Non-linear dynamic data reconciliation for industrial processes

This paper investigates and improves a technique known as Nonlinear Dynamic Data Reconciliation (NDDR) for a real industrial process. NDDRS is a technique for data reconciliation that requires an objective function to be minimised subject to both algebraic and differential, equality and inequality constraints. These constraints are obtained from the mathematical description of the process and ensure that the measurement data can be optimised to conform as closely as possible to the true behaviour of the process. One of the difficulties of using the original NDDR is that a rigorous process dynamic model is required as a constraint. Unfortunately it is very hard to establish a rigorous dynamic model for a complex industrial process, particularly for data reconciliation purpose. A transfer function matrix model has been introduced in this new NDDR method. Therefore the rigorous dynamic model is avoided. The real industrial data from FCCU is used to illustrate the efficiency of the new NDDR method

Reference model based maintenance of control system performance for industrial processes

In the last decade, fault tolerant controls (FTC) have enjoyed tremendous success to effectively accommodate defects in sensors, actuators, or plants. However, little of them considered what should be done once a control system performance is degraded during the operation. The aim of this paper is to maintain the performance of a control system at an acceptable level based on a pre-defined reference model. A maintenance approach is proposed and experimented in this paper. The method is to insert a compensator into the faulty control system and make the compensator and the faulty open loop system working together to track the pre-defined reference model. The proposed method is illustrated by reference to a mini process rig and shows the potential to industrial processes

Reference Model Based Maintenance of Control System Performance for Industrial Processes

In the last decade, fault tolerant controls (FTC) have enjoyed tremendous success to effectively accommodate defects in sensors, actuators, or plants. However, little of them considered what should be done once a control system performance is degraded during the operation. The aim of this paper is to maintain the performance of a control system at an acceptable level based on a pre-defined reference model. A maintenance approach is proposed and experimented in this paper. The method is to insert a compensator into the faulty control system and make the compensator and the faulty open loop system working together to track the pre-defined reference model. The proposed method is illustrated by reference to a mini process rig and shows the potential to industrial processes

Non-linear Dynamic Data Reconciliation For Industrial Processes

This paper investigates and improves a technique known as Nonlinear Dynamic Data Reconciliation (NDDR) for a real industrial process. NDDRS is a technique for data reconciliation that requires an objective function to be minimised subject to both algebraic and differential, equality and inequality constraints. These constraints are obtained from the mathematical description of the process and ensure that the measurement data can be optimised to conform as closely as possible to the true behaviour of the process. One of the difficulties of using the original NDDR is that a rigorous process dynamic model is required as a constraint. Unfortunately it is very hard to establish a rigorous dynamic model for a complex industrial process, particularly for data reconciliation purpose. A transfer function matrix model has been introduced in this new NDDR method. Therefore the rigorous dynamic model is avoided. The real industrial data from FCCU is used to illustrate the efficiency of the new NDDR method