Development of fibre optic sensors for dye concentration measurement / Chong Su Sin

Chong , Su Sin (2016) Development of fibre optic sensors for dye concentration measurement / Chong Su Sin. PhD thesis, Universiti Malaya.

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Abstract

The quality of industrial effluent needs to be monitored to prevent environmental pollution. Continuous development in non-invasive sensing technologies is expected to improve the efficiency and precision of various types of sensors for detecting chemical species in various industrial processes. Among the existing sensors, fibre optic sensors are widely accepted because of their minute size and non-destructive characteristics, which offer considerable advantages under diverse conditions compared to traditional sensors. In this work, three fibre optic sensors: tapered plastic optical fibre sensors (TPOF), core-mismatch interferometer fibre optic sensors (CMFOS) and non-adiabatic tapered fibre optic sensor (NATFOS) were developed in order to measure various concentrations of Remazol Black B (RBB) dye solutions. TPOF is based on the intensity modulation while NATFOS and CMFOS are based on the wavelength modulation approach. Optimizing fiber configuration can improve the sensitivity of a sensor. The performance of the sensors in terms of sensitivity, regression coefficient values, standard deviation, limit of detection and linearity was evaluated. Cross sensitivity due to temperature variation and the presence of other contaminants in the analyte were also investigated as they could affect the measurement accuracy of the developed sensors. Two methods were used to overcome the cross-sensitivity issue: multi-parameter sensing approach, as demonstrated in CMFOS and compensation of the cross-sensitivity effect, as demonstrated in TPOF and NATFOS respectively. Predictive models for the preliminary stage of designing a structure for RBB dye concentration detection were also developed. The results showed that the developed sensors could be potentially used for online monitoring of wastewater quality in the future. Soft computing techniques, including multiple linear regression, central composite design and adaptive neuro-fuzzy inference system (ANFIS) were used as support tools for analyzing data to predict RBB concentrations. In addition, a new approach for analyzing dye concentration by combining two typical responses – absorption and spectral shift, was also introduced to infer RBB dye concentrations using NATFOS. Eventually, the objectives of the research were achieved. The developed sensors had different sensitivities and detection limits. TPOF could measure dye concentration of as low as 7.80 ppm while CMFOS and NATFOS could measure dye concentration of up to 800ppm. The sensing errors caused by the cross sensitivity effect were minimized and the accuracy of the sensing capability was improved by the established predictive models.

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