Water Quality Measuring Station

By -

pH, Turbidity and temperature measurement



This project is carried out for the measurement of quality of water for the Metropolia UAS using sensors and microcontroller. Water is indispensable for the living beings, and clean water is critical because of the modern world pollutions into the water in different forms. This project focuses on how we can measure and analyse the quality of water.

This project focuses on measuring pH, Turbidity and Temperature of water. These three factors are considered because pH determines either the water be acidic or basic, Turbidity helps to determine the amount of solid particle,
Where as these values may slightly differ with the change in temperature.

The need for maintaining the quality of water is essential since the water is used or con-sumed by living beings in many different ways. Whether it is for a human being or aquatic plants or fishes, the properly maintained water is crucial for the proper sustainability. The measurement was first carried out by taking each sensor separately. After the completions of analysing sensors result, the circuit was combined to work all sensors together in the same microcontroller. The data was presented in serial monitor and then to the LabVIEW. LabVIEW shows the graph of each voltage retrieved from the sensors. Finally, the value of all the sensors was presented clearly in the user interface made using LabVIEW.


Process

The project started with taking a measurement of each value of pH, turbidity and temperature from each sensor separately. After getting the successful results all the sensors connected to the microcontroller to make the complete device. For the first measurement, after assembling all the sensors and circuit as required, tap water of school testing.

Measurement started with a testing temperature of water by taking the hot and cold water with different temperature values. Since the turbidity sensor used in WQMS project is only applicable for qualitative analysis but not the quantitative analysis, only the voltage derived from the sensor is calculated according to the change in voltage with the modification of dirt particles in the water. For measuring the turbidity, dirt particles are added to the water with the random amount and the value of voltage achieved shows as expected. The value of voltage decreases as the dirt particles added to the water which proves that the sensor works as expected. Finally, to measure the pH value, different kind of liquid which contains acid or base are used to test the device.


Results

When the finalised device used to measure the quality or normal tap water, the device provided the pH value of 7.02 and temperature of 23 degree Celsius and providing the turbidity value of close to the pure water (500mV). This is the first test analysed after the device completion. For the confirmation of device working properly, different measurements were taken at the different temperatures, pH value and turbidity.
After the device was ready to take the measurements, the parameter used for quality analysis was compared with the reference devices. First of all, the temperature was recorded with the reference thermometer as well as with WQMS at the same water. Cold water was taken at the beginning and slowly little by the little random amount of boiled water (temperature was close to 100°C ) was added.

Comments

Post a Comment

Popular posts from this blog

KNN Algorithm

By -
Image
When do we use KNN algorithm? KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry. To evaluate any technique we generally look at 3 important aspects: 1. Ease to interpret output 2. Calculation time 3. Predictive Power Let us take a few examples to  place KNN in the scale : KNN algorithm fairs across all parameters of considerations. It is commonly used for its easy of interpretation and low calculation time. How does the KNN algorithm work? Let’s take a simple case to understand this algorithm. Following is a spread of red circles (RC) and green squares (GS) : You intend to find out the class of the blue star (BS) . BS can either be RC or GS and nothing else. The “K” is KNN algorithm is the nearest neighbors we wish to take vote from. Let’s say K = 3. Hence, we will now make a circle with BS as center just as big as to enclose only three data points on the p...
Read more

Use amCharts to visualize Google Analytics data

By -
Image
Use amCharts to visualize Google Analytics data We can use amCharts to visualize google analytical data.Before we can extract data from your Analytics account, you will need to set up an application and API access to it and further more steps are available. An example : link : https://www.amcharts.com/kbase/use-amcharts-to-visualize-google-analytics-data/
Read more

What are the steps used in Machine Learning?

By -
What are the steps used in Machine Learning? There are 5 basic steps used to perform a machine learning task: Collecting data : Be it the raw data from excel, access, text files etc., this step (gathering past data) forms the foundation of the future learning. The better the variety, density and volume of relevant data, better the learning prospects for the machine becomes. Preparing the data : Any analytical process thrives on the quality of the data used. One needs to spend time determining the quality of data and then taking steps for fixing issues such as missing data and treatment of outliers. Exploratory analysis is perhaps one method to study the nuances of the data in details thereby burgeoning the nutritional content of the data. Training a model : This step involves choosing the appropriate algorithm and representation of data in the form of the model. The cleaned data is split into two parts – train and test (proportion depending on the p...
Read more