Data-driven optimization of rice husk waste management through an integrated machine learning and community-based pyrolysis approach - Hanif Yusran Makarim; Muhammad Daffa Anrizky; Bondan Attoriq; Daniel Evan Koyongian; Rafa Adhi Negoro (Institute for Advanced Science Social, and Sustainable Future, 2026)
Background: Indonesia’s energy landscape currently pivots between two bifaceted issues: the stagnation of the national energy transition and the inefficiencies of decentralized waste management. Despite East Java producing 9.27 million tons of dry-milled rice (GKG) in 2024, the resulting 1.85 Mt of rice husk remains an underutilized bio-resource. This wasted potential coincides with a sluggish renewable energy trajectory, where the 15.25% share by mid-2025 significantly trails the 23% national target. Methods: A data-driven framework integrating feedstock characterization, experimental data, and literature benchmarks was applied to evaluate catalytic fast pyrolysis and upgrading pathways for rice husk. Machine-learning-assisted correlation analysis and multi-objective optimization (NSGA-II) were used to benchmark key process variables, product yields, and fuel quality trade-offs. Findings: The technical foundation, built on detailed feedstock characterization, reveals that the CFP process yields 46.9 wt% bio-oil, which is further refined to a 32.2 wt% biodiesel-equivalent yield. To enhance operational precision, various ML algorithms were evaluated; the Extra Trees model coupled with Non-dominated Sorting Genetic Algorithm II (NSGA-II) demonstrated superior predictive performance with an R2 of up to 0.96 and an RMSE <1 MJ/kg for calorific value prediction, showing strong accuracy for O/C ratio and CO2 fraction estimation. Techno-economic assessment confirms the framework's viability for pilot-scale implementation, projecting a positive NPV of IDR 50.4 million, an IRR of 23.78%, and a 2.93-year payback period. While sensitivity analysis highlights exchange rate volatility as a key financial risk, the model successfully positions farmers as active stakeholders in the value chain. Conclusion: The integrated CFP–ML framework demonstrates technical and economic viability for decentralized rice husk valorization, positioning farmers as active stakeholders in the renewable energy value chain and offering a scalable, bottom-up solution to support Indonesia’s energy transition in agricultural regions. Novelty/Originality of this article: By synthesizing mechanistic process design with data-driven decision support, this study provides a scalable, bottom-up pathway for decentralized waste-to-energy systems in agricultural regions.