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Gonzalo Guillengosalbez - One of the best experts on this subject based on the ideXlab platform.

  • optimal Design of flexible heat integrated crude oil distillation units using surrogate models
    Chemical Engineering Research & Design, 2021
    Co-Authors: Megan Jobson, Dauda Ibrahim, Gonzalo Guillengosalbez
    Abstract:

    Abstract The Design of distillation columns often considers a given fixed feedstock and nominal operating conditions. Here we present an optimization-based approach for the optimal Design of these units considering a flexible operation under a range of potential feedstocks. Our method combines an artificial neural network with a support vector machine to model the crude oil distillation unit. The artificial neural network model predicts the performance of the distillation unit for a given crude oil feedstock whilst the support vector machine classifier filters out Infeasible Design alternatives from the solution space (i.e., Designs that are unlikely to converge when simulated using a rigorous model). The inputs to the artificial neural network include the column structural variables and operating conditions, whilst the outputs are process variables linked to the column performance. The artificial neural network models and support vector machines constructed for different crude oil feedstocks are integrated into a two-stage optimization framework in order to optimize the column structural variables and operating conditions, where the minimum utility demand is estimated using the pinch analysis. An effective solution strategy that combines stochastic and deterministic optimization algorithms is applied to search for economically viable and flexible Design alternatives that can operate over a given range of crude oil feedstocks while satisfying the product quality specifications. The capabilities of the proposed approach are illustrated using an industrially-relevant case study, where we clearly show that the proposed approach can identify Design alternatives capable of handling various feedstocks effectively.

  • optimization based Design of crude oil distillation units using surrogate column models and a support vector machine
    Chemical Engineering Research & Design, 2018
    Co-Authors: Dauda Ibrahim, Megan Jobson, Jie Li, Gonzalo Guillengosalbez
    Abstract:

    Abstract This paper presents a novel optimization-based approach for the Design of heat-integrated crude oil distillation units, which are widely used in refineries. The methodology presented combines, within a unified framework, surrogate distillation column models based on artificial neural networks, feasibility constraints constructed using a support vector machine, and pinch analysis to maximize heat recovery, in order to optimize the distillation column configuration and its operating conditions. The inputs to the surrogate column model are given by the column structure and operating conditions, while the outputs are related to the column performance. The support vector machine classifier filters Infeasible Design alternatives from the search space, thus reducing computational time, and ultimately improves the quality of the final solution. The overall optimization problem takes the form of a mixed-integer nonlinear program, which is solved by a genetic algorithm that seeks the Design and operating variables values that minimize the total annualized cost. The capabilities of the proposed approach are illustrated using an industrially–relevant case study. Numerical results show that promising Design alternatives can be obtained using the proposed method. The approach can help engineers to Design and operate petroleum refineries optimally, where these are expected to continue to play a major role in the energy mix for some years.

  • surrogate models combined with a support vector machine for the optimized Design of a crude oil distillation unit using genetic algorithms
    Computer-aided chemical engineering, 2017
    Co-Authors: Dauda Ibrahim, Megan Jobson, Jie Li, Gonzalo Guillengosalbez
    Abstract:

    Abstract This paper introduces a novel optimization-based framework for the Design of a crude oil distillation unit. The approach presented integrates surrogate models based on artificial neural networks (ANN) with feasibility constraints generated using a support vector machine (SVM) in order to optimise the column configuration and its operating conditions. The SVM filters Infeasible Design options from the solution space of the Design problem, which reduces the computational effort and ultimately improves the quality of the final solution. Rigorous process simulations are used to build the surrogate model, while pinch analysis is employed to determine the maximum heat recovery and minimum utility costs. The objective is to minimise the total annualized cost, which is optimised by combining a genetic algorithm with the surrogate model. The approach is illustrated in an industrially relevant case study.

Dauda Ibrahim - One of the best experts on this subject based on the ideXlab platform.

  • optimal Design of flexible heat integrated crude oil distillation units using surrogate models
    Chemical Engineering Research & Design, 2021
    Co-Authors: Megan Jobson, Dauda Ibrahim, Gonzalo Guillengosalbez
    Abstract:

    Abstract The Design of distillation columns often considers a given fixed feedstock and nominal operating conditions. Here we present an optimization-based approach for the optimal Design of these units considering a flexible operation under a range of potential feedstocks. Our method combines an artificial neural network with a support vector machine to model the crude oil distillation unit. The artificial neural network model predicts the performance of the distillation unit for a given crude oil feedstock whilst the support vector machine classifier filters out Infeasible Design alternatives from the solution space (i.e., Designs that are unlikely to converge when simulated using a rigorous model). The inputs to the artificial neural network include the column structural variables and operating conditions, whilst the outputs are process variables linked to the column performance. The artificial neural network models and support vector machines constructed for different crude oil feedstocks are integrated into a two-stage optimization framework in order to optimize the column structural variables and operating conditions, where the minimum utility demand is estimated using the pinch analysis. An effective solution strategy that combines stochastic and deterministic optimization algorithms is applied to search for economically viable and flexible Design alternatives that can operate over a given range of crude oil feedstocks while satisfying the product quality specifications. The capabilities of the proposed approach are illustrated using an industrially-relevant case study, where we clearly show that the proposed approach can identify Design alternatives capable of handling various feedstocks effectively.

