High-precision Prediction Research Articles

Construction and control parameter matching method of hydraulic pile hammer surrogate model combining transfer algorithm and knowledge embedding

Due to its complex application environment, hydraulic pile hammers are difficult to model and accurately grasp their motion laws in automation control. Therefore, to address the high-precision prediction problem of the motion law of hydraulic pile hammer systems under complex working conditions, a knowledge embedding surrogate model based on a dual neural network is designed, which can be divided into two parts: feature extraction network and prediction network. On this basis, the model introduces physics knowledge embedding technology, adds inequality constraints based on physical laws to the loss function, and introduces transfer learning algorithms to maintain high accuracy of the model under limited data conditions. The results showed that the PSM (γ = 0.5) model designed in the study had a median of -0.001 and a variance of 0.038 under undisturbed conditions, demonstrating high prediction stability. Under perturbation conditions, the median of the PSM (γ = 1.5) model was -0.003, with a variance of 0.031, indicating the highest prediction accuracy. From this, the research model has good prediction accuracy under complex working conditions, which can lay a technical foundation for predicting the motion law of hydraulic pile hammers and automatic control.

Study of spot distance on resistance spot welding quality: a 1DCNN-BiLSTM-Attention-based online inspection method

Abstract Resistance spot welding (RSW) is widely employed in the automotive and home appliance industries due to its high efficiency, low cost, and suitability for automation. However, traditional quality detection methods rely on destructive testing, leading to inefficiencies and resource wastage. This paper presents a novel quality inspection model for RSW that utilizes a one-dimensional convolutional neural network, bidirectional long short-term memory network, and attention mechanism (1DCNN-BiLSTM-Attention) to address the challenges of extracting temporal data under varying spot distances. The model integrates a residual linking mechanism and Kolmogorov–Arnold Networks (KAN) to enhance feature extraction and performance. Experimental results reveal that the model demonstrates strong predictive capabilities across different spot distances, with particularly notable performance at 10 mm spacing, achieving a mean absolute error (MAE) of 0.0632, a root mean square error (RMSE) of 0.0603, and an R² value of 0.7513. These findings underscore the model’s ability to provide high-precision predictions, even under conditions influenced by significant shunt effects.

Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype

This study proposed a method to predict the crushing force of controlled-release fertilizer granules based on their phenotypic characteristics to prevent coating damage during production, transport, and fertilization, which could affect nutrient diffusion rates. The phenotypic features, including sphericity, particle size, and texture, of three commonly used controlled-release fertilizers were obtained using machine vision, while the crushing force was measured using a universal testing machine. A principal component analysis was applied for data reduction, and the optimal parameters for the support vector machine (SVM) were selected using particle swarm optimization (PSO) combined with k-fold cross-validation. A particle swarm optimization–support vector machine (PSO-SVM) model was then developed to predict the crushing force based on fertilizer shape features. Compared with the traditional method, the innovation of this paper is that a non-destructive prediction method is proposed, which enables high-precision predictions of the crushing force by integrating multi-dimensional phenotypic features and an intelligent optimization algorithm. Comparative tests with a random forest regression, the K-nearest neighbor, a back propagation (BP) neural network, and a long short-term memory (LSTM) neural network have demonstrated that the PSO-SVM model outperforms these methods in terms of mean absolute error, root mean square error, and correlation coefficient, underscoring its effectiveness. The proportion of predictions within the −10% to +10% error range reached 0.82, 0.82, and 0.86 for the three fertilizers, confirming the high reliability and accuracy of the PSO-SVM method for non-destructive testing.

Spatiotemporal prediction of solidified dendrites based on convolutional long-short-term neural network

The formation and growth of dendrites during the solidification of metals are critical factors influencing material properties. Accurate prediction of dendritic growth morphology and evolution is of great significance for materials science research and industrial applications. Traditional simulation methods, such as the phase-field method, provide high-precision results but are computationally expensive and time-consuming. Recently, with the advancement of deep learning technology, data-driven approaches have shown great potential in materials science. This paper proposes the use of Convolutional Long-Short-Term Memory Network (ConvLSTM) to capture spatiotemporal dependencies for predicting the spatiotemporal evolution of dendritic solidification in materials. By training on historical data of dendritic growth, the ConvLSTM neural network model predicts the future growth morphology of the dendritic microstructure during solidification. The model accurately forecasts the future morphology and appearance of the microstructure. Case studies were conducted using a generated dataset of dendritic solidification simulations, and the results demonstrate that the ConvLSTM model effectively captures the complex dynamic characteristics of dendritic growth and provides high-precision prediction results. This offers new insights for material design and optimization.

