Prediction Of Rate Research Articles

Construction of Prediction Models of Mass Ablation Rate for Silicone Rubber-Based Flexible Ablative Composites Based on a Small Dataset

The prediction of the ablation rate of silicone rubber-based composites is of great significance to accelerate the development of flexible thermal protection materials. Herein, a method which combines uniform design experimentation, active learning, and virtual sample generation was proposed to establish a prediction model of the mass ablation rate based on a small dataset. Briefly, a small number of sample points were collected using uniform design experimentation, which were marked to construct the initial dataset and primitive model. Then, data points were acquired from the sample pool and iterated using various integrated algorithms through active learning to update the above dataset and model. Finally, a large number of virtual samples were generated based on the optimal model, and a further optimized prediction model was achieved. The results showed that after introducing 300 virtual samples, the average percentage error of the gradient boosting decision tree (GBDT) prediction model on the test set decreased to 3.1%, which demonstrates the effectiveness of the proposed method in building prediction models based on a small dataset.

A novel hybrid ANN-GB-LR model for predicting oil and gas production rate

The Oil and Gas Production Rate (OGPR) is one of the most significant processes that play an essential role in the oil industry. Predicting OGPR is critical for effective reservoir management and enhancing oil recovery. Traditional methods (TMs) and numerical simulations (NS) often struggle to process and analyze nonlinear, complex, and massive datasets. To avoid these challenges, artificial intelligence (AI) techniques and machine learning (ML) models have been proposed as an alternative solution due to their high efficiency and rapidity in handling complex data. In this study, a new hybrid model is developed by combining the strengths of Artificial Neural Networks (ANN) and Gradient Boosting (GB), using Linear Regression (LR) as a meta-model by stacking technique. It captures nonlinear relationships effectively and manages outliers, enhancing prediction accuracy. The novelty of this study lies in the hybrid ANN-GB-LR model's ability to integrate various machine learning techniques into a robust framework, leveraging the high learning capacity of ANN, the robust handling of outliers by GB, and the straightforward interpretability of LR. This creative combination handles the limitations of individual models and enhances the general predictive performance. The model was trained and tested using actual field data from the Halewah field in Yemen. Evaluation metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2), were utilized to evaluate and compare the hybrid model with other ML models: Random Forest (RF), XGBoost (XGB), LR, Light Gradient Boosting Machine (LGBM), GB, and K-nearest neighbors (KNN). The hybrid ANN-GB-LR model achieved superior results, with an R2 of 0.998, an RMSE of 11.06 for oil flow rate predictions, and an R2 of 0.98 and an RMSE of 172.15 for gas flow rate predictions. These results significantly surpass the other models, demonstrating the hybrid model's outstanding ability to capture complex data and provide accurate predictions. The ANN-GB-LR model surpasses Traditional Methods in predicting OGPRs. It shows a strong and reliable tool for optimizing reservoir management. This study establishes a new standard for predictive modeling in the oil industry, providing a framework for future research to apply hybrid models in handling complex datasets.

Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks

Advancements in water electrolysis technologies are crucial for green hydrogen production. Proton exchange membrane water electrolysis (PEMWE) is characterized by its efficiency and environmental benefits. The prediction and optimization of hydrogen production rates (HPRs) in PEMWE systems is difficult and still challenging because of the complexity of the system as well as the operational parameters. The integration of artificial intelligence (AI) and machine learning (ML) appears to be effective in optimization within the energy sector. Hence, this work employs the artificial neural network (ANN) to develop a model that accurately predicts HPR in PEMWE setups. A novel approach is introduced by employing the Levenberg–Marquardt backpropagation (LMBP) algorithm for training the ANN. This model is designed to predict HPR based on critical operational parameters, including anode and cathode areas (mm2), cell voltage (V) and current (A), water flow rate (mL/min), power (W), and temperature (K). The optimized ANN configuration features an architecture with 7 input nodes, two hidden layers of 64 neurons each, and a single output node. The performance of the ANN model was evaluated against conventional regression models using key metrics: mean squared error (MSE), coefficient of determination (R2), and mean absolute error (MAE). The findings of this study reveal that the developed ANN model significantly outperforms traditional models, achieving an R2 value of 0.9989 and an MAE of 0.012. In comparison, random forest (R2 = 0.9795), linear regression (R2 = 0.9697), and support vector machines (R2 = − 0.4812) show lower predictive accuracy, underscoring the ANN model's superior performance. This work demonstrates the efficiency of the LMBP in enhancing hydrogen production forecasts and sets a foundation for future improvements in PEMWE efficiency. By enabling precise control and optimization of operational parameters, this study contributes to the broader goal of advancing green hydrogen production as a viable and scalable alternative to fossil fuels, offering both immediate and long-term benefits to sustainable energy initiatives.

