Yogan, Jaya Kumar and Esvaran, Surend and M Fuzi, Siti Fatimah Zaharah and Masngut, Nasratun and Azizan, Fathin Ayuni (2024) Development Of Biochar Yield Prediction Model From Food Waste Pyrolysis. In: 19th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2024.
![[img]](http://eprints.utem.edu.my/style/images/fileicons/text.png) |
Text
Development Of Biochar Yield Prediction Model From Food Waste Pyrolysis.pdf - Published Version Download (417kB) |
Abstract
The accumulation of waste in natural surroundings has dramatically increased, necessitating new, sustainable solutions for food waste management. Food waste, a significant environmental challenge, can be transformed into biochar. As a carbon-rich substance formed from biomass pyrolysis, biochar has garnered interest for its potential as a sustainable soil amendment, carbon sequestration technique, and renewable energy source. However, biochar yield from food waste pyrolysis is influenced by parameters such as pyrolysis condition and feedstock composition. Accurate biochar yield prediction is crucial for effective food waste management, optimizing resource utilization, and minimizing environmental impact. Artificial intelligence (AI) algorithms have shown potential in modeling complex systems and predicting outcomes. This study aims to investigate the performance of AI-driven models for predicting biochar yield from food waste pyrolysis. Models such as Linear Regression, Random Forest, K-Nearest Neighbors, and Convolutional Neural Networks (CNNs) are evaluated. Data preprocessing, specifically feature scaling and logarithmic transformation, were found to play a pivotal role in enhancing the model performance. These advancements will contribute to more sustainable biochar production.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Divisions: | Faculty of Information and Communication Technology |
| Depositing User: | NURHASHIRAH BORHAN |
| Date Deposited: | 31 Jul 2026 08:11 |
| Last Modified: | 31 Jul 2026 08:12 |
| URI: | http://eprints.utem.edu.my/id/eprint/30249 |
| Statistic Details: | View Download Statistic |
Actions (login required)
 |
View Item |