摘要
Sensor network synthesis means to select certain variables to be measured, to select suitable sensors, to observe all the important variables, and to get reliable and accurate process data. The steady state online data reconciliation can remove the influences of errors in the measurements on the acquired data, and get consistent process data that satisfy the process constraints. These two techniques have been studied often but separately. This paper proposed to integrate these two techniques into a unified system to get better information about the process data. An industrial application of the integrated system to a crude oil distillation unit was also described, where valid process data were obtained and applied for online process optimization system, which led to a profit increase of about four million RMB annually.
Sensor network synthesis means to select certain variables to be measured, to select suitable sensors, to observe all the important variables, and to get reliable and accurate process data. The steady state online data reconciliation can remove the influences of errors in the measurements on the acquired data, and get consistent process data that satisfy the process constraints. These two techniques have been studied often but separately. This paper proposed to integrate these two techniques into a unified system to get better information about the process data. An industrial application of the integrated system to a crude oil distillation unit was also described, where valid process data were obtained and applied for online process optimization system, which led to a profit increase of about four million RMB annually.
