Mixed integer linear programming(MILP) approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material,energy,and other balance constrai...Mixed integer linear programming(MILP) approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material,energy,and other balance constrains.But the efficiency will decrease significantly when this method is applied in a large-scale problem because there are too many binary variables involved.In this article,an improved method is proposed in order to generate gross error candidates with reliability factors before data rectification.Candidates are used in the MILP objective function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates.Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.展开更多
Gross error detection has great importance and is the necessary step in process data reconciliation.Many methods have been published to solve this problem,but no one can guarantee consistently finding all of gross err...Gross error detection has great importance and is the necessary step in process data reconciliation.Many methods have been published to solve this problem,but no one can guarantee consistently finding all of gross errors.A combinatory method based upon measurement test (MT) and nodal test (NT) is developed for practical use. The MT-NT combinatory approach makes use of both MT and NT tests and avoids any artificial manipulation.It also eliminates the huge combinatorial problem that is created in the combinatory method based upon nodal test or in the serial elimination method in the case of more than one gross errors in a large process system.展开更多
基金Supported by the National High Technology Research and Development Program of China (2007AA40702 and 2007AA04Z191)
文摘Mixed integer linear programming(MILP) approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material,energy,and other balance constrains.But the efficiency will decrease significantly when this method is applied in a large-scale problem because there are too many binary variables involved.In this article,an improved method is proposed in order to generate gross error candidates with reliability factors before data rectification.Candidates are used in the MILP objective function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates.Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.
文摘Gross error detection has great importance and is the necessary step in process data reconciliation.Many methods have been published to solve this problem,but no one can guarantee consistently finding all of gross errors.A combinatory method based upon measurement test (MT) and nodal test (NT) is developed for practical use. The MT-NT combinatory approach makes use of both MT and NT tests and avoids any artificial manipulation.It also eliminates the huge combinatorial problem that is created in the combinatory method based upon nodal test or in the serial elimination method in the case of more than one gross errors in a large process system.
