝑀34 𝐼4 + 𝐼2 (𝑅𝑆 + jω𝐿2 + jω𝐿3 ) = 0
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Eq. (A.6) is obtained from Eq. (A.3) simply.
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jω𝑀34 𝐼2 + 𝐼4 (𝑅4 + 𝑅𝑟 + jω𝐿4 ) = 0
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jω𝑀34 𝐼2 + 𝐼4 (𝑅4 + jω𝐿4 ) = 0 −𝑗𝜔𝑀34 𝐼 𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4 2
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𝐼4 =
(A.2)
(A.3)
(A.3)
(A.6)
Substitute Eq. (A.6) to Eq. (A.2).
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jω𝑀12 𝐼1 + jω𝑀34 𝐼4 + 𝐼2 (𝑅𝑆 + jω𝐿2 + jω𝐿3 ) = 0
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jω𝑀12 𝐼1 + jω𝑀34
(A.2)
−𝑗𝜔𝑀34 𝐼 + 𝐼2 (𝑅𝑆 + jω𝐿2 + jω𝐿3 ) = 0 𝑅𝑟 + 𝑅4 + 𝑗𝜔𝐿4 2
2 𝜔2 𝑀34 jω𝑀12 𝐼1 + ( + (𝑅𝑆 + jω𝐿2 + jω𝐿3 ))𝐼2 = 0 𝑅𝑟 + 𝑅4 + 𝑗𝜔𝐿4
A=
2 𝜔2 𝑀34
𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4
+ (𝑅𝑆 + jω𝐿2 + jω𝐿3 )
(A.7)
jω𝑀12 𝐼1 + 𝐴𝐼2 = 0 𝐼2 = −
𝑗𝜔𝑀12 𝐴
𝐼1
(A.5)
Substitute Eq. (A.5) to Eq. (A.1). jω𝑀12 𝐼2 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1
jω𝑀12 (−
𝑗𝜔𝑀12 𝐴
(A.1)
)𝐼1 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1
2 ω2 𝑀12 𝐼1 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1 𝐴 2 ω2 𝑀12 + (𝑅1 + jω𝐿1 ))𝐼1 = 𝑈1 𝐴 𝐴
𝐼1 =
2 𝐴(𝑅1 +𝑗𝜔𝐿1 )+𝜔2 𝑀12
𝑈1
So the equations are obtained as following:
2 𝐴(𝑅1 +𝑗𝜔𝐿1 )+𝜔2 𝑀12
2 𝜔2 𝑀34
𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4
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close all
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A=
figure
%absolute permeability
u0 = 12.57 * 10^(-7);
%turns of coil
𝐴
𝐼1
−𝑗𝜔𝑀34 𝐼 𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4 2
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𝐼4 =
𝑗𝜔𝑀12
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𝐼2 = −
𝑈1
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𝐴
𝐼1 =
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(
+ (𝑅𝑆 + jω𝐿2 + jω𝐿3 )
Appendix B
(A.4)
(A.4)
(A.5)
(A.6)
(A.7)
N1 = 10; N2 = 10;
R1 = 0.1*N1;
R4 = 0.1*N2;
f = 10000;
omega = 2*pi*f;
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%height, inner diameter, outer diameter of coil
th = 7*10^(-3);
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r1 = 7.5*10^(-3);
r2 = 12.5*10^(-3);
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U1 = 0.3;
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Rr = 0;
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%permeability
u1 = 10500;
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k12=1-30/u1;
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u2 = 11500;
k34=1-30/u2;
%inductance
L1 = u0*u1*N1^2*th*log(r2/r1)/(2*pi)
L2 = u0*u1*th*log(r2/r1)/(2*pi);
L3 = u0*u2*th*log(r2/r1)/(2*pi);
L4 = u0*u2*N2^2*th*log(r2/r1)/(2*pi)
M12 = k12*(L1*L2)^0.5;
M34 = k34*(L3*L4)^0.5;
Conductance = [0, 0.5, 1, 3.3, 5, 10, 16.1, 20, 25, 33.3];
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Rs = 1000./Conductance;
A=omega^2*M34^2/(Rr+R4+j*omega*L4) + Rs+j*omega*(L2+L3);
I2= -j*omega*M12*I1./A;
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I4 = -j*omega*M34*I2./(R4+Rr+j*omega*L4);
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U4 = I4*10000;
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abs(U4)
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plot(Conductance, abs(U4),'ko'); hold on;
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I1 = A*U1./(A*(R1+j*omega*L1)+omega^2*M12^2);
PII:
S0924-4247(19)31591-2
DOI:
https://doi.org/10.1016/j.sna.2019.111761
Reference:
SNA 111761
To appear in:
Sensors and Actuators: A. Physical
Received Date:
3 September 2019
Revised Date:
6 November 2019
Accepted Date:
18 November 2019
Please cite this article as: Song KH, Hyon J, Chol KG, Chol YS, Hyok KY, A New Design of Inductive Conductivity Sensor for Measuring Electrolyte Concentration in Industrial Field, Sensors and Actuators: A. Physical (2019), doi: https://doi.org/10.1016/j.sna.2019.111761
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
A New Design of Inductive Conductivity Sensor for Measuring Electrolyte Concentration in Industrial Field Kang Hui Song*, Jang Hyon, Kim Gum Chol, Yu Song Chol, Kim Yong Hyok Faculty of Electronics, Kim Chaek University of Technology, 60-Gyougu, Yonggwang Street, Pyongyang, DPR of Korea
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Graphical Abstract
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Email: [email protected]
Highlights
We suggest extended model of TICS, which matches well with reality. Extended model includes equivalent loss resistance and mutual coupling factor. Virtual short of sensing amplifier eliminate the affection of permeability change. We design sensor structure, hardware and software of new sensing module. We compared new module with a commercial water quality meter F-74 (Horiba, Japan).
