Use of a novel autoassociative neural network for nonlinear steady-state data reconciliation

A novel autoassociative neural-network-based estimator for nonlinear steady-state data reconciliation was developed, which is a modified autoassociative feedforward neural network. The main difference between them lies in the minimization of an objective function that includes material imbalance terms of flow rates and compositions as well as the traditional least-square prediction term. Accordingly, this neural network, with the material balance-related equations included in the objective criterion, can perform simultaneously the following basic functions necessary for proper steady-state data rectification: (1) eliminate the nonrandom errors, such as the biases and gross erros, from measurements; (2) filter out the random errors from measured data; and (3) estimate the values of unmeasured process variables, provided data redundancy prevails. This novel neural-network-based reconciliation method is demonstrated on a flotation system.