Mahdi Faramarzi

Nitrate and Sulfate Estimations in Water Sources Using a Planar Electromagnetic Sensor Array and Artificial Neural Network Method

The primary advantages of planar electromagnetic sensors can be listed as low cost, convenient, suitable for in situ measurement systems, rapid reaction, and highly durable. In this paper, the outputs of a planar electromagnetic sensors array were observed and analyzed after testing it with different types of water samples at different concentrations. The output parameters were derived to decompose by wavelet transform. The energy and mean features of decomposed signals were extracted and used as inputs for an artificial neural network (ANN) model. The analysis model was targeted to classify the amount of nitrate and sulfate contamination in water. Nitrates and sulfate samples in the form of KNO3 and K2SO4, each having different concentrations between 5 and 114 mg dissolved in 1 L of distilled water, were used. Furthermore, the analysis model was tested with seven sets of mixed KNO3 and K2SO4 water samples. A three-layer multilayer perceptron is used as a classifier. It is understood from the results that the model can detect the presence of nitrate and sulfate added in distilled water and is capable of distinguishing the concentration level in the presence of other types of contamination with a root mean square error (RMSE) of 0.0132. The validity of the ANN model was verified by removing the ANN model in estimating the water contamination, where the RMSE rose to 0.0977. The system and approach presented in this paper have the potential to be used as a useful low-cost tool for water source monitoring.

A review on the design and development of turbidimeter

Purpose – This paper aims to present a review of the design and development of the turbidimeter for measuring the turbidity level in water. Monitoring the turbidity level of water is important because it is related to public health. Design/methodology/approach – A precise and reliable turbidimeter can provide vital data that reveals the water condition level. Several turbidimeter units are discussed briefly. Three types of turbidimeter design – single beam, ratio and modulated four beams – are elaborated with some illustrations of the design concept. Various improvements and innovations for upgrading turbidimeter design are also discussed. Findings – This paper elaborated on a new method of estimating the water turbidity level in water samples using an optical tomography system based on the independent component analysis method. The results showed that a tomography-based turbidimeter can measure slight changes in the level of turbidity when the volume of contaminants is changed slightly. The turbidimeter ...

The application of the Radial Basis Function Neural Network in estimation of nitrate contamination in Manawatu river

The Radial Basis Function (RBF) Neural Network has shown its strong capability in pattern recognition, classification and function approximation problems. In this paper, the RBF neural network is used to classify different levels of nitrate contamination in river water. The planar electromagnetic sensors have been subjected to different water samples contaminated by nitrate and output signals have been extracted. These signals are derived and its suitable features are extracted by using three different features; energy, mean and skewness. These features are inputted to the RBF neural network consequently, for the classification of different levels of nitrate concentration in water. The result shows that the planar electromagnetic sensor with the assistance of the RBF neural network can be a good alternative to current laboratory testing methods.

Gas Bubbles Investigation in Contaminated Water Using Optical Tomography Based on Independent Component Analysis Method

This paper presents the results of concentration profiles for gas bubble flow in a vertical pipeline containing contaminated water using an optical tomography system. The concentration profiles for the bubble flow quantities are investigated under five different flows conditions, a single bubble, double bubbles, 25% of air opening, 50% of air opening, and 100% of air opening flow rates where a valve is used to control the gas flow in the vertical pipeline. The system is aided by the independent component analysis (ICA) algorithm to reconstruct the concentration profiles of the liquid-gas flow. The behaviour of the gas bubbles was investigated in contaminated water in which the water sample was prepared by adding 25 mL of colour ingredients to 3 liters of pure water. The result shows that the application of ICA has enabled the system to detect the presence of gas bubbles in contaminated water. This information provides vital information on the flow inside the pipe and hence could be very significant in increasing the efficiency of the process industries.

Comparisons between radial basis function and multilayer perceptron neural networks methods for nitrate and phosphate detections in water supply

This paper presents the comparisons between two models to classify nitrate and phosphate contamination in water supply based on artificial intelligence with multiple inputs parameters. The planar electromagnetic sensor array has been subjected to different water samples contaminated by nitrate and phosphate where output signals have been extracted. In the first method, the signals from the planar electromagnetic sensor array were derived to decompose by Wavelet Transform (WT). The energy and mean features of decomposed signals were extracted and used as inputs for an Artificial Neural Network (ANN) multilayer perceptron (MLP) and Radial Basis Function (RBF) neural networks models. The analysis models were targeted to classify the amount of nitrate and phosphate contamination in water supply. The result shows that the planar electromagnetic sensor array with the assistance of the MLP neural network method is the best alternative as compared to RBF neural network method.

A Tomography System Based on Optical and Electrodynamic Sensors

This paper presents an investigation on the use of tomography system using using optical and electrodynamic sensors. The system obtains data from both sensors which detect the flow in a process pipe. Information on the flow is processed in order to display the image reconstruction of a solid flow.