The industrial ammonia soda process models based on mass balance and energy balance were developed, and the objective functions for data reconciliation and mixed integer nonlinear programming (MINLP)were established.B...The industrial ammonia soda process models based on mass balance and energy balance were developed, and the objective functions for data reconciliation and mixed integer nonlinear programming (MINLP)were established.By training the parameters with process data obtained from a plant producing 600000 t·a-1 of 99.5% soda,the models could be used to simulate different operating situations.Simulation data would be the data source of process optimization and data reconciliation.展开更多
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 con...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 applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive 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
文摘The industrial ammonia soda process models based on mass balance and energy balance were developed, and the objective functions for data reconciliation and mixed integer nonlinear programming (MINLP)were established.By training the parameters with process data obtained from a plant producing 600000 t·a-1 of 99.5% soda,the models could be used to simulate different operating situations.Simulation data would be the data source of process optimization and data reconciliation.
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 applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive 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.
