The Experts below are selected from a list of 485913 Experts worldwide ranked by ideXlab platform
Jie Zhang - One of the best experts on this subject based on the ideXlab platform.
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exergetic optimisation of atmospheric and vacuum distillation system based on bootstrap aggregated neural network models
Exergy for A Better Environment and Improved Sustainability 1: Fundamentals, 2018Co-Authors: F N Osuolale, Jie ZhangAbstract:This paper presents a bootstrap aggregated neural network-based strategy for the modelling and optimisation of crude distillation unit incorporating the second law of thermodynamics. Exergy analysis pinpoints the location and magnitude of the losses and is a tool for determining how efficient a Process is. Exergy analysis of Processes gives insights into the overall energy use evaluation of the Process, potentials for efficient energy use of such Processes can then be identified, and energy-saving measures of the Processes can be suggested. The focus is to improve the exergy efficiency of the crude distillation and hence reduce the energy consumption. To overcome the difficulties in developing detailed mechanistic models, data-driven models such as artificial neural network (ANN) models can be utilised. Real-time optimisation of distillation columns is made feasible by using ANN models which can be quickly developed from Process Operation data. To enhance the reliability of ANN models, bootstrap aggregated neural network (BANN) is used in this study. A further advantage of BANN is that model prediction confidence bounds can be obtained. BANN models for exergy efficiency and product qualities are developed from simulated Process Operation data and are used to maximise exergy efficiency while satisfying product quality constraints. The standard error of the individual neural network predictions is taken as the indication of model prediction reliability and is incorporated in the optimisation objective function. Application to a crude distillation system (comprising of ADU and VDU) shows good improvement in the exergy efficiency of the unit and no additional costs of equipments. A further analysis was to investigate the effects of preflash units on the exergy efficiency of the ADU and VDU. The analysis gives realistic and promising results. The method could be applicable in determining feasible and energy-efficient operating and design conditions for the crude distillation unit.
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energy efficiency optimisation for distillation column using artificial neural network models
Energy, 2016Co-Authors: F N Osuolale, Jie ZhangAbstract:This paper presents a neural network based strategy for the modelling and optimisation of energy efficiency in distillation columns incorporating the second law of thermodynamics. Real-time optimisation of distillation columns based on mechanistic models is often infeasible due to the effort in model development and the large computation effort associated with mechanistic model computation. This issue can be addressed by using neural network models which can be quickly developed from Process Operation data. The computation time in neural network model evaluation is very short making them ideal for real-time optimisation. Bootstrap aggregated neural networks are used in this study for enhanced model accuracy and reliability. Aspen HYSYS is used for the simulation of the distillation systems. Neural network models for exergy efficiency and product compositions are developed from simulated Process Operation data and are used to maximise exergy efficiency while satisfying products qualities constraints. Applications to binary systems of methanol-water and benzene-toluene separations culminate in a reduction of utility consumption of 8.2% and 28.2% respectively. Application to multi-component separation columns also demonstrate the effectiveness of the proposed method with a 32.4% improvement in the exergy efficiency.
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multi objective optimisation of atmospheric crude distillation system Operations based on bootstrap aggregated neural network models
Computer-aided chemical engineering, 2015Co-Authors: F N Osuolale, Jie ZhangAbstract:Abstract This paper presents a new methodology for optimising the energy efficiency of a crude distillation unit without trading off the product quality and Process throughput. It incorporates the second law of thermodynamics which indicates how well a system is performing compared to the optimum possible performance and hence gives a good indication of the actual energy use of a Process. Bootstrap aggregated neural networks are used for enhanced model accuracy and reliability. In addition to the Process Operation objectives, minimising the model prediction confidence bound is incorporated in multi-objective optimisation to improve the reliability of the optimisation results. The economic analysis of the recoverable energy (sum of internal and external exergy losses) reveals the energy saving potential of the method. The proposed method will aid the design and Operation of energy efficient crude distillation columns.
Ridong Zhang - One of the best experts on this subject based on the ideXlab platform.
