AIMC Topic: Biofuels
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Ensemble machine learning prediction of anaerobic co-digestion of manure and thermally pretreated harvest residues.
Bioresource technology
May 3, 2024
This study aimed to clarify the statistical accuracy assessment approaches used in recent biogas prediction studies using state-of-the-art ensemble machine learning approach according to 10-fold cross-validation in 100 repetitions. Three thermally pr...
Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach.
Environmental science and pollution research international
Apr 23, 2024
This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such ...
Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon.
Sensors (Basel, Switzerland)
Apr 15, 2024
This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strate...
Enhancing biomass conversion to bioenergy with machine learning: Gains and problems.
The Science of the total environment
Apr 8, 2024
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Rec...
Machine learning for high solid anaerobic digestion: Performance prediction and optimization.
Bioresource technology
Apr 4, 2024
Biogas production through anaerobic digestion (AD) is one of the complex non-linear biological processes, wherein understanding its dynamics plays a crucial role towards process control and optimization. In this work, a machine learning based biogas ...
Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review.
Environmental science and pollution research international
Feb 20, 2024
Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biog...
Optimization of biogas production from straw wastes by different pretreatments: Progress, challenges, and prospects.
The Science of the total environment
Sep 17, 2023
Lignocellulosic biomass (LCB) presents a promising feedstock for carbon management due to enormous potential for achieving carbon neutrality and delivering substantial environmental and economic benefit. Bioenergy derived from LCB accounts for about ...
A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches.
Bioresource technology
Jul 17, 2023
Biofuels from lignocellulosic biomass converted via thermochemical technologies can be renewable and sustainable, which makes them promising as alternatives to conventional fossil fuels. Prior to building industrial-scale thermochemical conversion pl...
Real-Time Sensor Data Profile-Based Deep Learning Method Applied to Open Raceway Pond Microalgal Productivity Prediction.
Environmental science & technology
May 26, 2023
Microalgal biotechnology holds the potential for renewable biofuels, bioproducts, and carbon capture applications due to unparalleled photosynthetic efficiency and diversity. Outdoor open raceway pond (ORP) cultivation enables utilization of sunlight...
Machine learning prediction of contents of oxygenated components in bio-oil using extreme gradient boosting method under different pyrolysis conditions.
Bioresource technology
Apr 8, 2023
This work aims to develop a prediction model for the contents of oxygenated components in bio-oil based on machine learning according to different pyrolysis conditions and biomass characteristics. The prediction model was constructed using the extrem...
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