Design, Implementation and Characterization of Stand-Alone Prototypes for Water Conductivity Assessment José Gouveia1, Pedro M. Ramos1, Helena Ramos1, Artur Ribeiro1, M. D. Pereira1,2 1
Instituto de Telecomunicações, DEEC, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001, Lisboa, Portugal Phone: +351-218418473; Fax +351-218417672, e-mail:
[email protected] 2 ESTSetúbal-LabIM/IPS, Rua do Vale de Chaves, Estefanilha, 2910-761 Setúbal, Portugal Phone: +351-265790000; Fax +351-265721869, e-mail:
[email protected]
Abstract1— Human life quality and preservation of ecosystems are two major topics that depend on environmental quality. In this paper particular attention is dedicated to water conductivity assessment in estuarine zones. Estuarine zones are generally surrounded by people concentration, that use those places as residential zones, and also by industrial plants that take advantage of water availability to generally cool, and sometimes warm (e.g., liquid natural gas plants), their industrial equipments. At the same time boats, particularly those that transport hydrocarbons, represent also a danger that can affect water quality. This paper includes the design and implementation of a low-cost conductivity sensor, its metrological characterization and a detailed description of the calibration techniques used to compensate errors caused by temperature variation.
I. INTRODUCTION The growing attention on environmental issues increased the necessity of developing sensors to monitor water quality in remote locations. Conductivity, salinity and total dissolved solids (TDS) on the open sea and inside estuaries, where the salty tide meets the fresh water currents, are three major parameters that are always used in water quality monitoring systems since the occurrence of pollution events and conductivity variations are generally correlated [1-2]. Conductivity has a strong dependence on temperature. Ionic salts conductivity temperature dependence is about 2%/ºC. Due to this fact, temperature variations cause frequent problems in conductivity measurements especially when the solution under test exhibits rapid varying temperature or when conductivity variations are reduced. In conductivity measurements for liquids, there are two major types of sensors: inductive and resistive [3]. The former are based on contactless sensors and the latter are based on metallic electrodes with direct contact with the electrolyte. Resistive sensors can have 2, 3 or 4 electrodes and they are more sensitive and less expensive to build. The four electrodes conductivity cell assures that polarization at the current electrodes has a minimal effect on conductivity measurements. Fouling and corrosion that affect substantially two- and three-electrode cells [4-5] has minimal
The work was supported by project DICSAP/WCA - IT/LA/298/2005 financed by Instituto de Telecomunicações.
effects on conductivity measurement accuracy of four-electrode cells. Controlling the ratio between the voltage, over the sensing terminals, and the current delivered by the cell it is possible to implement a dirty cell detector, thus minimizing cell maintenance routines for cleaning purposes. This paper dedicates a particular attention to the calibration of temperature sensors and associated measuring channels that can be used to improve conductivity measurement accuracy. The stand-alone prototype for water conductivity assessment presented in this paper is a sensing element to be included in a multiple parameter wireless distributed network for water quality assessment. This network provides capabilities to detect pollution events by aggregating data from multiple sensing units, located in different geographical places, improving reliability by minimizing effects caused by individual sensors’ failures and to promote remote monitoring and control of the environmental variables.
II. SYSTEM DESCRIPTION A. Sensing Units The measurement system includes two sensing units: a four-electrode conductivity sensor and an integrated temperature sensor. Figure 1 represents the conductivity sensing unit and associated circuitry. A tubular cylindrical structure is used to implement the conductivity sensing unit. The current terminals (HI CUR and LO CUR) are not on the edges of the tubular structure to minimize the cell’s sensitivity to external disturbances caused for example by the proximity of metallic materials that can be near the cell periphery. Another reason to extend the tubular structure is to increase the measurement sensitivity by reducing the fraction of the injected current that loops the current terminals outside the cell. The sensing terminals (HI POT and LO POT) are located in symmetrical position across the centre of the cell and far away from the current terminals to obtain a uniform electrical field between these terminals.
To measure the temperature an TMP36 temperature sensor is used together with an AD974 16-bit ADC. The temperature measurements are taken each 100 ms. Figure 2 represents the diagram block of the conductivity sensor conditioning circuit.
G I
R
HI POT
LO POT
Uout
HI CUR
LO CUR
Fig. 1. Conductivity cell and associated circuitry (HI/LOW CUR- high and low current terminals, HI/LOW POT- high and low sensing terminals, G- generator, I- cell injected current, Uout- output voltage). The voltage across resistor (R) is a measure of the current injected into the conductivity cell (I) and the ratio of voltage between sensing terminals (Uout) and the previous current is a measure of electrolyte conductivity. Geometrical dimensions of the cell were designed to obtain a cell constant [6] approximately equal to 100 m-1 which means that the resistive range varies between 10 Ω and 100 kΩ when the electrolyte conductivity varies between 1 mS/m and 10 S/m. This range includes the typical conductivity range of estuarine waters. The temperature calibration system includes multiple temperature sensors. This increases temperature measurement accuracy providing a more accurate compensation of conductivity measurements errors caused by temperature variations. Temperature measurement is provided by a three terminal monolithic sensor (AD22103) whose main metrological characteristics include: a temperature coefficient of 28 mV/°C, for a nominal power supply voltage (VS) equal to 3.3 V; accuracy better than 2.5% of full-scale (FS) range; and linearity better than 0.5% of FS.