Corrosion Behavior of Aluminum in Fossil Diesel Fuel and Biodiesel From Chicken Eggshell‐Alumina‐Catalyzed Waste Cooking Oil

ABSTRACTTo curb environmental pollution emanating from the exhausts of engines using fossil diesel fuel (DF), studies on the use of biodiesel from renewable energy source are ongoing. However, engine components have been observed to corrode easily in biodiesels. In this study, a comparative study of the corrosion rate of aluminum coupon (AC) in biodiesel from waste cooking oil over chicken eggshell‐supported alumina catalyst (WCOB) and fossil DF was executed. Weight loss method was used to examine the corrosion rate. Highest corrosion rates of 0.0381 and 0.0052 mm year−1 were recorded for AC in WCOB and DF, respectively. The developed mathematical models proved effective for corrosion rate prediction. At optimum prediction, the minimum corrosion rates for the AC in WCOB and DF were 1.822 × 10−3 and 1.222 × 10−3 mm year, respectively. Characterization revealed pits formation and presence of oxygen and carbon on AC surface with loss of Al ions from the AC surface into WCOB and DF after corrosion. In conclusion, all results revealed high corrosion rates of AC in WCOB than DF.

Towards accurate modelling of rock surface exposure dating using luminescence to estimate post-exposure erosion rate

Depth-dependent luminescence in the top few millimetres of rock surface emerges as a potential tool to estimate rock surface exposure age and post-exposure erosion rate. It relies on the principle that the luminescence depth profile (LDP) propagates deeper with the time of sunlight exposure and moves to shallower depth with the erosion rate. The propagation of LDP is generally assumed to follow the first-order kinetic (FOK) model, except for a few recent studies. The FOK model predicts an exponential decay of infrared stimulated luminescence (IRSL) signal with light exposure time, which rarely corroborates experimental observation; IRSL signal decay is much slower than exponential decay. The faster decay of IRSL, predicted by the FOK model, results in faster propagation of LDP and thus always underestimates the exposure age and translates into a higher erosion rate. Interestingly, the slower-than-exponential decay of the IRSL signal can be better explained by general order kinetics (GOK). Thus, recent studies on rock surface luminescence dating have employed the GOK model. However, the GOK model is yet to be explored to predict post-exposure erosion rates. Here, we apply the GOK model and theoretically demonstrate the impact of the order of kinetics on the calibration and propagation of LDP in the presence of erosion and how the LDP's transient to steady state transition depends on the order of kinetics. We have performed a series of synthetic tests to assess the impact of selecting an incorrect model on the prediction of erosion rate. Finally, using the revised rate equation, the erosion rates are recalculated for natural samples (data available in the literature: Lehmann et al. (2019b)) and the impact of GOK on the predicted erosion rate is discussed.

Entrainment rate predictions of axis-symmetric non-swirling jets using free-jet-theory, Reynolds-averaged Navier-Stokes modelling and large-eddy-simulations resolved up to Kolmogorov scale

Jet entrainment - relevant to mixers, sprays and combustion technologies - has been the subject of this work. We limit our considerations to air jets issued from convergent nozzles of diameter smaller than 25.4 mm, the nozzle exit Reynolds number in the range 30,000 to 100,000 and Mach numbers not exceeding 0.4. The emphasis is on jet self-similarity region (60 < x/d0 < 210) and the key question is with which accuracy can Computational Fluid Dynamics (RANS and LES) and free-jet theory predict jet entrainment. Seven jets have been considered.The realizable k-є model has outperformed the other models and provides the entrainment predictions within ±6 % margin from the measured data. The standard k-є and the Shear-Stress Transport (SST) k-ω models deliver entrainment figures which are larger than the measured data by 22 % – 24 % whilst predictions of either the Reynolds Stress Model (RSM) or Re-Normalization Group (RNG) k-є models can be off (too large) by as much as 34 % and 40 %, respectively. Such a clarity in classification of turbulence models has been obtained after minimization of numerical related errors to a degree which was not achievable in the past. The Panchapakesan&Lumly's jet has been computed using the Large Eddy Simulations with the filter size of the order of Kolmogorov scale throughout the jet e.g. at the inlet, potential core and the far field. Excellent predictions of the jet spread rate, velocity profiles and the entrainment have been obtained at the expense of huge computational resources.The well-known engineering correlation m˙e/m0˙=0.32(x/d0) provides entrainment figures that are by 10 % or less larger than the measured values.

