ANN Model Research Articles

Mathematical and ANN Modelling for Convective Drying of Spanish Cherry Seeds: Bioactive Degradation, Energy Efficiency, and Mass Transfer Evaluation

Mathematical and ANN Modelling for Convective Drying of Spanish Cherry Seeds: Bioactive Degradation, Energy Efficiency, and Mass Transfer Evaluation

Global Sensitivity Analysis of the Fundamental Frequency of Jacket-Supported Offshore Wind Turbines Using Artificial Neural Networks

Determining the fundamental frequency of Offshore Wind Turbines (OWTs) is crucial to ensure the reliability and longevity of the structure. This study presents a global sensitivity analysis of the fundamental frequency of OWTs on jacket foundations. Monte Carlo sampling was employed to generate a diverse set of wind turbines, emplacements, and jacket designs, ensuring that the generated samples are realistic and yield relevant conclusions. The fundamental frequency and its partial derivatives were obtained via a previously developed ANN model. The relative sensitivities were computed to facilitate the comparison of their influence. The results demonstrate that wind turbine properties are the most relevant variables affecting the fundamental frequency, with a decrement in frequency caused by tower height and rotor-nacelle assembly mass, as well as an increment due to the section dimensions of the tower, particularly at its base. Soil properties have a significant effect on foundation stiffness for soft and light soils but can be neglected for hard and heavy soils. The diameter and thickness of the braces also show different relevance depending on their dimensions, producing rigid links between legs for greater sections. This study provides a measure of the variables influencing the fundamental frequency, facilitating a deeper comprehension of this phenomenon.

Quantum intrusion detection system using outlier analysis

In the field of cybersecurity, hackers often enter computer systems despite current security measures, owing to the huge amount of network traffic that makes intruder identification difficult. Differentiating between authorized traffic and abnormal data produced by Distributed Denial of Service (DDoS) attackers is still a major difficulty. This research provides a unique technique that uses Quantum Machine Learning (QML) to improve security protocols for secure communication between two parties. Our strategy enhances detection accuracy and speed by using the characteristics of quantum neural networks. The approach includes creating a dataset from network traffic patterns, preprocessing it, and then turning it into quantum bits via angle embedding. The research uses outlier analysis, min-entropy, and quantum state fidelity to distinguish between normal and abnormal data patterns. The dispersed randomness of network header data is measured using entropy, which aids in identifying security concerns. The suggested QML-based methodology outperforms conventional approaches and current models, including AMM-CNN and ANN models, with a detection accuracy of 99.87% for DDoS attacks. This advancement leads to more effective and secure communication networks.

An AI-driven approach for modeling the compressive strength of sustainable concrete incorporating waste marble as an industrial by-product.

A key goal of environmental policies and circular economy strategies in the construction sector is to convert demolition and industrial wastes into reusable materials. As an industrial by-product,Waste marble (WM), has the potential to replace cement and fine aggregate in concrete which helps with saving natural resources and reducing environmental harm. While many studies have so far investigated the effect of WM on compressive strength (CS), it is undeniable that conducting experimental activities requires time, money, and re-testing with changing materials and conditions. Hence, this study seeks to move from traditional experimental approaches towards artificial intelligence-driven approaches by developing three models-artificial neural network (ANN) and hybrid ANN with ant colony optimization (ACO) and biogeography-based optimization (BBO) to predict the CS of WM concrete. For this purpose, a comprehensive dataset including 1135 data records is employed from the literature. The models' performance is assessed using statistical metrics and error histograms, and a K-fold cross-validation analysis is applied to avoid overfitting problems, emphasize the models' reliable predictive capabilities, and generalize them. The statistical metrics indicated that the ANN-BBO model performed best with a correlation coefficient (R) of 0.9950 and root mean squared error (RMSE) of 1.2017MPa. Besides, the error distribution results revealed that the ANN-BBO outperformed the ANN and ANN-ACO with a narrower range of errors so that 98% of the predicted data points in the training phase by the ANN-BBO model experienced errors in the range of [-10%, 10%], whereas for the ANN-ACO and ANN models, this percentage was 85% and 79%, respectively. Additionally, the study employed SHapley Additive exPlanations (SHAP) analysis to clarify the impact of input variables on prediction accuracy and found that the specimen's age is the most influential variable. Eventually, to validate the ANN-BBO, a comparison was performed with the results of previous studies' models.

