Artificial Neural Network Model Research Articles

Prediction of Rheological Properties of Flour From Physicochemical Properties Using Multiple Regression Techniques and Artificial Neuronal Networks

ABSTRACTThis study has two main objectives: (i) to determine the physicochemical and rheological properties of different flours and (ii) to estimate the alveograph parameters obtained as a result of experimental studies. In this context, physicochemical (protein, ash, falling number, wet gluten, gluten index, Zeleny, and delayed sedimentation) and alveograph parameters (P, L, G, W, P/L, and IE) of 150 different bread and pastry flours were determined. Multiple regression analysis (MRA) and artificial neural network (ANN) methods were then used to predict alveograph results from this experimentally obtained data set. Root mean square error (RMSE), mean absolute error (MAE), Nash‐Sutcliffe (NSEC), and relative error (RE) performance statistics were used to evaluate the CS prediction capabilities of the methods. It was found that the flours were in the range of 11.01%–13.82% protein, 325–403 s falling number, and 30–61 mL Zeleny and delayed sedimentation values. The ANN method showed better predictive performance than the regression‐based method. W was the best estimated parameter in the ANN model. This was followed by G, L, Ie, P/L, and P values. Considering the RMSE value of the W parameter, it was observed that the ANN method provided an improvement of 5.16, 1.76, and 2.15 times compared to the regression method for the training, validation, and test sets, respectively.

Integration of CFD Analysis and Artificial Neural Networks for Estimation of Erosion Rate in Oil and Gas Pipelines

Transporting oil and gas via pipelines has been creating countless challenges; sand particles deposit and erode the pipelines wall. Generally, conventional erosion rate prediction models are conservative due to numerous generalizations and theories. Finite Element Analysis via computational fluid dynamics (CFD) approach has proven to be useful to solve sand deposition problems and to estimate the eroded pipe due to its capability to precisely determine erosion deficiencies and make predictions with great precision. In this study, a CFD model was developed to calculate sand erosion rates in a 2-inch, 90° elbow pipe. Effects of particle sizes, sand flowrates, fluids velocities and pipe diameters on erosion rates were studied. The model was validated against literature data, and it was then used to generate data via sensitivity analysis simulations. The data became the basis for developing artificial neural network (ANN) models, which were then deployed in the environment of a process simulation software called Symmetry. Based on this approach, a variety of pipelines can be modelled, and maximum erosion rates of pipelines are calculated using deployed ANN models. Hence, a comprehensive study on the fluid flow dynamics and erosion rates of the pipelines can be evaluated simultaneously.

Coupling physics in artificial neural network to predict the fatigue behavior of corroded steel wire

To accurately predict the fatigue life of corroded steel wire, the authors proposed an artificial neural network (ANN) model and a probabilistic physics-guided neural network (PPgNN) model. Factors including stress range, mean stress, and corrosion rate were considered as input features of these two neural networks. The ANN model exhibited the best prediction accuracy with a determination coefficient of 0.94; however, it cannot capture the dispersion of fatigue life. Through introduction of physics constraints, PPgNN model was able to predict the standard deviation of fatigue life. The results showed that 94.79% of the dataset was within the 95% confidence interval.

A novel ZIF-8 mediated nanocomposite colorimetric sensor array for rapid identification of matcha grades, validated by density functional theory

The rapid and intelligent assessment of matcha grades remains crucial in quality control. This study proposed a colorimetric sensor array (CSA) mediated with zeolitic imidazolate framework-8 (ZIF-8) to identify matcha grades. The ZIF-8 mediated CSA was innovatively developed by encapsulating the pH indicator and metal porphyrin within the ZIF-8 structure, improving their stability and functionality. ZIF-8 mediated CSA exhibited significantly increased sensitivity towards matcha samples driven by the preconcentration of volatile organic compounds (VOCs) facilitated by ZIF-8. As a result, the response signal values of the CSA increased by 1.13–4.75 times after ZIF-8 mediation. Subsequently, qualitative models for identifying matcha grades were established using K-Nearest Neighbor and Artificial Neural Network (ANN). The ANN model showed the higher discrimination accuracy. Compared with common CSA, the ANN model based on ZIF-8 mediated CSA achieved a better identification rate of 95 %, and recognition accuracy was improved by 7.5 % in the model. These results indicated that the ZIF-8 mediated CSA with a porous structure demonstrated enhanced specificity in capturing VOCs. Ultimately, the density functional theory has confirmed that the ZIF-8-mediated CSA exhibits high selectivity towards matcha's characteristic VOCs. These results highlight the potential of this novel CSA for the rapid identification and grading of matcha.

