Robust model predictive control of a pilot plant distillation column

Martin, P.; Odloak, Darci; Kassab, Fuad

doi:10.1016/j.conengprac.2012.10.004

Cited by 34 publications

(17 citation statements)

References 22 publications

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“…Because the settling time of the system's slowest response is about 5 minutes, t f is selected 6 minutes to be longer than the settling time. However, the sample time T s should be selected sufficiently small to have an acceptable approximation in Equation (7). In the current study, T s is 0.05 minute, which is about 100th of the closed-loop settling time.…”

Section: Simulation Resultsmentioning

confidence: 96%

“…A decentralized control scheme is presented by Bisgaard et al to achieve optimal and economic performance using controlling both pressure and temperature. The development and application of a robust MPC to a pilot‐plant ethanol‐water distillation column is described in the work of Martin et al where the authors have also investigated how this controller can maintain the performance of the system in the presence of multimodel uncertainty compared to a linear controller. In the work of Karacan et al, an adaptive generalized predictive controller is designed to achieve the optimal operating conditions of a binary packed distillation column.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical optimal control of a binary distillation column

Ramezani

Sadati

2018

Optim Control Appl Methods

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Summary This paper presents a two‐level optimal control for the binary distillation column. From the control point of view, the binary distillation column is a high‐order system consisting of several interconnected subsystems (trays) that aim to maximize output purity. Controlling the system in a hierarchical manner not only decreases complexity and solution time but also can improve the control structure's reliability. In this study, the distillation column is decomposed into NT second‐order subsystems, each representing one stage (one tray). Accordingly, the cost function is decomposed, and thus, the overall problem is converted into NT lower‐order subproblems where each tray has its own controller. A two‐level interaction prediction approach provides optimal control for the overall system. The task of the first level is to solve the subproblems using the predicted values of the coordination parameters, whereas the second level acts as a coordinator to update the coordination parameters. Simulation results show the capability and efficacy of the proposed two‐level control method in finding the optimal solution with less complexity and lower solution time than those of the centralized method.

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Section: Simulation Resultsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Hierarchical optimal control of a binary distillation column

Ramezani

Sadati

2018

Optim Control Appl Methods

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“…, u u u u u u u u u (6) Note that in the coefficient matrix, if the off-diagonal terms are set to zeros, the experiments will be equivalent to sequential tests where inputs are changed one at a time. Next, the above equation is premultiplied by the inverse of the input data matrix.…”

Section: Proposed Methodologymentioning

confidence: 99%

Identification of MIMO Continuous-Time Models Using Simultaneous Step Inputs

Ahmed

Imtiaz

2015

Ind. Eng. Chem. Res.

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A new approach for identification of multi-input multi-output (MIMO) continuous-time transfer function models using simultaneous step input signals is proposed. MIMO processes exhibit directionality which implies that the output gains depend on the input magnitudes as well as the ratios of the inputs. Due to this directionality issue, MIMO models estimated by sequentially changing one input at a time often do not result in satisfactory tracking performance when used for model based control. In the proposed methodology all of the inputs are changed simultaneously to resemble controlled conditions. While the identification tests remain multi-input in its true sense, the parameter estimation steps involve estimation of the parameters of a single transfer function at a time. Moreover, the time delays are estimated in the same way as the coefficients in the model. Simulation results are presented to demonstrate the robustness of the methodology and its applicability to processes with high input−output dimensions. Identification and control results of a simulated distillation column are also presented; a dynamic matrix controller (DMC) results in better control performance with the model estimated using the proposed methodology than with the model estimated using sequential inputs.

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“…One of recent studies published by [18] shows how MPC is used as a master MIMO controller for slave PID control loops in order to decrease power consumption of the column. Paper [19] focuses on design of a MPC controller for industrial distillation column, with extended objective function related to economic profit. A robust MPC to a pilot plant ethanol-water distillation column is described in [20].…”

Section: Introductionmentioning

confidence: 99%

“…Paper [19] focuses on design of a MPC controller for industrial distillation column, with extended objective function related to economic profit. A robust MPC to a pilot plant ethanol-water distillation column is described in [20]. Study with MPC control strategy applied to methanol-water binary batch distillation column can be found in [21].…”

Section: Introductionmentioning

confidence: 99%