Innovative Method to Select Optimal Plugging Materials for Preventing Loss Circulation in Deep Fractured Reservoirs Using Machine Learning

Summary Lost circulation, a critical issue in drilling operations caused by drilling fluid loss into formation fractures, is a significant barrier in the exploration and production of oil, natural gas, and geothermal reservoirs. Effective design of the plugging formula to mitigate such losses is vital for the successful extraction of these resources. To efficiently design the plugging formula, in this paper we determine the key performance parameters of plugging materials based on the formation mechanism of the plugging zone, using them as feature input variables. We then use multitask learning (MTL) to establish a high-precision prediction model for the plugging formula, followed by the development of a mathematical optimization model for selecting performance parameters of the plugging formula, with displacement pressure and cumulative loss volume as the objective functions. An improved particle swarm optimization (PSO) algorithm is used to solve this mathematical model and determine the characteristic parameters of the plugging formula. Based on these parameters, the appropriate types of plugging materials, including bridging materials, fillers, and deformable reinforcement materials, are identified for the formula. The results show that the improved PSO algorithm outperforms the basic PSO algorithm, genetic algorithms, and whale optimization algorithms in solving the mathematical optimization model, with a performance improvement of about 10%. Additionally, sensitivity analysis confirms the model’s robustness, revealing that bridging materials play a critical role in the effectiveness of the plugging formula. As the variety of bridging, filling, and deformable reinforcement materials increases, their displacement pressure improves. More specifically, the analysis explores how the friction coefficient, D90 particle-size distribution, thermostability, compressive strength, and acid solubility of bridging materials affect displacement pressure and cumulative loss volume. Experimental findings validate that the innovative method to select optimal plugging materials for deep fractured reservoirs, leveraging MTL and intelligent optimization, facilitates the swift and effective development of deep fracture plugging strategies. This method not only assures effective fracture plugging but also minimizes material consumption in the formulations, thereby reducing overall material costs. The proposed method provides new novel perspectives and a theoretical foundation for the design of the deep fractured reservoir plugging formula.

Associations between volatile organic compounds exposure and multiple oxidative damage biomarkers: Method development, human exposure, and application for e-waste pollution prediction

Atmospheric monitoring studies reveal substantial health risks from exposure to volatile organic compounds (VOCs) for workers and nearby children in e-waste recycling areas (ERA), yet internal exposure risks are seldom examined. To address this, we developed a method to simultaneously analyze 12 urinary VOC metabolites (mVOCs) and oxidative damage biomarkers (ODBs) in workers and children from ERA and general adults from control areas using ultrahigh performance liquid chromatography coupled with quadrupole/orbitrap high-resolution mass spectrometry (UPLC-Orbitrap-HRMS). The results showed that e-waste workers exhibited significantly higher levels of VOC exposure and ODBs than e-waste children and control adults. Exceeding 91.1 %, 69.1 %, 20.8 %, 19.7 %, and 3.26 % of e-waste workers faced non-carcinogenic risk from exposure to acrolein, acrylonitrile, acrylamide, 1,3-butadiene, and 1,2-dichloroethane, respectively. The weighted quantile sum, quantile g-computation, and Bayesian kernel machine regression models consistently indicated significant positive associations between these VOC mixtures and cholesterol ODB levels (i.e., glycocholic acid, cholic acid, and glycochenodeoxycholic acid), highlighting the necessity for improved protective measures for occupational workers. Interestingly, cholesterol ODBs significantly mediated the association between VOCs exposure and nucleic acid ODBs, accounting for 12.0–26.0 % of the association with 8-hydroxy-2′-deoxyguanosine (an oxidative DNA damage biomarker) and 25.4–53.4 % with 8-hydroxyguanosine (an oxidative RNA damage biomarker). This suggests that cholesterol ODBs potentially serve as better indicators of health risks from VOC exposure than nucleic acid ODBs. Additionally, the combination of mVOCs and ODBs (Mean AUC: 0.906, ACC: 0.821) as exposure fingerprints outperformed either mVOCs (Mean AUC: 0.878, ACC: 0.802) or ODBs (Mean AUC: 0.843, ACC: 0.768) alone in predicting the presence of e-waste pollution, underscoring the importance of integrating exposure and effect biomarker fingerprints to accurately capture e-waste pollution characteristic. Our findings offer a novel approach for screening e-waste pollution in unknow e-waste recycling sites and provide a foundation for developing high-precision prediction models for other polluting industries.