A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh

Flash floods are catastrophic global events, especially in northeast Bangladesh, and assessing flash flood susceptibility is crucial for preparedness and mitigation. Traditional geographic system-based flash flood susceptibility mapping struggles to capture flash floods’ non-linear and complex nature. However, machine learning models have recently emerged as an efficient alternative to address these limitations. This study evaluates four machine learning models—Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Random Forest – Gradient Boosting (RFGB) Hybrid, and Categorical Boosting (CatBoost)—for flash flood susceptibility. It categorizes areas into five susceptibility levels: very low, low, moderate, high, and very high. Covering 24,424.25 km2 across eight districts, the study uses 400 points (200 flood and 200 non-flood) for training and validation, based on field investigation, historical flood information, Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar data using Google Earth Engine, and insights from the local people. The models’ predictive performances are evaluated by incorporating topographical attributes and rainfall indices and using accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) score, with 70% of the data for training and 30% for testing. The ANN model performs best with rainfall indices, achieving high accuracy (maximum AUC = 0.802). The RFGB hybrid model shows excellent training accuracy (AUC ≥ 0.971) but suffers from overfitting during validation (AUC ≤ 0.674), requiring careful hyperparameter adjustment. The CatBoost model effectively uses both rainfall indices and terrain features, achieving AUC = 0.701 in training and AUC = 0.667 in validation. The ANN model conservatively includes the largest area (2198.3 km2) under ’very high’ susceptibility. This study’s flash flood susceptibility maps, which include rainfall extremes, are more robust than those without, helping local administrative authorities and national flood practitioners prepare for flash floods.

Environmentally Friendly Synthesis of Gelatin Hydrogel Nanoparticles for Gastric Cancer Treatment, Bisphenol A Sensing and Nursing Applications: Fabrication, Characterization and ANN Modeling

This study presents a dual application approach for the environmentally friendly synthesis of gelatin hydrogel nanoparticles with potential applications in gastric cancer treatment, bisphenol A (BPA) sensing, and nursing. Gelatin hydrogel nanoparticles were synthesized using a green and freeze-drying method, avoiding the use of toxic chemicals and solvents. The nanoparticles showed excellent biocompatibility and promising potential for drug delivery system (DDS) in gastric cancer treatment. The controlled release of anticancer drugs from the gelatin nanoparticles was showed, highlighting their potential in targeted therapy. Additionally, the gelatin hydrogel nanoparticles were explored for BPA sensing. BPA is a widely used chemical known for its adverse effects on human health. The gelatin nanoparticles showed high selectivity and sensitivity towards BPA detection, making them suitable for environmental monitoring and health applications using scanning electron microscope (SEM). Also, in this study, an artificial neural network (ANN) was used to estimate the release of docetaxel (%) at 72 hours, the release of paclitaxel (%) at 72 hours, tensile strength with sample (wt%), and porosity (%) in broader ranges than the experimental samples. The environmentally friendly synthesis of gelatin hydrogel nanoparticles presented in this study offers a versatile platform with dual applications in gastric cancer treatment and sensing of harmful chemicals. The obtained results show the potential of these nanoparticles for innovative therapeutic and diagnostic strategies in healthcare and environmental monitoring. The study showed the development of sustainable and multifunctional nanomaterials for various biomedical applications. The modeling of the neural network predictions shows that increasing the sample (wt%) and porosity (%) leads to an increase in the release of docetaxel (%) at 72 hours, the release of paclitaxel (%) at 72 hours, and tensile strength. As porosity decreases, the release of docetaxel increases, and the release of paclitaxel and tensile strength also increase. Additionally, the prediction errors of the ANN in this study were evaluated using linear regression, showing acceptable error rates compared to the target results obtained from the experimental tests.

