关键词: 数据校正, 变负荷, 稳态检测, 多工况聚类

Abstract: Real-time optimization and process control are based on the process model, which is tuned by data reconciliation and parameter estimation. The results of data reconciliation and parameter estimation for variable load are not accurate, if the change of model parameters is nonlinear and the measured data have gross errors. As a result, efficiency of real-time optimization and process control will be decreased. This paper proposed an approach for data reconciliation and parameter estimation in the presence of variable load. The approach includes steady states detection and sampling, multiple operation conditions clustering, data reconciliation and parameter estimation with multiple data sets. The reliable process data are selected by steady states detection and sampling, and then multiple operation conditions clustered by the volatility of operation conditions and nonlinear characteristics of system can be used to decrease the influence of nonlinear change of model parameters. Data reconciliation and parameter estimation with multiple data sets can minimize the offsets between measured and reconciled data. Based on the process data measured from the real plant, numerical results demonstrated effectiveness of application of the proposed method to an air separation process.

Key words: data reconciliation, variable load, steady-state detection, multiple operation conditions clustering