Sensor networks are important tools to gather information to properly monitor the plant. This information is further filter and processed to obtain good process state estimators.

Recently a cutset-based tree enumeration method has been shown to be efficient for solving linear sensor network design problems (Mayur and Bagajewicz, 2006a, 2006b). Linear systems are characterized for handling the estimation of material flows only (Mass balance). The algorithms have been shown to consistently give optimum sensor networks for large scale linear systems with short computation time.

In this work, Mayur and Bagajewicz's methods are extended to solve the nonlinear sensor network design problem. The concept of cutsets for the nonlinear case is revisited and a new decomposition procedure, suitable for this case is also discussed.

We will show also how and why the computational time, which ended up being acceptable for large linear systems, becomes a problem in the case of nonlinear ones when the global optimum is desired. A short-cut method that further improves computation time for solving large scale design problems is then proposed. Examples will be provided.

References

Gala, M. and Bagajewicz, M. J. Rigorous Methodology for the Design and Upgrade of Sensor Networks Using Cutsets. Ind. Eng. Chem. Res. 2006a, 45(20), 6687-6697.

Gala, M. and Bagajewicz, M. J. Efficient Procedure for the Design and Upgrade of Sensor Networks Using Cutsets and Rigorous Decomposition. Ind. Eng. Chem. Res. 2006b; 45(20), 6679-6686.

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