Estimation of gas hold-up in bubble columns using wall pressure fluctuations and machine learning

We present a novel approach to estimate gas hold-up in bubble columns using wall pressure fluctuations – without requiring knowledge of operating flow regime, column configuration or physical properties. The approach uses machine learning and pressure fluctuations acquired from a wall mounted pressure sensor. The approach is validated by carrying out experiments with different physical properties of liquid (deionised water, deionised water – alcohol [ethanol, propanol and butanol] mixture up to 2 % alcohol, deionised water – glycerine (30 %), tap water). The majority of the data was obtained with 0.1 m diameter bubble column. Data with tap water in this column was obtained for four different spargers. Data with tap water was also obtained for 0.15 m and 0.33 m diameter bubble columns. Gas velocity was varied up to 0.1 m/s. The covered physical properties, sparger configurations, column diameter and gas velocities span different flow regimes. An artificial neural network (ANN) based model was developed to relate characteristics of wall pressure fluctuations and superficial gas velocity to the gas hold-up. The approach was validated by comparing predicted results with the experimental data unseen by the ANN model. The approach demonstrates the feasibility of using wall pressure fluctuations with machine learning to estimate key characteristics without prior knowledge of column configuration, flow regimes or physical properties.

Phosphate removal from aqueous solutions using reversal mode electrocoagulation with iron and aluminum electrodes: RSM optimization and ANN modeling

Abstract Phosphate pollution significantly contributes to eutrophication and the degradation of aquatic ecosystems. The removal of phosphate from wastewater before discharging into the environment is essential for the sustainability of the ecosystem. This work focuses on using a polarity reversal mode electrocoagulation (PRM-EC) system integrated with iron (Fe) and aluminum (Al) electrodes to remove phosphate from wastewater. The conditions for the removal process were optimized using response surface methodology (RSM). Central composite design (CCD) was used to design the experiment and numerical optimization was utilized to find the optimal conditions. The phosphate removal efficiency could reach 93.12% at a current density of 40 A m−2, time of 30 min, pH of 6.4, and electrode distance of 10.5 mm. The energy consumption was about 0.4 kW m−3. The artificial neural network (ANN) modeling showed that the current density was the most influencing factor, followed by time, pH, and electrode distance. The mechanism underlying the PRM-EC process encompassed electrode dissolution, floc formation, phosphate adsorption, and precipitation. The findings in the work show that PRM-EC is an environmentally friendly and effective solution for phosphate removal.

Weighted average algorithm: a novel meta-heuristic optimization algorithm based on the weighted average position concept

To address the ever-increasing complexity of real-world engineering challenges, meta-heuristic algorithms have been extensively studied and applied. However, balancing exploration and exploitation remains a significant challenge. In this paper, a novel meta-heuristic optimization algorithm based on the weighted average position concept, and named weighted average algorithm (WAA), is proposed and implemented. In this algorithm, the weighted average position for the whole population is first established at each iteration. Subsequently, WAA applies one of two movement strategies to balance exploration and exploitation, determined by a parameter function that depends on random constants and iteration numbers. To validate the effectiveness and reliability of WAA, it is applied to various optimization challenges, amongst which unconstrained benchmark functions and constrained engineering challenges. Based on the Friedman and Wilcoxon analyses, it can be concluded that the proposed algorithm obtained the best performance for the considered benchmark functions and engineering problems. The WAA is applied to prediction and optimization of surface waviness in Wire Arc Additive Manufacturing (WAAM) components. First, two prediction models relating WAAM process parameters and surface waviness are established based on an Artificial Neural Network (ANN) optimized by WAA, and Particle Swarm Optimization (PSO), respectively. The WAA optimized ANN (WAA/ANN) model outperforms both the standard ANN models and those optimized by PSO. Finally, leveraging the developed WAA/ANN prediction model, WAA and other optimization algorithms are applied to minimize waviness of a WAAM component, with WAA exhibiting promising performance.

Artificial neural network guided optimization of limiting factors for enhancing photocatalytic treatment of textile wastewater using UV/TiO₂ and kinetic studies

Wastewater effluents discharged from textile industries are characterized by excessive chemical and biochemical oxygen demands with significant amounts of harmful synthetic dyes that spoil the healthy environment. The present investigation focused on process optimization to develop an effective strategy for degrading and decolorizing wastewater from textile industries through an advanced photocatalysis oxidation process. The synergistic effect of Titanium dioxide (TiO2) nanoparticles as a photocatalyst with ultraviolet irradiation was applied as a novel technique to detoxify wastewater effluents from textile industries. In addition, the process was modeled and optimized using response surface methodology (RSM) and artificial neural networks (ANN). The kinetics of the photocatalytic degradation of wastewater effluents from textile industries were analyzed. The optimal condition predicted by the RSM was similar to the experimental outcomes, further, the predicted values from the ANN model had confirmed the prediction. The most significant limiting parameters, such as catalyst dosage, pH, and irradiation time, were optimized systematically as TiO2 dosage of 429.31mg/l, pH of 8.89, and irradiation time of 5h, respectively. Under these optimal conditions, COD reduction and decolorization of 90 and 88%, respectively, were achieved.

