MAXIMUM-LIKELIHOOD DATA RECTIFICATION - STEADY-STATE SYSTEMS

A maximum likelihood rectification (MLR) technique that poses the data -rectification problem in a probabilistic framework and maximizes the probability of the estimated plant states given the measurements is pr oposed. This approach does not divide the sensors into ''normal'' and ''gross error'' classes, but uses all of the data in the rectification , each sensor being appropriately weighted according to the laws of pr obability. In this manner, the conventional assumption of no sensor bi as is avoided and both random errors (noise) and systematic errors (gr oss errors) are removed simultaneously. A novel technique is introduce d that utilizes historical plant data to determine a prior probability distribution of the plant states. This type of historical plant infor mation, which contains the physical relationships among the variables (mass balances, energy balances, thermodynamic constraints), as well a s statistical correlations among the variables, has been ignored in pr ior data-rectification schemes. This approach can use the historical p lant information to solve a new class of data-rectification problems i n which there are no known model constraints. The MLR method is demons trated on data from a simulated flow network and a simulated heat-exch anger network. The MLR technique provides considerably improved perfor mance over existing data-reconciliation schemes in these examples.