Abstract-The tuning of state-space model predictive control (MPC) based on reverse engineering has been investigated in literature using the inverse optimality problem ( [1] and [2]). The aim of the inverse optimality is to find the tuning parameters of MPC to obtain the same behavior as an arbitrary lineartime-invariant (LTI) controller (favorite controller). This requires equal control horizon and prediction horizon, and loop-shifting is often used to handle non-strictly-proper favorite controllers. This paper presents a reverse-engineering tuning method for MPC based on transfer function formulation, also known as generalized predictive control (GPC). The feasibility conditions of the matching of a GPC with a favorite controller are investigated. This approach uses a control horizon equal to one and does not require any loop-shifting techniques to deal with non-strictlyproper favorite controllers. The method is applied to a binary distillation column example.

Reliable energy supply becomes increasingly complex in hybrid energy networks, due to increasing amounts of renewable electricity and more dynamic demand. Accurate modeling of integrated electricity and gas distribution networks is required to quantify operational bottlenecks in these networks and to increase security of supply. In this paper, we propose a hybrid network solver to model integrated electricity and gas distribution networks. A stochastic method is proposed to calculate the security of supply throughout the networks, taking into account the likelihood of events, operational constraints and dynamic supply and demand. The stochastic method is evaluated on a real gas network case study. The calculated security of supply parameters provide insight into the most critical parts of the network and can be used for future network planning. The capabilities of the coupled hybrid energy network simulation are demonstrated on the real gas network coupled to a simplified electricity network. Results demonstrate how combined simulation of electricity and gas networks facilitate the control design and performance evaluation of regional hybrid energy networks.

Due to natural depletion many mature fields are currently suffering from productivity issues due to halite precipitation in the North Sea. Main actions to prevent or solve this type of scaling issues include increasing flowing pressures (topside choking or velocity strings), but often considered easiest and most economical are fresh water washes to dissolve downhole halite deposits. The main objective of this study is to increase production by optimizing washing sequences. Having an accurate washing schedules predicted allows to anticipate on production degradation and its mitigating actions. To predict production decline due to halite, a virtual flow meter is developed combining a well model (Vertical Lift Performance, known as VLP curve), reservoir model (Inflow Performance Relation, known as IPR curve) and a dynamic model for the extra pressure drops due to deposition. The flow is calculated including the additional pressure drop. The virtual flow meter is calibrated using the last 3-6 months of production data. The Well Desalting Planning Tool (WDPT) makes real-time estimates of short-term pressure drops for each well and finally determines a date to water wash. It shows the schedule in the Planning Dashboard of the web-based WDPT tool. The software can be used to assist the operations department in prioritizing platform visits and optimize production. WDPT is developed as part of company Digital Oil Field (DOF).

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