This paper presents a new Lyapunov-based nonlinear adaptive observer and joint unscented Kalman filter to precisely estimate the states and the parameters for the low-order lumped model of the multi-phase flow at the gas refinery. The main focus of the study is to estimate the discharge coefficient of the orifice meter installed on the interconnection lines of the subsystems (parameters) and the total oil and gas mass flows (states). The adaptive estimation is conducted using the real-time measurements including choke pressure, bottom line pressure, single-phase gas flow, and single-phase liquid flow in the refinery outlet. To check the stability and performance of the system against changes, the Lyapunov theory has been used. In all stages, the investigations were based on the data collected from the actual process in the South Pars Gas Complex, Iran. Using the dynamic HYSYS simulation, it is found that the proposed adaptive observer is capable of estimating the oil and gas flows and identifying the discharge coefficient of the flow meter at issue. To show the performance of the proposed adaptive observer, it is evaluated against and compared with the unscented Kalman filter. The comparison of the results obtained from the proposed observer, unscented Kalman filter, and dynamic HYSYS simulation with data collected from the actual process of the refinery shows the appropriate performance of the both estimation algorithms in detecting the changes in liquid and gas flow rates and the consistency of their results with the real process in the South Pars Gas Complex. The simulations reveal that low-order lumped model is sufficient for estimation of parameters and states of the multiphase flow entering the gas refinery.
Evaluation of Coordination of Emergency Response Team through the Social Network Analysis. Case Study: Oil and Gas Refinery