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Fig.1. Principle of TICS
Abstract
We have presented an extended model to analyze the characteristics of transformer type inductive conductivity sensor (TICS) and described a new sensor design based on it. The model contains equivalent loss resistances (R1 and R4) and mutual coupling factors (k12 and k34) of two transformers. With studying on this model, we have confirmed to be able to improve the sensing performance with a proper design of signal receiver and prefer virtual short method to the repeater one. Based on this virtual short method, we have designed a new sensor module and tested it with several corrosive solutions such as sulfuric acid, sodium hydroxide and sea water. And the sensing data have been
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hardly affected by the permeability change of magnetic cores. The drive voltage amplitude dynamic control method can improve the sensitivity in low measuring range and expand the measuring range. The sensitivity, response, recovery time, stability and comparison with a commercial water quality
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meter F-74 (Horiba, Japan) were evaluated.
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Keywords: conductivity sensor, contactless, toroidal, permeability, inductive
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1. Introduction
It is very important to measure the electrolyte concentration rapidly and exactly in oceanographic
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studies and in process control of the industrial fields.
solution.
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The electrolyte concentration is the most commonly estimated by the electric conductivity of the
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The traditional method for measuring the conductivity is the conductive one. In this method two or more metal electrodes are used to apply a voltage and measure the electric current flowing through the solution. These sensors are the most generally used in many laboratories because of its high sensitivity, simple structure and the ease to miniaturize [1, 2]. However, these are affected by
polarisation and the metal electrodes can be damaged by the corrosive electrolyte. So it is not recommended to use the conductive sensor in the industrial field for a long time. The inductive conductivity sensor is based on the electromagnetic induction and it has some advantages such as a robust structure and a low maintenance cost over the conductive sensor. These sensors have no bare metal electrode directly contacted with the solution, so they are not damaged by
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the chemical corrosive solutions such as sulphuric acid and sodium hydroxide. There are two kinds of the inductive conductivity sensor, eddy current type and transformer type.
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The eddy current type is based on that the magnetic permeability of the solution depends on the electrolyte concentration. Jaime et al. [3] used the eddy current type sensor (two solenoid coils) to
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measure the conductivity of underground water and they used a frequency above 100 KHz. Liao et al.
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[4] studied the sensing performance of different coplanar dual-coil geometries and they used 12 MHz
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frequency. Li et al. [5] studied a novel inspection method that uses dual-coil inductance to evaluate the quality of raw milk and they used 9MHz. Ding et al. [6] proposed a new inductive salt solution
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concentration sensor with digital frequency output and a planar structure sensing coil which is small
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in size and easy to integrate and they used a frequency about 10MHz. The eddy current type inductive sensor has several advantages such as an ease to miniaturize and a good stability. But this method uses a high frequency up to 10MHz, so the sensing circuitry is relatively complicated. The transformer type sensor has a robust structure and a good linearity. It is easy to install and convenient to use in the industrial field. Striggow et al. [7] presented the results of a general
theoretical investigation of three commonly used types of inductive conductivity sensors, i.e., the single transformer, the double transformer and the double transformer with an additional loop. A.J. Fougere et al. [8. 9] presented improved transformer type inductive sensor for long-term deployment in biologically active ocean regions and their sensor was not affected by the external field. Ribeiro et al. [10] presented an inductive sensor constructed as a double transformer, to be utilized to measure
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the water salinity in the sea and estuaries. Linda et al. [11, 12] presented the design, development and testing of a salinity sensor system for estuary studies and the sensor was based on the double
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transformer principle. Wu et al. [13] reported the principle of the transformer type inductive conductivity sensor and presented a mathematical model to show the characteristics of the sensor.
