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Papers
61,005 resultsShowing papers similar to Comparative Analysis of Machine Learning Approaches to Predict Impact Energy of Hydraulic Breakers
ClearNeural network based prediction of the efficacy of ball milling to separate cable waste materials
Researchers developed a neural network — a type of artificial intelligence — to predict how well ball milling separates copper from plastic (PVC) in cable recycling, finding that the weight of cables loaded and the force of impact were the most critical factors. Machine learning tools like this could help scale up plastic and metal recycling to industrial levels.
Fine-tuning optimization of poly lactic acid impact strength with variation of plasticizer using simple supervised machine learning methods
Researchers applied machine learning methods — including K-nearest neighbors, support vector regression, and artificial neural networks — to predict and optimize the impact strength of polylactic acid (PLA) with different plasticizers, finding KNN offered the best balance of accuracy and stability for guiding formulation experiments.
Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers
Researchers developed a water cycle algorithm-optimized neural network to predict electrical power output from combined cycle power plants, demonstrating improved prediction accuracy compared to standard optimization algorithms on a publicly available dataset.
Machine Learning-Based Models for Basic Sediment & Water and Sand-Cut Prediction in Matured Niger Delta Fields
Researchers developed neural network-based models to predict basic sediment and water (BS&W) and sand-cut production from wellhead variables in 43 mature oilfields in the Niger Delta, using 457 datasets in both multiple-inputs single-output and multiple-inputs multiple-output network architectures. Model performance was evaluated against new test datasets using statistical measures including coefficient of determination.
Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach
Researchers applied machine learning methods to predict the loading frequency of nickel-titanium shape memory alloys based on experimental data from cyclic tensile tests. They tested multiple algorithms across different loading frequencies and found that machine learning could effectively classify the frequency conditions. The study demonstrates the potential of data-driven approaches for characterizing the behavior of these materials used in engineering applications.
A data-driven approach for the assessment of the thermal stratification of reservoirs based on readily available data
Researchers used a data-driven machine learning approach to assess the thermal structural integrity of materials under variable conditions, providing predictive models that can reduce reliance on costly physical testing. The methodology has broader applications for materials used in environments with high thermal stress.
Artificial Neural Networks and Gradient Boosted Machines Used for Regression to Evaluate Gasification Processes: A Review
This review examined the use of artificial neural networks and gradient boosted machine learning models as regression tools for evaluating and optimizing gasification waste-to-energy processes, finding these approaches can effectively predict process performance across diverse gasification conditions.
Predicting Aquaculture Water Quality Using Machine Learning Approaches
Researchers compared four machine learning approaches for predicting water quality parameters in industrial aquaculture systems, finding that back propagation and radial basis function neural networks outperformed support vector machine models for most parameters. The models achieved sufficient accuracy to support real-time management decisions without continuous in-situ monitoring.
Investigation Study of Structure Real Load Spectra Acquisition and Fatigue Life Prediction Based on the Optimized Efficient Hinging Hyperplane Neural Network Model
Not relevant to microplastics — this paper develops an optimized neural network model for predicting real-world load spectra and fatigue life of mechanical structures, achieving a fatigue life prediction accuracy of 93.56% for engineering applications.
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials
Researchers used machine learning and Bayesian network analysis on 4D microscopy data from cracking metal samples to identify which microstructural features best predict how small fatigue cracks grow and in which direction. The resulting analytical model outperformed existing fatigue metrics, offering a more accurate tool for predicting when and how structural metal components will fail under repeated stress.
Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges
This review covers machine learning methods applied to predicting and understanding mechanical properties of materials from large datasets. It is an engineering informatics paper and is not related to microplastics or environmental health.
Evaluation and Prediction of Production Yields in Plastic Manufacturing Industry Using Artificial Neural Network
This study evaluated and predicted production yield in a plastic manufacturing company using artificial neural network modeling. Predictive tools that improve manufacturing efficiency can reduce material waste and off-specification plastic products that may contribute to environmental plastic pollution.
Eco-Transformation of construction: Harnessing machine learning and SHAP for crumb rubber concrete sustainability
Researchers applied machine learning algorithms, including random forest and AdaBoost models, to predict the strength of concrete made with recycled crumb rubber from waste tires — a material that can reduce microplastic pollution from tire wear. The random forest model achieved strong accuracy (R² of 0.87), and found that rubber content and concrete age are the biggest factors influencing strength.
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
An Efficient Data Driven-Based Model for Prediction of the Total Sediment Load in Rivers
A data-driven machine learning model was developed to predict total sediment load in rivers using readily available hydrological and morphological variables, outperforming conventional empirical sediment transport equations in accuracy. The model provides a practical tool for river management applications where comprehensive physical measurements are unavailable.
A Bayesian-optimized convolutional neural network bidirectional gated recurrent unit model for dynamometer card reconstruction in beam pumping units
**TLDR:** This research developed a computer system that uses artificial intelligence to better monitor oil pumps by analyzing electrical signals. The system can more accurately predict when oil pumps need maintenance or adjustments, which could help prevent equipment failures. While this study focuses on industrial oil extraction rather than direct human health impacts, improved monitoring systems could reduce environmental risks from pump malfunctions.
Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs
Researchers evaluated machine learning models — including Gradient Boosting, XGBoost, and CatBoost — for predicting hydraulic performance of gabion weirs, finding that boosting ensemble methods accurately estimated discharge coefficients for these environmentally friendly water management structures.
Advancements in predictive maintenance techniques for enhancing machine tool reliability
This review examines advances in predictive maintenance techniques for manufacturing machine tools, describing how data analytics, machine learning, and real-time sensor technologies are used to forecast tool failures and enable proactive maintenance, reducing costly downtime compared to traditional reactive approaches.
Fast prediction and control of air core in hydrocyclone by machine learning to stabilize operations
This study developed a machine learning model to rapidly predict and control the air core behavior inside hydrocyclones used for wastewater treatment and microplastic removal, enabling more stable and efficient operation. The model reduced the need for manual adjustment and improved separation consistency.
Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach
Researchers used artificial neural network models to predict heavy metal contamination in soils near illegal landfills close to agricultural areas. The study found that illegal landfilling significantly impacts surrounding soil quality and proposes these predictive models as effective tools for environmental risk management and decision-making.
Coupling life prediction of bending very high cycle fatigue of completion strings made of different materials using deep wise separable convolution
Not relevant to microplastics — this study uses deep learning to predict the fatigue life of nickel-based alloy completion strings used in oil well engineering, with no connection to microplastic pollution.
Continuum approach to fatigue life prediction based on defect size
Researchers developed a continuum-based fatigue life prediction model that incorporates defect size as a key parameter, addressing limitations of conventional cycle-counting methods that assume constant amplitude loading and are insufficient for multiaxial fatigue scenarios.
Fatigue Assessment Comparison between a Ship Motion-Based Data-Driven Model and a Direct Fatigue Calculation Method
Researchers compared a ship motion-based data-driven model against a direct fatigue calculation method for assessing structural fatigue in ocean-crossing vessels subjected to wave-induced loads. The study evaluated how well motion sensor data can predict stress variations in ship structures, offering a practical alternative to computationally intensive direct methods.
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.