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Biofuels

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Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon.

Sensors (Basel, Switzerland)
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...

RSM and ANN methodologies in modeling the enhanced biodiesel production using novel protic ionic liquid anchored on g-CN@FeO nanohybrid.

Chemosphere
Herin, a new nanohybrid acid catalyst was fabricated for the efficient biodiesel production. At the first, magnetic porous nanosheets of graphitic carbon nitride (g-CN@FeO) was prepared and then functionalized with sulfonic acid. Next, the preparatio...

Ensemble machine learning prediction of anaerobic co-digestion of manure and thermally pretreated harvest residues.

Bioresource technology
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...

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass.

The Science of the total environment
Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine le...

Machine learning-aided unveiling the relationship between chemical pretreatment and methane production of lignocellulosic waste.

Waste management (New York, N.Y.)
Chemical pretreatment is a common method to enhance the cumulative methane yield (CMY) of lignocellulosic waste (LW) but its effectiveness is subject to various factors, and accurate estimation of methane production of pretreated LW remains a challen...

Assessing global carbon sequestration and bioenergy potential from microalgae cultivation on marginal lands leveraging machine learning.

The Science of the total environment
This comprehensive study unveils the vast global potential of microalgae as a sustainable bioenergy source, focusing on the utilization of marginal lands and employing advanced machine learning techniques to predict biomass productivity. By identifyi...

Application of triple-branch artificial neural network system for catalytic pellets combustion.

Journal of environmental management
On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to...

Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects.

Bioresource technology
This review explores the critical role of machine learning (ML) in enhancing microalgae bioprocesses for sustainable biofuel production. It addresses both technical and economic challenges in commercializing microalgal biofuels and examines how ML ca...

Unravelling barriers associated with dissemination of large-scale biogas plant with analytical hierarchical process and fuzzy analytical hierarchical process approach: Case study of India.

Bioresource technology
This study explores why large-scale biogas plants are not widely installed in India despite the wealth of biomass resources. The methodology includes an extensive literature review and surveyed biogas experts in different sectors, such as private, pu...

Applying machine learning and genetic algorithms accelerated for optimizing ethanol production.

The Science of the total environment
Corn straws can produce bioethanol via simultaneous saccharification and co-fermentation (SSCF). However, identifying optimal combinations of operating parameters from numerous possibilities through a cost-effective strategy to improve SSCF efficienc...