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design and implementation of hybrid modeling and pfc for oxygen content regulation in a coke furnace
IEEE Transactions on Industrial Informatics, 2018Co-Authors: Ridong Zhang, Qibing JinAbstract:This paper proposes the implementation of hybrid data driven modeling and predictive functional control (PFC) strategy for regulation of oxygen in an industrial coke furnace. A comprehensive model that incorporates simple step-response test and nonlinear optimization using neural network is first developed through Process Operation data. Then, a nonlinear PFC is designed to improve the dynamic response and steady Operation. The proposed PFC overcomes the disadvantages of proportional-integral-derivative or linear advance control strategies because the developed Process model yields better Process dynamics prediction and facilitates the subsequent PFC controller to improve Process Operation. In addition, the linear iterative form of the controller design is implemented such that engineers can easily apply it to industrial Processes. Results are shown by way of simulations and experimental tests to demonstrate the effectiveness of the proposed strategy.
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state space model predictive control for advanced Process Operation a review of recent development new results and insight
Industrial & Engineering Chemistry Research, 2017Co-Authors: Ridong Zhang, Furong GaoAbstract:Model predictive control (MPC) has acquired lots of developments and extensive applications in various industries during the past 40 years. For the early version of basic MPC strategies, there are challenges in both control performance and applications. On the basis of such backgrounds, state space models that contain more system information on the controlled system than conventional models and make the controller design straightforward under MIMO situations were introduced to the MPC framework, and many academic results have been put forward. One of the challenging missions for current MPC research is still focused on the optimal control of systems with improved (or robust) control performance. Therefore, it is of significance to investigate new developments of MPC. In order to improve the control performance of MPC, we introduced a new comprehensive state space model formulation strategy in which state variables and the output tracking errors are combined and can be regulated separately. On the basis of...
Matthew Ellis - One of the best experts on this subject based on the ideXlab platform.
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economic model predictive control theory computational efficiency and application to smart manufacturing escholarship
2015Co-Authors: Matthew EllisAbstract:The chemical industry is a vital sector of the US economy. Maintaining optimal chemical Process Operation is critical to the future success of the US chemical industry on a global market. Traditionally, economic optimization of chemical Processes has been addressed in a two-layer hierarchical architecture. In the upper layer, real-time optimization carries out economic Process optimization by computing optimal Process Operation set-points using detailed nonlinear steady-state Process models. These set-points are used by the lower layer feedback control systems to force the Process to operate on these set-points. While this paradigm has been successful, we are witnessing an increasing need for dynamic market and demand-driven Operations for more efficient Process Operation, increasing response capability to changing customer demand, and achieving real-time energy management. To enable next-generation market-driven Operation, economic model predictive control (EMPC), which is an model predictive control scheme formulated with a stage cost that represents the Process economics, has been proposed to integrate dynamic economic optimization of Processes with feedback control.Motivated by these considerations, novel theory and methods needed for the design of computationally tractable economic model predictive control systems for nonlinear Processes are developed in this dissertation. Specifically, the following considerations are addressed: a) EMPC structures for nonlinear systems which address: infinite-time and finite-time closed-loop economic performance and time-varying economic considerations such as changing energy pricing; b) two-layer (hierarchical) dynamic economic Process optimization and feedback control frameworks that incorporate EMPC with other control strategies allowing for computational efficiency; and c) EMPC schemes that account for real-time computation requirements. The EMPC schemes and methodologies are applied to chemical Process applications. The application studies demonstrate the effectiveness of the EMPC schemes to maintain Process stability and improve economic performance under dynamic Operation as well as to increase efficiency, reliability and profitability of Processes, thereby contributing to the vision of Smart Manufacturing.