A surrogate-based flow-field prediction and optimization strategy for hypersonic thrust nozzle

The thrust nozzle is a component that directly generates thrust, which is of great importance in the design of a hypersonic propulsion system. In this study, a surrogate-based flow-field prediction and optimization strategy for hypersonic thrust nozzles is proposed based on reduced-order modeling of the flow field. The effects of various methods on flow field prediction accuracy are studied, including domain decomposition-based proper orthogonal decomposition and Kriging-based surrogate models. A high-precision prediction of the viscid flow field can be achieved when an arbitrary nozzle contour parametrized by non-uniform rational B-spline is given. It is revealed that domain decomposition methods and improvements to the orthogonal basis effectively enhance the accuracy of flow field reconstruction. Compared to the full flow field surrogates, the near-wall region surrogates predict wall pressure more effectively and yield higher thrust accuracy. A hybrid infill sampling criterion is also proposed to improve the prediction accuracy of the model. Combining that with the surrogate-based flow-field prediction model and the intelligent optimization algorithms, an optimized nozzle contour is obtained, whose performance is 0.5% higher than that designed using the Rao method.

Finite basis topologies for multiloop high-multiplicity Feynman integrals

In this work, we systematically analyze Feynman integrals in the ‘t Hooft-Veltman scheme. We write an explicit reduction resulting from partial fractioning the high-multiplicity integrands to a finite basis of topologies at any given loop order. We find all of these finite basis topologies at two loops in four external dimensions. Their maximal cut and the leading singularity are expressed in terms of the Gram determinant and Baikov polynomial. By performing an integration-by-parts reduction without any cut constraint on a numerical probe for one of these topologies, we show that the computational complexity drops significantly compared to the conventional dimensional regularization scheme. Formally, our work implies an upper bound on the rigidity of special functions appearing in the iterated integral solutions at each loop order in perturbative quantum field theory. Phenomenologically, the integrand-level reduction we present will substantially simplify the task of providing high-precision predictions for future high-multiplicity collider observables. Published by the American Physical Society 2024

Prediction of Sunspot Activity Based on Differential Evolution Algorithm and BP Neural Network Model

In this research report, the prediction method of sunspot activity is deeply discussed, and a variety of statistical and data science models are used to make comprehensive prediction. Research focuses include the start time and duration of the solar maximum in the solar cycle, as well as the prediction of the number and area of sunspots. By capturing the periodicity of solar activity, Pearson correlation coefficient is used to analyze the relationship between the maximum solar activity and the number of sunspots, and the adaptive multivariate nonlinear regression-BP neural network model and particle swarm optimization BP neural network are combined to make high-precision prediction. The research results provide a scientific basis for the prediction of space weather, ionospheric state and communication system reliability.

Building Energy Consumption Prediction and Optimization

As the proportion of building energy consumption in global energy consumption is significant and continues to rise, accurate prediction and optimization of building energy consumption are of great significance. This research aims to develop high-precision prediction models and innovative optimization strategies to address the problems of insufficient accuracy of existing prediction methods and poor optimization effects. By collecting and integrating data through multiple channels, a hybrid model architecture combining LSTM, CNN, and the attention mechanism is selected, and an automatic search and optimization method for hyperparameters is adopted. In terms of data processing, multi-source heterogeneous data fusion, data cleaning and enhancement based on spatiotemporal features, and innovative feature engineering are carried out. In the experiments, the dataset is divided considering the dynamic use of buildings and environmental changes, and indicators related to energy costs are introduced for evaluation. The research successfully constructed a model for accurately predicting building energy consumption, proposed effective optimization strategies, reduced energy consumption, and ensured comfort, providing a basis for practical applications through sensitivity analysis and robustness tests.

A noninvasive blood glucose detection method with strong time adaptability based on fuzzy operator decision fusion and dynamic spectroscopy characteristics of PPG signals.