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Wu et al. reported that a soft magnetic material with high permeability is required as inductive core
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to improve the sensor resolution and the appropriate change of excited current and frequency can be
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also useful for the optimization of sensor sensitivity.
The transformer type inductive conductivity sensor has one or more magnetic cores. And the
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permeability values of them are changed by temperature and pressure. It is very important to
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compensate or eliminate the affection of magnetic core permeability change for improving the sensing performance.
In this paper we have suggested the extended model of TICS including the magnetic loss resistance and mutual coupling factor. Then we have compared the output signal of the model in the cases of repeater and virtual short. From the investigation, it becomes found that the output signal of
the model simulation using virtual short amplifier configuration is independent of the permeability values of magnetic cores and is proportional to the conductivity of the solution. We designed a new module based on the virtual short configuration to measure conductivity and concentration of the electrolyte with TICS. The inductive conductivity sensors are widely applied in many oceanographic research and hydro-meteorological measurement. However, they are very
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expensive and their salinity measuring range is below 5%. In the module design we kept simple circuitry, high resolution, low power consumption, low cost, and low maintenance cost in mind. So
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newly designed sensor module has only about 30$ cost. And its conductivity measuring range is up
This paper consists of 5 sections.
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to 350mS/cm, which is higher than the maximum conductivity of sodium chloride solution at 25℃.
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In section 2, we present the extended model of TICS and study the affection of the input
magnetic core.
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impedance of signal amplifier to the output signal dependence on the permeability change of the
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In section 3, the module design is presented and it includes the sensor structure design, hardware
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design and software design.
In section 4, the permeability change compensating ability, sensitivity, linearity, response, recovery time, stability and the comparison with a commercial water quality meter are evaluated. Section 5 concludes the paper and describes our future work.
2. Sensing model of TICS
Fig.1. Principle of TICS
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Fig. 1 shows the principle of the transformer type inductive conductivity sensor.
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TICS consists of drive coil, sense coil and temperature sensor. Applying an alternating voltage to the drive coil induces an ionic electric current in the solution around the sensor. This ionic electric
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current also induces an electric current in the sense coil which is proportional to the conductivity of
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the solution. This electric current induced in the sense coil is converted to the conductivity or the
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concentration of the solution. Here two coils are encapsulated by chemical-resistant plastic material, so they are not attacked by the corrosive electrolytes.
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The double transformer type inductive conductivity sensor can be equalized as shown in Fig.2.
Fig.2. Sensor model of TICS: P.C. is for Primary Coil and S.C. is for Secondary Coil
R1, L1 and N1 are the magnetic loss resistance, self-inductance and turn number of drive coil of the sensor or primary coil of the first transformer respectively. RS is the resistance of the electrolyte under test. L2 and N2 are the self-inductance and turn number of secondary coil of the first transformer respectively and N2 = 1. L3 and N3 are the self-inductance and turn number of primary coil of the second transformer respectively and N3 = 1.
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R4, L4 and N4 are the magnetic loss resistance, self-inductance and turn number of sense coil of the sensor or secondary coil of the second transformer respectively. M12 and M34 are the mutual
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inductances of the two transformers respectively.
M14 is the mutual inductance between drive coil and sense coil, although it is not illustrated in fig.2.
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M14 is not discussed in this paper because the high permeability magnetic core and the shielding
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plate between the drive coil and sense coil can reduce M14 to nearly zero. As for M13 and M24,
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although they are not illustrated in fig.2, M13 is same as M12 and M24 is same as M34 because L2 and L3 are made by the identical electrical current loop of the solution under test.
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By Kirchhoff’s law and Ohm’s law of closed circuit, we can get the following equations: jω𝑀12 𝐼2 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1
(1)
jω𝑀12 𝐼1 + jω𝑀34 𝐼4 + 𝐼2 (𝑅𝑆 + jω𝐿2 + jω𝐿3 ) = 0
(2)
jω𝑀34 𝐼2 + 𝐼4 (𝑅4 + 𝑅𝑟 + jω𝐿4 ) = 0
From the three equations as above, we can get four formulas as following:
(3)
𝐴
𝐼1 =
2 𝐴(𝑅1 +𝑗𝜔𝐿1 )+𝜔2 𝑀12
𝐼2 = − 𝐼4 =
A=
2 𝜔2 𝑀34
𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4
𝑗𝜔𝑀12 𝐴
𝑈1
(4)
𝐼1
(5)
−𝑗𝜔𝑀34 𝐼 𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4 2
(6)
+ (𝑅𝑆 + jω𝐿2 + jω𝐿3 )
(7)
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(The equations induction process is attached in appendix A.) M12 and M34 are mutual inductances of the transformers and they are as following:
(8)
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𝑀12 = 𝑘12 √𝐿1 𝐿2 , 𝑀34 = 𝑘34 √𝐿3 𝐿4
Eq.4-7 is the extended model including magnetic loss resistance and mutual coupling factor of
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TICS and we can analyze the characteristics of the sensor.
