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Acid Gas Removal

The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform

Stephen E Zitney – 1st expert on this subject based on the ideXlab platform

  • Optimal control system design of an Acid Gas Removal unit for an IGCC power plants with CO2 capture
    , 2020
    Co-Authors: Dustin Jones, Debangsu Bhattacharyya, Richard Turton, Stephen E Zitney

    Abstract:

    Future IGCC plants with CO{sub 2} capture should be operated optimally in the face of disturbances without violating operational and environmental constraints. To achieve this goal, a systematic approach is taken in this work to design the control system of a selective, dual-stage Selexol-based Acid Gas Removal (AGR) unit for a commercial-scale integrated Gasification combined cycle (IGCC) power plant with pre-combustion CO{sub 2} capture. The control system design is performed in two stages with the objective of minimizing the auxiliary power while satisfying operational and environmental constraints in the presence of measured and unmeasured disturbances. In the first stage of the control system design, a top-down analysis is used to analyze degrees of freedom, define an operational objective, identify important disturbances and operational/environmental constraints, and select the control variables. With the degrees of freedom, the process is optimized with relation to the operational objective at nominal operation as well as under the disturbances identified. Operational and environmental constraints active at all operations are chosen as control variables. From the results of the optimization studies, self-optimizing control variables are identified for further examination. Several methods are explored in this work for the selection of these self-optimizing control variables. Modifications made tomore » the existing methods will be discussed in this presentation. Due to the very large number of candidate sets available for control variables and due to the complexity of the underlying optimization problem, solution of this problem is computationally expensive. For reducing the computation time, parallel computing is performed using the Distributed Computing Server (DCS®) and the Parallel Computing® toolbox from Mathworks®. The second stage is a bottom-up design of the control layers used for the operation of the process. First, the regulatory control layer is designed followed by the supervisory control layer. Finally, an optimization layer is designed. In this paper, the proposed two-stage control system design approach is applied to the AGR unit for an IGCC power plant with CO{sub 2} capture. Aspen Plus Dynamics® is used to develop the dynamic AGR process model while MATLAB is used to perform the control system design and for implementation of model predictive control (MPC).« less

  • Acid Gas Removal from synGas in IGCC plants
    Integrated Gasification Combined Cycle (IGCC) Technologies, 2020
    Co-Authors: Debangsu Bhattacharyya, Richard Turton, Stephen E Zitney

    Abstract:

    Abstract In this chapter, a number of traditional as well as novel technologies for Acid Gas Removal from synthesis Gas are discussed. For the traditional technologies, physical-, chemical-, and hybrid-solvent-based technologies are discussed. A number of novel technologies are under development, mainly with the goal of reducing the cost of CO 2 capture. Some of these novel technologies are still at the lab-scale, while others are being evaluated at pilot-scale or higher. These technologies include various warm Gas cleanup technologies such as those based on solid sorbents as well as those based on membrane technologies where the synergistic sorption-enhanced water-Gas shift reaction is a valuable option for CO 2 capture cases. Other novel approaches under investigation are cryogenic, chemical looping, and Gas hydrate technologies. Further research will help to realize the full potential of these novel technologies.

  • State estimation of an Acid Gas Removal (AGR) plant as part of an integrated Gasification combined cycle (IGCC) plant with CO2 capture
    , 2020
    Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E Zitney

    Abstract:

    An accurate estimation of process state variables not only can increase the effectiveness and reliability of process measurement technology, but can also enhance plant efficiency, improve control system performance, and increase plant availability. Future integrated Gasification combined cycle (IGCC) power plants with CO2 capture will have to satisfy stricter operational and environmental constraints. To operate the IGCC plant without violating stringent environmental emission standards requires accurate estimation of the relevant process state variables, outputs, and disturbances. Unfortunately, a number of these process variables cannot be measured at all, while some of them can be measured, but with low precision, low reliability, or low signal-to-noise ratio. As a result, accurate estimation of the process variables is of great importance to avoid the inherent difficulties associated with the inaccuracy of the data. Motivated by this, the current paper focuses on the state estimation of an Acid Gas Removal (AGR) process as part of an IGCC plant with CO2 capture. This process has extensive heat and mass integration and therefore is very suitable for testing the efficiency of the designed estimators in the presence of complex interactions between process variables. The traditional Kalman filter (KF) (Kalman, 1960) algorithm has been used as amore » state estimator which resembles that of a predictor-corrector algorithm for solving numerical problems. In traditional KF implementation, good guesses for the process noise covariance matrix (Q) and the measurement noise covariance matrix (R) are required to obtain satisfactory filter performance. However, in the real world, these matrices are unknown and it is difficult to generate good guesses for them. In this paper, use of an adaptive KF will be presented that adapts Q and R at every time step of the algorithm. Results show that very accurate estimations of the desired process states, outputs or disturbances can be achieved by using the adaptive KF.« less

