AIMC Topic: Biomass

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Thermogravimetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn.

Marine pollution bulletin Apr 17, 2024
A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the ...
Biomass Support Vector Machine Machine Learning Neural Networks, Computer Thermogravimetry Thermodynamics Pyrolysis
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Enhancing Aboveground Biomass Prediction through Integration of the SCDR Paradigm into the U-Like Hierarchical Residual Fusion Model.

Sensors (Basel, Switzerland) Apr 11, 2024
Deep learning methodologies employed for biomass prediction often neglect the intricate relationships between labels and samples, resulting in suboptimal predictive performance. This paper introduces an advanced supervised contrastive learning techni...
Deep Learning Image Processing, Computer-Assisted Biomass Algorithms Neural Networks, Computer
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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...
Biomass Machine Learning Biofuels
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Machine learning and statistical physics modeling of tetracycline adsorption using activated carbon derived from Cynometra ramiflora fruit biomass.

Environmental research Apr 1, 2024
The current investigation reports the usage of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), the two recognized machine learning techniques in modelling tetracycline (TC) adsorption onto Cynometra ramiflora fruit ...
Biomass Water Pollutants, Chemical Machine Learning Fruit Tetracycline Charcoal Adsorption Neural Networks, Computer
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Machine learning application for predicting key properties of activated carbon produced from lignocellulosic biomass waste with chemical activation.

Bioresource technology Mar 21, 2024
The successful application of gradient boosting regression (GBR) in machine learning to forecast surface area, pore volume, and yield in biomass-derived activated carbon (AC) production underscores its potential for enhancing manufacturing processes....
Temperature Machine Learning Charcoal Biomass
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Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning.

Journal of environmental management Mar 12, 2024
Robust quantification of vegetative biomass using satellite imagery using one or more forms of machine learning (ML) has hitherto been hindered by the extent and quality of training data. Here, we showcase how ML predictive demonstrably improves when...
Farms Biomass Satellite Imagery Machine Learning Australia
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Machine learning models for predicting biochar properties from lignocellulosic biomass torrefaction.

Bioresource technology Mar 2, 2024
This study developed six machine learning models to predict the biochar properties from the dry torrefaction of lignocellulosic biomass by using biomass characteristics and torrefaction conditions as input variables. After optimization, gradient boos...
Charcoal Machine Learning Temperature Biomass
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Artificial intelligence methods for modeling gasification of waste biomass: a review.

Environmental monitoring and assessment Feb 26, 2024
Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasific...
Environmental Monitoring Algorithms Biomass Machine Learning Humans Artificial Intelligence
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Effects of CO concentration and time on algal biomass film, NO3-N concentration, and pH in the membrane bioreactor: Simulation-based ANN, RSM and NSGA-II.

Journal of environmental management Dec 19, 2023
The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass fi...
Biomass Bioreactors Neural Networks, Computer Carbon Dioxide Hydrogen-Ion Concentration
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Artificial intelligence to explain the variables that favor the cyanobacteria steady-state in tropical ecosystems: A Bayeasian network approach.

Anais da Academia Brasileira de Ciencias Dec 1, 2023
The steady-state is a situation of little variability of species dominance and total biomass over time. Maintenance of cyanobacteria are often observed in tropical and eutrophic ecosystems and can cause imbalances in aquatic ecosystem. Bayeasian netw...
Biomass Lakes Ecosystem Cyanobacteria Artificial Intelligence Eutrophication
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