The legal requirement to monitor discharges of harmful substances in industrial waste waters is presented in Chapter One, which also discusses the merits of using automated on-line analytical instruments for this purpose. Flow injection analysis with solid-state UV-visible detection is proposed as a potential on-line effluent monitoring technique, and the principles and advantages of this methodology are summarised.
Chapter Two describes the development of a portable, automated FI monitor for on-line determination of ammonia in liquid effluents. The development process culminates with deployments of the system at two chemical production sites, and validated results are presented for on-line analyses of real effluents.
The principles of multivariate calibration of spectrophotometric data are summarised in Chapter Three, and five commonly applied techniques (DMA, SMLR, PCR, PLS1 and PLS2) are described and compared. These multivariate calibration techniques are then applied in Chapter Four for the quantification of metal ions in model effluent systems, using diode-array spectral data sets. The relative predictive performances of the techniques are compared for both simple and more complex multicomponent systems.
Flow injection and multivariate calibration techniques are combined in Chapter Five, in which the development of a method for the determination of BTEX compounds in effluents is described. UV absorbance spectra are obtained for synthetic aqueous mixtures using an FI-diode array system, and SMLR, PCR, PLS1 and PLS2 are employed to quantify individual and total BTEX compounds. An FI solvent extraction method is also described for the analysis of a real effluent matrix.
The thesis concludes with an examination of a recursive digital filtering technique which has potential applications for on-line effluent monitoring. Chapter Six describes the principles of the Kalman filter, and presents results for both multivariate calibration and baseline drift correction of multicomponent spectral data sets, performed using different forms of the Kalman filter algorithm.
Copyright © 1996 Kevin N. Andrew. All rights reserved.
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