Simulating combined SO2 and CO2 capture from combustion flue gas
- Verheyen, Vincent, Cousins, Ashleigh, Pearson, Pauline, Puxty, Graeme, Jiang, Kaiqi, Garg, Bharti, Zhai, Rongrong, Ott, Petro, Feron, Paul
- Authors: Verheyen, Vincent , Cousins, Ashleigh , Pearson, Pauline , Puxty, Graeme , Jiang, Kaiqi , Garg, Bharti , Zhai, Rongrong , Ott, Petro , Feron, Paul
- Date: 2019
- Type: Text , Journal article
- Relation: Greenhouse Gases : Science and Technology Vol. 9, no. 6 (2019), p. 1087-1095
- Full Text:
- Reviewed:
- Description: The requirement to pre‐treat flue gas prior to the CO2 capture step is an economic challenge when using aqueous amine absorbents for capturing CO2 from coal‐fired power station flue gases. A potentially lower cost alternative is to combine the capture of both CO2 and SO2 from the flue gas into a single process, removing the requirement for the desulfurization pre‐treatment step. The CSIRO's CS‐Cap process uses a single aqueous amine absorbent to capture both of these acid gases from flue gas streams. This paper covers the initial simulation of this process applied to both brown and black coal flue gases. Removal of absorbed SO2 is achieved via reactive crystallization. This is simulated here using a ‘black box’ process, resulting in a K2SO4 product. Different operating conditions have been evaluated that increase the sulfate concentration of the absorbent in the SO2 capture section of the process, which is expected to increase the efficiency of the reactive crystallization step. This paper provides information on the absorption of SO2 into the amine solution, and heat and mass balances for the wider process. This information will be required for further detailed simulation of the reactive crystallization step, and economic evaluation of the CS‐Cap process. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.
- Authors: Verheyen, Vincent , Cousins, Ashleigh , Pearson, Pauline , Puxty, Graeme , Jiang, Kaiqi , Garg, Bharti , Zhai, Rongrong , Ott, Petro , Feron, Paul
- Date: 2019
- Type: Text , Journal article
- Relation: Greenhouse Gases : Science and Technology Vol. 9, no. 6 (2019), p. 1087-1095
- Full Text:
- Reviewed:
- Description: The requirement to pre‐treat flue gas prior to the CO2 capture step is an economic challenge when using aqueous amine absorbents for capturing CO2 from coal‐fired power station flue gases. A potentially lower cost alternative is to combine the capture of both CO2 and SO2 from the flue gas into a single process, removing the requirement for the desulfurization pre‐treatment step. The CSIRO's CS‐Cap process uses a single aqueous amine absorbent to capture both of these acid gases from flue gas streams. This paper covers the initial simulation of this process applied to both brown and black coal flue gases. Removal of absorbed SO2 is achieved via reactive crystallization. This is simulated here using a ‘black box’ process, resulting in a K2SO4 product. Different operating conditions have been evaluated that increase the sulfate concentration of the absorbent in the SO2 capture section of the process, which is expected to increase the efficiency of the reactive crystallization step. This paper provides information on the absorption of SO2 into the amine solution, and heat and mass balances for the wider process. This information will be required for further detailed simulation of the reactive crystallization step, and economic evaluation of the CS‐Cap process. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.
PFARS : Enhancing throughput and lifetime of heterogeneous WSNs through power-aware fusion, aggregation, and routing scheme
- Khan, Rahim, Zakarya, Muhammad, Tan, Zhiyuan, Usman, Muhammad, Jan, Mian, Khan, Mukhtaj
- Authors: Khan, Rahim , Zakarya, Muhammad , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Mukhtaj
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Communication Systems Vol. 32, no. 18 (Dec 2019), p. 21
- Full Text:
- Reviewed:
- Description: Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application-specific or too complex that make their implementation unrealistic, specifically, in a resource-constrained environment. In this paper, we propose a novel node-level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.
- Authors: Khan, Rahim , Zakarya, Muhammad , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Mukhtaj
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Communication Systems Vol. 32, no. 18 (Dec 2019), p. 21
- Full Text:
- Reviewed:
- Description: Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application-specific or too complex that make their implementation unrealistic, specifically, in a resource-constrained environment. In this paper, we propose a novel node-level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.
- «
- ‹
- 1
- ›
- »