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In the model, L1, L2, L3 and L4 are self-inductances of the coils and they are dependent on the permeability of the magnetic core materials.
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Here, k12 and k34 are the mutual coupling factors of the transformers and they are also dependent
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on the permeability of the magnetic core.
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We simulated the relationship between U1 and I4 using MATLAB software. (The MATLAB m files are attached in appendix B.)
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Fig.3 Calculation results based on the extended model: The signal voltage of the sensor with
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different magnetic cores to several conductance in the case of repeater configuration circuit (a); and
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virtual short configuration circuit (b); (c) The signal voltage change and error when the permeability changes from 0 to 20000 in the case of virtual short; (d) The signal voltage change depending on
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drive frequency and permeability in the case of virtual short.
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Fig.3 is the illustration of calculation results based on the new model. The relationships of conductance versus output signal are shown in the case of 𝑅𝑟 → ∞ (Fig.3a) and in the case of 𝑅𝑟 = 0 (Fig.3b). When Rr→ ∞, the output signal is changed remarkably by the change of the permeability of the magnetic core. However when Rr = 0, the output signal is scarcely changed by the change of the permeability. This result gives us a
possibility to design a new sensor independent of the permeability change of the magnetic core. Let’s suppose some relational expressions as following are true. (9)
𝑅4 ≪ 𝑁42 𝑅𝑆 , 𝑅4 ≪ ω𝐿4
(10)
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𝑅1 ≪ 𝑁12 𝑅𝑆 , 𝑅1 ≪ ω𝐿1
𝑘12 = 𝑘34 = 1
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𝑅𝑟 = 0
(11)
(12)
Then we can get equation 13 from the equations 4-7.
𝑈1
=
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𝐼4 =
𝑁1 𝑁4 𝑅𝑆
𝑈1
𝑁1 𝑁4
𝐺
(13)
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Equation 13 shows that when Rr = 0, output signal I4 is proportional to the conductance of the
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solution, G, and is nearly independent of the permeability of the magnetic core. Eq.13 is equal to the equation illustrated in literature [7].
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This means that our new model is more universal than before.
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The mutual coupling factors k12 and k34 converge to 1 as the permeability becomes larger. In fact they are nearly 1 when the permeability of the magnetic core is over 10000. The signal voltage of the sensor with the magnetic core of permeability over 10000 is hardly affected by the permeability change (Fig.3c). The equivalent loss resistances R1 and R4 depend on the turn number of drive coil and sense
coil. They are also dependent on the frequency. From the investigation with our new model it becomes found that the rational turn numbers of drive coil and sense coil are 10 respectively. The signal voltage is hardly affected when the drive frequency over 10kHz is used (Fig.3d). A frequency higher than 10KHz may increase the magnetic loss, as a drive frequency 10KHz is selected.
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3. Design and Implementation
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3.1. Sensor Structure Design
Characteristic parameters of magnetic core
Value
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Parameter
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Table 1
HS10
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Model
Mn-Zn ferrite 10000±25%
Relative loss coefficient, tanδ/μi, (×10-6)
30 (100kHz)
Saturation flux density, Bs, mT
380 (H=1194A/m, 25℃)
Residual flux density, Br, mT
120 (H=1194A/m, 25℃)
Coercive field, Hc, A/m
5
Curie temperature, Tc, ℃
120
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Initial Permeability, μi
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Material
Density, db, kg/m3
4.9
Resistivity, ρv, Ωm
0.2
According to eq.13, the signal current is independent on permeability of magnetic core. However there are some preconditions for eq.13. They are the conditions 9 – 12. In the condition 11, k is mutual coupling factor. This becomes closer to 1 when the permeability of magnetic core becomes
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larger. From the conditions 9 and 10 the magnetic loss must be so small that it may be neglected. In a word we have to use magnetic cores which have high permeability and small magnetic loss. HS10
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(TDK company) is selected as the most suitable magnetic core for our purpose. The properties of HS10 are given in Table 1.
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The sensor structure and its fabrication steps are given in Fig.4.
Fig.4 Sensor structure design and assembling steps: (a) step1; (b) step 2; (c) step 3; (d) completed model and the photo The drive coil and sense coil are fixed on the PCB and welded to the lead wire (Fig.4a). The main body encapsulates the two coils and PCB (Fig.4b). The seal ring, fixing cap and gland are assembled each other (Fig.4c).
In fig.4d the completed structure and the photo are given.
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The seal ring and gland are for water resistant structure and the fixing cap is for setup in tank.
