Steady-state optimization and nonlinear model-predictive control of a reactive distillation process using the software platform do-mpc

Tătulea-Codrean, Alexandru; Haßkerl, Daniel; Urselmann, Maren; Engell, Sebastian

doi:10.1109/cca.2016.7588015

Cited by 5 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mathematical formulation of the model of the reactive distillation column using the MESH equations results in a set of 186 differential and 288 algebraic equations. In contrast to refs and , we use a dynamic model of the reactive distillation process; i.e., we consider the rate of change of the liquid phase molar fraction of each component at each equilibrium stage and of the molar holdup of the evaporator. A library of correlations for the thermophysical properties (see Table S3 in the Supporting Information), the UNIQUAC model, models for the relative volumetric hold-up and the pressure drop for each element of the packing section according to ref complete the model equations.…”

Section: Process Modelmentioning

confidence: 99%

“…Different state estimation techniques were investigated for this case study using a simplified model. 24 Furthermore, simulation studies for the setup of the NMPC in general with respect to the choice of the underlying cost function were investigated in ref 25. This paper provides a comprehensive description of the results that were obtained by bringing the hardware and software components together and realizing economics optimizing control at the real column from a process technology viewpoint.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Performance Optimization of a Pilot-Scale Reactive Distillation Process by Economics Optimizing Control

Haßkerl

Lindscheid

Subramanian

et al. 2018

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

The most prominent example of process intensification by process integration is reactive distillation (RD), which can realize a high reactant conversion and a high purity of the target product in equilibrium-limited reaction systems. Reactive distillation is mostly used for single reactions, but realizations for multiple-reaction systems leading to more than one target product can also be found. In multipurpose chemical processes, the target product of a multiple-reaction system can be switched during the production. When combining fluid separation and multiple-reaction systems, e.g., in a RD column, it is challenging to find operating conditions that lead to the most profitable operation of the process. Economics optimizing control where an economic objective is employed in a model-predictive control framework has the potential to drive the process to the economic optimum during operation based upon a process model and online measurements. However, so far only few applications of such advanced process control (APC) concepts to reactive distillation processes have been realized and these do not concern multiple-reaction systems. In this contribution, we describe the application of economics optimizing control to a multiproduct RD process. The selected case study is a homogeneously catalyzed two-step transesterification reaction that is performed in a RD column in pilotplant scale.

show abstract

Section: Process Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Performance Optimization of a Pilot-Scale Reactive Distillation Process by Economics Optimizing Control

Haßkerl

Lindscheid

Subramanian

et al. 2018

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…Model predictive control (MPC) is an advanced optimisation-based control technique well known in chemical and petrochemical industries [1,2]. It offers many advantages compared with other methods as it achieves fast time responses and can be applied to a variety of linear or non-linear, mono or multivariable systems with constraints.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the impact of online identification on model predictive current control applied to permanent magnet synchronous motors

Sawma

Khatounian

Idkhajine

et al. 2017

IET Electric Power Applications

View full text Add to dashboard Cite

Model predictive control (MPC) is a model-based control strategy that introduces many advantages but also some drawbacks. The biggest challenge that faces MPC controller is its sensitivity towards parametric variation. This study aims to robustify MPCs by adding a least mean square (LMS) identification algorithm within their structure. The feasibility of this method is tested on a simple example which is the MPC current control of a permanent magnet synchronous motor driven by a two-level power converter. Many tests are firstly performed without including inside the MPC algorithms the LMS online tuning procedure. These tests are later compared with the ones obtained with the same control technique but where the identification algorithm is turned on. Results clearly show all the benefits of using this approach on the controller performances.

show abstract

Delta-operator-based adaptive model predictive control and online optimization of a natural gas liquefaction process

Won

Lee

2017

Chemical Engineering Science

View full text Add to dashboard Cite

Steady-state optimization and nonlinear model-predictive control of a reactive distillation process using the software platform do-mpc

Cited by 5 publications

References 18 publications

Dynamic Performance Optimization of a Pilot-Scale Reactive Distillation Process by Economics Optimizing Control

Dynamic Performance Optimization of a Pilot-Scale Reactive Distillation Process by Economics Optimizing Control

Analysis of the impact of online identification on model predictive current control applied to permanent magnet synchronous motors

Delta-operator-based adaptive model predictive control and online optimization of a natural gas liquefaction process

Contact Info

Product

Resources

About