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17 pages, 1732 KiB

Open AccessArticle

Online Machine Vision-Based Modeling during Cantaloupe Microwave Drying Utilizing Extreme Learning Machine and Artificial Neural Network

by Guanyu Zhu, G. S. V. Raghavan, Wanxiu Xu, Yongsheng Pei and Zhenfeng Li

Foods 2023, 12(7), 1372; https://doi.org/10.3390/foods12071372 - 23 Mar 2023

Cited by 5 | Viewed by 2069

Abstract

Online microwave drying process monitoring has been challenging due to the incompatibility of metal components with microwaves. This paper developed a microwave drying system based on online machine vision, which realized real-time extraction and measurement of images, weight, and temperature. An image-processing algorithm was developed to capture material shrinkage characteristics in real time. Constant-temperature microwave drying experiments were conducted, and the artificial neural network (ANN) and extreme learning machine (ELM) were utilized to model and predict the moisture content of materials during the drying process based on the degree of material shrinkage. The results demonstrated that the system and algorithm operated effectively, and ELM provided superior predictive performance and learning efficiency compared to ANN. Full article

(This article belongs to the Section Food Engineering and Technology)

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23 pages, 1831 KiB

Open AccessArticle

Estimating the Moisture Ratio Model of Cantaloupe Slices by Maximum Likelihood Principle-Based Algorithms

by Guanyu Zhu, G. S. V. Raghavan and Zhenfeng Li

Plants 2023, 12(4), 941; https://doi.org/10.3390/plants12040941 - 19 Feb 2023

Cited by 2 | Viewed by 1509

Abstract

As an agricultural plant, the cantaloupe contains rich nutrition and high moisture content. In this paper, the estimation problem of the moisture ratio model during a cantaloupe microwave drying process was considered. First of all, an image processing-based cantaloupe drying system was designed and the expression of the moisture ratio with regard to the shrinkage was built. Secondly, a maximum likelihood principle-based iterative evolution (MLP-IE) algorithm was put forward to estimate the moisture ratio model. After that, aiming at enhancing the model fitting ability of the MLP-IE algorithm, a maximum likelihood principle-based improved iterative evolution (MLP-I-IE) algorithm was proposed by designing the improved mutation strategy, the improved scaling factor, and the improved crossover rate. Finally, the MLP-IE algorithm and MLP-I-IE algorithm were applied for estimating the moisture ratio model of cantaloupe slices. The results showed that both the MLP-IE algorithm and MLP-I-IE algorithm were effective and that the MLP-I-IE algorithm performed better than the MLP-IE algorithm in model estimation and validation. Full article

(This article belongs to the Section Plant Modeling)

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19 pages, 1039 KiB

Open AccessArticle

Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

by Safoura Zadhossein, Yousef Abbaspour-Gilandeh, Mohammad Kaveh, Mariusz Szymanek, Esmail Khalife, Olusegun D. Samuel, Milad Amiri and Jacek Dziwulski

Energies 2021, 14(16), 4838; https://doi.org/10.3390/en14164838 - 9 Aug 2021

Cited by 30 | Viewed by 3395

Abstract

The study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of cantaloupe drying using various afore-mentioned input parameters. The results indicated that a rise in microwave power and a decline in sample thickness can significantly decrease the specific energy consumption (SEC), energy loss, exergy loss, and improvement potential (probability level of 5%). The mean SEC, energy efficiency, energy loss, thermal efficiency, dryer efficiency, exergy efficiency, exergy loss, improvement potential, and sustainability index ranged in 10.48–25.92 MJ/kg water, 16.11–47.24%, 2.65–11.24 MJ/kg water, 7.02–36.46%, 12.36–42.70%, 11.25–38.89%, 3–12.2 MJ/kg water, 1.88–10.83 MJ/kg water, and 1.12–1.63, respectively. Based on the results, the use of higher microwave powers for drying thinner samples can improve the thermodynamic performance of the process. The ANFIS model offers a more accurate forecast of energy and exergy indices of cantaloupe drying compare to ANN model. Full article

(This article belongs to the Special Issue Energy Systems and Applications in Agriculture)

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