MODELS FOR CONDUCTANCE MEASUREMENTS IN QUALITY ASSURANCE OF WATER ANALYSIS

Models for the prediction of conductance in nonbrine water samples thr ough the measurement of ionic concentrations and other parameters are compared. Such predictions are often used for quality assurance purpos es by comparing them with actual measurements to determine whether gro ss analysis errors have been made. A currently recommended method for making such predictions is a semiempirical relation that adapts the De bye-Huckel-Onsager equation to mixed electrolyte systems by incorporat ing modified definitions of ionic charge and concentration. The limita tions of this model are examined, and extensions to it are considered. Other predictive methods, including multiple linear regression (MLR), principal components regression (PCR), partial least-squares (PLS) re gression, continuum regression (CR), and neural networks (NN), are als o considered. Models employ the concentrations of 10 ions as well as, in some cases, additional water quality measurements. Best results wer e obtained with an extended form of the Debye-Huckel-Onsager equation and an optimized MLR model. PCR, PLS, CR, and NN did not offer signifi cant advantages.