A New Robust Data Reconciliation Method Based on Inequality Constraints for Industrial Processes
Data reconciliation plays an important role in the process control as measured signals are often contaminated by measurement errors. The simultaneous reconciliation of flow rate and composition measurements results in nonlinear constraints. A Correntropy based data reconciliation method is proposed for the type of bilinear systems with specific to the flow rate and measurement composition. Correntropy is a robust estimator and is effective in reducing the effect of random and gross errors. Inequality constraints or bounds are rather necessary in some cases to adjust parameter estimates to be physically meaningful. Based on the above two points, a new robust data reconciliation method based on Correntropy estimator and inequality constraints for industrial processes is proposed in this work, and the rationality and effectiveness of the method are verified.
A discrete multi-objective state transition algorithm for sensors placement problem in industrial processes
With the increasing quantity and importance of wastewater treatment plants, how to obtain all-round and high-quality data to ensure the stable and efficient operation of the plants draws more and more attention. In order to achieve an organic balance between information richness and cost, a discrete multi-objective optimization method based on the state transition algorithm is proposed to arrange flow rate sensors in the whole plants.