PPG signals are a new means of non-invasive detection of blood glucose, but there are still shortcomings of poor time adaptability and low prediction accuracy of blood glucose quantitative models. Few studies discuss prediction accuracy in the case of a large time interval span between modeling and prediction. This paper proposes an automatic optimal threshold baseline removal algorithm based on variational mode decomposition (AOT-VMD), which can adaptively eliminate high-frequency noise and baseline interference for each decomposed IMF modal component and reduce the baseline difference of PPG signals from different days. Furthermore, a fuzzy integral multi-model decision fusion algorithm based on error weight is proposed. The fuzzy integral operator is introduced to make the features with large contributions in each sub-model maintain a high-weight value in the overall prediction mechanism, which improves the prediction accuracy of blood glucose. In this paper, a self-developed portable PPG glucose meter is used to collect PPG signals, and the true blood glucose values for 8 consecutive days are collected by CGM. The proposed algorithm is used to build a model with the first day's data and predict the blood glucose values for the remaining 7 days. The experimental results show that the AOT-VMD preprocessing algorithm and the quantitative regression algorithm of the fuzzy integral multiple model decision fusion algorithm proposed in this paper perform well in measurement accuracy and time adaptability compared with the traditional methods. In addition, the proposed method requires less invasive calibration samples in the modeling stage, achieving high-precision prediction for a long period. 100% of the samples are located in areas A and B of the Clarke area in this experiment, and the algorithm has strong time generalization ability. This innovative method can promote the development of a home blood glucose noninvasive detector.

Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method

In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R2 values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations.

High-precision prediction of microalgae biofuel production efficiency: employing ELG ensemble method

Microalgae biofuels are considered a significant source of future renewable energy due to their efficient photosynthesis and rapid growth rates. However, practical applications face numerous challenges such as variations in environmental conditions, high cultivation costs, and energy losses during production. In this study, we propose an ensemble model called ELG, integrating Empirical Mode Decomposition (EMD), Long Short-Term Memory (LSTM), and Gradient Boosting Machine (GBM), to enhance prediction accuracy. The model is tested on two primary datasets: the EIA (U.S. Energy Information Administration) dataset and the NREL (National Renewable Energy Laboratory) dataset, both of which provide extensive data on biofuel production and environmental conditions. Experimental results demonstrate the superior performance of the ELG model, achieving an RMSE of 0.089 and MAPE of 2.02% on the EIA dataset, and an RMSE of 0.1 and MAPE of 2.21% on the NREL dataset. These metrics indicate that the ELG model outperforms existing models in predicting the efficiency of microalgae biofuel production. The integration of EMD for preprocessing, LSTM for capturing temporal dependencies, and GBM for optimizing prediction outputs significantly improves the model’s predictive accuracy and robustness. This research, through high-precision prediction of microalgae biofuel production efficiency, optimizes resource allocation and enhances economic feasibility. It advances technological capabilities and scientific understanding in the field of microalgae biofuels and provides a robust framework for other renewable energy applications.

DeepBP: Ensemble deep learning strategy for bioactive peptide prediction

BackgroundBioactive peptides are important bioactive molecules composed of short-chain amino acids that play various crucial roles in the body, such as regulating physiological processes and promoting immune responses and antibacterial effects. Due to their significance, bioactive peptides have broad application potential in drug development, food science, and biotechnology. Among them, understanding their biological mechanisms will contribute to new ideas for drug discovery and disease treatment.ResultsThis study employs generative adversarial capsule networks (CapsuleGAN), gated recurrent units (GRU), and convolutional neural networks (CNN) as base classifiers to achieve ensemble learning through voting methods, which not only obtains high-precision prediction results on the angiotensin-converting enzyme (ACE) inhibitory peptides dataset and the anticancer peptides (ACP) dataset but also demonstrates effective model performance. For this method, we first utilized the protein language model—evolutionary scale modeling (ESM-2)—to extract relevant features for the ACE inhibitory peptides and ACP datasets. Following feature extraction, we trained three deep learning models—CapsuleGAN, GRU, and CNN—while continuously adjusting the model parameters throughout the training process. Finally, during the voting stage, different weights were assigned to the models based on their prediction accuracy, allowing full utilization of the model's performance. Experimental results show that on the ACE inhibitory peptide dataset, the balanced accuracy is 0.926, the Matthews correlation coefficient (MCC) is 0.831, and the area under the curve is 0.966; on the ACP dataset, the accuracy (ACC) is 0.779, and the MCC is 0.558. The experimental results on both datasets are superior to existing methods, demonstrating the effectiveness of the experimental approach.ConclusionIn this study, CapsuleGAN, GRU, and CNN were successfully employed as base classifiers to implement ensemble learning, which not only achieved good results in the prediction of two datasets but also surpassed existing methods. The ability to predict peptides with strong ACE inhibitory activity and ACPs more accurately and quickly is significant, and this work provides valuable insights for predicting other functional peptides. The source code and dataset for this experiment are publicly available at https://github.com/Zhou-Jianren/bioactive-peptides.

