Fast Economic Model Predictive Control for a Gas Lifted Well Network ⁎ ⁎The authors gratefully acknowledge the financial support from Research Council of Norway through IKTPLUSS Young Researcher Grant and SFI SUBPRO programs
| Autor: | Johannes Jaeschke, Dinesh Krishnamoorthy, Eka Suwartadi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Model predictive control Nonlinear system 020901 industrial engineering & automation 020401 chemical engineering Control and Systems Engineering Computer science 02 engineering and technology Sensitivity (control systems) Oil and gas production 0204 chemical engineering Economic model predictive control |
| Zdroj: | IFAC-PapersOnLine. 51:25-30 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2018.06.350 |
| Popis: | This paper considers the optimal operation of an oil and gas production network by formulating it as an economic nonlinear model predictive control (NMPC) problem. Solving the associated nonlinear program (NLP) can be computationally expensive and time consuming. To avoid a long delay between obtaining updated measurement information and injecting the new inputs in the plant, we apply a sensitivity-based predictor-corrector path-following algorithm in an advanced-step NMPC framework. We demonstrate the proposed method on a gas-lift optimization case study and compare the performance of the path-following economic NMPC to a standard economic NMPC formulation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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