Solution of porous media inverse drying problems using a combination of stochastic and deterministic methods

Junior, Jader Lugon; Neto, Antônio José da Silva

doi:10.1590/s1678-58782011000400003

Cited by 8 publications

(1 citation statement)

References 12 publications

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“…Nevertheless, the peaberry recorded a coefficient of variation lower than 10% in both shear and compression in all the evaluated positions, meaning that the failing force is more homogeneous for this type of grain (Lee et al, 2017), also, coefficients of variation lower than 10% are characteristic of deterministic processes, therefore, the behaviour of the peaberry in compression and shear disregarding the position could be accurately predicted (Afonso et al, 2003; Lugon Jr, and Silva Neto, 2011; Parra‐Coronado et al, 2008). In contrast, the standard bean failed at a more extensive force range, suggesting they will fail at different forces when grinding a mass of these beans.…”

Section: Resultsmentioning

confidence: 98%

Peaberry shape and size influence on different coffee postharvest processes

Duque‐Dussán,

Figueroa‐Varela,

Sanz‐Uribe

2023

J Food Process Engineering

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In many coffee‐producing countries, the ellipsoidal‐shaped seeds called peaberries are often labeled as a defect because of their shape and reduced size, going against the market demand for large‐sized standard coffee beans. Nevertheless, the peaberry natural occurrence on the coffee plantations is significant, accounting for 5%–7% of the total harvested coffee for Coffea arabica L. and Coffea canephora, the most planted species worldwide. Nevertheless, recent growth in the peaberry market has happened due to exceptional cupping scores for this specific bean type; however, the relationship between these scores and the shape of the bean was not yet recorded in the literature. Therefore, this research aimed to evaluate and compare the impact of the shape and size of the peaberry against the standard beans in different postharvest processes: Drying, roasting (colorimetry and inner roasting profiles), grinding (compressive and shear force tests) and overall quality by cupping analysis. Coffea arabica L. var. Cenicafé 1 was used throughout all the experiments, where advanced methods were used to increase the accuracy of the results and deeply characterize the process behavior. The results of this research allow to understand the peaberry postharvest behavior better and add significant value to this often‐underrated bean condition. The peaberries demonstrated shape influence in the different evaluated parameters, allowing them to dry faster, roast evenly, avoid burnt spots, and collapse at homogeneous forces while attaining the same high cup scores as a standard coffee bean.Practical applicationsUnderstanding the peaberries' shape and size influence different postharvest processes is crucial to comprehend their value. By gaining insights, into how these unique beans interact with stages of processing, producers can control and predict their behavior leading to more consistent and optimized results. The findings of this research provide an opportunity to fully incorporate peaberries into the coffee product without considering them as defective beans or expanding market trends. Generally, peaberries were often seen as undesirable because they did not match the bean size and shape. However, their exceptional performance in drying, roasting, grinding and cupping dispels these misconceptions, highlighting their value and contribution to coffee quality. As a result, coffee producers can embrace peaberries as valuable beans without discarding or downgrading them. This newfound appreciation for peaberries does not only reduce waste, but it also diversifies coffee offerings to meet consumer preferences and enriches the industry.

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Section: Resultsmentioning

confidence: 98%

Peaberry shape and size influence on different coffee postharvest processes

Duque‐Dussán,

Figueroa‐Varela,

Sanz‐Uribe

2023

J Food Process Engineering

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Artificial Neural Networks

Biondi Neto,

da Cunha Pires Soeiro,

de Campos Velho

et al. 2023

Computational Intelligence Applied to Inverse Problems in Radiative Transfer

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Simultaneous estimation of unknown parameters using a-priori knowledge for the estimation of interfacial heat transfer coefficient during solidification of Sn–5wt%Pb alloy—an ANN-driven Bayesian approach

2019

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The present methodology focuses on model reduction in which the prevalent one-dimensional transient heat conduction equation for a horizontal solidification of Sn-5wt%Pb alloy is replaced with Artificial Neural Network (ANN) in order to estimate the unknown constants present in the interfacial heat transfer coefficient correlation. As a novel approach, ANN-driven forward model is synergistically combined with Bayesian framework and Genetic algorithm to simultaneously estimate the unknown parameters and modelling error. Gaussian noise is then added to the temperature distribution obtained using the forward approach to represent real-time experiments. The hallmark of the present work is to reduce the computational time of both the forward and the inverse methods and to simultaneously estimate the unknown parameters using a-priori engineering knowledge. The results of the present methodology prove that the simultaneous estimation of unknown parameters can be effectively obtained only with the use of Bayesian framework.

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Solution of porous media inverse drying problems using a combination of stochastic and deterministic methods

Abstract: In the present work the inverse problem of simultaneous heat and mass transfer modeled by Luikov equations is studied using a hybrid combination of the Levenberg-Marquardt (LM)

Cited by 8 publications

References 12 publications

Peaberry shape and size influence on different coffee postharvest processes

Peaberry shape and size influence on different coffee postharvest processes

Artificial Neural Networks

Simultaneous estimation of unknown parameters using a-priori knowledge for the estimation of interfacial heat transfer coefficient during solidification of Sn–5wt%Pb alloy—an ANN-driven Bayesian approach

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