The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform
Stephen E Zitney - One of the best experts on this subject based on the ideXlab platform.
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Optimal control system design of an Acid Gas Removal unit for an IGCC power plants with CO2 capture
2020Co-Authors: Dustin Jones, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract: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
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Acid Gas Removal from synGas in IGCC plants
Integrated Gasification Combined Cycle (IGCC) Technologies, 2020Co-Authors: Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract: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.
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State estimation of an Acid Gas Removal (AGR) plant as part of an integrated Gasification combined cycle (IGCC) plant with CO2 capture
2020Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract: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
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Sensor placement algorithm development to maximize the efficiency of Acid Gas Removal unit for integrated Gasification combined cycle (IGCC) power plant with CO{sub 2} capture
2020Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract:Future integrated Gasification combined cycle (IGCC) power plants with CO{sub 2} capture will face stricter operational and environmental constraints. Accurate values of relevant states/outputs/disturbances are needed to satisfy these constraints and to maximize the operational efficiency. Unfortunately, a number of these process variables cannot be measured while a number of them can be measured, but have low precision, reliability, or signal-to-noise ratio. In this work, a sensor placement (SP) algorithm is developed for optimal selection of sensor location, number, and type that can maximize the plant efficiency and result in a desired precision of the relevant measured/unmeasured states. In this work, an SP algorithm is developed for an selective, dual-stage Selexol-based Acid Gas Removal (AGR) unit for an IGCC plant with pre-combustion CO{sub 2} capture. A comprehensive nonlinear dynamic model of the AGR unit is developed in Aspen Plus Dynamics® (APD) and used to generate a linear state-space model that is used in the SP algorithm. The SP algorithm is developed with the assumption that an optimal Kalman filter will be implemented in the plant for state and disturbance estimation. The algorithm is developed assuming steady-state Kalman filtering and steady-state operation of the plant. The control system is considered tomore » operate based on the estimated states and thereby, captures the effects of the SP algorithm on the overall plant efficiency. The optimization problem is solved by Genetic Algorithm (GA) considering both linear and nonlinear equality and inequality constraints. Due to the very large number of candidate sets available for sensor placement and because of the long time that it takes to solve the constrained optimization problem that includes more than 1000 states, 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®. In this presentation, we will share our experience in setting up parallel computing using GA in the MATLAB® environment and present the overall approach for achieving higher computational efficiency in this framework.« less
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Adaptive Kalman filter for estimation of environmental performance variables in an Acid Gas Removal process
2013 American Control Conference, 2013Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract:In this paper, adaptive Kalman filter (KF) algorithms are implemented in an Acid Gas Removal (AGR) process for estimating the key environmental performance variables. It was found that by using a KF where the measurement noise covariance matrix (R) is adopted based on the residual sequence, the composition of the top and bottom streams from the H2S absorber in the AGR process could be estimated accurately even in the presence of large noise-to-signal ratio and poor initial guesses for R. Estimation accuracy of a KF, where the process noise covariance matrix (Q) is adopted, is found to be superior in comparison to the traditional KF, even in the presence of large mismatches between the linear and nonlinear models and a poor initial guess for Q.
Debangsu Bhattacharyya - One of the best experts on this subject based on the ideXlab platform.
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Optimal control system design of an Acid Gas Removal unit for an IGCC power plants with CO2 capture
2020Co-Authors: Dustin Jones, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract: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
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Acid Gas Removal from synGas in IGCC plants
Integrated Gasification Combined Cycle (IGCC) Technologies, 2020Co-Authors: Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract: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.
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State estimation of an Acid Gas Removal (AGR) plant as part of an integrated Gasification combined cycle (IGCC) plant with CO2 capture
2020Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract: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
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Sensor placement algorithm development to maximize the efficiency of Acid Gas Removal unit for integrated Gasification combined cycle (IGCC) power plant with CO{sub 2} capture
2020Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract:Future integrated Gasification combined cycle (IGCC) power plants with CO{sub 2} capture will face stricter operational and environmental constraints. Accurate values of relevant states/outputs/disturbances are needed to satisfy these constraints and to maximize the operational efficiency. Unfortunately, a number of these process variables cannot be measured while a number of them can be measured, but have low precision, reliability, or signal-to-noise ratio. In this work, a sensor placement (SP) algorithm is developed for optimal selection of sensor location, number, and type that can maximize the plant efficiency and result in a desired precision of the relevant measured/unmeasured states. In this work, an SP algorithm is developed for an selective, dual-stage Selexol-based Acid Gas Removal (AGR) unit for an IGCC plant with pre-combustion CO{sub 2} capture. A comprehensive nonlinear dynamic model of the AGR unit is developed in Aspen Plus Dynamics® (APD) and used to generate a linear state-space model that is used in the SP algorithm. The SP algorithm is developed with the assumption that an optimal Kalman filter will be implemented in the plant for state and disturbance estimation. The algorithm is developed assuming steady-state Kalman filtering and steady-state operation of the plant. The control system is considered tomore » operate based on the estimated states and thereby, captures the effects of the SP algorithm on the overall plant efficiency. The optimization problem is solved by Genetic Algorithm (GA) considering both linear and nonlinear equality and inequality constraints. Due to the very large number of candidate sets available for sensor placement and because of the long time that it takes to solve the constrained optimization problem that includes more than 1000 states, 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®. In this presentation, we will share our experience in setting up parallel computing using GA in the MATLAB® environment and present the overall approach for achieving higher computational efficiency in this framework.« less
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Adaptive Kalman filter for estimation of environmental performance variables in an Acid Gas Removal process
2013 American Control Conference, 2013Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract:In this paper, adaptive Kalman filter (KF) algorithms are implemented in an Acid Gas Removal (AGR) process for estimating the key environmental performance variables. It was found that by using a KF where the measurement noise covariance matrix (R) is adopted based on the residual sequence, the composition of the top and bottom streams from the H2S absorber in the AGR process could be estimated accurately even in the presence of large noise-to-signal ratio and poor initial guesses for R. Estimation accuracy of a KF, where the process noise covariance matrix (Q) is adopted, is found to be superior in comparison to the traditional KF, even in the presence of large mismatches between the linear and nonlinear models and a poor initial guess for Q.
