Author 's accepted manuscript, published in Control Engineering Practice 16 (2008) AbstractFault diagnosis methods based on process history data have been studied widely in recent years, and several successful industrial applications have been reported. Improved data validation has resulted in more stable processes and better quality of the products. In this paper, an on-line fault detection and isolation system consisting of a combination of principal component analysis (PCA) and two neural networks (NNs), radial basis function network (RBFN) and self-organizing map (SOM), is presented. The system detects and isolates faulty operation of the analyzers in an ethylene cracking furnace. The test results with real-time process data are presented and discussed.

Flotation is a difficult process to run efficiently. One way to make flotation performance better is to improve cell level control. However, controlling pulp levels in flotation cells is a complex control task because of strong interactions between the levels in flotation cells. Therefore advanced controllers are needed to give good level control. This paper deals with a model of six flotation cells in series. Simulations are performed to compare different control strategies. Four control strategies are considered: one SISO controller and three different MIMO controllers including a new multivariable controller. It is shown that level control performances of the MIMO controllers are significantly better than that of the classical SISO controller.

Flotation is a difficult process to run efficiently. One way to make flotation performance better is to improve cell level control. However, controlling pulp levels in flotation cells is a complex control task because of strong interactions between the levels in flotation cells. Therefore advanced controllers are needed to give good level control. This paper deals with a model of six flotation cells in series. Simulations are performed to compare different control strategies. Four control strategies are considered: one SISO controller and three different MIMO controllers including a new multivariable controller. It is shown that level control performances of the MIMO controllers are significantly better than that of the classical SISO controller.