Dynamic simulation and control of direct rotary dryers

Cubillos, Francisco; Zavala, Eder; Shene, Carolina; Álvarez, Pedro

doi:10.1016/s0956-7135(97)00077-7

Cited by 17 publications

(9 citation statements)

References 8 publications

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“…The experiments conducted by [13] have not studied the effect of the dryer dimensions on mass transfer coefficient between gas and solid. Experimental results of this study were compared to different studies Dynamic model based on the conservation equations of mass and energy were developed by [14], these models reproduce the dynamic behaviour observed qualitatively. These models were used for the control of industrial rotary dryer.…”

Section: Introductionmentioning

confidence: 94%

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Recent Patent and Modelling of Phosphate Rotary Dryer

Raffak¹,

Mohamed²,

Mabsate³

et al. 2008

ENG

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We review recent patents on a phosphate rotary dryer, and we present in this paper two-fold. The first aim is the modelling of the phosphate rotary dryer and presentation of the dynamic model which we elaborated for the simulation. So far, no studies to investigate this have been reported. The second aim is to realize tests on the rotary dryer existing in Oued-Zem Complex of drying Khouribga-Morocco. In the present study, an improved numerical heat and mass transfer model has been developed for the rotary dryer used for drying the phosphate. The developed model is obtained on the basis of the equations of heat and mass transfer between the gaseous and solid phases. These equations are simplified by using suitable assumptions. The latter were validated by the tests carried out on the location. In order to better understand the behaviour of the product during drying process and to validate the mathematical model developed, we conducted tests on a rotary dryer installed in Oued-Zem Complex of drying. The purpose of these tests was the determination of phosphate humidity and temperature gas profiles along the dryer. The computer results show that the present model can predict the length of the dryer as well as axial solid and gas temperature distributions with reasonably good accuracy. The model developed is in a dynamic regime and it makes possible to take into account the thermal convective process, the porosity of the product as well as the generation of humidity. The results of the simulation of the model are compared with the experimental data.

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Section: Introductionmentioning

confidence: 94%

“…S * = S S ir 1 S ir (14) Where K m 2 ( ) is the permeability, K r is the relative permeability, S saturation, S * is the normalised saturation, S ir is the saturation below which the liquid is no longer a continuous phase.…”

Section: Calculation Of Gas and Solid Velocitymentioning

confidence: 99%

Recent Patent and Modelling of Phosphate Rotary Dryer

Raffak¹,

Mohamed²,

Mabsate³

et al. 2008

ENG

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“…Rotary drying control has been assessed by Perez-Correa et al (1998). A nonlinear dynamic model was specified based on mass and energy balances.…”

Section: Feedforward and Feedback Controlmentioning

confidence: 99%

Control and Monitoring of Food-Manufacturing Processes

McFarlane

2013

Handbook of Food Factory Design

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This chapter begins with descriptions of the building blocks of control systems, which include the sensors for process parameters and product attributes, as well as the control modules, operator facilities and reporting devices. In the later parts of the chapter, methods for the incorporation of these building blocks in control schemes and information systems are reviewed, with special reference to system reliability and database management. In the final section, there is a guide for matching instrumentation and process equipment in specific forms of processing. InstrumentationSensors may be categorized as follows:• General purpose sensors for process conditions • Sensors for humidity and moisture • Chemical composition sensors • Devices for measurement of size, shape, and color • Devices for sorting and sensing of foreign bodies.The first category differs from the others because sensing of process conditions is a common requirement in all sectors of manufacturing industry; food manufacturers can therefore take advantage of the economy of scale and degree of reliability associated with long and widespread use.In all the other categories, not only are there the costs of developing or adapting sensors for particular applications, but there are also the delays and uncertainty associated with such developments. A lack of suitable in-line sensors was highlighted as far back as the mid-1980s (Wren 1985. Brimelow (2001), Tothill (2003), Patel and Beveridge (2003) and Zhou and Therdthai (2012) provide comprehensive descriptions of current instrumentation and sensor technology. Despite the developments that have occurred in recent years, there is still much to be done. Measurement of food quality parameters in real time still largely remains an elusive goal.

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“…A model applicable for solids exhibiting strong sorption properties, characterised by variable diffusivity of moisture, was proposed by Shene, Cubillos, Pérez, and Álvarez (1996). It was later extended to a dynamic model to be applied in process control (Pérez-Correa, Cubillos, Zavala, Shene, & Álvarez, 1998). Models for application in process control have also been developed by Yliniemi (1999) and Iguaz, Budman, and Douglas (2002).…”

Section: Previous Attempts At Modelling Rotary Dryersmentioning

confidence: 99%