Assessment of environmental data quality and its effect on modelling error of full-scale plants with a closed-loop mass balancing.

Environmental plants are notorious for poor data quality and sensor reliability due to the hostile environment in which the measurement equipment has to function, where the measurements and flow rate equipment in plants must be mutually consistent. The aim of this study is to detect any error in the measured data in an environmental plant and reconcile the data with some gross errors by using a closed data reconciliation of mass balance and the Lagrange multiplier method. A data reconciliation method based on closed-loop mass balance is suggested in order to reduce or remove error within data and obtain reliable process data. The proposed method is applied to a full-scale plant to detect the gross error in measured data, investigate the effects of erroneous data on modelling errors and compare the modelling performances of the faulty data and reconciled data. The results show that the proposed method can efficiently detect any gross error in data, estimate the error-free data by a reconciliation method and enhance the modelling accuracy by using reconciled data. This study provides a simple way to incorporate prior knowledge of plant modelling of a closed-loop mass balancing to identify any gross error and reconcile the faulty measurements.