Trends in Modeling and Sensing Approaches for Drying Control

Freire, Fábio Bentes; Vieira, Gustavo Nakamura Alves; Freire, José Teixeira; Mujumdar, Arun S.

doi:10.1080/07373937.2014.925471

Cited by 30 publications

(12 citation statements)

References 38 publications

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“…Kadlec et al [7] present a review of articles about soft sensing in the process industry, including an introduction about the subject. In addition, as pointed out by Freire et al [8] , the application of soft sensor modeling for the estimation of the moisture content of dried material featured recently in scientific literature.…”

Section: Introductionmentioning

confidence: 98%

Control of the Moisture Content of Milk Powder Produced in a Spouted Bed Dryer Using a Grey-Box Inferential Controller

Vieira

Freire

2015

Drying Technology

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Although the moisture content of dried products is an important variable on industrial dryers, it is often not measured directly for control purposes. Alternative and simpler meters might provide information to be used by a physical-mathematical model to estimate the moisture content. When this procedure is applied to a control strategy, an inferential controller is developed. In this paper, a physical-mathematical model was used to infer the moisture content of milk powder produced in a spouted bed dryer.Afterwards, simulations of an inferential proportional-integral controller were carried out, using the inlet air heating rate as the manipulated variable. The physical-mathematical model used in the procedure was a hybrid one, which considers mass and energy balances and one term which is estimated by an artificial neural network. The controller parameters (controller gain and integral time) were tuned by trial and error. Even though the procedure was quite simple, it was proven to be effective in yielding a stable closedloop response for both servo and regulatory control of the (inferred) powder moisture content.

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Section: Introductionmentioning

confidence: 98%

Control of the Moisture Content of Milk Powder Produced in a Spouted Bed Dryer Using a Grey-Box Inferential Controller

Vieira

Freire

2015

Drying Technology

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“…[6][7][8] Particularly, by utilizing suitable control strategies, a high-quality energy management can be achieved, since in this case the controlled system can operate closer to the designed conditions. [9] However, control of the drying process has received little attention in the literature. This fact can be attributed to the complex nature of the developed mathematical models and the numerical techniques used to solve them.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Modeling and Control of a Conveyor Belt Grain Dryer Using a Simplified Type 2 Neuro-Fuzzy Controller

2015

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In this paper, a nonlinear autoregressive with exogenous input (NARX) network was utilized to model a conveyor-belt grain dryer using a set of input-output data which was collected during an experiment to dry paddy grains. The resulting NARX model has achieved a remarkable modelling accuracy compared to other previously reported modelling techniques. To control the considered dryer, a simplified Type-2 adaptive neuro-fuzzy inference system (ANFIS) controller was proposed. The effectiveness of this controller was demonstrated by several performance tests conducted by computer simulations. Moreover, a comparative study with other related controllers further confirmed the superiority of the proposed dryer controller.

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“…This technique has been successfully applied in different applications, such as to estimate the higher heating values of biomass fuels [13] or to predict air quality parameters [14], its use in drying applications is still incipient. Freire et al [15] discuss various applications involving control and sensing techniques for spouted bed drying including adaptive and neural based algorithms. Hybrid neural network and first principle models, also known as grey-box models as opposed to black box where no model form is assumed or white box models that are purely theoretical, can likewise be found in the literature [16] [21].…”

Section: Introductionmentioning

confidence: 99%

A Neural Based Modeling Approach for Drying Kinetics Analysis of Mint Branches and Their Fractions (Leaves and Stems)

Rosanova¹,

Maia²,

Freire³

et al. 2017

ACES

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This work is aimed at investigating regular mint (Mentha × villosa) drying behavior and assessing how the heterogeneous composition of plants affects their drying kinetics. Drying kinetics and sorption isotherms were evaluated for whole branches and their fractions (leaves and stems). Stems and leaves were characterized by measurement of dimensions, apparent density and initial moisture content. The moisture sorption isotherms were obtained under temperatures of 30˚C, 40˚C and 50˚C for branches, stems and leaves and the data were fitted to the GAB model. Mint branches and their fractions were oven dried at temperatures from 40˚C to 70˚C and were obtained kinetic curves for each part. Water sorption patterns were similar for leaves and stems and the GAB model described well the sorption behavior of both materials. At a constant temperature, the drying rates were higher for leaves in comparison to stems and the differences increased as the temperature was raised. Therefore, depending on drying conditions, the moisture distribution in dried branches might be significantly different. Since the leaves constitute the major fraction in branches, the drying rates of branches were closer to those of leaves. The kinetic curves were fitted to a diffusion model based on an analytical solution of Fick's second diffusion law and to an empirical model based on artificial neural network (ANN). The results showed that the model based on the ANN predicted the drying kinetics of the different parts better than the diffusive model. A single network was built to describe the kinetic behavior of branches and fractions in the whole range of temperatures investigated. The diffusive model based on fitting effective diffusivity did not provide good predictions of moisture content, probably because neither the dependence of effective diffusivity on the moisture content nor the heterogenei- 155ty and shrinking of static beds were considered.

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Trends in Modeling and Sensing Approaches for Drying Control

Cited by 30 publications

References 38 publications

Control of the Moisture Content of Milk Powder Produced in a Spouted Bed Dryer Using a Grey-Box Inferential Controller

Control of the Moisture Content of Milk Powder Produced in a Spouted Bed Dryer Using a Grey-Box Inferential Controller

Intelligent Modeling and Control of a Conveyor Belt Grain Dryer Using a Simplified Type 2 Neuro-Fuzzy Controller

A Neural Based Modeling Approach for Drying Kinetics Analysis of Mint Branches and Their Fractions (Leaves and Stems)

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