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We present a method for joint state and parameter estimation for natural gasnetworks where gas pressures and flows through a network of pipes depend ontime-varying injections, withdrawals, and compression controls. The estimationis posed as an optimal control problem constrained by coupled partialdifferential equations on each pipe that describe space- and time-dependentdensity and mass flux. These are discretized and combined with nodal conditionsto give dynamic constraints for posing the estimation as nonlinear leastsquares problems. We develop a rapid, scalable computational method forperforming the estimation in the presence of measurement and process noise.Finally, we evaluate its effectiveness using a data set from a capacityplanning model for an actual pipeline system and a month of time-series datafrom its supervisory control and data acquisition (SCADA) system.

Dynamic State and Parameter Estimation for Natural Gas Networks using Real Pipeline System Data