Model simplification and time-scale assumptions applied to distillation modelling

Dones, Ivan; Preisig, Heinz A.

doi:10.1016/j.compchemeng.2009.11.002

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…The SANMPC algorithm here proposed is limited to all those model simplification techniques that reduce the number of the dynamic equations contained in the full model. For instance, if a plant is modeled using a network approach, , then lumping the capacities (i.e., the control volumes) reduces the number of differential equations . If the model has multiple time-scale behavior, then singular perturbation can be used for the reduction of the dynamics of the model. , For one-dimensional distributed systems, the aggregation model reduction method , is another solution to reduce the model’s dynamics.…”

Section: Self-adaptive Nonlinear Model Predictive Controlmentioning

confidence: 99%

Nonlinear Model Predictive Control: A Self-Adaptive Approach

Dones

Manenti

Preisig

et al. 2010

Ind. Eng. Chem. Res.

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Model predictive control (MPC) is an online application based on dynamic models. Its application faces two major obstacles: (i) computational constraints and (ii) the need to accurately simulate the process by a model that properly predicts how the plant will behave in the future. Implementation of MPC is not always possible in large-scale or industrial applications due to the computational complexity of MPC and to the dimensionality of the models. To facilitate MPC implementations, this paper proposes a self-adaptive approach based on simplified (or reduced-order) nonlinear models. The proposed methodology yields an MPC that adjusts the dimension of the model according to both the current process conditions and the control objectives. The self-adaptive approach is described and validated on an industrial case study, a C4-splitter.

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Section: Self-adaptive Nonlinear Model Predictive Controlmentioning

confidence: 99%

Nonlinear Model Predictive Control: A Self-Adaptive Approach

Dones

Manenti

Preisig

et al. 2010

Ind. Eng. Chem. Res.

Self Cite

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“…As discussed in the literature, model simplification and reduction are important areas in process system engineering and control theory. − Simplified and reduced models are used both to gain insight into the process behavior and to ease the computational efforts of simulation and analysis . For distillation columns, several model reduction and simplification methods have been developed. ,− In this paper, the balanced truncation method is considered.…”

Section: Introductionmentioning

confidence: 99%

Application of Balanced Truncation to Nonlinear Systems

Dones

Skogestad

Preisig

2011

Ind. Eng. Chem. Res.

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The balanced truncation method for reducing the size of a model was originally developed for linear systems. When extended to nonlinear systems, some considerations must be faced. First of all, the calculation of the balancing transformation matrix is not unique. This may results in nonphysical values for the reconstructed states, which may lead to failure, for example, in thermodynamic routines. To reduce this problem, it is recommended to include all the states in the balancing outputs. To further reduce the effect of nonlinearties in the original model, it is recommended to use a linearizing static transformation of the states, if available. In this paper, distillation column models are used as a case study, and, in this case, a logarithmic transformation of the compositions is beneficial.

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“…One approach to revamping in the PPD stage to improve profit is performed by applying rigorous process modeling (Dones and Preisig, 2010;Gadalla et al, 2013). Another type is focused on the methodology of process control (Ganguly and Saraf, 1993).…”

Section: Previous Studiesmentioning

confidence: 99%

A Framework to Systematize Technology of Preliminary Process Design for Plant Lifecycle Engineering

Yamada

Kawamura

Fuchino

2017

J. Chem. Eng. Japan / JCEJ

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The process and plant design stages, in particular the preliminary process design (PPD) stage, are performed repeatedly to cope with environmental changes at a plant during its lifecycle. The stages after the original design are called revamp design stages. As the PPD is repeated, operational exibility is decreased. As a result, slight deviations from the intended critical process conditions during operation can lead to problems. However, in many cases, the causes of an operational problem are not identi ed, leading to safety issues. To overcome this, a framework including two Business Process Models (BPMs) is developed and modeled using IDEF0 with a hierarchical nature and ability to describe the PDCA engineering cycle. One BPM is developed to acquire the information on critical process conditions generated in the PPD stage. The other BPM is developed to systematize the acquired generated information into technology for the PPD by associating the information with design variables. The former BPM must rst be developed as a reference model for the latter as a requirement. Furthermore, the framework is clearly divided into the above-mentioned two BPMs to manage the technology throughout the plant lifecycle while keeping consistent linkage to each other. Finally, it is con rmed that the developed framework has the necessary structure to systematize the technology of the PPD throughout the plant lifecycle by tracing a practical example problem of a distillation column in the framework.

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Model simplification and time-scale assumptions applied to distillation modelling

Cited by 5 publications

References 42 publications

Nonlinear Model Predictive Control: A Self-Adaptive Approach

Nonlinear Model Predictive Control: A Self-Adaptive Approach

Application of Balanced Truncation to Nonlinear Systems

A Framework to Systematize Technology of Preliminary Process Design for Plant Lifecycle Engineering

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