Two-stage biohydrogen and methane production from sugarcane-based sugar and ethanol industrial wastes: A comprehensive review

P Sukphun, W Wongarmat, T Imai, S Sittijunda… - Bioresource …, 2023 - Elsevier
The transition to renewable energy sources is crucial to ensure a sustainable future.
Although the sugar and ethanol industries benefit from this transition, there are untapped …

Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion

Y Zhang, Y Feng, Z Ren, R Zuo, T Zhang, Y Li… - Bioresource …, 2023 - Elsevier
The ideal conditions for anaerobic digestion experiments with biochar addition are
challenging to thoroughly study due to different experimental purposes. Therefore, three tree …

Microbial volatile organic compounds as novel indicators of anaerobic digestion instability: potential and challenges

E Nie, P He, W Peng, H Zhang, F Lü - Biotechnology Advances, 2023 - Elsevier
The wide application of anaerobic digestion (AD) technology is limited by process
fluctuations. Thus, process monitoring based on screening state parameters as early …

Artificial intelligence and machine learning for smart bioprocesses

SK Khanal, A Tarafdar, S You - Bioresource Technology, 2023 - Elsevier
In recent years, the digital transformation of bioprocesses, which focuses on
interconnectivity, online monitoring, process automation, artificial intelligence (AI) and …

Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning

Y Li, Z Xue, S Li, X Sun, D Hao - Bioresource Technology, 2023 - Elsevier
Ensuring the maturity of green waste compost is crucial to composting processes and quality
control of compost products. However, accurate prediction of green waste compost maturity …

Novel cofactor regeneration-based magnetic metal–organic framework for cascade enzymatic conversion of biomass-derived bioethanol to acetoin

RK Gupta, SKS Patel, JK Lee - Bioresource Technology, 2024 - Elsevier
Upgrading biomass-derived bioethanol to higher-order alcohols using conventional
biotechnological approaches is challenging. Herein, a novel, magnetic metal–organic …

Machine learning approach for determining and optimizing influential factors of biogas production from lignocellulosic biomass

A Sonwai, P Pholchan, N Tippayawong - Bioresource Technology, 2023 - Elsevier
Abstract Machine learning (ML) was used to predict specific methane yields (SMY) with a
dataset of 14 features from lignocellulosic biomass (LB) characteristics and operating …

Machine learning-based optimization of catalytic hydrodeoxygenation of biomass pyrolysis oil

X Chen, A Shafizadeh, H Shahbeik, S Rafiee… - Journal of Cleaner …, 2024 - Elsevier
Bio-oil derived from biomass pyrolysis contains various oxygenated compounds,
compromising its quality. Catalytic hydrodeoxygenation (HDO) holds promise for upgrading …

Metaheuristic optimization of data preparation and machine learning hyperparameters for prediction of dynamic methane production

A Meola, M Winkler, S Weinrich - Bioresource Technology, 2023 - Elsevier
Abstract Machine learning algorithms provide detailed description of the anaerobic digestion
process, but the impact of data preparation procedures and hyperparameter optimization …

[HTML][HTML] State estimation of a biogas plant based on spectral analysis using a combination of machine learning and metaheuristic algorithms

LA Putra, M Köstler, M Grundwürmer, L Li, B Huber… - Applied Energy, 2025 - Elsevier
The continuous monitoring of the state variables of a biogas plant remains a challenge due
to the necessity of an appropriate measuring device. The collection, transportation, and …