Efficient removal of perfluorooctanoic acid from aqueous matrices using cationic surfactant functionalized graphene oxide nanocomposite: RSM and ANN modeling, and adsorption behaviour

Perfluorooctanoic acid (PFOA), a persistent and recalcitrant emerging organic contaminant, has garnered worldwide attention due to its pervasiveness and strong bioaccumulation, emphasizing the urgent necessity for effective removal strategies. To address PFOA contamination in the global aquatic environment, this study prepared cetyltrimethylammonium bromide (CTAB)-functionalized graphene oxide (GO-C) nanocomposites. The functionality of GO-C is controlled by varying loading ratios of CTAB to achieve satisfactory performance. After preliminary experiments that confirmed the superior performance of GO-C0.5 in PFOA removal, the response surface methodology (RSM) revealed the effects of different process parameters, which followed the sequence: adsorbent dose > pH > time. The optimum PFOA removal of 98.5 % was achieved at adsorbent dose: 200 mg/L, pH: 3.5, and time: 50 min, at an experimental temperature of 298 K. Furthermore, a predictive model using an artificial neural network was developed for PFOA adsorption, which closely aligns with the experimental data (R2: 0.9999). The PFOA adsorption was most accurately represented by Freundlich isotherm (R2: 0.9747) and pseudo-second-order kinetics (R2: 0.9995), with a maximum experimental adsorption capacity of ~709.94 mg/g under RSM optimized conditions. The adsorption mechanism involves electrostatic and hydrophobic interactions and hydrogen bonding, as confirmed by the spectroscopic and zeta potential analyses. Lastly, the competitive adsorption and regeneration experiments underscore the potential of GO-C nanocomposite in removing PFOA. Overall, this study provides insights into the development and application of sustainable and promising adsorbent for the removal of PFOA in wastewater stream.

A hybrid technique to enhance the rainfall-runoff prediction of physical and data-driven model: a case study of Upper Narmada River Sub-basin, India

Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments.

An Integrated Machine Learning Workflow to Estimate In Situ Stresses Based on Downhole Sonic Logs and Laboratory Triaxial Ultrasonic Velocity Data

AbstractThe optimum performance of various subsurface operations such as stimulation treatments, wellbore drilling, horizontal well placement, underground mining, and tunneling rely on accurate estimation of the in situ stresses. This study presents an integrated machine learning (ML) workflow for the reliable determination of the in situ stress in subsurface rock formations. The study workflow was completed in three phases. In the first phase, six supervised ML regression techniques were employed to develop the stress prediction models using laboratory true triaxial ultrasonic velocity tests (labTUV) data of subsurface rock samples retrieved from well 16A(78)‐32 located at the Utah FORGE geothermal site. In the second phase, subsurface geological formations were classified into rock facies using an unsupervised K‐means clustering algorithm. Finally, in the third phase, the optimized ML models were employed for predicting the in situ stresses in the corresponding rock facies in the well 16A(78)‐32 using field sonic logs. A comparison of evaluation metrics revealed the superior performance of ANN models for horizontal and vertical stress predictions with root mean squared error of 1.5, 0.6, and 1.7 MPa, and determination coefficient (R2) of 0.98, 0.98, and 0.96, for the testing/validation phases, respectively. The generalization capability of ML models was explored by uncovering the underlying physics. The mathematical expressions of constitutive relations were extracted from three ANN models. Further, a total of five rock facies were identified in the subsurface geological formations. The novel workflow would be capable of delivering reliable in situ stress profiles in subsurface geological formations without performing expensive field tests.

