1Dept. of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A. P., India
2Dept. of Physics, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, A. P., India
3Dept. of Mechanical Engineering, Sasi Institute of Technology and Engineering, Tadepalligudem, A. P, India
4Dept. of Mechanical Engineering, Aditya University, Surampalem, A. P., India
5Dept. of Basic Sciences and Humanities, Narasaraopet Engineering College, A. P., India
6Dept. of Basic Sciences and Humanities, Usha Rama College of Engineering & Technology, A. P., India
7Dept. of Botany and Microbiology, Acharya Nagarjuna University, Guntur, A. P., India
8Dept. of Physics, Aditya University, Surampalem, A. P., India
*Corresponding Author: Chandolu Nagamani, Dept. of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A. P., India
The biowaste to wealth is a pillar of circular bioeconomy tactics, which aim at turning organic waste like food wastage, farm residues and city sludge into a range of high value products such as biogas, biofertilizers, biochar and platform chemicals. Models based on conventional methods which depend on mechanistic kinetic equations such as Monod or Gompertz, frequently have problems with the intrinsic heterogeneity of feedstocks nonlinear microbial dynamics and multiple process variables. Machine learning (ML) stands out as a revolutionary tool that can be used to overcome these problems. It utilizes data driven algorithms to forecast output, tweak inputs and allow live control with an accuracy never seen before. In this work discusses in detail the use of machine learning in the main recycling pathways such as anaerobic digestion (AD) for the production of methane, thermochemical methods such as pyrolysis and gasification for the production of syngas/bio-oil composting for the production of nutrients, rich humus and fermentation for the production of biohydrogen.
Keywords: Machine learning; Biowaste; Bioeconomy; XGBoost; SVM.
How to cite this article: Nagamani C, Rao YD, Kumar MVP, Rajendra K, Gosu SJ, Stuthi PD, Babu BK, Poornima K.., Machine Learning for Analyzing the Biowaste to Wealth Approach: A briefreview...Int J Drug Deliv Technol. 2026; 16(11s): 789-794; DOI: 10.25258/ijddt.16.11s.80
Source of support: Nil.
Conflict of interest: None