AI Medical Compendium Journal:
Bioresource technology

Showing 51 to 60 of 116 articles

Machine learning application for predicting key properties of activated carbon produced from lignocellulosic biomass waste with chemical activation.

Bioresource technology
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....

Understanding cellulose pyrolysis via ab initio deep learning potential field.

Bioresource technology
Comprehensive and dynamic studies of cellulose pyrolysis reaction mechanisms are crucial in designing experiments and processes with enhanced safety, efficiency, and sustainability. The details of the pyrolysis mechanism are not readily available fro...

Machine learning models for predicting biochar properties from lignocellulosic biomass torrefaction.

Bioresource technology
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...

Automated quantification of lipid contents of Lipomyces starkeyi using deep-learning-based image segmentation.

Bioresource technology
Intracellular lipid droplets (LDs), subcellular organelles playing a role in long-term carbon storage, have immense potential in biofuel and dietary lipid production. Monitoring the state of LDs in living cells is of utmost importance for quick bioma...

Data-driven analysis and prediction of wastewater treatment plant performance: Insights and forecasting for sustainable operations.

Bioresource technology
This study presents a comprehensive performance and forecasting analysis of the As-Samra wastewater treatment plant (WWTP) in Jordan, with two main objectives. Firstly, a thorough evaluation of the plant's performance is conducted. The analysis invol...

Machine learning-based model construction and identification of dominant factor for simultaneous sulfide and nitrate removal process.

Bioresource technology
Accurate water quality prediction models are essential for the successful implementation of the simultaneous sulfide and nitrate removal process (SSNR). Traditional models, such as regression and analysis of variance, do not provide accurate predicti...

Role of modeling and artificial intelligence in process parameter optimization of biochar: A review.

Bioresource technology
Enhancement of crop yield, conservation and quality upgradation of soil, and efficient water management are the main objectives of sustainable agriculture and mitigating climate change's impact on agriculture. In recent days, biochar, obtained via th...

A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches.

Bioresource technology
Biofuels from lignocellulosic biomass converted via thermochemical technologies can be renewable and sustainable, which makes them promising as alternatives to conventional fossil fuels. Prior to building industrial-scale thermochemical conversion pl...

Prediction of heavy metals adsorption by hydrochars and identification of critical factors using machine learning algorithms.

Bioresource technology
Hydrochar has become a popular product for immobilizing heavy metals in water bodies. However, the relationships between the preparation conditions, hydrochar properties, adsorption conditions, heavy metal types, and the maximum adsorption capacity (...

Machine learning prediction of contents of oxygenated components in bio-oil using extreme gradient boosting method under different pyrolysis conditions.

Bioresource technology
This work aims to develop a prediction model for the contents of oxygenated components in bio-oil based on machine learning according to different pyrolysis conditions and biomass characteristics. The prediction model was constructed using the extrem...