Real-Time Optimization of Gas Processing Plants

Author(s):  
Saeid Mokhatab ◽  
William A. Poe ◽  
John Y. Mak
Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1179
Author(s):  
Pedro A. Delou ◽  
Leonardo D. Ribeiro ◽  
Carlos R. Paiva ◽  
Jacques Niederberger ◽  
Marcos Vinícius C. Gomes ◽  
...  

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.


Energy ◽  
2012 ◽  
Vol 39 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Gene A. Bunin ◽  
Zacharie Wuillemin ◽  
Grégory François ◽  
Arata Nakajo ◽  
Leonidas Tsikonis ◽  
...  

Author(s):  
Zhongyou Wu ◽  
Yaoyu Li

Real-time optimization of wind farm energy capture for below rated wind speed is critical for reducing the levelized cost of energy (LCOE). Performance of model based control and optimization techniques can be significantly limited by the difficulty in obtaining accurate turbine and farm models in field operation, as well as the prohibitive cost for accurate wind measurements. The Nested-Loop Extremum Seeking Control (NLESC), recently proposed as a model free method has demonstrated its great potential in wind farm energy capture optimization. However, a major limitation of previous work is the slow convergence, for which a primary cause is the low dither frequencies used by upwind turbines, primarily due to wake propagation delay through the turbine array. In this study, NLESC is enhanced with the predictor based delay compensation proposed by Oliveira and Krstic [1], which allows the use of higher dither frequencies for upwind turbines. The convergence speed can thus be improved, increasing the energy capture consequently. Simulation study is performed for a cascaded three-turbine array using the SimWindFarm platform. Simulation results show the improved energy capture of the wind turbine array under smooth and turbulent wind conditions, even up to 10% turbulence intensity. The impact of the proposed optimization methods on the fatigue loads of wind turbine structures is also evaluated.


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