An Improved Fick Model for Predicting Carbonation Depth of Concrete

Concrete carbonation can weaken its strength, cause the corrosion of steel reinforcement, and shorten its service life. Predicting the concrete carbonation depth is a critical aspect of assessing concrete durability. Currently, mathematical models for the concrete carbonation depth, exemplified by the Fick model, suffer from a low fitting accuracy and limited applicability due to the complexity and variability of concrete materials and service environments. In light of this, this work proposes an improved Fick model that incorporates a correction term to effectively enhance the curve fitting accuracy. The correction term in the improved model provides a reasonable adjustment for deviations in the development pattern of the concrete carbonation depth from the Fick model under different conditions, thereby broadening the applicability of the new model compared to the Fick model. Several sets of experimental data on the concrete carbonation depth are used to validate the universality and superiority of the new model. The results of the case studies indicate that the average prediction error and standard deviation of the new model are significantly smaller than those of the Fick model. For the first two examples, in most situations, the average prediction error and standard deviation of the new model are less than 50% of those of the Fick model, with the lowest average prediction error being only 4% and the lowest standard deviation being only 2% of the Fick model’s respective values. For the third example, the new model demonstrates superior predictive capability for the later-stage concrete carbonation depth compared to the Fick model and the ANN model. Specifically, for the carbonation depth of the concrete on the 56th day, the relative error between the predicted value of the new model and the measured value is only 2%, which is much smaller than the 27% of the Fick model and the 12% of the ANN model. These results demonstrate the unique advantage of the proposed model in predicting the carbonation depth, especially when only a limited amount of experimental data are available.

Use of Deep-Learning-Accelerated Gradient Approximation for Reservoir Geological Parameter Estimation

The estimation of space-varying geological parameters is often not computationally affordable for high-dimensional subsurface reservoir modeling systems. The adjoint method is generally regarded as an efficient approach for obtaining analytical gradient and, thus, proceeding with the gradient-based iteration algorithm; however, the infeasible memory requirement and computational demands strictly prohibit its generic implementation, especially for high-dimensional problems. The autoregressive neural network (aNN) model, as a nonlinear surrogate approximation, has gradually received increasing popularity due to significant reduction of computational cost, but one prominent limitation is that the generic application of aNN to large-scale reservoir models inevitably poses challenges in the training procedure, which remains unresolved. To address this issue, model-order reduction could be a promising strategy, which enables us to train the neural network in a very efficient manner. A very popular projection-based linear reduction method, i.e., propel orthogonal decomposition (POD), is adopted to achieve dimensionality reduction. This paper presents an architecture of a projection-based autoregressive neural network that efficiently derives an easy-to-use adjoint model by the use of an auto-differentiation module inside the popular deep learning frameworks. This hybrid neural network proxy, referred to as POD-aNN, is capable of speeding up derivation of reduced-order adjoint models. The performance of POD-aNN is validated through a synthetic 2D subsurface transport model. The use of POD-aNN significantly reduces the computation cost while the accuracy remains. In addition, our proposed POD-aNN can easily obtain multiple posterior realizations for uncertainty evaluation. The developed POD-aNN emulator is a data-driven approach for reduced-order modeling of nonlinear dynamic systems and, thus, should be a very efficient modeling tool to address many engineering applications related to intensive simulation-based optimization.

Assessing the impact of recycled concrete aggregates and metakaolin on the mechanical properties of pervious concrete

