Artificial intelligence: Is it a good fit for drying?

Martynenko, Alex

doi:10.1080/07373937.2017.1362153

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…Application of artificial intelligence methods in drying is still in developmental stages and its continued development is anticipated in other to cater to new needs and new solutions; therefore, studies in this area are still relevant. The gap inapplicability of artificial intelligent techniques has been partly attributed by Martynenko (2017) to the lack of commercially available artificial intelligent systems for drying of agricultural products and bio-related materials. This is a result of difficulties associated with the interpretation of artificial intelligence language for the realistic needs of drying community or probably due to the availability of simpler but less accurate alternatives such as Proportional Integral Derivative controller.…”

Section: Introductionmentioning

confidence: 99%

Drying characteristics of yam slices (Dioscorea rotundata) in a convective hot air dryer: application of ANFIS in the prediction of drying kinetics

Ojediran

Okonkwo

Adeyi

et al. 2020

Heliyon

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This study applied Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the moisture ratio (MR) during the drying process of yam slices (Dioscorea rotundata) in a hot air convective dryer. Also the effective diffusivity, activation energy, and rehydration ratio were calculated. The experiments were carried out at three (3) drying air temperatures (50, 60, and 70 C), air velocities (0.5, 1, and 1.5 m/s), and slice thickness (3, 6, and 9 mm), and the obtained experimental data were used to check the usefulness of ANFIS in the yam drying process. The result showed efficient applicability of ANFIS in predicting the MR at any time of the drying process with a correlation value (R 2 ) of 0.98226 and root mean square error value (RMSE) of 0.01702 for the testing stage. The effective diffusivity increased with an increase in air velocity, air temperature, and thickness and the values (6.382E -09 to 1.641E -07 m 2 /s). The activation energy increased with an increase in air velocity, but fluctuate within the air temperatures and thickness used (10.59-54.93 KJ/mol). Rehydration ratio was highest at air velocityÂair tem-peratureÂthickness (1.5 m/sÂ70 C Â 3 mm), and lowest at air velocity Â air temperatureÂthickness (0.5 m/ sÂ70 C Â 3 mm). The result showed that the drying kinetics of Dioscorea rotundata existed in the falling rate period. The drying time decreased with increased temperature, air velocity, and decreased slice thickness. These established results are applicable in process and equipment design, analysis and prediction of hot air convective drying of yam (Dioscorea rotundata) slices.

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Section: Introductionmentioning

confidence: 99%

Drying characteristics of yam slices (Dioscorea rotundata) in a convective hot air dryer: application of ANFIS in the prediction of drying kinetics

Ojediran

Okonkwo

Adeyi

et al. 2020

Heliyon

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“…With further research, artificial intelligence can be considered to be used in the sterilization area for modeling, analysis, detection, and key point control in microwave processing. These emerging technologies will help to optimize the processing conditions, improve product quality, as well as reduce costs and energy consumption (Martynenko, 2018). Table 1 lists some of the research reports on microwave in liquid food processing in the last 15 years.…”

Section: Other Research On Microwave In Fluid Food Heating and Steril...mentioning

confidence: 99%

Research progress in fluid and semifluid microwave heating technology in food processing

et al. 2022

Comp Rev Food Sci Food Safe

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Microwave is a form of electromagnetic radiation that has high penetration and heating efficiency in food processing. Uneven heating is the main problem of microwave processing, especially in solid foods. Fluid and semifluid media, which are good carriers in microwave processing, have uniform dielectric properties and good material fluidity. Herein, we review the development, application prospects, and limitations of microwave in fluid and semifluid food processing and the research progress in microwave heating with steam as carrier. The mixture of generated steam and tiny micro droplets from food material under the action of microwave can absorb microwave and transfer heat evenly, which effectively improves the uniformity of microwave heating. Due to the relatively uniform dielectric properties and consistent texture of fluid and semifluid food materials, uneven heating phenomenon during their microwave processing can be significantly inhibited. Based on the development of microwave heating technology and equipment design, the microbial inactivation and enzyme inhibition in fluid and semifluid food were improved and food product with better retention of nutrients and sensory profile were produced. Also, microwave radiation can be used to prepare the printing material or process the printed product for 3D food printing, which enhances the added value of 3D printed products and the personalization of food manufacturing. In future research, intelligent control technology can be applied in the microwave processing of fluid and semifluid food materials for various applications. Therefore, the processing conditions can be adjusted automatically.

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“…[3] Artificial intelligence techniques are also found in drying. [6] They are used for process variable prediction [10][12] [15], process control [9][11] [13] or for soft sensors [14]. Artificial neural networks (ANN) are sucsefully applied on all these fiels on their own, or combined with other AI or conventional techniques.…”

Section: Introductionmentioning

confidence: 99%

Semi empirical models for drying of agricultural products by used structured artificial neural network

Bessenyei

Kurják

Beke

2018

Proceedings of 21th International Drying Symposium

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We compared a semi empirical and an empirical model. The empirical model is a multilayer ANN. The semi empirical model is a custom multilayer ANN. It is a structured model, and we define the structure by hand before the training of the network. The influence of the neuron numbers on the accuracy of the models was also investigated by statistical approach.We found that the custom multilayer ANNs developed like this, are suitable for modelling the drying process of agricultural materials. They also provide the ability to improve the applicability of the empirical models. Furthermore, the semi empirical model has a higher sensitivity on neuron number applied.

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Artificial intelligence: Is it a good fit for drying?

Cited by 15 publications

References 15 publications

Drying characteristics of yam slices (Dioscorea rotundata) in a convective hot air dryer: application of ANFIS in the prediction of drying kinetics

Drying characteristics of yam slices (Dioscorea rotundata) in a convective hot air dryer: application of ANFIS in the prediction of drying kinetics

Research progress in fluid and semifluid microwave heating technology in food processing

Semi empirical models for drying of agricultural products by used structured artificial neural network

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