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
The seabed oil gas pipeline network is simplified to a network with stochastic edge weight by means of the fuzzy graphics theory. With the help of network analysis, fuzzy mathematics, and stochastic theory, the prob...The seabed oil gas pipeline network is simplified to a network with stochastic edge weight by means of the fuzzy graphics theory. With the help of network analysis, fuzzy mathematics, and stochastic theory, the problem of reliability analysis for the seabed oil gas pipeline network under earthquakes is transformed into the calculation of the transitive closure of fuzzy matrix of the stochastic fuzzy network. In classical network reliability analysis, the node is supposed to be non invalidated; in this paper, this premise is modified by introducing a disposal method which has taken the possible invalidated node into account. A good result is obtained by use of the Monte Carlo simulation analysis.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 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.
文摘The seabed oil gas pipeline network is simplified to a network with stochastic edge weight by means of the fuzzy graphics theory. With the help of network analysis, fuzzy mathematics, and stochastic theory, the problem of reliability analysis for the seabed oil gas pipeline network under earthquakes is transformed into the calculation of the transitive closure of fuzzy matrix of the stochastic fuzzy network. In classical network reliability analysis, the node is supposed to be non invalidated; in this paper, this premise is modified by introducing a disposal method which has taken the possible invalidated node into account. A good result is obtained by use of the Monte Carlo simulation analysis.
