Optimized Multivariable Predictive Control of an Industrial Distillation Column Considering Hard and Soft Constraints

Kniese, D.-W.; Haber, Robert; Volk, U.; Hahn, R. S.

doi:10.1016/s1474-6670(17)35790-7

Cited by 8 publications

(1 citation statement)

References 3 publications

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“…The distillation is the most studied unit operation in terms of control [4]. For controlling the system Proportional-Integral-Derivative (PID) controllers were used [5].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Simulation Control in a Cryogenic Distillation Column

Kállai

Szepesi

Mizsey

2020

Pollack

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Chemical industry has a high demand for ethylene quantity, especially with high quality. This paper discusses dynamic simulation models of an ethaneethylene high-pressure cryogenic rectification column with Unisim Design process simulator software. Distillation is one of the most essential technologies in chemical industry, it is important that the operation of the procedure can be modeled not only in steady-state mode but also in a dynamic way. The goal during this study is to make simulations with system-controlling and to investigate the effect the disturbance on the behavior of the columns.

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“…The distillation is the most studied unit operation in terms of control [4]. For controlling the system Proportional-Integral-Derivative (PID) controllers were used [5].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Simulation Control in a Cryogenic Distillation Column

Kállai

Szepesi

Mizsey

2020

Pollack

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Data-driven Bayesian approach for control loop diagnosis

Huang

2008

2008 American Control Conference

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Many methods and algorithms have been proposed for control performance monitoring and process monitoring. However, there are few methods available for synthesis of different monitoring algorithms to form a control loop diagnostic system. Determination of the underlying reason of poor control performance is challenging. In this paper, we investigate a novel data-driven Bayesian approach for control loop diagnosis. The new approach can synthesize information from different monitoring techniques to give an appropriate inference even if the performance of each individual monitor may be low. Some other merits of the new approach include, for example, probabilistic inferences which can be easily used by optimal decision making system, robustness to missing data, and ability to incorporate a priori knowledge. Simulation of Bayesian diagnostic system for a binary distillation column is presented. Data missing handling feature using causality structure and marginalization is discussed. Performance of the Bayesian diagnostic system is examined under different operating modes to demonstrate the information synthesizing ability of the proposed approach.

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