Population growth and urban expansion have increased impervious surfaces, leading to environmental issues such as stormwater runoff and groundwater depletion. Traditional concrete, while durable, contributes to these problems. There is a need for sustainable construction materials that can address environmental challenges and reduce the consumption of natural resources. Pervious concrete, enhanced with recycled aggregates (RCA) and supplementary cementitious materials (SCMs) like metakaolin (MK) present a potential solution. This study aims to improve the mechanical and durability properties of pervious concrete by incorporating RCA and MK, assessing their effects on workability, density, porosity, permeability, compressive strength and flexural strength. The incorporation of RCA resulted in increased aggregate porosity compared to natural aggregates. Workability was assessed using the slump test, density was measured, and porosity and permeability were evaluated. The results showed that incorporation of RCA generally decreased workability, density and strength, while the addition of MK improved these properties by refining the microstructure. Specifically, mixes with 10% MK demonstrated better performance in terms of increased compressive and flexural strengths, improved density and reduced porosity. The ANN model showed high accuracy in predicting compressive and flexural strengths. The study concludes that RCA and MK can enhance the mechanical and durability properties of pervious concrete.

RSM, SVM and ANN modeling of the properties of self-compacting concrete with natural mordenite-rich tuff and recycled glass

RSM, SVM and ANN modeling of the properties of self-compacting concrete with natural mordenite-rich tuff and recycled glass

Gender Disparities in Book Circulation at a Chinese University Library (2012–2022)

The study explores the significance of gender differences in higher education, using book circulation data from Nanjing Normal University libraries between 2012 and 2022. It aims to analyze gender disparities and calculate the mathematical characteristics of gender prediction probability using the ANN model. The time-series ARIMA model also forecasts the average number of books that male and female teachers and teacher candidates are expected to borrow and renew in the next three years. The findings indicate that men tend to be more focused and persistent readers despite women having a more extensive network of book borrowing ties. Male graduate students and faculty members demonstrate significantly greater individual variability in book renewals than their female counterparts. Additionally, the maximum value of book renewals for male graduate students and male faculty members is notably higher than that of female graduate students and female faculty members. Book renewal frequency is a crucial factor in ANN gender prediction. On average, male readers incur higher overdue fines than female readers, and the overdue fines for both genders during the COVID-19 pandemic were considerably higher than usual. The actual monthly average renewal rates in 2022 exhibited significant fluctuations across genders and academic statuses, highlighting the limitations of the ARIMA model and other factors influencing renewal rates.

Forecasting Carbon Dioxide Emission Regional Difference in China by Damping Fractional Grey Model

The emission of carbon dioxide is the main reason for many global warming problems. Although China has made tremendous efforts to reduce carbon emission, the space–time dynamics of the carbon emission trend is still imbalanced. To forecast CDED in China, the Dagum Gini coefficient was applied to measure regional CDED. Then, a grey correlation model was used to select potential influence factors and a wrapping method for selecting the optimal subset. DGMC is proposed to forecast CDED. The research results showed that the DGMC generalization performance is significantly superior to other models. The MAPE of DGMC in six cases are 1.18%, 1.11%, 0.66%, 1.13%, 1.27% and 0.51%, respectively. The RMSPEPR of DGMC in six cases are 1.08%, 1.21%, 0.97%, 1.36%, 1.41% and 0.57%, respectively. The RMSPEPO of DGMC in six cases are 1.29%, 0.69%, 0.02%, 0.58%, 0.78% and 0.32%, respectively. In future trends, the eastern carbon dioxide emission intraregional differences will decrease. Additionally, the intraregional differences in western and middle-region carbon dioxide emissions will expand. Interregional carbon emission difference will display a narrowing trend. Compared with the traditional grey model and ANN model, integrating the influence factor information significantly improved forecasting accuracy. The proposed model will present better balanced historical information and accurately forecast future trends. Finally, policy recommendations are proposed based on the research results.

