Application of machine learning in anaerobic digestion: Perspectives and challenges
Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with
simultaneous production of renewable energy and nutrient-rich digestate. AD process …
simultaneous production of renewable energy and nutrient-rich digestate. AD process …
Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review
The increased water scarcity, depletion of freshwater resources, and rising environmental
awareness are stressing for the development of sustainable wastewater treatment …
awareness are stressing for the development of sustainable wastewater treatment …
[HTML][HTML] Assessing optimization techniques for improving water quality model
In order to keep the" good" status of coastal water quality, it is essential to monitor and
assess frequently. The Water quality index (WQI) model is one of the most widely used …
assess frequently. The Water quality index (WQI) model is one of the most widely used …
Machine-learning-based prediction and optimization of emerging contaminants' adsorption capacity on biochar materials
Biochar materials have recently received considerable recognition as eco-friendly and cost-
effective adsorbents capable of effectively removing hazardous emerging contaminants (eg …
effective adsorbents capable of effectively removing hazardous emerging contaminants (eg …
Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green
This study focuses on the potential capability of numerous machine learning models, namely
CatBoost, GradientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree …
CatBoost, GradientBoosting, HistGradientBoosting, ExtraTrees, XGBoost, DecisionTree …
Review on machine learning-based bioprocess optimization, monitoring, and control systems
Abstract Machine Learning is quickly becoming an impending game changer for
transforming big data thrust from the bioprocessing industry into actionable output. However …
transforming big data thrust from the bioprocessing industry into actionable output. However …
[HTML][HTML] Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning …
Experimental studies using a substantial number of datasets can be avoided by employing
efficient methods to predict the mechanical properties of construction materials. The …
efficient methods to predict the mechanical properties of construction materials. The …
A review of artificial intelligence in water purification and wastewater treatment: Recent advancements
Artificial intelligence (AI) is an emerging powerful novel technology that can model real-time
problems involving numerous intricacies. The modeling capabilities of AI techniques are …
problems involving numerous intricacies. The modeling capabilities of AI techniques are …
Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
Plain weave composite is a long-lasting type of fabric composite that is stable enough when
being handled. Open-hole composites have been widely used in industry, though they have …
being handled. Open-hole composites have been widely used in industry, though they have …
Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
Abstract Predicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting
Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of …
Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of …