Richard Turton - One of the best experts on this subject based on the ideXlab platform.
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Optimal control system design of an Acid Gas Removal unit for an IGCC power plants with CO2 capture
2020Co-Authors: Dustin Jones, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract: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
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Acid Gas Removal from synGas in IGCC plants
Integrated Gasification Combined Cycle (IGCC) Technologies, 2020Co-Authors: Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract: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.
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State estimation of an Acid Gas Removal (AGR) plant as part of an integrated Gasification combined cycle (IGCC) plant with CO2 capture
2020Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract: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
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Sensor placement algorithm development to maximize the efficiency of Acid Gas Removal unit for integrated Gasification combined cycle (IGCC) power plant with CO{sub 2} capture
2020Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract:Future integrated Gasification combined cycle (IGCC) power plants with CO{sub 2} capture will face stricter operational and environmental constraints. Accurate values of relevant states/outputs/disturbances are needed to satisfy these constraints and to maximize the operational efficiency. Unfortunately, a number of these process variables cannot be measured while a number of them can be measured, but have low precision, reliability, or signal-to-noise ratio. In this work, a sensor placement (SP) algorithm is developed for optimal selection of sensor location, number, and type that can maximize the plant efficiency and result in a desired precision of the relevant measured/unmeasured states. In this work, an SP algorithm is developed for an selective, dual-stage Selexol-based Acid Gas Removal (AGR) unit for an IGCC plant with pre-combustion CO{sub 2} capture. A comprehensive nonlinear dynamic model of the AGR unit is developed in Aspen Plus Dynamics® (APD) and used to generate a linear state-space model that is used in the SP algorithm. The SP algorithm is developed with the assumption that an optimal Kalman filter will be implemented in the plant for state and disturbance estimation. The algorithm is developed assuming steady-state Kalman filtering and steady-state operation of the plant. The control system is considered tomore » operate based on the estimated states and thereby, captures the effects of the SP algorithm on the overall plant efficiency. The optimization problem is solved by Genetic Algorithm (GA) considering both linear and nonlinear equality and inequality constraints. Due to the very large number of candidate sets available for sensor placement and because of the long time that it takes to solve the constrained optimization problem that includes more than 1000 states, 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®. In this presentation, we will share our experience in setting up parallel computing using GA in the MATLAB® environment and present the overall approach for achieving higher computational efficiency in this framework.« less
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Adaptive Kalman filter for estimation of environmental performance variables in an Acid Gas Removal process
2013 American Control Conference, 2013Co-Authors: Prokash Paul, Debangsu Bhattacharyya, Richard Turton, Stephen E ZitneyAbstract:In this paper, adaptive Kalman filter (KF) algorithms are implemented in an Acid Gas Removal (AGR) process for estimating the key environmental performance variables. It was found that by using a KF where the measurement noise covariance matrix (R) is adopted based on the residual sequence, the composition of the top and bottom streams from the H2S absorber in the AGR process could be estimated accurately even in the presence of large noise-to-signal ratio and poor initial guesses for R. Estimation accuracy of a KF, where the process noise covariance matrix (Q) is adopted, is found to be superior in comparison to the traditional KF, even in the presence of large mismatches between the linear and nonlinear models and a poor initial guess for Q.
Jurgen Karl - One of the best experts on this subject based on the ideXlab platform.
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modelling and assessment of Acid Gas Removal processes in coal derived sng production
Applied Thermal Engineering, 2015Co-Authors: E I Koytsoumpa, K Atsonios, K D Panopoulos, Sotirios Karellas, Emmanuel Kakaras, Jurgen KarlAbstract:Abstract Solid fuel conversion into Substitute Natural Gas (SNG) enables its use in remote heat and power applications via storage and transportation through the existing natural Gas infrastructure. The product Gas of an allothermal coal Gasification process, requires cleaning and conditioning before the final methanation process. Catalysts' restrictions and grid requirements emerge the need of CO 2 -and sulfur species Removal before the methanator. This paper investigates the different Acid Gas Removal processes through a comparison of their final integration on the coal-to-SNG production chain. Among these technologies, absorption with physical (i.e. Rectisol™, Selexol™) or chemical (K 2 CO 3 , MDEA) solvents which have been implemented in various clean synGas production applications are compared for their efficiency and feasibility. The paper presents conceptual designs comparison, mass and energy analyses of the four processes integrated in the coal-to-SNG system, based on AspenPlus™ modelling.
