Infrared Drying Process of an Experimental Water Painting: Model Predictive Control

Dufour, Pascal; Blanc, D.; Touré, Youssoufi; Laurent, Pierre

doi:10.1081/drt-120028233

Cited by 21 publications

(7 citation statements)

References 11 publications

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“…As a result, moisture content M is spatially distributed along the z direction. Moreover, as pointed out in other works, 31,[59][60][61] the temperature difference between the top and bottom layers is negligible since the coating is usually very thin (around 20 mm for basecoat and 100 mm in total 62 ). Thus, the temperature of the paint is assumed to be spatially uniform during the drying process.…”

Section: Drying Process Of Automotive Coatingsmentioning

confidence: 68%

“…A similar control strategy using linear MPC can be found in Vada et al 30 To incorporate nonlinear PDE systems into linear MPC frameworks, one possible approach is to linearize the nonlinear PDE at nominal operating points and design the controller based on the linearized model. 1,14,31 Dufour et al 14,31,32 proposed a two-level optimization scheme that uses this approach for infrared-convective drying of a paint film. The nonlinear PDE of the drying process is first linearized around a predefined operating point.…”

Section: Introductionmentioning

confidence: 99%

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Estimation and predictive control of nonlinear diffusion processes with application to drying of coatings

Cao

Ayalew

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article deals with the estimation and control of a class of distributed parameter processes dominated by nonlinear diffusion. The major challenges in control of such systems lie in the nonlinear infinite-dimensional nature of the systems and the lack of direct sensing for relevant system states. In the proposed scheme, the proper orthogonal decomposition-Galerkin method is adopted to derive a reduced-order model in terms of temporal coefficients from the original system described by nonlinear partial differential equation(s). To overcome the sensing problem, the unscented Kalman filter is implemented to estimate the temporal coefficients of the reduced-order model online and subsequently reconstruct the distributed system states. A set of improved sufficient conditions are also established to ensure stability of the estimation scheme. Then, once these difficulties are addressed, a nonlinear model predictive control scheme is formulated to achieve desired control objectives such as trajectory tracking for distributed states, energy optimization and quality control. The proposed estimation and control scheme is demonstrated via an application to infrared drying of coatings in the automotive industry.

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Section: Drying Process Of Automotive Coatingsmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Estimation and predictive control of nonlinear diffusion processes with application to drying of coatings

Cao

Ayalew

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Only a few publications [24][25][26][27] reported employing real experimental tests of control strategies on continuous IR dryers that are similar to an industrial dryer.…”

Section: Literature Review On Ir Dryingmentioning

confidence: 99%

Infrared Drying: From Process Modeling to Advanced Process Control

Dhib

2007

Drying Technology

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The article surveys the drying of solids materials and polymer solutions when infrared radiation (IR) is employed as the main heating source. The study reviews the current research trends of IR drying of specific applications. A case study similar to an industrial setting is presented to illustrate a model development and control scheme of an IR drying unit. The design and online implementation of an internal model controller (IMC) is discussed. The study demonstrates the controller capabilities to suppress random variations of the moisture content in the material entering the dryer. Simulation results also showed the success of model predictive control (MPC) multivariable controller ability, while handling process interactions and process constraints, to track setpoint changes in the humidity and temperature of the material exiting the dryer and to reject unmeasured stochastic disturbances in the inlet humidity stream. INTRODUCTIONEven though drying is an old and well-established process, the transport phenomena characterized by a simultaneous transfer of mass and energy is an interesting and fascinating area to study. In a recent survey, Mujumdar [1] reviewed the most important aspects of drying technology. He highlighted the R&D research trends in this area from conventional to new technologies. Energy saving and product quality are among the main concerns in industry today. Thermal drying, being defined as the removal of moisture from a solid, is one of the most ubiquitous and energy-intensive processes. Firstly, the energy required for a drying process can be supplied by different means to adapt the drying requirement of a variety of products and also to satisfy the industrial and market demand. Thus, efficient use and management of energy are very desirable in the design and operation of a drying unit. In fact, energy

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“…Model-based control systems have been applied to different industrial food dryers; see, e.g., McFarlane and Bruce, [10] Liu and Bakker-Arkema, [11,12] Didriksen, [13] and Shahosseiri et al [14] In Didriksen, [13] control is stated to be superior to traditional feedback control. In Dufour et al, [15] Abukhalifeh, [16] and Musch et al, [17] a model-predictive control has been applied to infrared drying processes and timber drying. Abdel-Jabbar et al [18] present simplified reduced-order linear models that can be used in synthesizing model-based control schemes for fluidized bed boilers.…”

mentioning

confidence: 99%

Simulation Model for the Model-Based Control of a Biofuel Dryer at an Industrial Combined Heat and Power Plant

Holmberg¹,

Ahtila²

2006

Drying Technology

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The primary aim of this article is to present a simulation model for a bark dryer integrated into a combined heat and power plant. The same model can be used for the model-based control of the dryer. The secondary aim is to evaluate how useful the control is from an economic point of view. Results show that the final fuel moisture content can be stabilised by controlling the drying temperature(s). On the other hand, the deviation in final bark moisture decreases even when the dryer has no control at all. Net incomes resulting from drying increase in most cases compared to dryers without control. The need for control cannot be justified on economic grounds.

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Infrared Drying Process of an Experimental Water Painting: Model Predictive Control

Cited by 21 publications

References 11 publications

Estimation and predictive control of nonlinear diffusion processes with application to drying of coatings

Estimation and predictive control of nonlinear diffusion processes with application to drying of coatings

Infrared Drying: From Process Modeling to Advanced Process Control

Simulation Model for the Model-Based Control of a Biofuel Dryer at an Industrial Combined Heat and Power Plant

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