Hierarchical control scheme for optimal setting of setpoints in a three-phase separator of a crude treatment train
One of the techniques to treat oil from oil wells is to use oil treatment trains to separate the three existing phases: gas, oil and water. One of the equipment that makes up the train is the three-phase separator. This work demonstrates the feasibility of using a hierarchical control scheme for onl...
- Autores:
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Universidad Católica de Pereira
- Repositorio:
- Repositorio Institucional - RIBUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.ucp.edu.co:10785/9999
- Acceso en línea:
- https://revistas.ucp.edu.co/index.php/entrecienciaeingenieria/article/view/1163
http://hdl.handle.net/10785/9999
- Palabra clave:
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc/4.0/deed.es_ES
| Summary: | One of the techniques to treat oil from oil wells is to use oil treatment trains to separate the three existing phases: gas, oil and water. One of the equipment that makes up the train is the three-phase separator. This work demonstrates the feasibility of using a hierarchical control scheme for online setting of optimal pressure and temperature setpoints and thus reducing energy consumption in the heat treater. So that the manipulated variables and the set points are within the limits of the safe operation, with this provides an economic and environmental benefit for this plant. The achievement of this optimization is the main contribution of this work. For the development of this work we designed an optimization algorithm based on three hierarchical layers where the first layer refers to direct control (PIDs), the second layer refers to the optimization of steady state objectives (SSTO), the third layer refers to local optimization of objectives (LSSO). For the solution to this problem we resorted to solutions of linear optimization and nonlinear optimization these algorithms are related to the linearized model of the plant, in other works are usually based on the theory MPC (Predictive Control Model). The simulation of the algorithms was done in Matlab and tested in a simulated plant in Hysys using the Active x server connector that connects the two software. In this experiment it was possible to save 30% of the energy consumption. In turn, it was found that it is possible to relate the plant model with the linear and non-linear optimization algorithms, which concluded that the implementation of these algorithms is feasible in this type of plants for process optimization. |
|---|