Measurement and Control

doi: 10.1177/002029400904200701pmid: N/A

Wang, Hong; Breikin, Tim

doi: 10.1177/002029400904200702pmid: N/A

Chai, Tianyou

doi: 10.1177/002029400904200703pmid: N/A

This paper describes the control and operational management process of plant-wide production processes and analyzes the relationship between the control system actions and the multiple global production indices that characterize quality, yield, costs, and consumptions etc. The existing research results in control and optimization are reviewed. From the angles of mathematical models-based, data-based and their combinations, the state-of-art of control and optimization is summarized. This is followed by the analysis on the challenging issues on the optimal control for plant-wide production processes in terms of process control, operational optimization and the realization of the control systems.

Miao, Yu; Su, Hongye; Gang, Rong; Chu, Jian

doi: 10.1177/002029400904200704pmid: N/A

Process data plays a vital role in industrial processes, which are the basis for process control, monitoring, optimization and business decision making. However, it is inevitable that process data measurements will be corrupted by random errors. Therefore, data reconciliation has been developed to improve accuracy of process data by reducing the effect of random errors. Unfortunately, reconciled values would be deteriorated by gross errors, which may be present during measurement. Therefore, gross error detection is necessary to guarantee the efficiency of data reconciliation, which has been developed to identify and eliminate gross errors in process data. In this paper, a review of data reconciliation and gross error detection and relevant industrial applications are presented. As the efficiency of data reconciliation and gross error detection largely depends upon the locations of sensors, sensor networks design is also included in the review. Meanwhile, some achievements of the authors are also included.

Zou, Wei; Yu, Junzhi; Xu, De

doi: 10.1177/002029400904200705pmid: N/A

Recently embedded technology has been widely applied to machine vision and embedded vision systems are more and more popular. This paper reviews the advances on embedded vision systems, and then compares and analyzes their frameworks in processing ability, cost and performance. A discussion is provided for some unsolved problems for embedded vision systems. Finally, the future of embedded vision system is outlined.

Richardson, Bryan

doi: 10.1177/002029400904200706pmid: N/A