Data-driven multi-objective optimization of aerodynamics and aeroacoustics in dual Savonius wind turbines using large eddy simulation and machine learning

Savonius rotor is a popular form of vertical-axis wind turbine (VAWT) for small-scale and urban applications because of its straightforward design and self-starting ability. Dual VAWTs present challenges in terms of wake interactions and noise, particularly in urban areas. Optimizing these parameters is essential for future wind energy adoption. This research is the first to analyze how the interaction of wakes from adjacent rotors, combined with a deflector, affects both the aerodynamic performance and noise levels of dual Savonius rotors. Large Eddy Simulation is applied, as it effectively captures detailed turbulent wind flows and their interactions with wind turbines. A multi-objective optimization method combining Machine Learning and Computational Fluid Dynamics (CFD) is developed to optimize rotors for maximum power efficiency and minimum noise, considering their wake interactions with a unique deflector system. First, the influence of geometric parameters on aerodynamics and aeroacoustics characteristics of rotors is analyzed, and the database is generated using Design of Experiment approach. Next, the CFD model is replaced by Artificial Neural Network (ANN) model established for predicting rotor performances. A Multi-Objective Genetic Algorithm method is used to optimize aerodynamics and aeroacoustics characteristics of rotors. Finally, optimal design parameters are identified from the Pareto front using the technique for order of preference by similarity to ideal solution decision-making method. The ANN model demonstrated high accuracy with an RANN2 of 0.995 and 0.971 for the average power coefficient (CP) and overall sound pressure level (OSPL) predictions, respectively. Multi-objective optimization revealed the best configuration of the deflector with bleed jets, improving the average CP up to 57.5% and reducing OSPL to an almost 5.2% compared to the dual rotor case at TSR = 0.8.

Multi-objective optimisation and verification of creep-resistant Ni-base superalloy for electron-beam powder-bed-fusion

This paper reports the use of integrated computational alloy design, coupled with a rapid printability screening method, to downselect from a total of 70000 datasets in design space to five candidates in the first step, and then from five to one in the second step. The new Ni-base superalloy with compositions of Ni-5.03Al-2.69Co-5.63Cr-0.04Hf-1.91Mo-2.36Re-3.32Ta-0.57Ti-8.46W-0.05C-0.019B exhibits an optimal balance of density (8.82 g/cm2), printability (freezing range of 107 °C), thermal stability (γ′-volume fraction of 50.7% at 980 °C and low Md‾ value) and creep (rupture time of 612 h at 980 °C/120 MPa). The micro-hardness varies mildly from 417.2 ± 18.5 to 434.7 ± 14.6 HV, suggesting good phase stability. This is substantiated by microstructure observations, which revealed the absence of a topologically close-packed phase. Machine-learning tools of the artificial neural network (ANN), random forest, and support vector regression, respectively, were used to predict creep rupture time. The ANN algorithm achieves the highest accuracy in predicting creep life. By recognising the “black box” nature of the ANN, interpretability analysis was conducted using the local interpretable model-agnostic method. The analysis supports that the ANN model truly learned meaningful functional relationships, and thus is judged as reliable. Feature correlation evaluation outcome emphasises the importance of incorporating microstructure-related input features.

Predicting Binge Eating Disorder Using Machine Learning Methods

Eating disorders are enduring conditions characterized by elevated rates of mortality and morbidity, presenting a serious threat to life. Among these disorders, binge eating disorder is the most prevalent. Therefore, it is an important health problem that often results in obesity worldwide. This study was conducted to evaluate the eating attitudes and behaviors of university students and predict binge eating disorder using machine learning methods. The study was carried out on 306 individuals (117 males, 189 females). Individuals' personal characteristics were questioned with the questionnaire form. The Bulimic Investigatory Test Edinburgh (BITE) test was used to determine whether individuals taking part in the study had binge eating disorder. In this study, in which binge eating disorder was classified, different artificial neural network models were created by changing the basic parameters, and the optimum model was assessed accordingly. Among the models created with different layers and activation functions, the optimum results were obtained using the number of fully connected layers as 2, first and second layers' sizes as 10, and ReLU, a non-linear activation function, in the Bilayered Neural Network structure. This study is the first trial in which binge eating disorder is predicted using machine learning methods, and we believe that machine learning is an important tool to help researchers and clinicians diagnose, prevent, and treat eating disorders at an early stage.