An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations

As urbanization accelerates, the management of urban solid waste poses increasingly intricate challenges. Traditional urban metrics, such as GDP and per capita consumption rates, have become inadequate for accurately reflecting the realities of waste generation; moreover, the linear correlation between these metrics and waste production is progressively diminishing. Consequently, this study introduces a novel methodology leveraging nighttime light remote sensing data to enhance the precision of urban solid waste production forecasts. By processing remote sensing data to mitigate noise and integrating it with conventional urban datasets, an innovative index system and predictive model were developed. Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. Furthermore, in light of the substantial costs associated with waste recovery route planning and site selection for treatment facilities, this research further devised a location and distribution framework for waste treatment centers based on high-precision predictions of waste production while employing multi-objective evolutionary algorithms (MOEAs) alongside the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. Distinct from prior studies, this study suggests that service point waste quantities are not fixed values but rather adhere to a normal distribution within specified ranges and thus provides a more realistic simulation of fluctuations in waste production while enhancing both the robustness and predictive accuracy of the model. In conclusion, by incorporating nighttime light remote sensing data along with advanced machine learning techniques, this study markedly improves forecasting accuracy for waste production while offering effective optimization strategies for site selection and recovery route planning—thereby establishing a robust data foundation aimed at refining urban solid waste management systems.

A Physics- and Data-Driven Study on the Ground Effect on the Propulsive Performance of Tandem Flapping Wings

In this paper, we present a physics- and data-driven study on the ground effect on the propulsive performance of tandem flapping wings. With numerical simulations, the impact of the ground effect on the aerodynamic force, energy consumption, and efficiency is analyzed, revealing a unique coupling effect between the ground effect and the wing–wing interference. It is found that, for smaller phase differences between the front and rear wings, the thrust is higher, and the boosting effect due to the ground on the rear wing (maximum of 12.33%) is lower than that on a single wing (maximum of 43.83%) For a larger phase difference, a lower thrust is observed, and it is also found that the boosting effect on the rear wing is above that on a single wing. Further, based on the bidirectional gate recurrent units (BiGRUs) time-series neural network, a surrogate model is further developed to predict the unsteady aerodynamic characteristics of tandem flapping wings under the ground effect. The surrogate model exhibits high predictive precision for aerodynamic forces, energy consumption, and efficiency. On the test set, the relative errors of the time-averaged values range from −4% to 2%, while the root mean squared error of the transient values is less than 0.1. Meanwhile, it should be pointed out that the established surrogate model also demonstrates strong generalization capability. The findings contribute to a comprehensive understanding of the ground effect mechanism and provide valuable insights for the aerodynamic design of tandem flapping-wing air vehicles operating near the ground.

Data-driven models for predicting compressive strength of 3D-printed fiber-reinforced concrete using interpretable machine learning algorithms

3D printing technology is growing swiftly in the construction sector due to its numerous benefits, such as intricate designs, quicker construction, waste reduction, environmental friendliness, cost savings, and enhanced safety. Nevertheless, optimizing the concrete mix for 3D printing is a challenging task due to the numerous factors involved, requiring extensive experimentation. Therefore, this study used three machine learning techniques, including Gene Expression Programming (GEP), Multi-Expression Programming (MEP), and Decision Tree (DT), to forecast the compressive strength of 3D printed fiber-reinforced concrete (3DP-FRC). The dataset comprises 299 data points with sixteen variables gathered from experimental research studies. For training the model, 70 % of the dataset was used, while the remaining 30 % was reserved for model testing. Several statistical metrics were utilized to evaluate the accuracy and applicability of the models. In addition, SHapley Additive exPlanations (SHAP), partial dependence plots, and individual conditional expectations approach were employed for the interpretability of the models. The proposed GEP, MEP, and DT models indicated enhanced efficacy, exhibiting correlation coefficient (R) scores of 0.996, 0.987, and 0.990, with mean absolute errors (MAE) of 1.029, 4.832, and 2.513, respectively. Overall, the established GEP model demonstrated exceptional performance compared to MEP and DT, showcasing high prediction precision in assessing the strength of 3DP-FRC. Moreover, a simple empirical formulation has been devised using GEP to predict the compressive strength, offering a simplified and efficient approach for predicting 3DP-FRC strength. The SHAP approach identified water, silica fume, fiber diameter, curing age, and loading directions as leading controlling parameters in predicting strength of 3DP-FRC. In summary, the proposed models can potentially minimize both the computational workload and the need for experimental trials in formulating the mixed design of 3D-printed concrete.