Elucidating synergistic effects and environmental value enhancement in infrared-Assisted Co-Pyrolysis of coal and polyvinyl chloride

Co-pyrolysis of high-alkali coal and polyvinyl chloride (PVC) through infrared heating is a promising approach for managing escalating PVC waste and converting low-grade coal resources efficiently. The synergistic effects during co-pyrolysis and thermal degradation of PVC were studied through thermogravimetric analysis (TG-FTIR) and spectral analysis. Furthermore, chlorine migration and chemical transformation integral to the process, along with catalytic interactions, were explored through chlorine mass balance assessment, employing X-ray photoelectron spectroscopy and ion chromatography to establish a groundbreaking understanding of these phenomena. The results of products exhibited a trend of initially increasing and then decreasing with increasing temperatures and mixing ratios. The maximum oil yield was 14.62 %, achieving at 600 °C and 20 %. Meanwhile, the content of aromatic hydrocarbons remained high level through the synergistic interaction of Lewis and Brønsted acid sites, nearly exceeding 60 %. The results showed that the Artificial Neural Network model had a high prediction accuracy with an R2 value of 0.99, while the decarboxylation effect dissociated the metal, resulting in an increase in the fixation of inorganic chlorine from 1 % to 66 % and a decrease in the chlorine content in the liquid phase. Innovatively, the study employs an ANN model for predicting the behavior of co-pyrolysis, exemplifying high accuracy in understanding complex thermal conversions of PVC and coal. Notably, the work challenges existing constraints by applying sophisticated analytical methods to elucidate the thermodynamics and kinetics of the co-pyrolysis processes, thereby enhancing the energetic and environmental value of the products.

Fusing Transformer and diffusion for high-resolution prediction of daylight illuminance and glare based on sparse ceiling-mounted input

Accurate prediction of workplane daylight illuminance and eye-height glare is crucial for lighting control. Existing studies used machine learning to predict illuminance at predetermined locations based on indoor sensors, but they may encounter challenges in scenarios 1) with flexible seating arrangements, 2) with dynamic shading devices, and 3) requiring the prediction of glare. To address these challenges, we proposed a novel method fusing Transformer and Diffusion models, with the input being data collected from sparse ceiling-mounted illuminance sensors, and the outputs being high-resolution workplane illuminance and glare. The model works well for rooms without and with dynamic roller shades. For the former, the mean absolute errors for illuminance below 3000 lx and Daylight Glare Index (DGI) are only 20.77 lx and 0.20, and the error rates in detecting illuminance <500 lx and DGI>22 are only 0.85 % and 5.55 %. For the more complicated latter case, the aforementioned four numbers are 34.78 lx, 0.59, 2.47 % and 23.13 %. The model significantly outperforms the linear and the ANN models, particularly in glare prediction. The influence of sensor number and placement strategy on model performance was also revealed. The model can potentially enhance lighting control, especially in cases with dynamic shading, with flexible seating arrangements, and where glare is of interest.

Designing of a proficient macro porous metallo-polymer of iron with anion functionality: A sustainable approach for arsenic remediation from water

In this study, a novel porous metallo-polymeric network named poly(Ferric trimethacrylate-co-4-Vinyl benzyl chloride), incorporating an iron moiety, was developed for arsenic remediation from water. The synthesis of crosslinked q(pFeVBC) was carried out via suspension polymerization and functionalizing it with hexamethylenetetramine, resulting in a porous three-dimensional structure. Characterization using FTIR, FESEM, XPS, XRD, and BET-SA analysis confirmed its properties. Various monomer ratios were tested to optimize q(pFeVBC) for arsenic removal. The q(pFeVBC) exhibited significant adsorption capacities (qe max exp) of 8.33 mg g−1 for As(V) and 7.9 mg g−1 for As (III), owe to its unique architecture (integrated iron moiety and anion functionality) and porous texture (SA: 112 m2 g−1, pore size: 1.27–2.07 µm), which is close to the capacity calculated by Langmuir model (qe max theo) 8.719 mg g−1 for As (V) and 8.463 mg g−1 for As (III). The PSO kinetic model showed excellent fitting for both arsenic forms. Thermodynamic studies indicated a spontaneous and endothermic adsorption process. Additionally, a 4:10:1 ANN model accurately predicted adsorption behavior.