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Each part is fabricated by using 3D printer and its material is ABS plastic.
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The thickness of the main body is 2mm.
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ABS plastic material has a high chemical resistance, a high water resistance and low cost.
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3.2. Hardware Design
The electronic circuit diagram is shown in Fig.5.
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The circuit consists of MCU, power supply, communication part, drive power amplifier, signal
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amplifier and temperature sensing circuit. We used STM32F103VET6 as the MCU. The input voltage of module is 12V and we used LM2576, LM1117 and TL431 as voltage regulators. The communication is done by MAX485.
The drive power amplifier and signal amplifier are for TICS. The drive power amplifier is to apply stable sine wave to drive coil and the sine wave is made by DAC (Digital to Analog Converter) of
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MCU.
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Fig.5 Electronic circuit diagram
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The signal amplifier is configured as virtual short as discussed in section 2. The virtual short
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configuration of the signal amplifier is shown in Fig.6.
Fig.6 Virtual short configuration
From the basic property of operation amplifier the relational expression as following is established. 𝑈𝑁 = 𝑈𝑃 = 𝑈𝑉𝐺
(14)
𝑈𝑉𝐺 is the virtual ground voltage.
So it is assumed that two terminals of sense coil are short-circuited. Because the negative input of operation amplifier has very large impedance, the electric current of sense coil flows through Rf. So there is an alternating voltage proportional to the intensity of the electric current flowing through the sense coil. The intensity of the electric current flowing through
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the sense coil is proportional to the conductivity of the solution.
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Fig.7 shows the photograph of sensor module.
Fig.7 Photograph of the sensor module
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The pressure sensor on the board is to measure precisely the level of salty water in a salt works
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and it is not associated with TICS being discussed in this paper. The power supply is designed for 12V input voltage and it takes area as large as 30% of all the board and it can be minimized as small as the situation may need.
In this module the signal generation and process are conducted in MCU and the complicated analog parts of TICS are simplified. In fact the analog circuit of our sensor module consists of two operation amplifiers for power amplification and virtual short configuration.
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3.3 Software Design
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The internal operation mechanism of MCU is illustrated in Fig.8.
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Fig.8 Internal operation mechanism of MCU
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The sine wave to drive the sensor is made by DAC1 which is controlled by DMA3 (Direct Memory Access). The signal from the virtual short circuit is converted to digital in ADC1 which is
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controlled by DMA4.
DMA3 and DMA4 are synchronized by TIM1 (Timer). The sensor signal is sampled in ADC 60 times every period and the sampled data is processed digitally by using FFT (Fast Fourier Transform).
Fig.9 Digital signal processing in MCU Fig.9 shows the DSP (Digital Signal Processing) in MCU. By using DSP unnecessary frequency
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components of signal are eliminated. The MCU calculated out the conductivity of the solution with only the main frequency 10 KHz component.
From the conductivity and the temperature of the solution the MCU gets out the concentration of
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Fig.10 shows the software algorithm of the MCU.
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the electrolyte.
Fig.10 Software algorithm and time consumption 4. Experiment Result and Discussion
4.1.
Response Characteristics
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4.1.1. Experiments with several resistors
An experimental investigation with several resistors was carried out to assess the response characteristics of the sensor module. The experiment conditions are given in Table 2.
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The experiment conditions to test the response characteristics.
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Table 2
Drive Coil
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Sense Coil
Turns
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Parameter
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Ambient Temperature
Permeability Inductance
10
10500
751μH
10
11500
822μH
25℃
Rf, Feedback Resistor
10kΩ
Frequency
10kHz
U1, Drive Voltage Amplitude
300mV
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Self-
The permeability, self-inductance and other magnetic properties were measured by a LCR meter (UT612, UNI-T) and Hysteresis Curves Test System (FE-2100SA).
The resistance values of resistors were measured exactly by Precision Multi-meter (8846A, Fluke).
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Schematic diagram and photograph of the experimental setup is shown in Fig. 11.
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Fig.11. Schematic diagram and photograph of experimental setup: (a) Schematic diagram; (b) a photograph.
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The resistor RS stands for the solution loop resistor (Fig.11a).
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The experiment conditions in Table 2 were used to simulate the sensor model by using MATLAB software.
(MATLAB m file for simulation is given in appendix B.)
The simulation data by using the sensor model and the experimental data by using several resistors were compared and the results are given in Fig.12.
Fig.12. Comparison the experimental data with the sensor model simulation data
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The experimental data curve is nearly coincident with the sensor model simulation curve. This shows that the sensor model suggested in this paper is consistent well with the real measuring and it
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can be used to investigate the TICS.
reference voltage of microprocessor.
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For a resistor with the conductance over 50mS the sensor signal voltage becomes over the ADC
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A lower amplitude drive voltage can be used to measure the conductance over 50mS.