  • optimization based Design of crude oil distillation units using surrogate column models and a support vector machine
    Chemical Engineering Research & Design, 2018
    Co-Authors: Dauda Ibrahim, Megan Jobson, Jie Li, Gonzalo Guillengosalbez
    Abstract:

    Abstract This paper presents a novel optimization-based approach for the Design of heat-integrated crude oil distillation units, which are widely used in refineries. The methodology presented combines, within a unified framework, surrogate distillation column models based on artificial neural networks, feasibility constraints constructed using a support vector machine, and pinch analysis to maximize heat recovery, in order to optimize the distillation column configuration and its operating conditions. The inputs to the surrogate column model are given by the column structure and operating conditions, while the outputs are related to the column performance. The support vector machine classifier filters Infeasible Design alternatives from the search space, thus reducing computational time, and ultimately improves the quality of the final solution. The overall optimization problem takes the form of a mixed-integer nonlinear program, which is solved by a genetic algorithm that seeks the Design and operating variables values that minimize the total annualized cost. The capabilities of the proposed approach are illustrated using an industrially–relevant case study. Numerical results show that promising Design alternatives can be obtained using the proposed method. The approach can help engineers to Design and operate petroleum refineries optimally, where these are expected to continue to play a major role in the energy mix for some years.

  • surrogate models combined with a support vector machine for the optimized Design of a crude oil distillation unit using genetic algorithms
    Computer-aided chemical engineering, 2017
    Co-Authors: Dauda Ibrahim, Megan Jobson, Jie Li, Gonzalo Guillengosalbez
    Abstract:

    Abstract This paper introduces a novel optimization-based framework for the Design of a crude oil distillation unit. The approach presented integrates surrogate models based on artificial neural networks (ANN) with feasibility constraints generated using a support vector machine (SVM) in order to optimise the column configuration and its operating conditions. The SVM filters Infeasible Design options from the solution space of the Design problem, which reduces the computational effort and ultimately improves the quality of the final solution. Rigorous process simulations are used to build the surrogate model, while pinch analysis is employed to determine the maximum heat recovery and minimum utility costs. The objective is to minimise the total annualized cost, which is optimised by combining a genetic algorithm with the surrogate model. The approach is illustrated in an industrially relevant case study.

Megan Jobson - One of the best experts on this subject based on the ideXlab platform.

  • optimal Design of flexible heat integrated crude oil distillation units using surrogate models
    Chemical Engineering Research & Design, 2021
    Co-Authors: Megan Jobson, Dauda Ibrahim, Gonzalo Guillengosalbez
    Abstract:

    Abstract The Design of distillation columns often considers a given fixed feedstock and nominal operating conditions. Here we present an optimization-based approach for the optimal Design of these units considering a flexible operation under a range of potential feedstocks. Our method combines an artificial neural network with a support vector machine to model the crude oil distillation unit. The artificial neural network model predicts the performance of the distillation unit for a given crude oil feedstock whilst the support vector machine classifier filters out Infeasible Design alternatives from the solution space (i.e., Designs that are unlikely to converge when simulated using a rigorous model). The inputs to the artificial neural network include the column structural variables and operating conditions, whilst the outputs are process variables linked to the column performance. The artificial neural network models and support vector machines constructed for different crude oil feedstocks are integrated into a two-stage optimization framework in order to optimize the column structural variables and operating conditions, where the minimum utility demand is estimated using the pinch analysis. An effective solution strategy that combines stochastic and deterministic optimization algorithms is applied to search for economically viable and flexible Design alternatives that can operate over a given range of crude oil feedstocks while satisfying the product quality specifications. The capabilities of the proposed approach are illustrated using an industrially-relevant case study, where we clearly show that the proposed approach can identify Design alternatives capable of handling various feedstocks effectively.

  • optimization based Design of crude oil distillation units using surrogate column models and a support vector machine
    Chemical Engineering Research & Design, 2018
    Co-Authors: Dauda Ibrahim, Megan Jobson, Jie Li, Gonzalo Guillengosalbez
    Abstract:

    Abstract This paper presents a novel optimization-based approach for the Design of heat-integrated crude oil distillation units, which are widely used in refineries. The methodology presented combines, within a unified framework, surrogate distillation column models based on artificial neural networks, feasibility constraints constructed using a support vector machine, and pinch analysis to maximize heat recovery, in order to optimize the distillation column configuration and its operating conditions. The inputs to the surrogate column model are given by the column structure and operating conditions, while the outputs are related to the column performance. The support vector machine classifier filters Infeasible Design alternatives from the search space, thus reducing computational time, and ultimately improves the quality of the final solution. The overall optimization problem takes the form of a mixed-integer nonlinear program, which is solved by a genetic algorithm that seeks the Design and operating variables values that minimize the total annualized cost. The capabilities of the proposed approach are illustrated using an industrially–relevant case study. Numerical results show that promising Design alternatives can be obtained using the proposed method. The approach can help engineers to Design and operate petroleum refineries optimally, where these are expected to continue to play a major role in the energy mix for some years.