RGMeta: Enhancing Cold-Start Recommendations with a Residual Graph Meta-Embedding Model

Traditional recommendation models grapple with challenges such as the scarcity of similar user or item references and data sparsity, rendering the cold-start problem particularly formidable. Meta-learning has emerged as a promising avenue to address these issues, particularly in solving the item cold-start problem by generating meta-embeddings for new items as their initial ID embeddings. This approach has shown notable success in enhancing the accuracy of click-through rate predictions. However, prevalent meta-embedding models often focus solely on the attribute features of the item, neglecting crucial user information associated with it during the generation of initial ID embeddings for new items. This oversight hinders the exploitation of valuable user-related information to enhance the quality and accuracy of the initial ID embedding. To tackle this limitation, we introduce the residual graph meta-embedding model (RGMeta). RGMeta adopts a comprehensive approach by considering both the attribute features and target users of both old and new items. Through the integration of residual connections, the model effectively combines the representation information of the old neighbor items with the intrinsic features of the new item, resulting in an improved initial ID embedding generation. Experimental results demonstrate that RGMeta significantly enhances the performance of the cold-start phase, showcasing its effectiveness in overcoming challenges associated with sparse data and limited reference points. Our proposed model presents a promising step forward in leveraging both item attributes and user-related information to address cold-start problems in recommendation systems.

The Relationship between Foreign Exchange rate prediction using Artificial Intelligence and Audit Effort

The Relationship between Foreign Exchange rate prediction using Artificial Intelligence and Audit Effort

Foreign Exchange Rates Prediction Based on Comparative Models

With the development of globalization, fluctuations in the foreign exchange market have the huge potential to influence global economy. Machine Learning technology shows great potential in the field of financial forecasting than traditional statistical methods in recent years. This research selected statistical model Autoregressive Integrated Moving Average (ARIMA) and machine learning models Linear Regression and Long- and Short-Term Memory (LSTM) to make training and testing of historical data on the forex market. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Squared were chosen as evaluation indexes to evaluate the performance of each model and compare their performance in forex exchange rate forecasts. The results show that ARIMA and LSTM could make better predictions than Linear Regression, between which LSTM has higher degree of fitting data. This research provides a detailed analysis comparing the performance of different models in forex exchange rate forecasting whose results are beneficial to guide forex market forecast in practical application and provide reference for future research.

Enhancing Tunnel Boring Machine Penetration Rate Predictions through Particle Swarm Optimization and Elman Neural Networks

Enhancing Tunnel Boring Machine Penetration Rate Predictions through Particle Swarm Optimization and Elman Neural Networks

A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction

A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction

Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling

In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. To build the dataset, 401 samples were collected from two fields in Prince Edward Island (PEI) and 122 samples from the New Brunswick (NB), Canada. In addition, soil moisture (SM), temperature (ST), and electrical conductivity (EC), alongside eight climatic variables including wind speed (WS), solar radiation (SR), relative humidity (RH), precipitation (PCP), air temperature (AT), dew point (DP), vapour pressure difference (VPD) and reference evapotranspiration (ETo) were also collected. Greedy stepwise (GS) approach was implemented for feature selection. Finally, different qualitative (scatter plot, line graph, Taylor diagram, box plot, and Rug plot), and quantitative (uncertainty analysis, root mean square error (RMSE), percent of BIAS (PBIAS), Nash Sutcliff efficiency (NSE) and RMSE-observations standard deviation ratio (RSR)) techniques were used for model evaluation and comparison. Results of feature selection approaches revealed that DP, AT, SM, and ST are the four most effective variables at CO2 prediction in PEI, while AT, RH, DP, and ST are the most effective in the NB study area. For optimum input scenario, the GS algorithm was applied, and results showed that a combination of DP, AT, ST, SM, and ETo was the best for the PEI study area, while for NB, all input variables should be involved. Our analysis, for prediction of CO2 fluxes, confirmed that the ICO-AR-RF model performed the best at both PEI (RMSE=0.70, NSE=0.76, PBIAS=-5.11, RSR=0.48) and NB (RMSE=0.74, NSE=0.75, PBIAS=3.23, RSR=0.50), followed by MS-RF and AR-RF. Uncertainty analysis showed that CO2 prediction is more sensitive to input scenario selection than models in both study areas. Results revealed that climatic variables are more effective in CO2 prediction than soil characteristics and the developed hybrid model ICO-AR-RF can be a promising tool for decision-makers and beneficial for stakeholders.