Debangsu Bhattacharyya – 2nd expert on this subject based on the ideXlab platform

  • Optimal control system design of an Acid Gas Removal unit for an IGCC power plants with CO2 capture
    , 2020
    Co-Authors: Dustin Jones, Debangsu Bhattacharyya, Richard Turton, Stephen E Zitney

    Abstract:

    Future IGCC plants with CO{sub 2} capture should be operated optimally in the face of disturbances without violating operational and environmental constraints. To achieve this goal, a systematic approach is taken in this work to design the control system of a selective, dual-stage Selexol-based Acid Gas Removal (AGR) unit for a commercial-scale integrated Gasification combined cycle (IGCC) power plant with pre-combustion CO{sub 2} capture. The control system design is performed in two stages with the objective of minimizing the auxiliary power while satisfying operational and environmental constraints in the presence of measured and unmeasured disturbances. In the first stage of the control system design, a top-down analysis is used to analyze degrees of freedom, define an operational objective, identify important disturbances and operational/environmental constraints, and select the control variables. With the degrees of freedom, the process is optimized with relation to the operational objective at nominal operation as well as under the disturbances identified. Operational and environmental constraints active at all operations are chosen as control variables. From the results of the optimization studies, self-optimizing control variables are identified for further examination. Several methods are explored in this work for the selection of these self-optimizing control variables. Modifications made tomore » the existing methods will be discussed in this presentation. Due to the very large number of candidate sets available for control variables and due to the complexity of the underlying optimization problem, solution of this problem is computationally expensive. For reducing the computation time, parallel computing is performed using the Distributed Computing Server (DCS®) and the Parallel Computing® toolbox from Mathworks®. The second stage is a bottom-up design of the control layers used for the operation of the process. First, the regulatory control layer is designed followed by the supervisory control layer. Finally, an optimization layer is designed. In this paper, the proposed two-stage control system design approach is applied to the AGR unit for an IGCC power plant with CO{sub 2} capture. Aspen Plus Dynamics® is used to develop the dynamic AGR process model while MATLAB is used to perform the control system design and for implementation of model predictive control (MPC).« less

  • Acid Gas Removal from synGas in IGCC plants
    Integrated Gasification Combined Cycle (IGCC) Technologies, 2020
    Co-Authors: Debangsu Bhattacharyya, Richard Turton, Stephen E Zitney

    Abstract:

    Abstract In this chapter, a number of traditional as well as novel technologies for Acid Gas Removal from synthesis Gas are discussed. For the traditional technologies, physical-, chemical-, and hybrid-solvent-based technologies are discussed. A number of novel technologies are under development, mainly with the goal of reducing the cost of CO 2 capture. Some of these novel technologies are still at the lab-scale, while others are being evaluated at pilot-scale or higher. These technologies include various warm Gas cleanup technologies such as those based on solid sorbents as well as those based on membrane technologies where the synergistic sorption-enhanced water-Gas shift reaction is a valuable option for CO 2 capture cases. Other novel approaches under investigation are cryogenic, chemical looping, and Gas hydrate technologies. Further research will help to realize the full potential of these novel technologies.

  • State estimation of an Acid Gas Removal (AGR) plant as part of an integrated Gasification combined cycle (IGCC) plant with CO2 capture
    , 2020
    Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E Zitney

    Abstract:

    An accurate estimation of process state variables not only can increase the effectiveness and reliability of process measurement technology, but can also enhance plant efficiency, improve control system performance, and increase plant availability. Future integrated Gasification combined cycle (IGCC) power plants with CO2 capture will have to satisfy stricter operational and environmental constraints. To operate the IGCC plant without violating stringent environmental emission standards requires accurate estimation of the relevant process state variables, outputs, and disturbances. Unfortunately, a number of these process variables cannot be measured at all, while some of them can be measured, but with low precision, low reliability, or low signal-to-noise ratio. As a result, accurate estimation of the process variables is of great importance to avoid the inherent difficulties associated with the inaccuracy of the data. Motivated by this, the current paper focuses on the state estimation of an Acid Gas Removal (AGR) process as part of an IGCC plant with CO2 capture. This process has extensive heat and mass integration and therefore is very suitable for testing the efficiency of the designed estimators in the presence of complex interactions between process variables. The traditional Kalman filter (KF) (Kalman, 1960) algorithm has been used as amore » state estimator which resembles that of a predictor-corrector algorithm for solving numerical problems. In traditional KF implementation, good guesses for the process noise covariance matrix (Q) and the measurement noise covariance matrix (R) are required to obtain satisfactory filter performance. However, in the real world, these matrices are unknown and it is difficult to generate good guesses for them. In this paper, use of an adaptive KF will be presented that adapts Q and R at every time step of the algorithm. Results show that very accurate estimations of the desired process states, outputs or disturbances can be achieved by using the adaptive KF.« less