  • surrogate models combined with a support vector machine for the optimized Design of a crude oil distillation unit using genetic algorithms
    Computer-aided chemical engineering, 2017
    Co-Authors: Dauda Ibrahim, Megan Jobson, Jie Li, Gonzalo Guillengosalbez
    Abstract:

    Abstract This paper introduces a novel optimization-based framework for the Design of a crude oil distillation unit. The approach presented integrates surrogate models based on artificial neural networks (ANN) with feasibility constraints generated using a support vector machine (SVM) in order to optimise the column configuration and its operating conditions. The SVM filters Infeasible Design options from the solution space of the Design problem, which reduces the computational effort and ultimately improves the quality of the final solution. Rigorous process simulations are used to build the surrogate model, while pinch analysis is employed to determine the maximum heat recovery and minimum utility costs. The objective is to minimise the total annualized cost, which is optimised by combining a genetic algorithm with the surrogate model. The approach is illustrated in an industrially relevant case study.

Jie Li - One of the best experts on this subject based on the ideXlab platform.

  • optimization based Design of crude oil distillation units using surrogate column models and a support vector machine
    Chemical Engineering Research & Design, 2018
    Co-Authors: Dauda Ibrahim, Megan Jobson, Jie Li, Gonzalo Guillengosalbez
    Abstract:

    Abstract This paper presents a novel optimization-based approach for the Design of heat-integrated crude oil distillation units, which are widely used in refineries. The methodology presented combines, within a unified framework, surrogate distillation column models based on artificial neural networks, feasibility constraints constructed using a support vector machine, and pinch analysis to maximize heat recovery, in order to optimize the distillation column configuration and its operating conditions. The inputs to the surrogate column model are given by the column structure and operating conditions, while the outputs are related to the column performance. The support vector machine classifier filters Infeasible Design alternatives from the search space, thus reducing computational time, and ultimately improves the quality of the final solution. The overall optimization problem takes the form of a mixed-integer nonlinear program, which is solved by a genetic algorithm that seeks the Design and operating variables values that minimize the total annualized cost. The capabilities of the proposed approach are illustrated using an industrially–relevant case study. Numerical results show that promising Design alternatives can be obtained using the proposed method. The approach can help engineers to Design and operate petroleum refineries optimally, where these are expected to continue to play a major role in the energy mix for some years.

  • surrogate models combined with a support vector machine for the optimized Design of a crude oil distillation unit using genetic algorithms
    Computer-aided chemical engineering, 2017
    Co-Authors: Dauda Ibrahim, Megan Jobson, Jie Li, Gonzalo Guillengosalbez
    Abstract:

    Abstract This paper introduces a novel optimization-based framework for the Design of a crude oil distillation unit. The approach presented integrates surrogate models based on artificial neural networks (ANN) with feasibility constraints generated using a support vector machine (SVM) in order to optimise the column configuration and its operating conditions. The SVM filters Infeasible Design options from the solution space of the Design problem, which reduces the computational effort and ultimately improves the quality of the final solution. Rigorous process simulations are used to build the surrogate model, while pinch analysis is employed to determine the maximum heat recovery and minimum utility costs. The objective is to minimise the total annualized cost, which is optimised by combining a genetic algorithm with the surrogate model. The approach is illustrated in an industrially relevant case study.

Xiaoping Du - One of the best experts on this subject based on the ideXlab platform.

  • towards a better understanding of modeling feasibility robustness in engineering Design
    Journal of Mechanical Design, 2000
    Co-Authors: Xiaoping Du
    Abstract:

    In robust Design, it is important not only to achieve robust Design objectives but also to maintain the robustness of Design feasibility under the effect of variations (or uncertainties). However, the evaluation of feasibility robustness is often a computationally intensive process. Simplified approaches in existing robust Design applications may lead to either over-conservative or Infeasible Design solutions. In this paper, several feasibility-modeling techniques for robust optimization are examined. These methods are classified into two categories: methods that require probability and statistical analyses and methods that do not. Using illustrative examples, the effectiveness of each method is compared in terms of its efficiency and accuracy. Constructive recommendations are made to employ different techniques under different circumstances. Under the framework of probabilistic optimization, we propose to use a most probable point (MPP) based importance sampling method, a method rooted in the field of reliability analysis, for evaluating the feasibility robustness. The advantages of this approach are discussed. Though our discussions are centered on robust Design, the principles presented are also applicable for general probabilistic optimization problems. The practical significance of this work also lies in the development of efficient feasibility evaluation methods that can support quality engineering practice, such as the Six Sigma approach that is being widely used in American industry.