Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Proposal Description
3.1.1. Hardware Description
- Microcontroller board: This was used as the central processing unit of the system, responsible for executing the software that controls the sensor. We chose M5Stack [28], a prototyping board based on the ESP32 [29] microcontroller, which provides a range of analog and digital inputs and outputs. This board was chosen for the design of our sensor due to its several advantageous features. The M5Stack board offers a color display that can be used to display the results of the sensor, as well as three buttons for operating the sensor. Additionally, it includes an SD card reader which can be used to store the results of the sensor and a small battery that allows for use of the sensor without being connected to a power source. These features make M5Stack an ideal choice for the design of our sensor. One of the drawbacks of the M5Stack is that it only includes three analog inputs, which is why it has been necessary to add a demultiplexer.
- LED light source: This was used to project the beam of light required for the nephelometry or turbidimetry measurement. We used an infrared LED (950 nm) and RGB LED to generate different wavelengths. The IR light was selected because the commercial turbidimeter uses IR light. This is because this wavelength has low energy and, therefore, will be little absorbed by the substances in the environment. The RGB wavelengths were used because the sediment and algae have different colors. Therefore, the absorption and scattering of these lights were different. Accordingly, the light that arrived at the photoreceptors was different.
- Optical sensors: We used a photodiode (1612660) for infrared light and a light-dependent resistor (LDR) for visible light. These sensors were used in pairs at angles of 90 and 180 degrees depending on whether we were using either the nephelometry or turbidimetry method. Figure 1 shows the sensors nephelometry vs. turbidimetry.
- Auxiliary elements: In order to connect the sensors and actuators to the microcontroller and adapt the different signals, we needed different resistors, as shown in Figure 2. An amplification stage was also necessary in the 90-degree infrared sensor. All these components are shown in the schematic circuit. Since the resistors have been adjusted at various times, it was thought to be more efficient to assemble the prototype on a proto board. Once the final design was reached, we then mounted the equivalent PCB board.
- Signal adapter circuit: To connect the sensors and actuators to the microcontroller and adapt the different signals, we needed various resistors, as shown in the circuit schematic. An amplification stage was also necessary for the 90-degree infrared sensor, for which an operational amplifier with three adjustable resistors was utilized. The M5Stack only has 3 analog inputs, but our design required 4; 2 for the infrared light sensors at 90 and 180 degrees, and 2 for the visible light sensors. To overcome this issue, we added a multiplexer which can select up to sixteen inputs in one process.
- Sensor Enclosure: To protect the sensor and its components, we designed and 3D printed an enclosure for the sensor. Having a protective case for the sensor reduced the risk of cable disconnections that can occur when using a proto board, as well as protecting the components from water damage. Additionally, it allowed for the precise positioning of the LEDs. Figure 3 shows the sensor.
3.1.2. Software Functionality
Algorithm 1: Master head algorithm. |
Given: refresh_time, samples_number PHASE=[“IR180”,“IR90”,“R180”,“R90”,“G180”,“G90”,“B180”,“B90”,“NONE”] configure_graphical_output_UI() configure_pinMode_for_each_in_out() Repeat For Each phase In PHASE select_multiplexer_input(phase) turn_on_appropriate_LED(phase) For Each count In 1..samples_number value = read_analog_value() save_value(phase, count, value) // o data[phase,count] = value draw_value_UI(value) wait(refresh_time) End For obtain_mean_and_deviation(phase) End For check_alarm_system() save_phases_in_SD() Until button_C is pressed |
3.2. Test Bench
Methodology
4. Results
4.1. Selection of the Better Combination of Resistance
4.2. Turbidity of Samples
4.3. IR LED
4.4. Use of Colour LEDs
4.5. Use of Neural Network
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Resistance LED (Ω) | 1200 | 560 | 220 | 150 | 100 | 68 | 33 |
Intensity LED (mA) | 3.5 | 7.09 | 17.73 | 24.14 | 35.29 | 53 | 100 |
Resistance photodiode (kΩ) | 100 | 330 | 1000 | 3000 | 8200 | 10,000 |
Parameters | Red 180° | Red 90° | Green 180° | Green 90° | Blue 180° | Blue 90° |
---|---|---|---|---|---|---|
Resistance 0 mg/L (kΩ) | 1.04 | 16.25 | 0.72 | 16.61 | 1.09 | 27.12 |
Resistance 100 mg/L (kΩ) | 1.24 | 84.70 | 0.97 | 40.71 | 1.53 | 65.70 |
Optimal resistance (kΩ) | 1.14 | 37.10 | 0.84 | 26.00 | 1.29 | 42.21 |
Voltage difference (V) | 0.153 | 1.280 | 0.245 | 0.707 | 0.280 | 0.685 |
Select resistance (kΩ) | 1.20 | 33.00 | 1.20 | 33.00 | 1.20 | 33.00 |
Voltage difference (V) | 0.145 | 1.276 | 0.238 | 0.698 | 0.280 | 0.674 |
Equation (3) | Equation (4) | |||
---|---|---|---|---|
Absolute (mg/L) | Relative (%) | Absolute (mg/L) | Relative (%) | |
Error calibration | 5.69 | 11.14 | 4.16 | 8.09 |
Error verification | 3.90 | 11.84 | 3.79 | 11.40 |
Equation (5) | Equation (6) | |||
---|---|---|---|---|
Absolute (mg/L) | Relative (%) | Absolute (mg/L) | Relative (%) | |
Error calibration | 5.73 | 19.17 | 6.85 | 29.49 |
Error verification | 6.78 | 30.58 | 8.62 | 53.33 |
Equation G | Equation H | |||
---|---|---|---|---|
Absolute (mg/L) | Relative (%) | Absolute (mg/L) | Relative (%) | |
Error calibration | 3.74 | 14.98 | 3.99 | 11.61 |
Error verification | 4.95 | 17.95 | 5.63 | 25.53 |
Absolute (mg/L) | Relative (%) | |
---|---|---|
Error calibration | 2.78 | 13.91 |
Error verification | 3.20 | 17.86 |
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Rocher, J.; Jimenez, J.M.; Tomas, J.; Lloret, J. Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies. Sensors 2023, 23, 3913. https://doi.org/10.3390/s23083913
Rocher J, Jimenez JM, Tomas J, Lloret J. Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies. Sensors. 2023; 23(8):3913. https://doi.org/10.3390/s23083913
Chicago/Turabian StyleRocher, Javier, Jose M. Jimenez, Jesus Tomas, and Jaime Lloret. 2023. "Low-Cost Turbidity Sensor to Determine Eutrophication in Water Bodies" Sensors 23, no. 8: 3913. https://doi.org/10.3390/s23083913