AIMC Topic: Biomass

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Nutrient based classification of Phyllospora comosa biomasses using machine learning algorithms: Towards sustainable valorisation.

Food research international (Ottawa, Ont.)
Sustainable seaweed value chains necessitate accurate biomass biochemical characterisation that leads to product development, geographical authentications and quality and sustainability assurances. Underutilised yet abundantly available seaweed speci...

Recent advances in dark fermentative hydrogen production from vegetable waste: role of inoculum, consolidated bioprocessing, and machine learning.

Environmental science and pollution research international
Waste-centred-bioenergy generation have been garnering interest over the years due to environmental impact presented by fossil fuels. Waste generation is an unavoidable consequence of urbanization and population growth. Sustainable waste management t...

Comparing statistical and deep learning approaches for simultaneous prediction of stand-level above- and belowground biomass in tropical forests.

The Science of the total environment
Accurate and cost-effective prediction of aboveground biomass (AGB), belowground biomass (BGB), and the total (ABGB) at stand-level within tropical forests is crucial for effective forest ecological management and the provision of forest ecosystem se...

Harnessing Artificial Intelligence for Sustainable Bioenergy: Revolutionizing Optimization, Waste Reduction, and Environmental Sustainability.

Bioresource technology
Assessing the mutual benefits of artificial intelligence (AI) and bioenergy systems, to promote efficient and sustainable energy production. By addressing issues with conventional bioenergy techniques, it highlights how AI is revolutionising optimisa...

A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste.

Journal of environmental management
The fourth industrial revolution will heavily rely on machine learning (ML). The rationale is that these strategies make various business operations in many sectors easier. ML modeling is the discovery of hidden patterns between multiple process para...

Graph-learning-based machine learning improves prediction and cultivation of commercial-grade marine microalgae Porphyridium.

Bioresource technology
A graph learning [Binarized Attributed Network Embedding (BANE)] model enhances the single-target and multi-target prediction performances of random forest and eXtreme Gradient Boosting (XGBoost) by learning complex interrelationships between cultiva...

Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle-Light Detection and Ranging and Machine Learning.

Sensors (Basel, Switzerland)
is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of biomass is important for plantation forest management and the prediction ...

Reducing cadmium and arsenic accumulation in rice grains: The coupled effect of sulfur's biomass dilution and soil immobilization analyzed using meta-analysis and machine learning.

The Science of the total environment
The biogeochemical cycling of sulfur (S) in paddy soil influences cadmium (Cd) and arsenic (As) migration. However, the impact of S application on Cd and As within the soil-rice system has not been fully explored. This study aimed to examine the effe...

Predicting photosynthetic bacteria-derived protein synthesis from wastewater using machine learning and causal inference.

Bioresource technology
Causal inference-assisted machine learning was used to predict photosynthetic bacterial (PSB) protein production capacity and identify key factors. The extreme gradient boosting algorithm effectively predicted protein content, while the gradient boos...

Machine learning prediction and exploration of phosphorus migration and transformation during hydrothermal treatment of biomass waste.

The Science of the total environment
Hydrothermal treatment (HTT) held promise for phosphorus (P) recovery from high-moisture biomass. However, traditional experimental studies of P hydrothermal conversion were time-consuming and labor-intensive. Thus, based on biomass characteristics a...