River modeling INDEX

A global sensitivity analysis was performed for the lead concentration in the arterial blood model (Fig. 7) over the simulation period for each parameter. Parameters considered for the sensitivity analysis are listed in Table 8 (Annex 1). The magnitude of sensitivity is shown by relative sensitivity index. It was observed that the most influential parameter is the porosity of the sediment of the river (phi sed)... 

The minimization of the quadratic performance index in Eq. (8-64), subject to the constraints in Eqs. (8-67) to (8-69) and the step response model in Eq. (8-61), can be formulated as a standard QP (quadratic programming) problem. Consequently, efficient QP solution techniques can be employed. When the inequality constraints in Eqs. (8-67) to (8-69) are omitted, the optimization problem has an analytical solution (Camacho and Bordons, Model Predictive Control, 2d ed., Springer-Verlag, New York, 2004 Maciejowski, Predictive Control with Constraints, Prentice-Hall, Upper Saddle River, N.J., 2002). If the quadratic terms in Eq. (8-64) are replaced by linear terms, an LP (linear programming) problem results that can also be solved by using standard methods. This MPC formulation for SISO control problems can easily be extended to MIMO problems. 

Ruslan Rainis, Kamarul Ismail and Hafizan Juahir, 2004. Modeling The Relationship Between River Water Quality Index (WQl) and Land Uses Using Artificial Neural Networks (ANN). Presented in JSPS Seminar, December 15-17, Kyoto, Japan. 

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