Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)

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Wet Grain Dehumidification Using a Mechanical System with ML-Based Optimization Techniques

Authors
G. Pavani1, K. Varun2, K. Vamsi Raghu Ram2, K. Thilakavathi2, K. Vineesha2, Hanisha Moditha Satya Sree Potluri3, Phani Prasanthi4, *
1Assistant Professor, Department of CSE(Data Science), Prasad V Potluri Siddhartha Institute of Technology, Kanru, Vijayawada, Andhra Pradesh, India
2UG students, Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India
3UG students, Department of Electronics and Communication Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India
4Professor, Department of Mechanical Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India
*Corresponding author. Email: phaniprasanthi.parvathaneni@gmail.com
Corresponding Author
Phani Prasanthi
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-940-7_41How to use a DOI?
Keywords
Grain Dehumidification; Mechanical systems; Machine learning methods; Optimization techniques
Abstract

Unseasonal rains in India often damage harvested grains, causing substantial losses for farmers. Grains awaiting transport to rice mills for dehusking and bagging are especially vulnerable when exposed to heavy rains after harvest. To mitigate this problem, a mechanical system was developed to reduce excess moisture content in wet grains. The system integrates heat exchangers and vibrators to accelerate the drying process, and its performance was assessed by analyzing key influencing parameters. Furthermore, machine learning techniques—including Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Random Forest (RF)—were applied to optimize system performance. These models identified optimal operating conditions that ensured high-quality output while improving energy efficiency. By helping farmers safeguard their harvests against unpredictable weather, the proposed system effectively reduces post-harvest losses and enhances profitability.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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<Previous Article In Volume
Volume Title
Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 December 2025
ISBN
978-94-6463-940-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-940-7_41How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - G. Pavani
AU  - K. Varun
AU  - K. Vamsi Raghu Ram
AU  - K. Thilakavathi
AU  - K. Vineesha
AU  - Hanisha Moditha Satya Sree Potluri
AU  - Phani Prasanthi
PY  - 2025
DA  - 2025/12/31
TI  - Wet Grain Dehumidification Using a Mechanical System with ML-Based Optimization Techniques
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 559
EP  - 571
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_41
DO  - 10.2991/978-94-6463-940-7_41
ID  - Pavani2025
ER  -