AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Biomass

Showing 71 to 80 of 137 articles

Clear Filters

Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning.

Environmental technology
Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at differe...

A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass.

International journal of molecular sciences
The higher heating value (HHV) is the main property showing the energy amount of biomass samples. Several linear correlations based on either the proximate or the ultimate analysis have already been proposed for predicting biomass HHV. Since the HHV ...

A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach.

Environmental pollution (Barking, Essex : 1987)
Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approa...

Evaluation of AquaCrop and intelligent models in predicting yield and biomass values of wheat.

International journal of biometeorology
AquaCrop is one of the dynamic and user-friendly models for simulating different conditions governing plant growth in the field. But this model requires many input parameters such as plant information, soil, climate, groundwater, and management facto...

Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste.

Waste management (New York, N.Y.)
The combination of machine learning and infrared spectroscopy was reported as effective for fast characterization of biomass and waste (BW). However, this characterization process is lack of interpretability towards its chemical insights, leading to ...

Machine learning for surrogate process models of bioproduction pathways.

Bioresource technology
Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commer...

Machine learning and statistical analysis for biomass torrefaction: A review.

Bioresource technology
Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kine...

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications.

Bioresource technology
Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which ...

Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships.

Bioresource technology
Machine learning methods have recently shown a broad application prospect in biomass gasification modeling. However, a significant drawback of the machine learning approaches is their poor physical interpretability when relying on limited experimenta...

Global stability of a continuous bioreactor model under persistent variation of the dilution rate.

Mathematical biosciences and engineering : MBE
In this work, the global stability of a continuous bioreactor model is studied, with the concentrations of biomass and substrate as state variables, a general non-monotonic function of substrate concentration for the specific growth rate, and constan...