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8.1 Conclusions
From the literature review done in this work, although there is some work done in the area of pro- cess synthesis trough Mixed Integer Non-Linear Programming (MINLP) optimization of homogeneous systems, when it comes to the particular case of CO2compression, it appears that this approach was never applied to a Carbon Capture and Storage (CCS) compression train system. This represents a gap in the area of process optimization and also, given the importance of reducing the costs of the entire CCS chain, an opportunity to provide a design tool that finds the best economical solution for a CO2compression train system.
In order to accomplish the objective of this work, component models relevant for the system were developed based on chemical engineering principle rules and the information gathered from the back- ground research (chapters 2, 3 and 4). Later on, a compression train flowsheet (including a set of centrifugal compressors, coolers and knock-out drums) was elaborated based on a case study and the models were tested against available data (chapter 5). Then, a proper superstructure was developed, the MINLP problem was formulated (chapter 6) and solved along with the respective assumptions that, in the last sections of this work, were tested to confirm their accuracy (chapter 7).
From the comparison made in chapter 5 between the simulation results from the flowsheet imple- mented in gPROMS against the data from the case report, it was observed a good correspondence since the deviation of all key outpus was smaller than 2%. Given the results, it was considered that the all the models developed for the compression system modeling toolkit were validated and represent both truthfully and accurately what happens in real conditions for the operating range of this work.
In the first section of chapter 7, the MINLP optimization problem formulated in chapter 6 was first discretized to many but small Non-Linear Programming (NLP) problems by fixing the number of compressors into different values, in order to have a better understanding of the system and how the decision variables affect the total cost and its major components. The optimal result from this optimization consists in a compression train with 4 compressor sections with a pressure ratio of 2.3, an objective function of 6.4 M$/yr (2011) and where Operating Expenditure (OPEX) represented about 85% of the total cost.
One unexpected behavior observed in this chapter was the fact that OPEX had an optimal number of compressor sections, since it contradicts what thermodynamically was expected. In other words, the more compression steps with intercooling are given the lower was the ideal compression work.
After analyzing the case, the cause has been identified as the balance between the reduction of the ideal work and the reduction of the compressors efficiency with the volume flowrate, caused by the compression of the gas.
Knowing the influence of the volume flowrate with the compressors efficiency, the optimization problem was reformulated and the pressure ratio was optimized for each specific compressor section.
The results were clear, as the balance of compression work performed by the new formulation resulted in a reduction of the total cost and the optimal train configuration changed from 4 to 5 compressors.
Typically, the compression cost ranges from 6 to 8 US$ per tonne of CO2(see [6]) against the 3.5
US$ got from the optimal train of this last formulation. Since the superstructure only represents the
”first half” of the train (only the compressors and coolers up to the dehydrator), the cost and respective train configuration were considered reasonable due to the cost per tonne of CO2being smaller than the typical cost but having the some order of magnitude.
To get a better understanding of the system, some assumption tests and sensibility analysis were conducted. The intercooling assumption was proven to be valid since, for the range of pressures considered in this work, the total cost of two compressors without intercooling was always higher than with intercooling. The optimal speed value was discovered, since the value from the report [27] was actually the optimal value (80 Hz), and it was concluded that a better compression work efficiency could be achieved if the speed of each compressor section/stage was controlled by a gear box. Also, one of the most important specifications that influence the design of the final train is the discharge pressure of the last compressor.
Due to this result and the fact that the pressure in the injection site increases with time as the holdup in the reservoir increases with the CO2 accumulation, a multi-period optimization was per- formed. This new formulation consisted in performing the same type of design optimization but taking in count in the objective function both design conditions and off-design performance. The resultant train got from this new formulation proved to have a significantly lower OPEX (4%) in the off-design conditions resulting in a lower total cost (2%) than the previous optimal train.
It is considered that the purpose of this work was accomplished since, not only the process syn- thesis problem was successfully solved but the initial formulation was improved in order to attain off-design performance. Also, the behavior of the system and the changes in the optimal train config- uration were analyzed for the key process variables.
The implementation of the entire compression train, the incorporation of a better costing model and the integration of the number of stages optimization are pointed out has the main key aspects to be improved in this work in the future, since with those changes, this tool can become much alike to the final version of a true self-configurable compression train system.
8.2 Future Work
Designing the compressors only in a section basis can be misleading and slightly lead to the high pressure ratio solution path, since only the first stage has the maximum efficiency calculated in the section design and, as the number of stages increases, the stage efficiency slightly decreases. In this sense, the development of the compressor section design by calculating the number of stages would be an important improvement to the global optimization problem, leading it to more accurate predictions and therefore a better train configuration.
Further development of some of the other component models would also improve the quality and accuracy of the results. For example, in theCoolerKODrum, the calculation of the number of tubes and their respective diameter and length would allow to get a better cost estimation and the calculation of the heat transfer coefficient. Also, the implementation of the mist eliminator efficiency would make the
model to take in count the effect of possible water droplets in the CO2stream that weren’t removed in the knock-out drum.
Like it was said in the previous section, the entire compression train system can now be imple- mented and optimized. By combining two sets of the superstructure implemented in this work with a dehydrator model between them, the total cost can be minimized and the effect of all decision vari- ables considered, like the number of compressors before and after the dehydrator, pressure ratio of all compressors or the recycling stream from the dehydrator, can be analyzed and quantified.
In order to determine the effect of different CO2sources in the optimal train design, different case studies have to be tested including admitting CO2sources with a typical composition from a capture plant with pre, post or oxy combustion technology.
Using the multi-period optimization methodology from this work, the entire compression train could be designed taking in count different off-design conditions. Another challenge related to this design strategy is to determine the best equilibrium of drive speeds that leads to a minimum OPEX of the train when it has to deliver different discharge pressures to the pipeline network or to process different loads of carbon dioxide.
The supercritical compression path isn’t the only option to compress the CO2. The liquefaction and pumping path can also be tested and optimized using the methodology from this work. Adding a liquefaction unit and CO2pump models to the flowsheet (already available in the gCCS library) would allow to perform a process synthesis of the new superstructure for CO2liquefaction. In an advanced phase, both paths could be merged into one superstructure that would make possible the automatic decision between both compression paths made by the optimization algorithm.
After the technology area of compression systems synthesis becomes developed, the possibility to combine the optimization of the train with other CCS chain components becomes a reality. Since some work has already been done in the area of power plant and capture plant MINLP optimization, besides all the interactions between the two systems and possible unanticipated problems from it, combining both power plant with capture technology and compression systems using their respective methodologies becomes a possibility.
Also, there is some work done in the area of pipeline network optimization of Liquified Natural Gas (LNG) production. Applying that methodology to the CO2 pipeline network, both compression system and pipeline transportation can be optimized together for both CO2 and LNG cases, since the compression train optimization methodology of this work can be easily adapted to the natural gas compression and liquefaction process.