Review of Energy-Related Machine Learning Applications in Drying Processes

D Đaković, M Kljajić, N Milivojević, Đ Doder… - Energies, 2023 - mdpi.com
Drying processes are among the most energy-intensive industrial processes. There is a
need for development of the efficient methods needed for estimating, measuring, and …

[HTML][HTML] Artificial neural networks (ANNs) and multiple linear regression (MLR) for prediction of moisture content for coated pineapple cubes

J Meerasri, R Sothornvit - Case Studies in Thermal Engineering, 2022 - Elsevier
The effects were investigated of edible coating and drying temperature (50, 65 and 80° C)
on the properties of dehydrated pineapple cubes. A comparative study was performed using …

Influences of Emerging Drying Technologies on Rice Quality

N Mahmood, Y Liu, X Zheng, Z Munir… - Food Research …, 2024 - Elsevier
Rice is an important staple food in the world. Drying is an important step in the post-harvest
handling of rice and can influence rice qualities and thus play a key role in determining rice …

Fast and precise DEM parameter calibration for Cucurbita ficifolia seeds

X Ding, B Wang, Z He, Y Shi, K Li, Y Cui, Q Yang - Biosystems Engineering, 2023 - Elsevier
The lack of discrete element method (DEM) models and calibration parameters for Cucurbita
ficifolia seeds, as well as low accuracy and efficiency of common parameters calibration …

Modeling and optimization of osmo‐sonicated dehydration of garlic slices in a novel infrared dryer using artificial neural network and response surface methodology

S Malakar, P Dhurve, VK Arora - Journal of Food Process …, 2023 - Wiley Online Library
This investigation aims to develop a novel infrared drying system and optimization of
ultrasound‐assisted osmotic dehydration of garlic slices. RSM and ANN approaches are …

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

M Kulisz, J Kujawska, M Cioch, W Cel, J Pizoń - Applied Sciences, 2024 - mdpi.com
In the context of escalating energy demands and the quest for sustainable waste
management solutions, this paper evaluates the efficacy of three machine learning methods …

Calibration of Small-Grain Seed Parameters Based on a BP Neural Network: A Case Study with Red Clover Seeds

X Ma, M Guo, X Tong, Z Hou, H Liu, H Ren - Agronomy, 2023 - mdpi.com
In order to enhance the accuracy of discrete element numerical simulations in the
processing of small-seed particles, it is essential to calibrate the parameters of seeds within …

Chemometrics for optimization and modeling of Cu (II) continuous adsorption onto carboxymethylcellulose-alginate encapsulated graphene oxide hydrogel beads

D Allouss, SE Marrane, Y Essamlali, A Chakir… - International Journal of …, 2024 - Springer
The discharge of liquid effluents bearing heavy metals such as Cu (II) into the environment
constitutes a source of pollution of both surface water and groundwater. Here, the …

[HTML][HTML] Optimization of extraction process of Astragali radix-Paeoniae radix alba by comparing BBD-RSM and SA-BPNN and antioxidant activity

Q Chen, L Yu, H Zhou, H Wan, Y Zhang, H Wan… - LWT, 2024 - Elsevier
Abstract The Box-Behnken design-response surface methodology (BBD-RSM) and Back-
propagation neural network prediction model optimized by Simulated annealing (SA-BPNN) …

Exploring drying kinetics and fate of nutrients in thermal digestion of solid organic waste

N Kumar, SK Gupta - Science of The Total Environment, 2022 - Elsevier
Thermal digestion has emerged as a novel technique for the rapid treatment of solid organic
waste (SOW). Dehydration mechanism and fate of nutrients during the thermal digestion of …