To overcome the problem that soft sensor models cannot be updated with the process changes, a soft sensor modeling algorithm based on hybrid fuzzy c-means （FCM） algorithm and incremental support vector machines （ISVM） is proposed. This hybrid algorithm FCMISVM includes three parts： samples clustering based on FCM algorithm, learning algorithm based on ISVM, and heuristic sample displacement method. In the training process, the training samples are first clustered by the FCM algorithm, and then by training each clustering with the SVM algorithm, a sub-model is built to each clustering. In the predicting process, when an incremental sample that represents new operation information is introduced in the model, the fuzzy membership function of the sample to each clustering is first computed by the FCM algorithm. Then, a corresponding SVM sub-model of the clustering with the largest fuzzy membership function is used to predict and perform incremental learning so the model can be updated on-line. An old sample chosen by heuristic sample displacement method is then discarded from the sub-model to control the size of the working set. The proposed method is applied to predict the p-xylene （PX） purity in the adsorption separation process. Simulation results indicate that the proposed method actually increases the model＇s adaptive abilities to various operation conditions and improves its generalization capability.
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.
A strategy for the integration of production planning and scheduling in refineries is proposed. This strategy relies on rolling horizon strategy and a two-level decomposition strategy. This strategy involves an upper level multiperiod mixed integer linear programming （MILP） model and a lower level simulation system, which is extended from our previous framework for short-term scheduling problems [Luo, C.E, Rong, G, ＂Hierarchical apthis extended framework is to reduce the number of variables and the size of the optimization model and, to quickly find the optimal solution for the integrated planning/scheduling problem in refineries. Uncertainties are also considered in this article. An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.