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The drive voltage is made by DAC of microprocessor and it can be controlled easily. By using this method so-called drive voltage amplitude dynamic control method this sensor
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module can measure the wide range conductance. In this paper the amplitude of the drive voltage is
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fixed at 300mV and this drive voltage amplitude dynamic control method is not used. Sequentially the influence of ferrite permeability change by temperature to the measuring was studied. The sensor module was designed by using virtual short circuit in order to reduce the influence of permeability change by temperature and other environmental conditions to the measuring. Firstly the permeability of the magnetic core was measured heating the sensor. Then the
sensing value was recorded heating the sensor with 100Ω precision resistor as a solution loop resistor (Fig.11a). The temperature coefficient of the resistor is about 0.005%/℃ and the resistance change of the resistor by temperature can be neglected. By using a constant temperature bath the sensor was heated from 20℃ to 70℃ with 5℃ interval and at each temperature keeping point the measuring
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was done several times for 20 minutes. The test results are given in Fig.13.
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Fig.13. The test result of heating the sensor with 100Ω precision resistor.
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In Fig.13 the permeability of the magnetic core has the maximum value at about 25℃ and the
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minimum value at about 60℃. The maximum value is 12000 and the minimum value is 10000. At the time the signal voltage is 289.3mV at 25℃ and 289.0mV at 60℃. This shows that the
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permeability change of magnetic core hardly affects the signal voltage and the virtual short circuit is
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effective to reduce the influence of the permeability change to the sensing value.
4.1.2. Experiments with solutions
An experiment was performed to test the sensitivity and linearity of the sensor module.
The sulphuric acid solution of which the molar conductivity is high relatively was used because the high conductivity over 350mS/cm should be measured. And the other reason why to select the sulphuric acid is to test the ability of the sensor to stand in the chemical corrosive solution such as the strong acid and alkali. 4 kinds of sulphuric acid solutions which had 1%, 3%, 5%, 8% concentrations respectively were
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prepared. The high purity sulphuric acid and distilled water was used for preparation of solutions. A constant temperature bath was used to keep the temperature constantly at 25℃.
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Each solution was measured 5 times at 25℃ and the measured values were averaged and
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The test results are presented in Fig.14
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recorded.
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Fig.14. Experimental data points and linear fitting result
The result of the experiment to measure the real electrolyte shows that our sensor module has a good linearity. The sensitivity is about 4mV per 1mS/cm and the measuring range is up to 350mS/cm.
If the drive voltage amplitude dynamic control method mentioned above is used, the sensitivity and the measuring range can be improved remarkably. And the sensor is not damaged by the sulphuric acid. So this sensor can be used to measure the concentration and conductivity of the sulphuric acid.
4.2. Response, recovery time and stability
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An experiment was performed to test the response and recovery time of the sensor module.
For the experiment 0.15M sodium hydroxide solution was used. For the solution preparation high
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purity sodium hydroxide and distilled water was used. The temperature of solution was kept
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constantly at 25℃.
Fig.15. (a): Response and recovery of the sensor module in case of empty container and 0.15M sodium hydroxide solution; (b): Stability of the sensing value in 0.15M sodium hydroxide solution at 25℃. In this experiment TICS was firstly immersed into the prepared sodium hydroxide solution for 3 minutes. The measurement was done once per 300 milliseconds. Then the sensor was exposed to
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atmosphere of laboratory for 3 minutes. Then the above steps were repeated several times. Fig.15a shows the response and recovery characteristics of the sensor module.
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As shown in Fig. 15a the measured response and recovery time were within one second.
Fig.15b shows the stability of sensing value in 0.15M sodium hydroxide solution at 25℃.
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Tiny measured value fluctuations (about 1.8mV) were observed and the uncertainty of the sensor
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module was calculated to be about 0.12% in the full scale.
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And the sensor is not damaged by the sodium hydroxide solution. So this sensor can be used to
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measure the concentration and conductivity of the sodium hydroxide.
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4.3. Characteristics Comparison
A Comparison test between the present sensor module and a commercial meter was performed. Water Quality Meter F-74 (Horiba, Japan) was used as the commercial meter. The conductivity electrode 9382-10D (Horiba, Japan) was used and its measuring range is below 100mS/cm.
Sea water concentrated 2.2% was used as a test solution and its conductivity measured by commercial water quality meter F-74 was 44.5mS/cm at 25℃. The signal voltage of the TICS module was 185.6mV and its conductance was 6.36mS. The conductivity and the conductance are related with each other as following: κ = G × 𝑘𝑐
(15)
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κ is the electrical conductivity, G is the conductance and kc is the cell constant. The cell constant is related with geometry of the sensor.