Alessandro Dal Pozzo - One of the best experts on this subject based on the ideXlab platform.
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environmental and economic performance assessment of alternative Acid Gas Removal technologies for waste to energy plants
Sustainable Production and Consumption, 2018Co-Authors: Alessandro Dal Pozzo, Giacomo Antonioni, Daniele Guglielmi, Alessandro TugnoliAbstract:Abstract Removal of Acid pollutants (HCl and SO2) is an important stage in waste incineration flue Gas cleaning. Several technological options for Acid Gas neutralisation are currently available in order to comply with the increasingly stringent emission limit values and the choice of the best solution for a specific plant should be based on the economic and environmental considerations implied in the concept of Best Available Technique. The present study analyses and compares state-of-the-art dry, semi-dry and wet process configurations for Acid Gas Removal in waste-to-energy plants. The performance of five representative process schemes was analysed: the streams associated with Acid Gas emission control were quantified via mass and energy balances and a life cycle perspective was applied in order to evaluate the inputs and outputs of the supply and disposal chains. The analysis pinpoints the key issues in terms of environmental and economic performance of the presented alternatives. Benefits and limits of the alternative technologies are discussed in view of different waste composition. The energy penalty associated with flue Gas reheat appears to be the main environmental drawback of wet methods, while the main contribution to the environmental footprint of dry methods is given by the production of solid reactants. Multi-stage treatment systems systematically show lower environmental impacts than the single stage counterparts, but their cost-effectiveness is limited by the disposal cost for the generated solid residues. The provided insights can contribute to a more effective implementation of the strategies of circular economy and cleaner production in the operation of a waste-to-energy plant.
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Analysis of Sustainable Technologies for Acid Gas Removal
2017Co-Authors: Alessandro Dal PozzoAbstract:Acid Gases, such as sulphur dioxide and hydrogen halides and – in a broad sense – carbon dioxide, are typical pollutants generated by combustion processes. Their Removal by means of solid sorbents represent an efficient and cost-effective approach in dry Acid Gas treatment systems for waste incineration flue Gas, while for CO2 capture the process is exploratively studied as a promising alternative to amine scrubbing. The present study addressed both aspects. In waste incineration flue Gas cleaning, Acid Gas Removal by sorbent injection is a well-established process. Nonetheless, a thorough understanding of the Gas-solid reactions involved in the process has not been reached yet and, thus, the operation of dry treatment systems is still highly empirical. In the present study, the process was analysed using different levels of detail: from the microscopic level of a lab-scale experimental campaign and phenomenological description of the kinetic and mass transfer phenomena governing the Gas-solid reaction to the macroscopic level of techno-economic and environmental assessment of alternative full-scale dry treatment systems. With respect to CO2 capture technologies, the process is still in the development stage and research is focused on the identification of highly-efficient sorbents. The present study analysed the enhancement of CO2 uptake potential of magnesium oxide, a promising sorbent for intermediate-temperature carbon capture, by means of coating with alkali metal molten salts. The joint analysis of Gas-solid reaction for flue Gas cleaning in two diverse contexts allowed the identification of common issues and of possible shared solutions.
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Enhanced modelling of heterogeneous Gas–solid reactions in Acid Gas Removal dry processes
Chemical Engineering Science, 2016Co-Authors: Giacomo Antonioni, Alessandro Dal Pozzo, Daniele Guglielmi, Alessandro Tugnoli, Valerio CozzaniAbstract:Acid Gases as hydrogen halides and sulphur oxides are typical pollutants of combustion processes. Their Removal from flue Gas can be performed via the injection of dry powdered sorbents, as calcium hydroxide. However, the efficiency of dry treatment methods is hindered by the limited final conversion of the solid reactant, due to an abrupt decline of its reactivity during the reaction process. Fundamental Gas–solid reaction models such as the shrinking core model and the grain model are able to reproduce this phenomenon only introducing an arbitrary value of the final conversion or an adjustable value of the solid-state diffusivity of the Gaseous reactant. In the present study, the conventional grain model approach was integrated with a crystallisation and fracture (CF) submodel, which links the chemical potential of nucleation to the work needed to displace the layer of solid product formed on the reaction interface. The decline in reactivity of the sorbent was accounted by a twofold effect of the product layer growth: (i) the increase of the characteristic length for solid-state diffusion, accounted for in the grain model, and (ii) the increase of the mechanical work required for nucleation as a function of product layer thickness, accounted for in the CF submodel. This approach, validated against literature data on the Ca(OH)2/HCl system, allowed reproducing the conversion of the solid reactant at different operating temperatures.
