AI Medical Compendium Journal:
Bioresource technology

Showing 1 to 10 of 116 articles

Generative deep learning model assisted multi-objective optimization for wastewater nitrogen to protein conversion by photosynthetic bacteria.

Bioresource technology
For decades, the photosynthetic bacteria (PSB)-based nitrogen treatment and valorization from wastewater have been explored. However, balancing nitrogen removal performance and resource recovery potential in PSB has remained a key unresolved issue fo...

Meta-tuning and fast optimization of machine learning models for dynamic methane prediction in anaerobic digestion.

Bioresource technology
This study evaluates the performance of several optimization algorithms for tuning a data preparation and hyperparameter optimization pipeline applied to machine and deep learning models predicting methane production. Bayesian ridge regression and re...

Data-driven insights for enhanced cellulose conversion to 5-hydroxymethylfurfural using machine learning.

Bioresource technology
Converting cellulose into 5-Hydroxymethylfurfural (HMF) provides a promising strategy for creating bio-based chemicals, offering sustainable alternatives to petroleum-based materials in polymers, biofuels, and pharmaceuticals. However, the efficient ...

Electrochemical activation of alum sludge for the adsorption of lead (Pb(II)) and arsenic (As): Mechanistic insights and machine learning (ML) analysis.

Bioresource technology
Alum sludge (AlS) has emerged as an effective adsorbent for anionic contaminants, with traditional activation methods like acid/base treatments and calcination employed to enhance its adsorption capacity. However, these approaches encounter significa...

Predicting single-cell protein production from food-processing wastewater in sequencing batch reactors using ensemble learning.

Bioresource technology
Producing single-cell protein (SCP) from food-processing wastewater offers a sustainable approach to resource recovery, animal feed production, and wastewater treatment. Decision-makers need accurate system performance data under variable influent co...

Pyrolysis mechanism study on xylose by combining experiments, chemical reaction neural networks and density functional theory.

Bioresource technology
Chemical reaction neural networks (CRNN) and density functional theory (DFT) are gaining attention in biomass pyrolysis mechanism research. Reaction pathways are often speculated based on a single method, influenced by expert knowledge. To address th...

Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges.

Bioresource technology
The lignocellulosic biorefinery involves pretreatment, enzymatic hydrolysis, mixed sugar fermentation, and optional anaerobic digestion. This pipeline could be effectively implemented through machine learning (ML)-guided process optimization and stra...

Enhanced nitrogen prediction and mechanistic process analysis in high-salinity wastewater treatment using interpretable machine learning approach.

Bioresource technology
This study introduces an interpretable machine learning framework to predict nitrogen removal in membrane bioreactor (MBR) treating high-salinity wastewater. By integrating Shapley additive explanations (SHAP) with Categorical Boosting (CatBoost), we...

Metabolic reprogramming and machine learning-guided cofactor engineering to boost nicotinamide mononucleotide production in Escherichia coli.

Bioresource technology
Nicotinamide mononucleotide (NMN) is a bioactive compound in NAD(P) metabolism, which exhibits diverse pharmaceutical interests. However, enhancing NMN biosynthesis faces the challange of competing with cell growth and disturbing intracellular redox ...

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...