We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Machine learning modeling of microplastics removal by coagulation in water and wastewater treatment
ClearEnhancing water quality prediction: a machine learning approach across diverse water environments
Researchers compared seven machine learning models for predicting water quality parameters using six years of wastewater treatment plant data. The gradient boosting model performed best overall, accurately predicting parameters related to water contamination. While the study focuses on general water quality rather than microplastics specifically, these predictive tools could be applied to monitoring microplastic-relevant conditions in treatment systems.
Revealing the removal behavior of five neglected microplastics in coagulation-ultrafiltration processes: Insights from experiments and predictive modeling
Researchers combined laboratory experiments with artificial neural network modeling to study how five commonly overlooked types of microplastics are removed during drinking water treatment. They found that coagulation alone removed 37-56% of the microplastics, while adding ultrafiltration removed virtually all remaining particles. The study provides new insights into the chemical and physical interactions that drive microplastic removal, which could help optimize water treatment processes.
Design of an Efficient Model for Microplastic Removal in Wastewater using Advanced Filtration, Nanotechnology, and Bioremediation
This paper proposed an advanced machine learning model to design and optimize microplastic removal in wastewater treatment, using process parameters to predict removal efficiency. The intelligent model outperformed conventional design approaches in predicting treatment outcomes.
Understanding and Improving Microplastic Removal during Water Treatment: Impact of Coagulation and Flocculation
Researchers systematically tested coagulation and flocculation for removing microplastics from drinking water, finding that removal efficiency depended strongly on plastic particle size and whether particles had been weathered, with smaller pristine particles being the hardest to remove.
Recent advances in microplastic removal from drinking water by coagulation: Removal mechanisms and influencing factors
A meta-analysis and random forest model found that coagulation can effectively remove microplastics from drinking water, with particle shape being the most important factor affecting removal efficiency, followed by coagulant type and dosage. Charge neutralization is the dominant mechanism for small microplastics, while adsorption bridging and sweeping work better for larger particles.
Efficient Data-Driven Machine Learning Models for Water Quality Prediction
This study tested machine learning methods for predicting water quality based on physical, chemical, and biological measurements. While focused on water safety testing rather than microplastics specifically, the automated classification tools developed here could help water treatment facilities quickly identify contaminated water. Better monitoring technology is important because current methods for detecting microplastics in water are slow and expensive.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System
Researchers reviewed the application of artificial intelligence and neural networks in water treatment coagulation systems. The study found that AI-based approaches can effectively predict water quality parameters and optimize chemical dosing, potentially improving the removal of contaminants including microplastics from drinking water treatment processes.
Coagulation technologies for separation of microplastics in water: current status
This review examines how coagulation water treatment technologies can remove microplastics from water. Conventional coagulation achieves 8-98% removal efficiency while electrocoagulation achieves 8-99%, depending on conditions, offering a potentially effective approach for reducing microplastics in drinking water and wastewater.
Microplastic removal in coagulation-flocculation: Optimization through chemometric and morphological insights
Researchers optimized the coagulation-flocculation process — a standard water treatment step where chemicals cause particles to clump and settle — for removing three types of microplastics: polypropylene, polyethylene, and polystyrene. Polystyrene was removed most efficiently, and adjusting pH, coagulant type, and dosage significantly improved removal rates, providing practical guidance for upgrading existing water treatment plants to better capture microplastics.
The removal of microplastics from water by coagulation: A comprehensive review
This review comprehensively examined coagulation as a technology for removing microplastics from drinking water and wastewater treatment plants, analyzing the mechanisms, influencing factors, and effectiveness of different coagulants for microplastic removal.
Machine Learning to Predict the Adsorption Capacity of Microplastics
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Microplastic removal by coagulation: a review of optimizing the reaction conditions and mechanisms
This review examines recent advances in using coagulation to remove microplastics from water and wastewater, analyzing how factors like coagulant type, dosage, pH, and particle shape affect removal efficiency. Researchers found that optimizing these reaction conditions is critical for maximizing microplastic removal while reducing energy costs. The study highlights significant knowledge gaps in understanding the mechanisms behind coagulation-based microplastic removal and calls for more extensive research.