In the case of the conductive electrode the cell constant is calculated as following: 𝐿 𝑆
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𝑘𝑐 =
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L is the distance between electrodes and S is the area of the electrodes.
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In the case of the inductive sensor the cell constant calculation is relatively complicated. The cell constant is 6.9968 in our case.
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The sea water was heated from 15℃ to 47℃ slowly and the conductivity value of the sensor module and the commercial meter was recorded at several temperature points together (Fig.11b).
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The temperature control magnetic stirrer was used to heat and stir the solution.
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Fig.16 shows the results.
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Fig.16 The result of comparison test with the commercial water quality meter F-74 (Horiba, Japan) The result shows that the maximum error between the sensor module and the commercial meter is 0.6mS/cm. The relative error of the sensor module was calculated to be 0.17% in the full scale. 5. Conclusion The simulation results using our new model of TICS have shown a good similarity to the experiment results. The higher permeability of magnetic core is, the more reliable this model is.
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The receiving signal is hardly affected when the impedance of signal receiver is zero.
The smart sensing device has been developed by using synchronization of DAC and ADC, digital signal processing by Fast Fourier Transform and discontinuous wave generation. This sensing device
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has low cost, low power consumption, simple circuitry, good linearity, high sensitivity and wide measuring range. An important problem in this sensing device is the contact resistance between the
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sensor and the PCB board. As time goes by, this resistance may become larger than the initial value
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because of the environmental condition. And this change may affect the sensing value. In the future we will investigate the frequency modulating method which uses the different 3 or more frequencies
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to eliminate the influence of the contact resistance. Next time the mathematical model to calculate the cell constant of TICS will be discussed in detail and this can be helpful to get the rational
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geometry of the sensor.
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Conflict of Interest No Interest.
Acknowledgement We thank Prof. Choe Yong Nam, the director of Mirae Centre of Science and Technology, Kim chaek University of Technology for providing a good experimental condition. We thank Prof. Kang Il Yong, the director of the School of Science and Engineering, Kim chaek University of Technology
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for his great theoretical support.
References
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[1] P.A. Ramon, L.G. Javier, Constant-phase element identification in conductivity sensors using a single square wave, Sens. Actuators A-Phys. 132 (2006) 122-128.
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[2] X. Huang, R.W. Pascal, K. Chamberlain, C.J. Banks, M. Mowlem, F. Morgan, A miniature,
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2011, 11, 3246–3252.
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high precision conductivity and temperature sensor system for ocean monitoring, IEEE Sens. J.
[3] http://dx.doi.org/doi:10.3390/s150920990
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[4] http://dx.doi.org/doi:10.1016/j.sna.2017.07.022
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[5] http://dx.doi.org/DOI: 10.1016/j.sna.2016.01.035 [6] http://dx.doi.org/doi:10.1016/j.sna.2017.05.043 [7] K. Striggow, R. Dankert, The exact theory of inductive conductivity sensors for oceanographic application, IEEE J. Ocean. Eng. 1985, 10, 175–179.
[8] A. J. Fougere, New non-external field inductive conductivity sensor (NXIC) for long term deployments in biologically active regions, OCEANS 2000 MTS/IEEE Conference and Exhibition, Vol. 1, pp. 623-630, Sept. 2000. [9] A. Fougere, M. St Germain, F.J. Kelly, Field evaluation of a revolutionary CTD design, OCEANS 2003 Proceedings, Vol. 4, pp. 2249-2253, Sept. 2003.
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[10] A.L. Ribeiro, H.M.G. Ramos, P.M. Ramos, J.M.D. Pereira, Inductive Conductivity Cell for Water Salinity Monitoring. Metrology for a Sustainable Development September, 17 – 22,
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2006, Rio de Janeiro, Brazil
[11] T.T. Pham, T. Green, J. Chen, P.Truong, A. Vaidya and L. Bushnell, A Salinity Sensor System
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for Estuary Studies, Applied Math Journal: Networks and Heterogeneous Media, vol. 4, no. 2,
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pp. 381-392, 2009.
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[12] R.T. Wood, A. Bannazadeh, N.Q. Nguyen, L.G. Bushnell, A salinity sensor for long-term data collection in estuary studies, In Proceedings of the IEEE OCEANS, Sydney, Australia, 24–27
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May 2010; pp. 1–6.
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[13] S. Wu, H. Lan, J.J. Liang, Y. Tian, Y. Deng, H.Z. Li, N. Liu, Investigation of the performance of an inductive seawater conductivity sensor, Sens. Trans. 186 (2015) 43-48.
Kang Hui Song was born in Sinuiju, DPRK, in 1989. He works on the Ph.D. degree in Kim Chaek University of Technology, Pyongyang, DPRK. His current research is the water quality sensing devices.
