No abstract

Solvent-based carbon capture is the most commercially-ready technology for economically and sustainably reaching carbon emission reduction targets in the power sector. Globally, the technology has been deployed to deal with flue gases from large scale power plants and different carbon-intensive industries. The success of the technology is due to significant R&D activities on the process development and decades of industrial experience on acid gas removal processes from gaseous mixtures. In this paper, current status of PCC based on chemical absorption-commercial deployment and demonstration projects, analysis of different solvents and process configurations-is reviewed. Although some successes have been recorded in developing this technology, its commercialization has been generally slow as evidenced in the cancellation of high profile projects across the world. This is partly due to the huge cost burden of the technology and unpredictable government policies. Different research directions, namely new process development involving process intensification, new solvent development and a combination of both, are discussed in this paper as possible pathways for reducing the huge cost of the technology.

Rotating packed bed (RPB) absorber using monoethanolamine (MEA) as the solvent to capture CO2 is modelled at steady state condition in this study according to the first principles in gPROMS ®. The effect of eight different kinetic reaction models and five enhancement factors is examined based on the newly developed model. Selection of kinetic model has significant effect on the carbon capture level (CCL) but the effect of enhancement factor relation is not important. The steady state process model is validated against the experimental data and showed good agreement. The average absolute relative deviation for 12 case-runs is 3.5%. In addition, process analysis is performed to evaluate the effect of four factors namely rotor speed, MEA concentration in lean MEA solution, lean MEA solution temperature and lean MEA solution flow rate on CCL. Finally, orthogonal array design (OAD) method is applied to analyse the simultaneous effect of the above-mentioned factors in the CCL and motor power of RPB absorber by considering 25 scenarios. The result of using OAD revealed that rotor speed has the most important effect on CCL, and after that lean MEA solution flow rate has the second importance. In addition, the OAD method is used to find the proper combination of four factors that resulted in about 90% CCL with low motor power.

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Intensified regenerator/stripper using rotating packed bed (RPB) for regeneration of rich-MEA solvent in post-combustion CO2 capture with chemical absorption process was studied through modelling and simulation in this paper. This is the first systematic study of RPB regenerator through modelling as there is no such publication in the open literature. Correlations for liquid and gas mass transfer coefficients, heat transfer coefficient, liquid holdup , interfacial area and pressure drop which are suitable for RPB regenerator were written in visual FORTRAN as subroutines and then dynamically linked with Aspen Plus ® rate-based model to replace the default mass and heat transfer correlations in the Aspen Plus ®. The model now represents intensified regenerator/stripper. Model validation shows good agreement between model predictions and experimental data from literature. Process analyses were performed to investigate the effect of rotor speed on the regeneration efficiency and regeneration energy (including motor power). The rotor speed was varied from 200 to 1200 rpm, which was selected to cover the validation range of rotor speed. Impact of reboiler temperature on the rate of CO2 stripping was also investigated. Effect of rich-MEA flow rate on regeneration energy and regeneration efficiency was studied. All the process analyses were done for wide range of MEA concentration (32.6 wt%, 50 wt% and 60 wt%). Comparative study between regenerator using packed column and intensified regenerator using RPB was performed and the study shows a size reduction of 9.691 times. This study indicates that RPB process has great potential in thermal regeneration application.

For large volumes of carbon dioxide (CO 2 ) onshore and offshore transportation, pipeline is considered the preferred method. This paper presents a study of the pipeline network planned in the Humber region of the UK. Steady state process simulation models of the CO 2 transport pipeline network were developed using Aspen HYSYS ® . The simulation models were integrated with Aspen Process Economic Analyser ® (APEA). In this study, techno-economic evaluations for different options were conducted for the CO 2 compression train and the trunk pipelines respectively. The evaluation results were compared with other published cost models. Optimal options of compression train and trunk pipelines were applied in an optimal case. The overall cost of CO 2 transport pipeline network was analyzed and compared between the base case and the optimal case. The results show the optimal case has an annual saving of 22.7 M€. For the optimal case, levelized energy and utilities cost is 7.62 €/t-CO 2 , levelized capital cost of trunk pipeline is about 8.11 €/t-CO 2 and levelized capital cost of collecting system is 2.62 €/t-CO 2 . The overall levelized cost of the optimal case was also compared to the result of another project to gain more insights for CO 2 pipeline network design.

There has been a shift to less carbon intensive fuels such as natural gas to meet energy demand due to increasing pressure to cut CO2 emissions. This has prompted a need to assess unconventional and contaminated natural gas reserves (which contains CO2 concentration of 20mol% or more). The CO2 capture process with MEA as the solvent is mostly adopted to treat contaminated natural gas. In this study, the option of using a blend of ionic liquids (IL) and MEA as a promising solvent in the process was investigated through modelling and simulation. A detailed rate-based model was developed for both MEA (30wt%) solvent and IL (30wt%)-MEA (30wt%) blend using Aspen Plus ® to assess both process and economic performances. The 1-Butylpyridinium ([bpy][BF4]) ionic liquid was selected in this study. The physiochemical properties of [bpy][BF4], predicted using Aspen Plus ® , showed good accuracy compared with experimental data. The results from this study showed about 15% and 7.44% lower energy consumption in the reboiler duty and CO2 removal cost respectively with aqueous [bpy][BF4]-MEA solvent compared to 30 wt% MEA solvent. It is concluded that the aqueous [bpy][BF4]-MEA solvent is therefore a promising solvent that could replace 30 wt% MEA solvent in this process.

Keywords:NARX neural networks Subcritical coal-fired power plant Drum-boiler gPROMS modelling and simulation a b s t r a c t There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of plant models. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not represent the process well. As a result, data-driven approach based on neural networks is chosen in this study. Models derived with this approach incorporate all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level).