Modeling and simulation of a beet pulp dryer for a training simulator

Merino, Alejandro; Alves, Raul; Acebes, Luis Felipe; Prada, César de

doi:10.1080/07373937.2016.1275674

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Likewise, many other researchers have used different ML‐based algorithms for the prediction of drying kinetics in various food materials such as seedy grape (Çakmak & Yıldız, 2011), apple (Menlik et al., 2010), carrot (Aghbashlo et al., 2011; Nazghelichi et al., 2011), potato (Islam et al., 2003), grain (H. Li & Chen, 2019), and sugar beet pulp (Merinoet al. et al., 2017). A summary of the application of the ML‐based framework in food drying application can be found in Table 1.…”

Section: In Food Processing Applicationsmentioning

confidence: 99%

Machine learning‐based modeling in food processing applications: State of the art

Khan

Sablani

Nayak

et al. 2022

Comp Rev Food Sci Food Safe

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Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better‐quality products. The development of a machine learning (ML)‐based approach to food processing applications is an exciting and innovative idea for optimizing process parameters and process kinetics to reduce energy consumption, processing time, and ensure better‐quality products; however, developing such a novel approach requires significant scientific effort. This paper presents and evaluates ML‐based approaches to various food processing operations such as drying, frying, baking, canning, extrusion, encapsulation, and fermentation to predict process kinetics. A step‐by‐step procedure to develop an ML‐based model and its practical implementation is presented. The key challenges of neural network training and testing algorithms and their limitations are discussed to assist readers in selecting algorithms for solving problems specific to food processing. In addition, this paper presents the potential and challenges of applying ML‐based techniques to hybrid food processing operations. The potential of physics‐informed ML modeling techniques for food processing applications and their strategies is also discussed. It is expected that the potential information of this paper will be valuable in advancing the ML‐based technology for food processing applications.

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Section: In Food Processing Applicationsmentioning

confidence: 99%

Machine learning‐based modeling in food processing applications: State of the art

Khan

Sablani

Nayak

et al. 2022

Comp Rev Food Sci Food Safe

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show abstract

“…Different authors investigated hot air drying of sugar beet pulp, but not in the presence of dolomite. The most of them tried to find the proper model to describe or simulate the drying process of sugar beet pulp [9,10]. In the present research, the influence of temperature and the addition of dolomite on the drying rate was observed and analyzed.…”

mentioning

confidence: 95%

The Influence of Temperature and Dolomite Addition on the Drying Kinetics of Sugar Beet Pulp (Beta Vulgaris L.)

Ghirişan¹,

Drăgan²,

Cota³

et al. 2018

Studia UBB Chemia

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The aim of this study is to investigate the hot air drying kinetics of sugar beet pulp, without and with dolomite addition, in order to establish the drying mechanism. The influence of temperature and dolomite addition on the drying rates is observed. The drying constant rates and the activation energies for different working conditions are determined. The obtained results show that the drying took place in falling rate period. The obtained activation energy values correspond to the mechanism mass transfer at low amount of dolomite addition and combined mechanism, mass transformation (evaporation process)-mass transfer, at higher amount.

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Chemical and physical properties of vacuum-dried red beetroot (Beta vulgaris) powders compared to other drying methods

Kerr

Varner

2019

Drying Technology

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Modeling and simulation of a beet pulp dryer for a training simulator

Cited by 4 publications

References 30 publications

Machine learning‐based modeling in food processing applications: State of the art

Machine learning‐based modeling in food processing applications: State of the art

The Influence of Temperature and Dolomite Addition on the Drying Kinetics of Sugar Beet Pulp (Beta Vulgaris L.)

Chemical and physical properties of vacuum-dried red beetroot (Beta vulgaris) powders compared to other drying methods

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