The renewed interest in the process data reconciliation by both the academic world and the process industries is mainly due to the introduction of new process control technologies such as the model predictive control, the real time dynamic optimization and the enterprise resource planning, all requiring a continuous (on-line) maintenance and optimization. The paper discusses the existing techniques for solving a data reconciliation problem, proposes some emerging methodologies, and introduces the open issue of the real-time nonlinear reconciliation based on detailed models
Abstract: The aim of this work is to compare Dynamic Data Reconciliation techniques by both theoreti...
In the digital era, data mining, analysis, and interpretation are of primary interest for industry 4...
In the digital era, data mining, analysis, and interpretation are of primary interest for industry 4...
The renewed interest in the process data reconciliation by both the academic world and the process i...
The use of data reconciliation techniques can considerably reduce the inaccuracy of process data due...
Data reconciliation is a model-based technique that reduces measurement errors by making use of redu...
The operation of power plants and chemical processes requires process measurements for optimal opera...
In a modern chemical plant, the implementation of a distributed control system leads to a large numb...
The paper deals with the integrated solution of different model-based optimization levels to face th...
The paper deals with the integrated solution of different model-based optimization levels to face th...
This paper investigates and improves a technique known as Nonlinear Dynamic Data Reconciliation (NDD...
This thesis presents, discusses and compares a set of methodologies and several appropriate combinat...
The paper deals with the integrated solution of different model-based optimization levels to face th...
The paper deals with the integrated solution of different model-based optimization levels to face th...
In the digital era, data mining, analysis, and interpretation are of primary interest for industry 4...
Abstract: The aim of this work is to compare Dynamic Data Reconciliation techniques by both theoreti...
In the digital era, data mining, analysis, and interpretation are of primary interest for industry 4...
In the digital era, data mining, analysis, and interpretation are of primary interest for industry 4...
The renewed interest in the process data reconciliation by both the academic world and the process i...
The use of data reconciliation techniques can considerably reduce the inaccuracy of process data due...
Data reconciliation is a model-based technique that reduces measurement errors by making use of redu...
The operation of power plants and chemical processes requires process measurements for optimal opera...
In a modern chemical plant, the implementation of a distributed control system leads to a large numb...
The paper deals with the integrated solution of different model-based optimization levels to face th...
The paper deals with the integrated solution of different model-based optimization levels to face th...
This paper investigates and improves a technique known as Nonlinear Dynamic Data Reconciliation (NDD...
This thesis presents, discusses and compares a set of methodologies and several appropriate combinat...
The paper deals with the integrated solution of different model-based optimization levels to face th...
The paper deals with the integrated solution of different model-based optimization levels to face th...
In the digital era, data mining, analysis, and interpretation are of primary interest for industry 4...
Abstract: The aim of this work is to compare Dynamic Data Reconciliation techniques by both theoreti...
In the digital era, data mining, analysis, and interpretation are of primary interest for industry 4...
In the digital era, data mining, analysis, and interpretation are of primary interest for industry 4...