A low-impact nature-based solution for reducing aquatic microplastics from freshwater ecosystems

Effective nature-based solutions (NBS) and strategies for freshwater microplastic (MP) pollution are beneficial for reducing ecological and human health risks. This study proposed an innovative NBS for the in-situ retention of aquatic MPs. By evaluating the tolerance and MP retention efficiency of different submerged macrophytes, Myriophyllum aquaticum was identified as a well-suited system for optimization as NBS for operational MP retainment practice. The response surface method and artificial neural network modeling were applied to determine the optimal operational strategy of this solution, which was determined to be at a flow rate of 60 L/h, aeration intensity of 5 m3/(m2·h), and plant density of 190 plants/m2. Under this strategy, an average MP retention of 93.38% was achieved for the actual tested lake. The retention of MPs was particularly effective for particle sizes larger than 100 μm (especially films and fragments) and for the 4 polymer types. At the same time, also total nitrogen and phosphorus levels in the treated waters were reduced by 80.0% and 78.4% respectively, reflecting the added environmental value for water purification. This NBS provides a feasible strategy for mitigating MP pollution, but further research is needed on its long-term applicability and potential ecological effects in a wider range of specific environments, and effective development of plant harvesting cycle strategies is also essential to achieve long-lasting MP pollution removal.

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.

Numerical Simulation and Artificial Neural Network Modeling of Cavitation on 2D Tandem Hydrofoils Arrangement

Abstract In the current research, we used a parametric approach to investigate cavitation with tandem-arranged Clark Y-11.7% hydrofoils. We simulated the tandem-arranged hydrofoil system using ANSYS FLUENT with the Zwart cavitation model combined with the Transition SST turbulence model. To substantiate the accuracy and reliability of the numerical model, computations were performed for an individual Clark Y-11.7% hydrofoil and compared to experimental results. The following parameters were studied in tandem arrangement: cavitation number(σ), horizontal direction spacing ratio between two hydrofoils(x), vertical direction spacing ratio between two hydrofoils(y), angle of attack (AOA) of the upstream hydrofoil (α 1), and angle of the downstream hydrofoil relative to the upstream hydrofoil (α 2). The lift coefficient (C L) was used to estimate due to the acceptable error. An optimal artificial neural network (ANN) model was trained based on the five-parameter combination from numerical simulations. The model’s weights and biases were presented, providing a prediction method for hydrofoils in tandem arrangement with cavitation. A global sensitivity analysis based on the trained model is implemented, which reveals the AOA of the downstream hydrofoil (α 2), the horizontal spacing ratio (x), and the AOA of the upstream hydrofoil (α 1) have a main effect on the performance of the tandem arrangement.

A Survey on Genetic Disease − Autism Spectrum Disorder Prediction and Classification in Machine Learning

Autism spectrum disorder (ASD) is a hereditary, neurological condition with many aetiologies that manifest in early childhood. Mental illnesses, including anxiety, poor communication, and a lack of recurrent interest, may result from ASD. It can be highly advantageous for children to improve their psychological wellness level if the ASD is recognized in the earlier years of life. Furthermore, machine learning (ML) approaches are now essential for diagnosing and categorising ASD. The creation of computer programmes that can acquire data and utilise it to gain knowledge for oneself is the main goal of this aspect of artificial intelligence. Many scholars have suggested various ML strategies for quickly and accurately detecting the various forms of ASD. This paper presents a survey on ASD prediction and classification using ML methods-based research articles from the year 2016 to 2023. Moreover, the current survey article discusses the performance assessment employing different metrics and made a comparative assessment to determine the ML model’s effectiveness. From this survey, it is identified that Artificial Neural Network model has attained better results than other ML algorithms. Moreover, further ASD studies employing an ML strategy for feature selection, prediction and classification can greatly benefit from this research.