Appendix A jω𝑀12 𝐼2 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1
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(A.1)
jω𝑀12 𝐼1 + jω𝑀34 𝐼4 + 𝐼2 (𝑅𝑆 + jω𝐿2 + jω𝐿3 ) = 0
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Eq. (A.6) is obtained from Eq. (A.3) simply.
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jω𝑀34 𝐼2 + 𝐼4 (𝑅4 + 𝑅𝑟 + jω𝐿4 ) = 0
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jω𝑀34 𝐼2 + 𝐼4 (𝑅4 + jω𝐿4 ) = 0 −𝑗𝜔𝑀34 𝐼 𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4 2
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𝐼4 =
(A.2)
(A.3)
(A.3)
(A.6)
Substitute Eq. (A.6) to Eq. (A.2).
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jω𝑀12 𝐼1 + jω𝑀34 𝐼4 + 𝐼2 (𝑅𝑆 + jω𝐿2 + jω𝐿3 ) = 0
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jω𝑀12 𝐼1 + jω𝑀34
(A.2)
−𝑗𝜔𝑀34 𝐼 + 𝐼2 (𝑅𝑆 + jω𝐿2 + jω𝐿3 ) = 0 𝑅𝑟 + 𝑅4 + 𝑗𝜔𝐿4 2
2 𝜔2 𝑀34 jω𝑀12 𝐼1 + ( + (𝑅𝑆 + jω𝐿2 + jω𝐿3 ))𝐼2 = 0 𝑅𝑟 + 𝑅4 + 𝑗𝜔𝐿4
A=
2 𝜔2 𝑀34
𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4
+ (𝑅𝑆 + jω𝐿2 + jω𝐿3 )
(A.7)
jω𝑀12 𝐼1 + 𝐴𝐼2 = 0 𝐼2 = −
𝑗𝜔𝑀12 𝐴
𝐼1
(A.5)
Substitute Eq. (A.5) to Eq. (A.1). jω𝑀12 𝐼2 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1
jω𝑀12 (−
𝑗𝜔𝑀12 𝐴
(A.1)
)𝐼1 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1
2 ω2 𝑀12 𝐼1 + 𝐼1 (𝑅1 + jω𝐿1 ) = 𝑈1 𝐴 2 ω2 𝑀12 + (𝑅1 + jω𝐿1 ))𝐼1 = 𝑈1 𝐴 𝐴
𝐼1 =
2 𝐴(𝑅1 +𝑗𝜔𝐿1 )+𝜔2 𝑀12
𝑈1
So the equations are obtained as following:
2 𝐴(𝑅1 +𝑗𝜔𝐿1 )+𝜔2 𝑀12
2 𝜔2 𝑀34
𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4
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close all
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A=
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%absolute permeability
u0 = 12.57 * 10^(-7);
%turns of coil
𝐴
𝐼1
−𝑗𝜔𝑀34 𝐼 𝑅𝑟 +𝑅4 +𝑗𝜔𝐿4 2
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𝐼4 =
𝑗𝜔𝑀12
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𝐼2 = −
𝑈1
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𝐴
𝐼1 =
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(
+ (𝑅𝑆 + jω𝐿2 + jω𝐿3 )
Appendix B
(A.4)
(A.4)
(A.5)
(A.6)
(A.7)
N1 = 10; N2 = 10;
R1 = 0.1*N1;
R4 = 0.1*N2;
f = 10000;
omega = 2*pi*f;
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%height, inner diameter, outer diameter of coil
th = 7*10^(-3);
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r1 = 7.5*10^(-3);
r2 = 12.5*10^(-3);
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U1 = 0.3;
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Rr = 0;
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%permeability
u1 = 10500;
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k12=1-30/u1;
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u2 = 11500;
k34=1-30/u2;
%inductance
L1 = u0*u1*N1^2*th*log(r2/r1)/(2*pi)
L2 = u0*u1*th*log(r2/r1)/(2*pi);
L3 = u0*u2*th*log(r2/r1)/(2*pi);
L4 = u0*u2*N2^2*th*log(r2/r1)/(2*pi)
M12 = k12*(L1*L2)^0.5;
M34 = k34*(L3*L4)^0.5;
Conductance = [0, 0.5, 1, 3.3, 5, 10, 16.1, 20, 25, 33.3];
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Rs = 1000./Conductance;
A=omega^2*M34^2/(Rr+R4+j*omega*L4) + Rs+j*omega*(L2+L3);
I2= -j*omega*M12*I1./A;
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I4 = -j*omega*M34*I2./(R4+Rr+j*omega*L4);
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U4 = I4*10000;
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abs(U4)
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plot(Conductance, abs(U4),'ko'); hold on;
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I1 = A*U1./(A*(R1+j*omega*L1)+omega^2*M12^2);