Artificial neural network modeling in predicting thermal performance and assessment of quality parameters during graphene‐based far‐infrared drying

Abdulaziz Nuhu JIBRIL; Wang Jiuyuan; Zaharaddeen Aminu Bello; Jingcheng Xi; Yan Shiwei; Jichao Huang; Kunjie Chen

doi:10.1002/jsfa.70549

March 9, 2026Journal of the Science of Food and Agriculture

Artificial neural network modeling in predicting thermal performance and assessment of quality parameters during graphene‐based far‐infrared drying

View at Publisher

Ask AI

Bookmark

Share

Key Points

This research aims to explore how artificial neural networks can predict thermal performance during graphene-based drying processes.
Utilized artificial neural networks for modeling thermal performance and quality parameters.
Conducted assessments of energy efficiency during drying processes.
Evaluated the effects of graphene heating on product quality.
Demonstrated a significant potential for energy efficiency improvement using graphene-based heating.
Showed that product quality remains uncompromised during the far-infrared drying process.

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

Modeling predicts thermal performance in graphene-based drying, suggesting improved energy efficiency without quality loss.

Abstract

This study highlights the potential of cutting-edge computational tools to enhance energy efficiency through graphene-based heating without compromising product quality.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

JIBRIL et al. (2026) studied this question.

synapsesocial.com/papers/69b25b3896eeacc4fcec9b97 https://doi.org/10.1002/jsfa.70549

View at Publisher

Ask AI

Bookmark

Share

Artificial neural network modeling in predicting thermal performance and assessment of quality parameters during graphene‐based far‐infrared drying

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Artificial neural network modeling in predicting thermal performance and assessment of quality parameters during graphene‐based far‐infrared drying

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading