AIMC Topic: Bioreactors

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Reliable protocol using gradient boosting decision tree with limited experimental data to modify membrane surface for enhanced resilience and nitrogen removal in biofilm system.

Bioresource technology
This study highlights the importance of advanced membrane modification optimization methods for Membrane biofilm reactors (MBfR). Using Response surface methodology (RSM) and Gradient Boosting Decision Tree (GBDT), a relationship between solution con...

Predicting anaerobic digestion stability in load-flexible operation using gas phase indicators and classification algorithms.

Bioresource technology
This study investigates early warning indicators for process instabilities in anaerobic digestion caused by shock-loadings in biogas plants, focussing on gas-phase parameters to avoid substrate analyses. With the increasing use of renewable energy so...

Machine learning algorithms for predicting membrane bioreactors performance: A review.

Journal of environmental management
Membrane bioreactors (MBR) are recognized as a sustainable technology for treating polluted effluents. Machine learning (ML) algorithms have emerged as a modeling option to predict pollutant removal and operational variables such as membrane fouling,...

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...

Development of artificial neural network model for anaerobic digestion-elutriated phase treatment.

Journal of environmental management
Nonlinear autoregressive exogenous (NARX) neural network models were used to forecast the time-series profiles of anaerobic digestion-elutriated phase treatment (ADEPT). Experimental data from the operation of the pilot plant and lab-scale reactor we...

Harnessing near-infrared and Raman spectral sensing and artificial intelligence for real-time monitoring and precision control of bioprocess.

Bioresource technology
Effective monitoring and control of bioprocesses are critical for industrial biomanufacturing. This study demonstrates the integration of near-infrared and Raman spectroscopy for real-time monitoring and precise control of gentamicin fermentation. Th...

Graph-based deep learning for predictions on changes in microbiomes and biogas production in anaerobic digestion systems.

Water research
Anaerobic digestion (AD), which relies on a complex microbial consortium for efficient biogas generation, is a promising avenue for renewable energy production and organic waste treatment. However, understanding and optimising AD processes are challe...

Enhanced prediction of partial nitrification-anammox process in wastewater treatment by developing an attention-based deep learning network.

Journal of environmental management
In the process of partial nitrification and anaerobic ammonia oxidation (anammox) for nitrogen removal, the process offers simple metabolic pathways, low operating costs, and high nitrogenous loading rates. However, since the partial nitrification-an...

Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm.

Bioresource technology
Microalgal-bacteria biofilm shows great potential in low-cost greywater treatment. Accurately predicting treated greywater quality is of great significance for water reuse. In this work, machine learning models were developed for simulating and predi...

Exploring interactive effects of environmental and microbial factors on food waste anaerobic digestion performance: Interpretable machine learning models.

Bioresource technology
Biogas yield in anaerobic digestion (AD) involves continuous and complex biological reactions. The traditional linear models failed to quantitatively assess the interactive effects of these factors on AD performance. To further explore the internal r...