A geographically weighted neural network model for digital soil mapping of heavy metal copper in coastal cities

Assessing the spatial distribution of heavy metals in soil is essential for mitigating risks to human health and ensuring the sustainable use of soil resources. This study proposed a geographically weighted neural network (GWNN) model, leveraging deep learning and geographically weighted regression (GWR). The model was designed based on the GWR concept to address the spatial autocorrelation of copper (Cu) in soil and incorporated convolutional neural network (CNN) to capture the nonlinear relationships between Cu and environmental covariates. The GWNN model was compared with GWR, extreme gradient boosting (XGBoost), and artificial neural network (ANN) models. XGBoost was employed to select important environmental covariates and spatial autocorrelation of Cu concentrations was assessed using Moran’s I. The model’s performance was evaluated using 10-fold cross-validation, and prediction uncertainty was quantified with 100 bootstrap models. The results indicated that temperature covariates were the most significant predictors of soil Cu concentrations. The R2 values for Cu prediction accuracy were 0.60 for GWNN, 0.53 for ANN, 0.49 for XGBoost, and 0.32 for GWR. The spatial distribution of Cu showed a trend of higher concentrations in the north and lower concentrations in the south, consistent with spatial clusters identified by local Moran’s I. The mean uncertainty of the 90% confidence interval for GWNN was 16.49%, closely aligning with XGBoost (15.44%) and ANN (16.29%) and significantly outperforming the GWR (18.25%). Overall, the GWNN model demonstrated strong predictive accuracy and low uncertainty, offering an improved approach for digital soil mapping applications.

An Experimental and ANN Analysis of Ammonia Energy Integration in Biofuel Powered Low-Temperature Combustion Engine to Enhance Cleaner Combustion

In the pursuit of global net-zero emissions targets and cleaner combustion technologies, researchers are increasingly turning to innovative solutions. Ammonia, as a carbon-free fuel, holds promise for achieving low emissions and efficient combustion. This study explores the potential synergies of various ammonia shares (20%, 40%, and 60%) combined with Citronella biofuel in a Low-Temperature Combustion (LTC) Reactivity Controlled Compression Ignition (RCCI) engine. Comparative analysis between a 40% ammonia blend Citronella biofuel (A40) and a Standard Diesel (SDL) in an RCCI engine demonstrates significant reductions in oxides of nitrogen (by 17%), carbon dioxide (by 6.6%), and smoke (by 9.8%), along with a notable enhancement in Brake Thermal Efficiency (BTE) by 5.4% for A40. The validation of results using an Artificial Neural Network (ANN) model shows robust coefficients of determination (97%), high R values (0.9945 to 0.9995), and low Root Mean Square Error values (ranging from 0.0219 to 0.0483). These findings strongly support the efficacy of the A40 blend, positioning it as a promising and viable alternative fuel for achieving cleaner combustion in LTC-RCCI engines.

Prediction of soil heavy metal content around mine tailings using multiple methods combined with transformed hyperspectral reflectance data

The extensive accumulation of tailings can potentially cause heavy metal contamination in the surrounding farmland soil. Accurately predicting the spatial distribution of heavy metals in farmland soil is crucial for assessing the potential environmental hazards of tailings.This study focuses on the spatial distribution and the quantitative prediction of heavy metals (chromium (Cr), vanadium (V), and copper (Cu)) in soils surrounding mine tailings using advanced spectral data analysis and multiple prediction models. The original hyperspectral reflectance data were processed using first-order differential (FD), second-order differential (SD), reciprocal logarithmic (LR), and continuum removal (CR) transformations to highlight the positions of characteristic bands. Multiple linear regression (MLR), stepwise linear regression (SLR), partial least squares regression (PLSR), random forest (RF), and back propagation artificial neural network (BP-ANN) models were used to establish inversion models for Cr, V, and Cu based on bands with high correlation coefficients. The performance of the inversion models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). The results indicate that the raw hyperspectral data from the measured soil exhibit a weak response to heavy metal content in the study area. However, applying FD, SD, and CR transformations significantly enhances the sensitivity of soil spectral data to heavy metal concentrations, facilitating subsequent modeling. Among these, the SD transformation is particularly beneficial for modeling the Cr and Cu elements in the soil. For the V element, the FD transformation yields data that are more suitable for modeling. Regarding the inversion models based on the measured spectral data, the BP-ANN model exhibited the best predictive performance. Specifically, when combined with SD spectral data, the BP-ANN achieved the highest predictive accuracy for Cu content (R² = 0.85, RPD = 2.12). The RF model demonstrated the next best performance, with its optimal inversion model also utilizing SD spectral data for predicting Cu content (R² = 0.76, RPD = 1.90). On the other hand, the MLR model exhibited the poorest performance and is unsuitable for predicting heavy metal content in the region using the measured spectral data. This study highlights the potential of spectral data in environmental monitoring and provides a technical reference for the inversion assessment and regulation of heavy metals in farmlands surrounding tailing sites.