Heavy metal contamination in water poses severe risks to both human and environmental health. This project explores an integrated approach to detect and remediate heavy metals in water by using sensor-based monitoring combined with sustainable remediation techniques. The focus is on developing and deploying an electrochemical sensor capable of real-time, precise detection of heavy metals such as lead, mercury, and cadmium. The sensor's high sensitivity and specificity make it an effective tool for monitoring contaminated water sources. In parallel, this project investigates biological remediation as a sustainable solution for heavy metal removal, leveraging natural processes to detoxify and eliminate contaminants. By merging advanced sensor technology with eco-friendly remediation methods, this approach offers a promising solution to the persistent challenge of heavy metal pollution. The findings done suggest that this integrated system not only enhances detection accuracy but also provides a more sustainable and efficient method for mitigating heavy metal contamination in water, contributing to improved public health and environmental sustainability.
Project Objectives
The project aims to investigate and evaluate various sensor technologies, such as optical, piezoelectric, and electrochemical sensors, for the detection of heavy metals in water. The most suitable sensor was selected based on criteria including efficiency, sensitivity, selectivity, and cost-effectiveness. This chosen sensor which is the electrochemical sensor will be developed into a robust monitoring system, complemented by algorithms and real-time data analysis code.
The system will incorporate decision-making logic to trigger remediation actions based on detected heavy metal levels and include a feedback control mechanism to regulate the remediation process. To ensure continuous monitoring, the design will be optimized for energy efficiency, low latency, and high accuracy in data processing. The system will be tested in real-world scenarios, such as wastewater and natural water bodies, to validate its performance under various conditions, and assess its safety, reliability, and robustness.
Methodology
This involves a water remediation system with an electrochemical sensor and a biological remediation process. The proposed solution will enable the remote monitoring of heavy metal detection systems. The proposed system is divided into four main units namely, the sensor unit, the remediation unit, the control unit, and the data unit.
Sensor unit:
The sensor unit comprises of all sensors used for heavy metal detection. The electrochemical sensor will be used for this project. Electrochemical sensors have a high sensitivity and allow real-time detection of heavy metals. Furthermore, they have a good sample rate and are portable which means that they can easily move between locations.
Control unit:
The control unit is the brain of the system. It is used for control and analysis purposes. The control unit is made up of an ESP32 microcontroller-based board. The Esp32 has the particularity to allow connection to Wi-Fi which is a great feature for remote data collection.
The ESP32 will be setup to connect to the Arduino Cloud website. This will make the collected data be easily saved on the cloud and be accessible to the users anywhere and at any time.
Furthermore, ESP32 boards have enough available pins to connect both our sensors and the other devices used in this project. Algorithms shall be created to trigger remediation actions based on sensor data collected.
Data unit:
The data unit is made of the data logger section and the Human Interface (HMI) section. The data logger section is used to save the collected data for further analysis. That section is made of a SD card which is connected to the ESP32 board. Using an SD card is the simplest way to save data for analysis. On the other hand, the HMI section is used for data visualization. Data visualization will be done by using a mobile device. The mobile device will allow the user to access the data anywhere by accessing the Arduino Cloud website.
Remediation Unit:
This unit is where heavy metal remediation will take place. The remediation unit will be connected physically and via data connections. Physically, the unit shall be connected to the water source via tubing for which water will flow in the system for tubing. For the data connection, the sensors shall be connected to the remediation unit. The sensors shall then send data to the data unit and then the control unit, remediation shall then take place depending on the data received.
RS Electronics Components
The components from RS Components will play a crucial role in the development and implementation of the sensor-based monitoring and remediation system. The Arduino Uno Rev 3 will serve as the primary microcontroller, responsible for interfacing with the electrochemical sensors, processing the acquired data, and controlling the system's operations. The Adafruit HUZZAH WiFi Development Board shall provide wireless connectivity, enabling real-time data transmission and remote monitoring of water quality. A power bank shall be used to power the entire system, ensuring that it remains operational even in remote or off-grid locations.
Wires and resistors will be used to connect the various components, forming a reliable and efficient circuit. Operational amplifiers (op-amps) will be employed to amplify the signals from the sensors, ensuring that the data collected is accurate and can be effectively processed by the Arduino. Additionally, the system will be designed with power efficiency in mind, using the low-power capabilities of the Arduino and the HUZZAH board, along with optimized code, to minimize energy consumption. The combined use of these components will allow for the creation of a robust, efficient, and scalable system for detecting and remediating heavy metals in water.
What Next
The next step in the project involves aligning the constructed prototype with the simulations to ensure consistency and accuracy. The construction phase should be completed using the selected components from RS Components, with careful attention to the proper integration and connection of each element. Simulations should be run to compare the expected outcomes with the actual performance of the prototype.
During this process, sensor calibration and data acquisition protocols shall be refined, ensuring that the data processing algorithms correspond precisely to the simulated models. Any discrepancies observed between the simulation results and the prototype's behaviour shall be thoroughly investigated and addressed, by modifying the hardware setup, adjusting the software algorithms, or revisiting the initial simulation assumptions. This phase is critical for validating the system's functionality and ensuring that the design is both reliable and effective before progressing to extensive testing and eventual deployment.
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