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economic model predictive control theory computational efficiency and application to smart manufacturing escholarship
2015Co-Authors: Matthew EllisAbstract:The chemical industry is a vital sector of the US economy. Maintaining optimal chemical Process Operation is critical to the future success of the US chemical industry on a global market. Traditionally, economic optimization of chemical Processes has been addressed in a two-layer hierarchical architecture. In the upper layer, real-time optimization carries out economic Process optimization by computing optimal Process Operation set-points using detailed nonlinear steady-state Process models. These set-points are used by the lower layer feedback control systems to force the Process to operate on these set-points. While this paradigm has been successful, we are witnessing an increasing need for dynamic market and demand-driven Operations for more efficient Process Operation, increasing response capability to changing customer demand, and achieving real-time energy management. To enable next-generation market-driven Operation, economic model predictive control (EMPC), which is an model predictive control scheme formulated with a stage cost that represents the Process economics, has been proposed to integrate dynamic economic optimization of Processes with feedback control.Motivated by these considerations, novel theory and methods needed for the design of computationally tractable economic model predictive control systems for nonlinear Processes are developed in this dissertation. Specifically, the following considerations are addressed: a) EMPC structures for nonlinear systems which address: infinite-time and finite-time closed-loop economic performance and time-varying economic considerations such as changing energy pricing; b) two-layer (hierarchical) dynamic economic Process optimization and feedback control frameworks that incorporate EMPC with other control strategies allowing for computational efficiency; and c) EMPC schemes that account for real-time computation requirements. The EMPC schemes and methodologies are applied to chemical Process applications. The application studies demonstrate the effectiveness of the EMPC schemes to maintain Process stability and improve economic performance under dynamic Operation as well as to increase efficiency, reliability and profitability of Processes, thereby contributing to the vision of Smart Manufacturing.
Matthew James Ellis - One of the best experts on this subject based on the ideXlab platform.
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economic model predictive control theory computational efficiency and application to smart manufacturing
2015Co-Authors: Matthew James EllisAbstract:The chemical industry is a vital sector of the US economy. Maintaining optimal chemical Process Operation is critical to the future success of the US chemical industry on a global market. Traditionally, economic optimization of chemical Processes has been addressed in a two-layer hierarchical architecture. In the upper layer, real-time optimization carries out economic Process optimization by computing optimal Process Operation set-points using detailed nonlinear steady-state Process models. These set-points are used by the lower layer feedback control systems to force the Process to operate on these set-points. While this paradigm has been successful, we are witnessing an increasing need for dynamic market and demand-driven Operations for more efficient Process Operation, increasing response capability to changing customer demand, and achieving real-time energy management. To enable next-generation market-driven Operation, economic model predictive control (EMPC), which is an model predictive control scheme formulated with a stage cost that represents the Process economics, has been proposed to integrate dynamic economic optimization of Processes with feedback control.Motivated by these considerations, novel theory and methods needed for the design of computationally tractable economic model predictive control systems for nonlinear Processes are developed in this dissertation. Specifically, the following considerations are addressed: a) EMPC structures for nonlinear systems which address: infinite-time and finite-time closed-loop economic performance and time-varying economic considerations such as changing energy pricing; b) two-layer (hierarchical) dynamic economic Process optimization and feedback control frameworks that incorporate EMPC with other control strategies allowing for computational efficiency; and c) EMPC schemes that account for real-time computation requirements. The EMPC schemes and methodologies are applied to chemical Process applications. The application studies demonstrate the effectiveness of the EMPC schemes to maintain Process stability and improve economic performance under dynamic Operation as well as to increase efficiency, reliability and profitability of Processes, thereby contributing to the vision of Smart Manufacturing.
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economic model predictive control theory computational efficiency and application to smart manufacturing
2015Co-Authors: Matthew James EllisAbstract:The chemical industry is a vital sector of the US economy. Maintaining optimal chemical Process Operation is critical to the future success of the US chemical industry on a global market. Traditionally, economic optimization of chemical Processes has been addressed in a two-layer hierarchical architecture. In the upper layer, real-time optimization carries out economic Process optimization by computing optimal Process Operation set-points using detailed nonlinear steady-state Process models. These set-points are used by the lower layer feedback control systems to force the Process to operate on these set-points. While this paradigm has been successful, we are witnessing an increasing need for dynamic market and demand-driven Operations for more efficient Process Operation, increasing response capability to changing customer demand, and achieving real-time energy management. To enable next-generation market-driven Operation, economic model predictive control (EMPC), which is an model predictive control scheme formulated with a stage cost that represents the Process economics, has been proposed to integrate dynamic economic optimization of Processes with feedback control.Motivated by these considerations, novel theory and methods needed for the design of computationally tractable economic model predictive control systems for nonlinear Processes are developed in this dissertation. Specifically, the following considerations are addressed: a) EMPC structures for nonlinear systems which address: infinite-time and finite-time closed-loop economic performance and time-varying economic considerations such as changing energy pricing; b) two-layer (hierarchical) dynamic economic Process optimization and feedback control frameworks that incorporate EMPC with other control strategies allowing for computational efficiency; and c) EMPC schemes that account for real-time computation requirements. The EMPC schemes and methodologies are applied to chemical Process applications. The application studies demonstrate the effectiveness of the EMPC schemes to maintain Process stability and improve economic performance under dynamic Operation as well as to increase efficiency, reliability and profitability of Processes, thereby contributing to the vision of Smart Manufacturing.
