Selection of Controlled Variables and Robust Setpoints

Enhanced control structure design for an industrial off-gas system: Simple reconfigurations benefit the economy

For control structure design of the industrial off-gas benchmark system, application of the Skogestad's state-of-art design procedure has suggested the scrubber inlet pressure (P _si in the roaster) and one of the fan speeds (N _fan1 or N__fan2 in the furnace) as the self-optimizing controlled variables CVs. In this study, we stress and advocate the gSOC-plus-BAB approach as an enhanced design toolkit for the classical and systematical design procedure. The gSOC (global self-optimizing control) is able to efficiently solve measurement combinations as CVs with improved economic performances, while the BAB (branch and bound) algorithm serves to fast screen promising measurement subsets for large-scale problems. Using the enhanced design for the off-gas system, our new findings are to control the combination of roaster's ID fan outlet pressures, 0.494P _IDfan1+0.506P _IDfan2 (setpoint: -257.04 Pa), and the furnace's fan pressure difference, P _IDfan1- P _IDfan2 (setpoint: 0). Such simple reconfigurations can dramatically reduce the average economic loss by 53.3% for the roaster and even achieve perfect optimal control for the furnace. Both steady state and dynamic evaluations are carried out to validate the reconfigured control structures. © 2017 Elsevier Inc. All rights reserved.

Regression based state space adaptive model of two-phase anaerobic reactor

In this paper, a linear state space model for the two-phase anaerobic reactor system was developed based on historical data. Subsequently, the model was used to predict its future behavior. The state space model developed involved correlation analysis and model development. The model would be updated at every time point when a new data set became available, giving it an "adaptive" feature. The model was then applied to monitor two-phase anaerobic co-digestion of a feed comprising 2 industrial secondary sludges and 2 industrial wastewaters. The case study showed the proposed model was able to provide good predictions of various process parameters. In addition, it also predicted impending process failure and this would have allowed the operator to take necessary measures to prevent or reduce impact of such failure during plant operation.