Harnessing artificial neural networks and linear regression models for modeling thermal modification processes: Characterization by FTIR and prediction of the mechanical properties of eucalyptus wood

In the present study, an artificial neural network model and multiple linear regression method were constructed to establish a relationship between the parameters of the thermal modification process and the mechanical properties of Eucalyptus camaldulensis wood samples. Three influencing parameters on the mechanical properties during heat treatment were considered input variables, namely water absorption, volumetric mass, and mass loss; the other parameters were kept constant (treatment temperature: 200, 220, 240, and 260 °C, and the duration: 5, 60, and 90 minutes). There were five neurons in the hidden layer that were used, as well as an output layer for the mechanical property. According to the results obtained, the mean square error (MSE) for the training, validation, and testing datasets were determined to be 0.21, 0.25, and 0.22 in the prediction modulus of rupture (MOR) and 0.019, 0.017, and 0.023 in the prediction modulus of elasticity (MOE). Higher coefficients of determination (R2) ranging from 0.93 to 0.98 were obtained for all datasets with the proposed ANN models, while the multiple linear regression models found the MSE to be 1.03 and 1.40 for MOR and MOE, respectively, as well as R2 to be 0.97 and 0.80, respectively. These results show that ANN models can be successfully used to predict the change in mechanical properties of wood during heat treatment.

Density Functional Theory (DFT) Study on the Nontoxic Alternative of Bisphenol A (BPA) Derivatives: A Comprehensive Review

AbstractBisphenol A is an oil‐derived, large market volume chemical with a wide spectrum of applications in plastics, adhesives, and thermal papers. However, bisphenol A and its derivative are not considered safe due to its endocrine disrupting properties and reproductive toxicity. A nontoxic alternative of bisphenol analogus has been proposed in this study using plant biomass. A study of different DFT‐based QSAR approaches has been done to show the significance of the conceptual DFT‐based selected descriptors with different QSAR models in the prediction of toxicity and to establish meaningful correlations between the molecular structure of the proposed compounds and their toxicological properties. Multiple regression analysis and ANN model are also suggested to use to relate the biological activity with the global and local reactivity descriptors.

Experimental Analysis of EDM Parameters on D2 Die Steel Using Nano-aluminum Composite Electrodes

This paper presents an experimental study on the electrical discharge machining (EDM) of AISI D2 die steel using an Al-Ni composite electrode. The investigation focuses on the influence of input parameters like current, pulse duration, and pulse interval, on key output parameters such as material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). EDM oil was employed as the dielectric fluid. Grey relational analysis (GRA) was utilized for designing and conducting the experiments using the Taguchi L9 method. The ANN model showed excellent predictive accuracy with a perfect correlation coefficient (R) of 1.00, indicating strong capability in forecasting MRR based on machining parameters. GRA further confirmed that higher current settings and longer pulse-off times effectively reduce tool wear, suggesting that the ANN model accurately reflects the conditions that minimize TWR. The ANN model achieved strong predictive accuracy for SR, with high correlation coefficients, although with slightly higher mean squared error (MSE) in testing.

DIAGNOSTIC EVALUATION OF URBAN METRO TRANSIT SYSTEM POST-COVID-19

Public transportation usage in Delhi has declined, with the Delhi Metro having a significant share. However, due to fare hikes and COVID-19 restrictions, the DM's share has been decreasing further. To improve ridership, a study is being conducted to evaluate the DM's performance and identify areas for improvement in passenger convenience and comfort. The Magenta line is investigated through an on-board survey to collect primary data. The survey covers commuter perceptions of safety &amp; security, financial &amp; economic factors, infrastructure &amp; comfort and functional &amp; operational features. The Relative Importance Index approach is used to analyse the data and evaluate DM performance. An ANN model is also presented to determine the factors influencing the choice to travel on the DM, with the “metro fare per trip” factor being a key consideration. Based on the analysis results, recommendations are made to improve the DM's performance. The study found that safety and security had the highest RII, followed by efficiency and viability, functional and operational features, infrastructure and comfort, and financial and economic factors. The subway fare had the lowest RII. The ANN model is adapted to understand the reasons behind low metro ridership.