Risk-based health-aware control of Åsgard subsea gas compression station
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Safe and efficient operation of subsea processing systems imposes strict requirements withrespect to equipment design and reliability. This is to avoid accidental shutdowns, whichcan lead to expensive maintenance engagements. For that reason, health monitoring methodsare applied to monitor and evaluate the condition of the overall system in real-time.However, when finding the optimal operation policy, the health condition is generally notreviewed directly. As a consequence, this may cause overly restrictive operation. Thisstudy will suggest to combine control and condition monitoring of the A° sgard gas compressionstation, in order to prevent the operation policy from being sub-optimal. In thismanner, the obtained optimal plan of action for operation will seek to sustain the reliabilityof the subsea system. This makes it possible to forecast the health of the system andmanage the operation accordingly, rather than just reacting to it. This thesis proposes a model predictive control (MPC) approach for integrating healthmonitoring and control. The scheme will seek to ensure safe operation and an economicoptimal control policy for the subsea station. Risk measures that estimate the risk of failureare used for condition monitoring purposes. In this work, Conditional Value-at-Risk(CVaR) with respect to the random variable remaining useful life (RUL) of equipment isincorporated into the optimal control problem to assess the condition of system equipment.CVaR estimates the risk of failure in a conservative manner by bringing the extremeRUL of equipment outcomes into focus for a confidence level, . The theoretical analysisshows that minimization of unavailability of equipment coincides with the maximizationof CVaR with respect to RUL of equipment. Control of the predicted CVaR with respectto RUL of equipment is employed to enforce safe operation until the next maintenanceengagement. The numerical simulations show that the predicted CVaR with respect to the RUL of equipmentdecreases with time until the next maintenance engagement, which is scheduled tohappen in five years. The average RUL of the 0.1% worst RUL outcomes has been calculatedto be to just above five years at the startup of the plant. A higher confidence levelgives rise to higher values for CVaR with respect to RUL of equipment. In this approach,maximizing profit in terms of gas production while maximizing average RUL of the 0.1%worst RUL outcomes gives a production profile where the gas production rate decreaseswith time. The overall conclusion from this work is that health-aware control with risk measuresfor condition monitoring has the potential to manage the reliability of a subsea plant. Nevertheless,the accuracy of the system model and the implementation of the risk measureestimate influence the ability of the controller to predict the risk of failure.