B. Analog and Digital Signal Processing For the conductivity sensor conditioning circuit an ADSPBF533 was used with an AD1836 ADC (for data acquisition and generation) and 64 MB of external memory. The codec chip includes two sigma-delta ADC converters with differential inputs, 24-bit resolution, 96 kS/s maximum sampling rate and input voltage range of ±3.08 V. The ADC data records are transmitted to the DSP by a SPI connection. One of the DAC’s in the AD1836 is used to generate the sine stimulus for the impedance measurement circuit. The DAC has 24-bit resolution and a maximum amplitude of 5.6 Vpp. The sine signal is defined with 40 points per period and is sampled at 96 kS/s to generate a 2.4 kHz sine wave. The DAC output voltage level can be digitally controlled with 1024 steps of linear attenuation. The multichannel codec (AD1836) that contains three stereo DACs and two stereo ADCs, with 24 bits resolution is included in the ADSPBF533 kit used.
Fig. 2. Diagram block of the conductivity sensor conditioning circuit and signal processing unit. The main tasks associated with the DSP unit include: generation of sinusoidal signals for testing purposes; flexible selection of frequency and amplitude of the stimulation signal; A/D and D/A conversions and data communication through a RS232 interface. Obviously, a lower cost, commercial conductivity measurement unit, based on the presented prototype, must consider other hardware solutions based on a microcontroller and commercial off-the-shelf (COTS) devices. C. Calibration To compensate measurement errors caused by temperature variation the temperature sensor units and associated signal conditioning channels are calibrated using a temperature stabilized water bath. In this paper, particular attention is dedicated to these errors since conductivity measurement accuracy is strongly affected by temperature variations (typical temperature cross sensitivity is equal to 2%/ºC). To minimize these errors a two step calibration procedure for the bath temperature sensors is used. In the first step, calibration factors for each temperature sensing unit are evaluated based on the relationship
Vsi = ( mi ⋅ T+bi ) ⋅
VPS , i= {1,2,3,4,5} 3.3
(1)
where Vsi is the temperature sensor voltage output, VPS is the sensor’s power supply voltage, T is the reference temperature and {mi,bi} represent the slope and origin ordinate of the best fitted experimental values straight line (LMS), respectively. Five temperature sensing units where characterized during the calibration phase and the temperature reference was provided by a liquid thermometer [7] with a measuring range between -10ºC and 50ºC and an accuracy of 0.1ºC.
Vo j =m j ⋅ Vin j + b j , j= {0,1,2,3}
(2)
where Voj is the signal conditioning output voltage of channel j, Vin j is the input voltage of each signal conditioning channel and {mj,bj} represent the slope and origin ordinate of the best fitted experimental values straight line (LMS), respectively. This calibration procedure assures that each sensing unit can be connected to any signal conditioning channel without requiring any additional calibration procedure. At the same time usage of multiple temperature sensing units provides a better accuracy, based on data averaging, and an improvement on system’s reliability. The measured temperature (T) for each association between a temperature sensor and a signal conditioning unit can be easily obtained from (1) and (2): 1 T= mi
⎛ 3.3 Vo j -b j ⎞ ⋅⎜ ⋅ -bi ⎟ , i= {1,2,3,4,5} and j= {0,1,2,3} (3) ⎜ VPS m j ⎟ ⎝ ⎠
Using the previous calibration method together with a Peltier controlled temperature bath [8] and a set of four temperature sensors, used to assure a stabilized condition for calibration temperature, each temperature sensor was characterized and accuracy of 0.1 ºC was obtained. Figure 3 represents the hardware block diagram used for temperature calibration purposes. PC Power supply
GPIB DAQ
V
V
I
A
SCB
CH2 CH3
- +
Termometer Multimeter
A
+ -
14L Vessel
Peltier Cells water Temperature sensor to calibrate
III. RESULTS Using the Peltier controlled temperature bath each temperature sensor was calibrated. As an example, Figure 4 represents the output voltage delivered by temperature sensor 1 for a bath temperature variation between 9.1 ºC and 44.9 ºC. 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0
10
20
T (ºC)
30
40
50
Fig. 4. Temperature sensor calibration results. The linear regression correlation factor (R) associated with the experimental data is almost unitary (R≅0.9996) and the linearity error is equal to 0.12% which confirms the specifications of the temperature sensor (ELin≤0.5%). Table I summarizes the combined slope and offset values of the linear regression calibration straight lines for each temperature sensor (i). Table I Combined slope and offset values of the linear regression calibration straight lines for each temperature sensor
CH0 CH1 Power Supply
sensing terminals (Uout) and cell’s exciting current (I); setup of acquisitions parameters (sampling rate, number of samples, excitation signal frequency and amplitude); data acquisition and evaluation of conductivity and temperature; conductivity temperature compensation; initialization of serial UART port and input-output RS232 data transmission. Additional software routines can also be developed to detect cell dirty events and other smart sensing features like auto-ranging, auto-averaging, auto-test and auto-calibration [10].
