Soft-sensors for process estimation and inferential control

Abstract This paper presents two adaptive estimators (software based sensors or ‘soft-sensors’) for inferring process outputs that are subject to large measurement delays, from other (secondary) outputs which may be sampled more rapidly. In other words, these estimators utilize plant data sampled at different rates. The parameters of the estimators can be continuously estimated and updated on-line, thus enabling the tracking of slow variations in process characteristics. The estimators employ either an input-output or a state space process description as the starting points for algorithm synthesis. In contrast to mechanistic model-based estimators such as Kalman filters, the proposed adaptive techniques require minimal design effort. The key contribution of the paper is thus the formulation of applications independent, adaptive multi-rate algorithms, which can provide accurate estimates of infrequently measured process outputs, from other more rapidly sampled secondary outputs. Theoretical developments are supported by results of recent applications to a variety of industrial scale processes: estimation of biomass concentration in an industrial mycelial fermentation; top product composition of a large industrial distillation tower and melt flow index on an industrial polymerization reactor. Measurements from established instruments such as off-gas carbon dioxide in the fermenter; overheads temperature in the distillation column and hydrogen concentration in the reactor were used as the secondary variables for the respective processes. The range of the applications is an indication of the utility of the techniques. A significant improvement in overall process control performance is also possible when estimated plant outputs, rather than the infrequently obtained measurements, are used for feedback control. This is demonstrated by non-linear simulation studies.