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The rise of new digital technologies and their applications in several areas pushes the process industry to update its methodologies with more intensive use of mathematical models—commonly denoted as digital twins—and artificial intelligence (AI) approaches to continuously enhance operational efficiency. In this context, Real-time Optimization (RTO) is a strategy that is able to maximize an economic function while respecting the existing constraints, which enables keeping the operation at its optimum point even though the plant is subjected to nonlinear behavior and frequent disturbances. However, the investment related to the project of commercial RTOs may make its application infeasible for small-scale facilities. In this work, an in-house, small-scale RTO is presented and its successful application in a real industrial case—a Natural Gas Processing Unit—is shown. Besides that, a new method for enhancing the efficiency of using sequential-modular simulator inside an optimization framework and a new method to account for the economic return of optimization-based tools are proposed and described. The application of RTO in the industrial case showed an enhancement in the stability of the main variables and an increase in profit of 0.64% when compared to the operation of the regulatory control layer alone.

Electric submersible pumps (ESPs) are one of the most widespread oil artificial lifting technologies. In the operation of an ESP there are a large number of parameters that must be monitored and held within operational constraints in order to guarantee stable and optimal operation. Manual control is subject to sub-optimal production and constant violation of operational limits, that can cause either a reduction in ESP lifetime or premature failure. Therefore, a proper automation strategy must be applied to support operators in order to ensure the best production rate with less energy cost. Previous literature has proposed the use of linear MPC based on system identification, however all relevant system variable measurements were considered available. In this paper, the problem of losing measurements of the state variables due to the aggressive subsea environment is addressed. We show that a non-adaptive single linear model strategy lacks in quality for state estimation and, therefore, a robust MPC is not possible under this configuration. In this work, an adaptive constrained MPC coupled with a model scheduling Kalman filter (MSKF) is proposed. Two model scheduling strategies based on linear interpolation of a pre-set number of local models are proposed and compared to successive linearization at every sampling time, based on Taylor series expansion of the nonlinear model. All strategies guarantee model accuracy and model stability over the whole operational range. The proposed scheduling strategies presented similar performance compared to the successive linearization strategy, avoiding the need of obtaining a local linear model at each sampling time.

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