Sallehuddin Ibrahim

Optical sensor configurations for process tomography

This paper describes an investigation into the optimum design of optical fibre sensing arrays to be incorporated in an optical tomographic measurement system for on-line monitoring of particles and droplets. Two approaches are considered to cover opaque and transparent materials; optical path length and optical attenuation. Four flow models are investigated: single-pixel flow representing a single particle or droplet, two-pixel flow as a simple check on aliasing in the reconstructed image, half flow representing half the sensing cross section filled with material and full flow, where the whole sensing cross section is full of material. Six projection geometries of the fibre sensors are considered. For tomographic imaging, the forward problem, which assumes particles are placed in specific places in the measurement cross section and calculates voltage outputs for the individual sensors, is modelled. The solutions from the forward problem are used to solve the inverse problem, which uses actual sensor voltage readings to estimate the spatial distribution of the material in the measurement cross section. The solution of the inverse problem is used to derive the linear back projection (LBP) and filtered LBP algorithms. In order to improve image quality, a hybrid reconstruction algorithm is implemented. This algorithm first checks if any sensors read zero and sets (locks for this estimation) all pixels associated with them to zero (no material). The algorithm then proceeds as for the LBP.

Planar Electromagnetic Sensor Based Estimation of Nitrate Contamination in Water Sources Using Independent Component Analysis

The main advantages of electromagnetic sensors can be listed as low-cost, convenient, suitable for in-situ measurement system, rapid response, and high durability. In this paper, the output parameters of the planar electromagnetic sensor have been observed with different kind of water samples at different concentrations. The output parameters have been derived and tested to be incorporated with independent component analysis (ICA) and used as inputs for an analysis model. The analysis model targeted to estimate the amount of nitrate contamination in water samples with the assistance of ICA based on FastICA fixed point algorithm under the contrast functions of pow3, tanh, gauss, and skew. Nitrates sample in the form of ammonium nitrates (NH4NO3), each of different concentration between 5 mg and 20 mg dissolved in 1 litre of deionized water (Milli-Q) was used as one of the main references. The analysis model was tested with eight sets of mixed NH4NO3 and (NH4)2HPO4 water samples. It is seen from the results that the model can acceptably detect the presence of nitrate added in Milli-Q water and capable of distinguishing the concentration level in the presence of other type of contamination. The system and approach presented in this paper has the potential to be used as a useful low-cost tool for water sources monitoring.

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.

Concentration measurements of bubbles in a water column using an optical tomography system

Optical tomography provides a means for the determination of the spatial distribution of materials with different optical density in a volume by non-intrusive means. This paper presents results of concentration measurements of gas bubbles in a water column using an optical tomography system. A hydraulic flow rig is used to generate vertical air-water two-phase flows with controllable bubble flow rate. Two approaches are investigated. The first aims to obtain an average gas concentration at the measurement section, the second aims to obtain a gas distribution profile by using tomographic imaging. A hybrid back-projection algorithm is used to calculate concentration profiles from measured sensor values to provide a tomographic image of the measurement cross-section. The algorithm combines the characteristic of an optical sensor as a hard field sensor and the linear back projection algorithm.

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 ...

Development of planar electromagnetic sensor array for nitrate and sulphate detection in natural water sources

Purpose – The purpose of this study is to determine the contamination level in natural water resources because the tremendous development in the agriculture sector has increased the amount of contamination in natural water sources. Hence, the water is polluted and unsafe to drink. Design/methodology/approach – Three types of sensor arrays were suggested: parallel, star and delta. The simulation of all types of sensor array was carried out to calculate the sensors’ impedance value, capacitance and inductance during their operation to determine the best sensor array. The contamination state was simulated by altering the electrical properties values of the environmental domain of the model to represent water contamination. Findings – The simulation results show that all types of sensor array are sensitive to conductivity, σ, and permittivity, ɛ (i.e. contaminated water). Furthermore, a set of experiments was conducted to determine the relationship between the sensor’s impedance and the water’s nitrate and su...

Lensed optical fiber sensors for on-line measurement of flow.

This paper describes a system using lensed optical fiber sensors that are arranged in the form of two orthogonal projections. The sensors are placed around a process vessel for upstream and downstream measurements. The purpose of the system is for on-line monitoring of particles and droplets being conveyed by a fluid. The lenses were constructed using a custom heating fixture. The fixture enables the lenses to be constructed with similar radii resulting in identical characteristics with minimum differences in transmitted intensity and emission angle. By collimating radiation from two halogen bulbs, radiation can be obtained by the sensors with radiation intensity related to the nature of the media. Each sensor interrogates a finite section of the measurement section. Each sensor provides a view. Parallel sensors provide a projection. Signal processing is carried out on the measured data in the time and frequency domains to investigate the latent information present in the flow signals.

Selective membrane for detecting nitrate based on planar electromagnetic sensors array

The applications of planar electromagnetic sensor are gaining worldwide attention since it was introduced due to simplicity, fast response, convenience, and low cost. Numerous membranes have been investigated to remove nitrates from aqueous solution. This paper aims to produce selective membrane for detecting nitrate ion based on planar electromagnetic sensors and compare it with conventional coating using acrylic lacquer. The preparation method of membrane in this study will reduce the cost of pretreatment as no addition of nutrients are required, thus presenting a promising alternative method. In view of this, a membrane of polymer dope of silica is selected as an alternative coater for the planar electromagnetic sensors array as to increase the selectivity in the detection of unwanted nitrate ions in aqueous solution. Finally, the methods in choosing the best coater in supporting the detection of nitrate contamination have been determined and it showed that the planar electromagnetic sensors array coated with the selective membrane yields absolute average sensitivity, |Z%| value range between the range of around 0.007 % to 223 %, when tested with nitrate concentrations of 5 ppm, 25 ppm, and 100 ppm, which is the highest absolute average sensitivity, |Z%| value range of the results.

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.

The Selection of Novel Planar Electromagnetic Sensors for the Application of Nitrate Contamination Detection

Twenty four novel planar electromagnetic sensors based on the combination of meander and interdigital sensors have been designed and fabricated using the simple PCB technology for the application of nitrate contamination detection. Experiments were conducted to obtain the impedance characterization for each sensor, and the results were used to estimate the important parameters that influence the performance of the sensors based on the equivalent electrical circuits. Furthermore, the best sensors have been tested to detect nitrates contamination in distilled water from two sets of experiments. Firstly, two nitrate forms, namely sodium nitrates (NaNO3) and ammonium nitrates (NH4NO3), each of different concentration between 5 mg and 20 mg dissolved in 1 litre of distilled water were used to observe the sensor response. Secondly, NaNO3 and NH4NO3 were mixed in several different ratios dissolved in 1 litre of distilled water and the responses of the sensors were observed. The outcomes concluded that a sensor with the combination of interdigital sensor enclosed with a meander sensor can very well detect the presence of nitrate added into distilled water and is capable of distinguishing the concentration level. This paper will discuss the process that was involved to select the best sensor for the application of nitrate contamination detection.