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
Researchers developed machine learning models to predict the settling velocity of microplastics in water, using particle shape, size, and density as inputs. The models outperformed traditional empirical equations, providing a more accurate tool for modeling microplastic transport and sedimentation.
Mathematical modeling of water flocculation process with high turbidity: studies and comparative analysis between methods and models
This study compared mathematical models for predicting flocculation kinetics in water treatment, an important process for removing suspended particles including microplastics. More accurate flocculation models help optimize water treatment efficiency and reduce the microplastic particles that pass through conventional treatment into drinking water.
Machine Learning-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
Seven machine learning algorithms were trained on 173 published measurements to predict how strongly organic contaminants adsorb onto different types of microplastics in freshwater. Accurate adsorption predictions are essential for assessing environmental risk, because microplastics that strongly bind pollutants become vectors that concentrate and transport toxic chemicals through aquatic food webs.
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning
Researchers used machine learning to predict microplastic levels in ocean surface waters and identify the key factors driving contamination. Their models found that geographic location, ocean currents, and proximity to populated coastlines were major predictors of microplastic abundance. This approach could help scientists map pollution hotspots without costly and time-consuming physical sampling.
A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years
This comprehensive review analyzes over 170 studies on using machine learning to predict water quality, covering both individual pollutant indicators and overall water quality indices. The authors highlight key challenges including data acquisition, model uncertainty, and the need to incorporate water flow dynamics into predictions. While broadly focused on water quality, these predictive tools are relevant to microplastics research because they could help forecast microplastic concentrations in water systems based on environmental conditions.
AI-Driven Framework Development for Predictive Classification of Microplastic Concentration of Aquatic Systems in the United States
Researchers compared four machine learning models—logistic regression, random forest, support vector machine, and a neural network—for predicting microplastic density in US coastal waters across three regions. The support vector machine performed best with 93.94% average accuracy, demonstrating the potential of AI-driven tools for microplastic monitoring.
Microplastics removal from aquatic environment by coagulation: Selecting the best coagulant based on variables determined from a systematic review
This systematic review and experimental study identifies the most effective methods for removing microplastics from water using coagulation, a common water treatment technique. Researchers tested different coagulants on three types of microplastics and found that aluminum-based coagulants were most effective. These findings could help water treatment plants better remove microplastics from the water supply before it reaches our taps.
A new modeling approach for microplastic drag and settling velocity
Researchers developed a novel machine learning-based modelling framework to predict drag coefficients and settling velocities for microplastics of varying shapes (1D, 2D, 3D, and mixed) in aquatic environments. The framework achieved coefficient of determination values of 0.86-0.95 for drag models, outperforming traditional theoretical and data-fitting approaches in both speed and accuracy.
Microplastics removal efficiency of drinking water treatment plant with pulse clarifier
Researchers evaluated how well a drinking water treatment plant using pulse clarification technology removes microplastics from river water sourced from the Ganges. Raw water contained about 18 microplastics per liter, and the treatment process removed 85% of them, with sand filtration being the most effective step. The study found that fibers and films were the most common microplastic shapes, and machine learning analysis revealed strong links between microplastic levels and water quality indicators like turbidity.
Microplastic removal by coagulation/flocculation: A review and bibliometric analysis
This review of existing research found that a common water treatment method called coagulation (where chemicals help clump particles together so they can be removed) works well at filtering out microplastics from drinking water and wastewater. The treatment is especially good at removing larger microplastic pieces, but struggles with the tiniest ones under 10 micrometers. This matters because microplastics are showing up everywhere in our water supply, and this research suggests we already have proven technology that could help reduce our exposure to these plastic particles.