A Hybrid Hierarchical Machine Learning Framework for Optimized Predictive Analytics in Bio-Ethanol Production

Author : SIVA NAGA KISHORE. BURAGADDA

Abstract :Machine learning has great potential in bio-production industries, and in the bio-ethanol industry in particular, where traditional models have under-represented these challenges due to their inability to deal with instability at process-level and multi-scale systems. To address these challenges, this study presents a Hybrid Hierarchical Machine Learning Framework, which combines a global root knowledge model with factory-specific knowledge branch models. This two-tiered architecture not only guarantees generalizability across diverse production sites but also allows for adaptability to localized variations, improving everything from accuracy to scalability. The root-level model learns high-level patterns with an ensemble of models, while the branch-level models consist of integrated learning components to learn complex interactions between features specific to each factory. We validate the framework on a real-world dataset of 86,754 records and achieve superior predictive performance in various metrics such as R², MSE, MAE, and SMAPE. Results show that algorithms such as XGBoost and Gradient Boosting are better than classic algorithms globally as well as locally. Additionally, the system allows for real-time adaptability and extensibility, which can handle dynamic inputs like environmental or economic factors. Thus, the framework can serve as an advanced decision-support tool for stakeholders keen on maximizing bio-ethanol yield, minimizing resource wastage, and ensuring production aligns with sustainability objectives. This work is a first step in several future directions, including deployment in real time with IoT integration, policy-based modeling of scenarios, and applications to other sectors of renewable energy. Thus, it is established in this study as a versatile foundation for data-driven innovation in the production of green energy

Keywords :Hybrid Hierarchical Model, Bio-Ethanol Production, Real Time Prediction, Multi Objective Optimization, Ensemble Learning, Root-Branch Framework

Conference Name :International Conference on Computer Science and Engineering (ICCSAE-25)

Conference Place Kollam, India

Conference Date 25th May 2025