Tubular photobioreactor design based on mixing intensity

A novel design methodology for tubular photobioreactors (TPBRs) for the mass culture of microalgae that can account for mixing intensity is presented. To date, TPBRs have been mainly designed and operated under the assumption of perfect mixing with regard to photosynthesis performance (light integration regime). In this work we show that this simplification has been leads to significant errors in the prediction of the optimal dilution rate and biomass productivity. To this end, computational fluid dynamics and light distribution model have been employed to calculate the trajectories and light histories I(t) of a microalgal cell population represented by 50 particles of 5 μm diameter. The density of the microalgal cells was set at 1000 kg m−3 and the tube diameters (D) were 14, 24, 44, 64 and 84 mm, with the circulation velocities (v) ranging from 0.4 to 1 m s-1. This has been coupled to a dynamic photosynthesis model in order to calculate the average photosynthetic response and hence the integration factors in TPBRs. It has been demonstrated that for a generic microalgal strain, the use of the light integration simplification (Γ = 1) would result in the prediction of an optimal dilution rate of 0.0315 h−1 (for D = 14 mm and v = 0.4 m/s as an example), which would lead to an actual biomass productivity of 182.5 g biomass m−3h−1 if the predicted integration factor (Γ = 0.578) is used whereas the newly proposed method predicts an optimal dilution rate of 0.0125 h−1 and a biomass productivity of 362.3 g biomass m−3h−1. This demonstrates that simplifying the light integration regime is inadequate for TPBRs design and operation, resulting in significant inaccuracies. The estimation charts and regressions proposed in this work to estimate actual integration factors will enable the development of an optimization method for TPBRs based on mixing intensity.

Bridging classical nucleation theory and molecular dynamics simulation for homogeneous ice nucleation.

Water freezing, initiated by ice nucleation, occurs widely in nature, ranging from cellular to global phenomena. Ice nucleation has been experimentally proven to require the formation of a critical ice nucleus, consistent with classical nucleation theory (CNT). However, the accuracy of CNT quantitative predictions of critical cluster sizes and nucleation rates has never been verified experimentally. In this study, we circumvent this difficulty by using molecular dynamics (MD) simulation. The physical properties of water/ice for CNT predictions, including density, chemical potential difference, and diffusion coefficient, are independently obtained using MD simulation, whereas the calculation of interfacial free energy is based on thermodynamic assumptions of CNT, including capillarity approximation among others. The CNT predictions are compared to the MD evaluations of brute-force simulations and forward flux sampling methods. We find that the CNT and MD predicted critical cluster sizes are consistent, and the CNT predicted nucleation rates are higher than the MD predicted values within three orders of magnitude. We also find that the ice crystallized from supercooled water is stacking-disordered ice with a stacking of cubic and hexagonal ices in four representative types of stacking. The prediction discrepancies in nucleation rate mainly arise from the stacking-disordered ice structure, the asphericity of ice cluster, the uncertainty of ice-water interfacial free energy, and the kinetic attachment rate. Our study establishes a relation between CNT and MD to predict homogeneous ice nucleation.

Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer

Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of ‘omics’ data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.

Ship sailing speed optimization considering dynamic meteorological conditions

Sailing speed optimization is a cost-effective strategy to improve ship energy efficiency and a viable way to fulfill emission reduction requirements. This study develops a novel ship sailing speed optimization method that considers dynamic meteorological conditions. We first develop an artificial neural network model for vessel fuel consumption rate (FCR) prediction based on a fusion dataset of ship noon reports and public meteorological data. Then, based on the predicted FCRs, the method repeatedly formulates a multistage graph based on the most recent forecasts, and optimal speeds for the remaining voyage are obtained until the vessel reaches the destination port. The computational efficiency of the optimization process is enhanced by progressively removing nodes without connections to successor nodes, starting from the penultimate stage. We examine the proposed method on two 11-day voyages of a dry bulk carrier. Results show that the proposed method demonstrates significant reductions in fuel consumption by 5.35% compared with a constant sailing speed scheme and by 7.34% compared with a static speed optimization model. In addition, the proposed model achieves similar fuel savings to those achieved by speed optimization based on actual meteorological conditions, enabling shipping companies to optimize ship sailing speeds in the absence of actual meteorological conditions. The proposed method can be applied to various types of vessels due to its flexibility and adaptability, making it a valuable tool for the shipping industry to reduce greenhouse gas (GHG) emissions, thereby supporting the International Maritime Organization (IMO)’s goal of reaching net-zero GHG emissions by around 2050.