F N Osuolale - One of the best experts on this subject based on the ideXlab platform.
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exergetic optimisation of atmospheric and vacuum distillation system based on bootstrap aggregated neural network models
Exergy for A Better Environment and Improved Sustainability 1: Fundamentals, 2018Co-Authors: F N Osuolale, Jie ZhangAbstract:This paper presents a bootstrap aggregated neural network-based strategy for the modelling and optimisation of crude distillation unit incorporating the second law of thermodynamics. Exergy analysis pinpoints the location and magnitude of the losses and is a tool for determining how efficient a Process is. Exergy analysis of Processes gives insights into the overall energy use evaluation of the Process, potentials for efficient energy use of such Processes can then be identified, and energy-saving measures of the Processes can be suggested. The focus is to improve the exergy efficiency of the crude distillation and hence reduce the energy consumption. To overcome the difficulties in developing detailed mechanistic models, data-driven models such as artificial neural network (ANN) models can be utilised. Real-time optimisation of distillation columns is made feasible by using ANN models which can be quickly developed from Process Operation data. To enhance the reliability of ANN models, bootstrap aggregated neural network (BANN) is used in this study. A further advantage of BANN is that model prediction confidence bounds can be obtained. BANN models for exergy efficiency and product qualities are developed from simulated Process Operation data and are used to maximise exergy efficiency while satisfying product quality constraints. The standard error of the individual neural network predictions is taken as the indication of model prediction reliability and is incorporated in the optimisation objective function. Application to a crude distillation system (comprising of ADU and VDU) shows good improvement in the exergy efficiency of the unit and no additional costs of equipments. A further analysis was to investigate the effects of preflash units on the exergy efficiency of the ADU and VDU. The analysis gives realistic and promising results. The method could be applicable in determining feasible and energy-efficient operating and design conditions for the crude distillation unit.
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energy efficiency optimisation for distillation column using artificial neural network models
Energy, 2016Co-Authors: F N Osuolale, Jie ZhangAbstract:This paper presents a neural network based strategy for the modelling and optimisation of energy efficiency in distillation columns incorporating the second law of thermodynamics. Real-time optimisation of distillation columns based on mechanistic models is often infeasible due to the effort in model development and the large computation effort associated with mechanistic model computation. This issue can be addressed by using neural network models which can be quickly developed from Process Operation data. The computation time in neural network model evaluation is very short making them ideal for real-time optimisation. Bootstrap aggregated neural networks are used in this study for enhanced model accuracy and reliability. Aspen HYSYS is used for the simulation of the distillation systems. Neural network models for exergy efficiency and product compositions are developed from simulated Process Operation data and are used to maximise exergy efficiency while satisfying products qualities constraints. Applications to binary systems of methanol-water and benzene-toluene separations culminate in a reduction of utility consumption of 8.2% and 28.2% respectively. Application to multi-component separation columns also demonstrate the effectiveness of the proposed method with a 32.4% improvement in the exergy efficiency.
-
multi objective optimisation of atmospheric crude distillation system Operations based on bootstrap aggregated neural network models
Computer-aided chemical engineering, 2015Co-Authors: F N Osuolale, Jie ZhangAbstract:Abstract This paper presents a new methodology for optimising the energy efficiency of a crude distillation unit without trading off the product quality and Process throughput. It incorporates the second law of thermodynamics which indicates how well a system is performing compared to the optimum possible performance and hence gives a good indication of the actual energy use of a Process. Bootstrap aggregated neural networks are used for enhanced model accuracy and reliability. In addition to the Process Operation objectives, minimising the model prediction confidence bound is incorporated in multi-objective optimisation to improve the reliability of the optimisation results. The economic analysis of the recoverable energy (sum of internal and external exergy losses) reveals the energy saving potential of the method. The proposed method will aid the design and Operation of energy efficient crude distillation columns.