Optimal operation of industrial compressor stations in systems with large energy consumption
File(s)
Author(s)
Xenos, Dionysios
Type
Thesis
Abstract
The aim of the thesis is to study the optimal operation of compressor stations in systems with large energy consumption such as process systems and natural gas networks. Compressor stations include several compressors in parallel which usually account for the major part of the total energy consumed in the system. Therefore, the efficient operation and maintenance of the compressors could save energy, and reduce operational costs.
The development of optimisation frameworks and optimisation models can determine the decisions which lead to the minimisation of costs operating compressor stations. The modelling of the behaviour of the compressors is also an important topic of the current PhD study. Different types of models should be used according to the level of optimisation level (online and real time, and offline), considering the available resources such as compressor maps or process data.
The thesis developed a comprehensive real time optimisation framework which can reduce the power consumption of compressor stations compared to the case of operation with the existing industrial practices. The thesis also developed a mixed integer linear programming multi-period optimisation model to minimise total costs of the operation, for example electricity costs, and start up and shut down costs. Another contribution of the thesis is the integration of operation and maintenance of the compressors considering different types of maintenance activities such as major overhauls and the washing of compressors. For example, the proposed integrated framework can be used to generate the schedules of the online and offline washing of the compressors compared to existing approaches described by fixed periodical washing or washing when the degradation of the condition of the compressors has reached unacceptable limits.
The optimisation frameworks was applied to two industrial case studies, namely one air separation plant involving a network of air compressors in BASF, Ludwigshafen, Germany and one export natural gas compressor station operated by Statoil in Norway. The final chapters of the thesis discuss the contributions and assumptions of each method, and present potential new research areas deriving from the PhD study.
The development of optimisation frameworks and optimisation models can determine the decisions which lead to the minimisation of costs operating compressor stations. The modelling of the behaviour of the compressors is also an important topic of the current PhD study. Different types of models should be used according to the level of optimisation level (online and real time, and offline), considering the available resources such as compressor maps or process data.
The thesis developed a comprehensive real time optimisation framework which can reduce the power consumption of compressor stations compared to the case of operation with the existing industrial practices. The thesis also developed a mixed integer linear programming multi-period optimisation model to minimise total costs of the operation, for example electricity costs, and start up and shut down costs. Another contribution of the thesis is the integration of operation and maintenance of the compressors considering different types of maintenance activities such as major overhauls and the washing of compressors. For example, the proposed integrated framework can be used to generate the schedules of the online and offline washing of the compressors compared to existing approaches described by fixed periodical washing or washing when the degradation of the condition of the compressors has reached unacceptable limits.
The optimisation frameworks was applied to two industrial case studies, namely one air separation plant involving a network of air compressors in BASF, Ludwigshafen, Germany and one export natural gas compressor station operated by Statoil in Norway. The final chapters of the thesis discuss the contributions and assumptions of each method, and present potential new research areas deriving from the PhD study.
Version
Open Access
Date Issued
2015-09
Date Awarded
2016-01
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
License URL
Advisor
Thornhill, Nina
Martinez-Botas, Ricardo
Sponsor
European Commission
Grant Number
PITN-GA-2010-264940
Publisher Department
Chemical Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)