V se n s o r ( V)
In the second step, calibration factors for each signal conditioning channel are evaluated based on
Stabilization Temperature sensors
Fig. 3. Temperature calibration hardware block diagram (SCB- signal conditioning box, PC- personal computer, DAQ-data acquisition board, GPIB- General Purpose Interface Bus, Chi- temperature measuring channel i). D. Software
Several functions and software modules for the DSP where developed to perform measuring, control and communication tasks. These functions and software modules include: implementation of the sine fitting algorithm [9] that extracts the amplitude and phase relation between voltage across cell
i 1 2 3 4 5
m 0.0272 0.0272 0.0270 0.0271 0.0267
b 0.2758 0.2717 0.2693 0.2671 0.2806
Mean STD
0.0270 0.0002
0.2729 0.0054
Figure 5 represents the admittance of the conductivity cell as a function of the temperature for a working frequency of 10 kHz. The linear interpolation relationship is Y =1.423 ⋅10-5 ⋅ T+3.529 ⋅10-4
(4)
where T is the temperature in ºC. The normalized slope is 2.23%/ºC which confirms the expect value of conductivity temperature dependence of ionic salts (about 2%/ºC) and the relative deviation between measured and interpolated values, computed as
(|Y| measured – |Y| interpolated) / |Y| interpolated · 100, is lower than 0.5%. 9
x 10
Linear interpolation of |Y| at 10 kHz
-4
|Y| (S)
8 7
Measurement Interpolation
6 5 16
18
20
22
24 26 28 Temperature °C
30
32
34
36
32
34
36
(|Ymeas| - |Yint|)/|Y(int)| (%)
Relative deviation at 10 kHz 1
0.5
0
-0.5 16
18
20
22
24 26 28 Temperature °C
30
Fig. 5. Admittance of the conductivity cell as a function of temperature variation for a working frequency of 10 kHz: measurements and linear interpolation results on top; (b) relative deviation between measurements and linear interpolation results on bottom. IV. CONCLUSIONS The proposed solution enables a flexible choice of amplitude and frequency of the test signal used to evaluate water conductivity. Auto-ranging and auto-averaging techniques are easily applied to extend measurement system dynamic range and to improve signal-to-noise ratio, respectively. An improved methodology to calibrate temperature sensors is presented providing a more accurate compensation of conductivity measurement errors caused by temperature variations. A low-cost smart sensing solution for a distributed water quality monitoring network is easily obtained by replacing the DSP by a microcontroller device.
REFERENCES [1] Béla G. Lipták, “Process Measurement and Analysis – Instrument Engineers’ Handbook”, CRC Press, fourth edition, pp. 1316-1322, 2003. [2] J. M. Dias Pereira, O. Postolache, P. M. Girão and H. G. Ramos, " Smart Oil and Conductivity Sensor for Water Quality Monitoring ", Proc Imeko TC4 Symp., Athenas, Greece, Vol. 2, pp. 417 - 422, September 2004. [3] K. Striggow and R. Dankert, “The Exact Theory of Inductive Conductivity Sensors for Oceanographic Applications”, IEEE Journal of Oceanic Engineering, Vol. OE-10, NO. 2, April 1985. [4] L. M. Gurriana, J. M. Dias Pereira and H. G. Ramos, “Development and Characterization of a pH and Conductivity Measurement System for Water Quality Assessment”, Proc. of the 5th Conference on Telecommunications, Tomar, April 2005. [5] Helena Ramos, L. Gurriana, O. Postolache, M. Pereira and P. Girão, “Development and Characterization of a Conductivity Cell for Water Quality Monitoring”, Third IEEE International Conference on Systems, Signals & Devices (SSD’2005), Sousse, Tunísia, March 2005. [6] http://www.eutechinst.com/techtips/tech-tips25.htm. [7] Labor-Feinthermometer , Model L25994, http://www.amarell.de/thermometer/laborthermometer.ht m. [8] H. G. Ramos, F. Assunção, A. Ribeiro and Pedro M. Ramos, “A low-cost temperature controlled system to test and characterize sensors”, IEEE Africon 2004, Gaborone, Botswana, vol. 1, pp. 457-460, Sept. 2004. [9] Pedro M. Ramos, Fonseca da Silva and A. Cruz Serra, “Improving sine-fitting algorithms for amplitude and phase measurements”, XVII IMEKO World Congress, Dubrovnik, Croatia, pp. 614-619, June 2003. [10] P. W. Chapman, “Smart Sensors”, ISA – Instrument Society of America, 1996.