Richard Turton – 3rd expert on this subject based on the ideXlab platform

  • Optimal control system design of an Acid Gas Removal unit for an IGCC power plants with CO2 capture
    , 2020
    Co-Authors: Dustin Jones, Debangsu Bhattacharyya, Richard Turton, Stephen E Zitney

    Abstract:

    Future IGCC plants with CO{sub 2} capture should be operated optimally in the face of disturbances without violating operational and environmental constraints. To achieve this goal, a systematic approach is taken in this work to design the control system of a selective, dual-stage Selexol-based Acid Gas Removal (AGR) unit for a commercial-scale integrated Gasification combined cycle (IGCC) power plant with pre-combustion CO{sub 2} capture. The control system design is performed in two stages with the objective of minimizing the auxiliary power while satisfying operational and environmental constraints in the presence of measured and unmeasured disturbances. In the first stage of the control system design, a top-down analysis is used to analyze degrees of freedom, define an operational objective, identify important disturbances and operational/environmental constraints, and select the control variables. With the degrees of freedom, the process is optimized with relation to the operational objective at nominal operation as well as under the disturbances identified. Operational and environmental constraints active at all operations are chosen as control variables. From the results of the optimization studies, self-optimizing control variables are identified for further examination. Several methods are explored in this work for the selection of these self-optimizing control variables. Modifications made tomore » the existing methods will be discussed in this presentation. Due to the very large number of candidate sets available for control variables and due to the complexity of the underlying optimization problem, solution of this problem is computationally expensive. For reducing the computation time, parallel computing is performed using the Distributed Computing Server (DCS®) and the Parallel Computing® toolbox from Mathworks®. The second stage is a bottom-up design of the control layers used for the operation of the process. First, the regulatory control layer is designed followed by the supervisory control layer. Finally, an optimization layer is designed. In this paper, the proposed two-stage control system design approach is applied to the AGR unit for an IGCC power plant with CO{sub 2} capture. Aspen Plus Dynamics® is used to develop the dynamic AGR process model while MATLAB is used to perform the control system design and for implementation of model predictive control (MPC).« less

  • Acid Gas Removal from synGas in IGCC plants
    Integrated Gasification Combined Cycle (IGCC) Technologies, 2020
    Co-Authors: Debangsu Bhattacharyya, Richard Turton, Stephen E Zitney

    Abstract:

    Abstract In this chapter, a number of traditional as well as novel technologies for Acid Gas Removal from synthesis Gas are discussed. For the traditional technologies, physical-, chemical-, and hybrid-solvent-based technologies are discussed. A number of novel technologies are under development, mainly with the goal of reducing the cost of CO 2 capture. Some of these novel technologies are still at the lab-scale, while others are being evaluated at pilot-scale or higher. These technologies include various warm Gas cleanup technologies such as those based on solid sorbents as well as those based on membrane technologies where the synergistic sorption-enhanced water-Gas shift reaction is a valuable option for CO 2 capture cases. Other novel approaches under investigation are cryogenic, chemical looping, and Gas hydrate technologies. Further research will help to realize the full potential of these novel technologies.

  • State estimation of an Acid Gas Removal (AGR) plant as part of an integrated Gasification combined cycle (IGCC) plant with CO2 capture
    , 2020
    Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E Zitney

    Abstract:

    An accurate estimation of process state variables not only can increase the effectiveness and reliability of process measurement technology, but can also enhance plant efficiency, improve control system performance, and increase plant availability. Future integrated Gasification combined cycle (IGCC) power plants with CO2 capture will have to satisfy stricter operational and environmental constraints. To operate the IGCC plant without violating stringent environmental emission standards requires accurate estimation of the relevant process state variables, outputs, and disturbances. Unfortunately, a number of these process variables cannot be measured at all, while some of them can be measured, but with low precision, low reliability, or low signal-to-noise ratio. As a result, accurate estimation of the process variables is of great importance to avoid the inherent difficulties associated with the inaccuracy of the data. Motivated by this, the current paper focuses on the state estimation of an Acid Gas Removal (AGR) process as part of an IGCC plant with CO2 capture. This process has extensive heat and mass integration and therefore is very suitable for testing the efficiency of the designed estimators in the presence of complex interactions between process variables. The traditional Kalman filter (KF) (Kalman, 1960) algorithm has been used as amore » state estimator which resembles that of a predictor-corrector algorithm for solving numerical problems. In traditional KF implementation, good guesses for the process noise covariance matrix (Q) and the measurement noise covariance matrix (R) are required to obtain satisfactory filter performance. However, in the real world, these matrices are unknown and it is difficult to generate good guesses for them. In this paper, use of an adaptive KF will be presented that adapts Q and R at every time step of the algorithm. Results show that very accurate estimations of the desired process states, outputs or disturbances can be achieved by using the adaptive KF.« less