Deep learning-based fast prediction of flow field around multiple bluff bodies

Bluff bodies are widely employed in practical engineering, but they also introduce complex wind-induced flow characteristics, particularly due to interference effects arising from the interaction of multiple bluff bodies. Traditional wind tunnel experiments and numerical simulation methods are time-consuming and costly, posing significant challenges to design and construction. This study employed deep learning technique to rapidly predict two-dimensional flow fields around multiple bluff bodies. First, a physics-informed deep learning model is proposed, incorporating residual equations representing physical laws, i.e. the continuity and momentum equations, as loss functions. By enforcing the model training to converge in directions consistent with actual physical laws, the physics-informed deep learning model achieves rapid and high-precision predictions of the flow fields around multiple bluff bodies. Additionally, by utilizing spatially permutation invariance of point cloud method on the input of deep learning model, the data input no longer requires strict spatial constraints as in traditional methods, making it more representative of real-world data and expanding the engineering application scenarios of deep learning networks.

Study on laser induced breakdown spectrum analysis of lithium in drilling cuttings

Abstract The elements of drilling cuttings are crucial information to be obtained in the downhole measurement while drilling for eological interpretation, resource assessment, and development decisions. However, it is difficult to achieve the analysis of light elements within natrium (Na) by the existing elemental analysis technology. So this paper studies the analysis of lithium by the laser-induced breakdown spectroscopy (LIBS) to promote the discovery, exploration and development of lithium resources. The spectra of 20 lithium-containing ore compositional analysis standards are collected to discuss the impact of the baseline correction methods such as the asymmetric least squares method (AsLS), adaptive iteratively reweighted penalized least squares method (airPLS), and asymmetrically reweighted penalized least squares method (arPLS), and the quantitative analysis models of multivariate linear regression (MLR), Bayesian regression (BPR) and Gaussian process regression (GPR), to verify the accuracy and reliability of the lithium analysis results. The results show that the baseline correction method can improve the analysis accuracy of lithium element, and the preferred AsLs+Gaussian regression is more suitable for the prediction of Li element concentration, with the correlation coefficient of 0.9300. As an effective strategy for high-precision prediction of lithium element concentration, it is applied to the drilling field data for the prediction of lithium element. This research provides an important theoretical support for the application of LIBS technology to lithium element analysis of drilling cutting.

Physics-informed dynamic mode decomposition for reconstruction and prediction of dense particulate pipe flows

Dynamic mode decomposition (DMD) effectively captures the growth and frequency characteristics of individual modes, enabling the construction of reduced-order models for flow evolution, thereby facilitating the prediction of fluid dynamic behavior. However, DMD's predictive accuracy is inherently constrained by its inability to inherently incorporate physical principles. Therefore, for dense particulate pipe flows with complex flow mechanisms, we introduce a physics-informed dynamic mode decomposition (PIDMD) approach, which augments the purely data-driven DMD framework by incorporating the conservation of mass as a constraint. This ensures that the extracted dynamic modes adhere to known physical principles. Initially, we apply the DMD to reconstruct and predict the velocity field, comparing the results against benchmark computational fluid dynamics-discrete element method (CFD-DEM) simulations. Findings indicate that while DMD can reconstruct the flow field simulated by CFD-DEM and provide predictions of future flow states, its predictive accuracy gradually deteriorates over time. Next, we utilize both PIDMD and DMD to reconstruct and predict particle volume fraction, evaluating both models based on CFD-DEM outcomes. The results indicate that both PIDMD and DMD can predict particle aggregation toward the center, but PIDMD provides more accurate predictions regarding the size of particle aggregations and their distribution near the tube wall. Furthermore, the average prediction error for particle volume fraction using PIDMD is 6.54%, which is lower than the error of 13.49% obtained by DMD. Both qualitative and quantitative comparisons highlight the superior predictive capability of PIDMD. The methodology developed in this study provides valuable insights for high-precision predictions of particulate flows.