Nonlinear model predictive control of a batch fluidized bed dryer for pharmaceutical particles

Gagnon, Francis; Desbiens, André; Poulin, Éric C.; Lapointe-Garant, Pierre-Philippe; Simard, Jean-Sébastien

doi:10.1016/j.conengprac.2017.04.009

Cited by 24 publications

(13 citation statements)

References 14 publications

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“…Therefore, (24) can be directly obtained. Similarly, taking into account (34), (25) can also be guaranteed. On the other hand, by substituting (9) into (36) and multiplying the right and left sides by G and its transpose, respectively, we have…”

Section: Resultsmentioning

confidence: 99%

Robust Model Predictive Control for a Class of Discrete-Time Markovian Jump Linear Systems With Operation Mode Disordering

Cai

et al. 2019

IEEE Access

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For a class of discrete-time Markovian jump linear systems subject to operation mode disordering, a robust model predictive control method can be proposed to solve this issue. A bijective mapping scheme between the original random process and a new random process is studied to cope with the problem of operation mode disordering. At each sampling time, the original ''min-max'' optimization problem is transformed into a convex optimization problem with linear matrix inequalities so that the complexity of solving the optimization problem can be greatly reduced. The sufficient stability condition of the Markovian jump linear systems can be achieved by using the Lyapunov stability theory. Moreover, a state feedback control law is obtained, which minimizes an infinite prediction horizon performance cost. Furthermore, the cases of uncertain and unknown transition probabilities are also considered in this paper. The simulation results show that the proposed method can guarantee the optimal control performance and the stability of Markovian jump linear systems. INDEX TERMS Robust model predictive control, Markovian jump linear systems (MJLSs), operation mode disordering, linear matrix inequalities (LMIs).

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Section: Resultsmentioning

confidence: 99%

Robust Model Predictive Control for a Class of Discrete-Time Markovian Jump Linear Systems With Operation Mode Disordering

Cai

et al. 2019

IEEE Access

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“…It is to be desired that the control law is easily understood by the operators. Hence, this research aims at developing an operation guidance system using nonlinear model predictive control (NMPC) [1][2][3][4] to reduce the HMT variation. The NMPC algorithm adopted a new 2D transient model to predict the future HMT.…”

Section: Introductionmentioning

confidence: 99%

Practical Operation Guidance on Thermal Control of Blast Furnace

Hashimoto¹,

Okamoto²,

Kaise³

et al. 2019

ISIJ Int.

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Blast furnaces are still manually operated in the steel industry. To realize an efficient and stable operation, the authors developed an operation guidance system for controlling hot metal temperature and applied it to actual furnaces. The system features a newly developed 2D transient model that describes the sluggish and complicated process dynamics. Based on the transient model, nonlinear model predictive control (NMPC) and moving horizon estimation (MHE) were adopted to provide operators with appropriate control actions while minimizing the undesirable influence of disturbances. The online validation results demonstrated that the developed operation guidance system successfully reduced the variance of HMT by 1.9°C.

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“…Previous studies related to fluidised bed control were mainly devoted to fluidised bed dryers using model predictive control (Gagnon et al, 2017;Wang et al, 2009) and distributed parameter control (Villegas et al, 2009). Due to the complex gas-solids multiphase flows involved in a coating process, knowledge on pellet agglomeration and size growth is still limited without a robust and reliable predictive model of flow behaviour.…”

Section: Introductionmentioning

confidence: 99%

Control of pellets coating in a Wurster fluidised bed by means of electrical capacitance tomography

Che

Wang

et al. 2019

Transactions of the Institute of Measurement and Control

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Wurster fluidised bed is commonly used for coating pellets in the pharmaceutical industry. Normally, the control in a Wurster fluidised bed for coating is based on point-based measurement, that is, pressure and optical probes. However, the point-based measurement methods only provide local flow information and cannot reveal the cross-sectional flow dynamics characteristics and it is difficult to control the whole process with limited data. In this paper, electrical capacitance tomography (ECT) is applied to reconstruct the solids distribution in a lab-scale Wurster fluidised bed for coating pellets and the measurements are used in a control loop of the process. The flow regime is identified from the ECT images using a simple but efficient approach. The objective of control is to keep stable minimum fluidisation and avoid undesired flow regime in the region between the outside wall and the Wurster tube, such as intermittent, plug and defluidisation. To achieve this target, a PID controller is applied to keep a low volume fraction in the annular region by adjusting the fluidisation air rate, and bang-bang control is applied to a peristaltic pump used for spraying coating solutions based on the detected flow regime. It has been found that the proposed control scheme based on the ECT measurement is effective for keeping a stable flow regime, reducing the degree of pellets agglomeration and avoiding defluidisation.

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Nonlinear model predictive control of a batch fluidized bed dryer for pharmaceutical particles

Cited by 24 publications

References 14 publications

Robust Model Predictive Control for a Class of Discrete-Time Markovian Jump Linear Systems With Operation Mode Disordering

Robust Model Predictive Control for a Class of Discrete-Time Markovian Jump Linear Systems With Operation Mode Disordering

Practical Operation Guidance on Thermal Control of Blast Furnace

Control of pellets coating in a Wurster fluidised bed by means of electrical capacitance tomography

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