Model-Based Co-Clustering in Customer Targeting Utilizing Large-Scale Online Product Rating Networks

Given the widely available online customer ratings on products, the individual-level rating prediction and clustering of customers and products are increasingly important for sellers to create targeting strategies for expanding the customer base and improving product ratings. However, the massive missing data problem is a significant challenge for modeling online product ratings. To address this issue, we propose a new co-clustering methodology based on a bipartite network modeling of large-scale ordinal product ratings. Our method extends existing co-clustering methods by incorporating covariates and ordinal ratings in the model-based co-clustering of a weighted bipartite network. We devise an efficient variational EM algorithm for model estimation. A simulation study demonstrates that our methodology is scalable for modeling large datasets and provides accurate estimation and clustering results. We further show that our model can successfully identify different groups of customers and products with meaningful interpretations and achieve promising predictive performance in a real application for customer targeting.

Study on the changing patterns of production performance of laying hens and their relationships with environmental factors in a large-scale henhouse

The production performance of laying hens is influenced by various environmental factors within the henhouse. The intricate interactions among these factors make the impact process highly complicated. The exact relationships between production performance and environmental variables are still not well understood. In this study, we measured the production performance of laying hens and various environmental variables across different parts of the henhouse, evaluated the weight of each environmental variable, and constructed a laying rate prediction model. Results displayed that body weight, laying rate, egg weight and eggshell thickness of hens decrease gradually from WCA to FA (P < 0.05). Serum levels of FSH and LH, as well as antibody level of H5 Re-13, gradually decrease from WCA to FA (P < 0.05). Moreover, the values for temperature (T), temperature-humidity index (THI), air velocity (AV), carbon dioxide (CO2), and particulate matter (PM2.5) gradually increase from WCA to FA (P < 0.05). Conversely, the relative humidity (RH) value gradually decreases from FA to WCA (P < 0.05). Additionally, the weights of the environmental variables, determined using a combination of the grey relational analysis (GRA) and analytic hierarchy process (AHP), were as follows in descending order: RH, THI, T, light intensity (LI), AV, PM2.5, NH3, and CO2. When the number of decision trees in the laying rate prediction model was set to 2,500, the results displayed a high level of agreement between the model's predictions and the observed outcomes. The model's performance evaluation yielded an R2 value of 0.89995 for the test set, suggesting strong predictive effects. In conclusion, the current study revealed significant differences in both the production performance of laying hens and the environmental variables across different parts of the henhouse. Furthermore, the study demonstrated that different environmental factors have distinct impacts on laying rate, with humidity and temperature identified as the primary factors. Finally, a multi-variable prediction model was constructed, exhibiting high accuracy in predicting laying rate.

ARIANT Assessments for CANDU Analysis Using RD-14M Experiments

ARIANT (AlgoRIthm for Analysis of Network Thermalhydraulics) is a Canadian Nuclear Laboratories system thermal-hydraulic code for the modeling and analysis of two-phase flow and heat transfer for pressurized heavy water reactors, light water pressurized water reactors, and advanced reactor applications. This paper presents ARIANT models and simulations of RD-14M experiments, including small-break loss-of-coolant accidents, large-break loss-of-coolant accidents, loss-of-flow accidents, station blackout, and natural circulation, that are representative of accident scenarios in a CANDU reactor. ARIANT predictions of pressures, flow rates, temperatures, and void fractions are compared against the steady-state and transient data over the course of the tests. The results show that ARIANT predicted the key parameters with reasonable accuracy, as well as the overall behavior of the five transient events. These assessments support ARIANT’s applicability to the corresponding CANDU design-basis accidents and demonstrate ARIANT as an alternative to existing system thermal-hydraulic codes for CANDU safety analysis.

Research on adaptive feature optimization and drilling rate prediction based on real-time data

In the drilling process, the rate of penetration (ROP) is an essential indicator of drilling efficiency. Accurate prediction of the rate of penetration (ROP) is crucial for improving drilling efficiency and minimizing costs. Conventional ROP predictions are typically made post-drilling, often resulting in low accuracy and limited applicability. To predict ROP more efficiently and accurately, an adaptive feature-optimized ROP prediction method based on real-time data was proposed. Initially, a fusion feature selection algorithm based on historical drilling data was designed and optimized using the Particle Swarm Optimization algorithm (PSO). On this basis, adaptive feature optimization was performed on real-time drilling datasets, and four different machine learning ROP prediction models were established using the selected feature parameters. The result shows that compared with the non-adaptive methods, the accuracy of the ROP prediction model using adaptive feature optimization is improved. The improvements are 27%, 44%, 47% and 47%, respectively, it demonstrates the significant advantages of using the designed adaptive feature optimization.