AIMC Topic: Biomass

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Unveiling the factors shaping variability in biomass productivity: Meta-analysis of outdoor pilot-scale microalgal cultures.

Bioresource technology
Meta-analysis and machine learning is used to investigate factors influencing variation in biomass productivity in outdoor algal systems. Understanding these factors is essential for optimizing algal systems. Mean productivity across the analysed stu...

Application of explainable machine learning in the production of pullulan by Aureobasidium pullulans CGMCCNO.7055.

International journal of biological macromolecules
The application of machine learning in pullulan biofermentation has demonstrated significant potential. Explainable machine learning enhances model transparency and interpretability by revealing the relationships between variables. In this study, we ...

Genetic algorithm-optimized artificial neural network for multi-objective optimization of biomass and exopolysaccharide production by Haloferax mediterranei.

Bioprocess and biosystems engineering
Microbial production of industrially important exopolysaccharide (EPS) from extremophiles has several advantages. In this study, key media components (i.e., sucrose, yeast extract, and urea) were optimized for biomass growth and extracellular EPS pro...

Integrating Proximal and Remote Sensing with Machine Learning for Pasture Biomass Estimation.

Sensors (Basel, Switzerland)
This study tackles the challenge of accurately estimating pasture biomass by integrating proximal sensing, remote sensing, and machine learning techniques. Field measurements of vegetation height collected using the PaddockTrac ultrasonic sensor were...

Machine learning-assisted prediction of gas production during co-pyrolysis of biomass and waste plastics.

Waste management (New York, N.Y.)
A general method for predicting gas yield is crucial in biomass and plastics co-pyrolysis. This study employed two machine learning methods to forecast gas yield in co-pyrolysis. Comparing the predictive performance of Support Vector Regression (SVR)...

Biological-chemical conversion process design and machine learning-related life cycle assessment: Bio-lubricant production in a real case study of South Korea.

Journal of environmental management
This study explores the production of poly alpha olefin (PAO) from biomass as an environmentally friendly alternative to fossil fuel-based methods, aiming to reduce greenhouse gas (GHG) emissions. The primary goal is to design a process for convertin...

Sustainable extraction of phytochemicals from Mentha arvensis using supramolecular eutectic solvent via microwave Irradiation: Unveiling insights with CatBoost-Driven feature analysis.

Ultrasonics sonochemistry
The present study revealed the higher extraction potential of sustainable choline chloride (ChCl) and ethylene glycol (EG) based deep eutectic solvent (DES) from Mentha arvensis via microwave irradiation. The categorical boosting (CatBoost) machine l...

Modeling and optimization of docosahexaenoic acid production by Schizochytrium sp. based on kinetic modeling and genetic algorithm optimized artificial neural network.

Bioresource technology
Docosahexaenoic acid (DHA), an essential ω-3 polyunsaturated fatty acid, is efficiently biosynthesized by Schizochytrium sp., yet its bioprocess optimization remains constrained by dynamic interdependencies between cultivation parameters and metaboli...

Machine learning unveils large-scale impact of Posidonia oceanica on Mediterranean Sea water.

The Science of the total environment
Posidonia oceanica is a protected endemic seagrass of the Mediterranean Sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset...

Integrated learning framework for enhanced specific surface area, pore size, and pore volume prediction of biochar.

Bioresource technology
Specific surface area, pore size, and pore volume are essential biochar properties. Optimization typically reduces yield by focusing on per gram of biochar. This work introduces new indicators and an integrated model to balance quality and quantity, ...