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Biomass

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Investigating quantitative approach for microalgal biomass using deep convolutional neural networks and image recognition.

Bioresource technology
The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) an...

Prediction of Individual Gas Yields of Supercritical Water Gasification of Lignocellulosic Biomass by Machine Learning Models.

Molecules (Basel, Switzerland)
Supercritical water gasification (SCWG) of lignocellulosic biomass is a promising pathway for the production of hydrogen. However, SCWG is a complex thermochemical process, the modeling of which is challenging via conventional methodologies. Therefor...

Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based k data.

Microbial cell factories
BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have...

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...

A machine learning-based approach for improving plasmid DNA production in Escherichia coli fed-batch fermentations.

Biotechnology journal
Artificial Intelligence (AI) technology is spearheading a new industrial revolution, which provides ample opportunities for the transformational development of traditional fermentation processes. During plasmid fermentation, traditional subjective pr...

Machine learning screening of biomass precursors to prepare biomass carbon for organic wastewater purification: A review.

Chemosphere
In the past decades, the amount of biomass waste has continuously increased in human living environments, and it has attracted more and more attention. Biomass is regarded as the most high-quality and cost-effective precursor material for the prepara...

Multi-output neural network model for predicting biochar yield and composition.

The Science of the total environment
In biomass pyrolysis for biochar production, existing prediction models face computational challenges and limited accuracy. This study curated a comprehensive dataset, revealing pyrolysis parameters' dominance in biochar yield (54.8 % importance). Py...

Accounting for minimum data required to train a machine learning model to accurately monitor Australian dairy pastures using remote sensing.

Scientific reports
Precision in grazing management is highly dependent on accurate pasture monitoring. Typically, this is often overlooked because existing approaches are labour-intensive, need calibration, and are commonly perceived as inaccurate. Machine-learning pro...

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...