Multilayer Perceptron Artificial Neural Network Research Articles

Multivariate Modelling Based on Isotopic, Elemental, and Fatty Acid Profiles to Distinguish the Backyard and Barn Eggs.

The ability to trace the origin of eggs from backyard-raised hens is important due to their higher market value compared to barn-raised eggs. This study aimed to differentiate eggs from these two rearing systems using isotopic, elemental, and fatty acid profiles of egg yolks. A total of 90 egg yolk samples were analyzed, analytical results being followed by statistical tests (Student's t-test) showing significant differences in δ18O, several elements (Mg, K, Sc, Mn, Fe, Ni, Cu, Zn, As, Cd, Ba, Pb), and fatty acids compositions (C23:0, C17:0, C18:0, C16:1n7, C18:1n9, C18:2n6, C20:1n7, C20:4n6, C20:5n3, C22:6n3), as well as in the ratios of SFA, PUFA, and UFA. The results indicated a nutritional advantage in backyard eggs due to their lower n-6 polyunsaturated fatty acid content and a more favorable n-6 to n-3 ratio, linked to differences in the hens' diet and rearing systems. To classify the production system (backyard vs. barn), three pattern recognition methods were applied: linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and multilayer perceptron artificial neural networks (MLP-ANN). LDA provided perfect initial separation, achieving 98.9% accuracy in cross-validation. k-NN yielded classification rates of 98.4% for the training set and 85.7% for the test set, while MLP-ANN achieved 100% accuracy in training and 92.3% in testing, with minor misclassification. These results demonstrate the effectiveness of fusion among isotopic, elemental, and fatty acid profiles in distinguishing backyard eggs from barn eggs and highlight the nutritional benefits of the backyard-rearing system.

Predicting Biohydrogen Production from Dark Fermentation of Organic Waste Biomass Using Multilayer Perceptron Artificial Neural Network (MLP–ANN)

The focus on sustainable energy has increased interest in biohydrogen production through dark fermentation of organic waste biomass, offering dual benefits of energy production and waste management. Optimizing this process is challenging due to complex interactions among substrate composition, microbial consortia, and fermentation parameters. A multilayer perceptron artificial neural network model was developed to predict biohydrogen yield from organic waste. The model, trained on 180 data points from 35 studies, uses inputs, such as substrate type, inoculum type, concentration, pH, and temperature, with hydrogen yield as the output. The multilayer perceptron artificial neural network model achieved high accuracy, with a root mean square error of 0.3838, a mean absolute percentage error of 0.1938, and a coefficient of determination of 0.8381. These results demonstrate the model's effectiveness in predicting biohydrogen production, providing a valuable tool for optimizing the fermentation process and advancing sustainable energy solutions.

Machine Learning for the Sustainable Management of Depth Prediction and Load Optimization in River Convoys: An Amazon Basin Case Study

The seasonal fluctuation of river depths is a critical factor in designing cargo capacity for river convoys and logistics processes used for grain transportation in northern Brazil. Water level variations directly impact the load capacities of pusher convoys navigating the Amazon rivers. This paper presents a machine learning model based on a multilayer perceptron artificial neural network developed with the aim of estimating the cargo capacities of river convoys one year in advance, which is essential for determining load capacities during dry periods. The prediction model was applied to the Tapajós River in the Amazon Basin, Brazil, where grain transportation is significant and relies on inland waterways. Navigability conditions were evaluated in terms of depth and geometric parameters. The results of this case study were satisfactory, validating the computational tool and enabling the assessment of capacity losses during dry periods and the identification of navigation bottlenecks. The main contributions of this work include optimizing river logistics, reducing costs, minimizing environmental impacts, and promoting the sustainable management of water resources in the Amazon. Conclusions drawn from the study indicate that the developed model is highly effective, with an R2 of 0.954 and RMSE of 0.095, demonstrating its potential to significantly enhance river convoy operations and support sustainable development in the region.

Chemical characterization of hibiscus rosa-sinensis plant fibers facilitated through design of experiments and artificial neural network hybrid approach

The integration of natural fibers into Fiber Reinforced Polymers (FRPs) has emerged as a promising avenue for sustainable and high-performance composite materials. Natural fibers, derived from plants, offer notable advantages such as renewability, low cost, and environmental friendliness. Among these natural fibers, Hibiscus Rosa-Sinensis (HRS) plant fibers have gained significant attention owing to their widespread availability and unique mechanical properties. In this study, HRS fibers were chemically treated using Sodium Hydroxide (NaOH), Potassium Permanganate (KMnO4), and Acetic Acid (CH3COOH) at different weight percentages (3, 4, 5 Wt.%) and solutionizing times (1, 2, 3 h) based on Taguchi’s L27 orthogonal array. The fibers, extracted from epidermis of the stems, underwent cleaning and chemical treatment after water retting. The crystallinity index, determined via X-ray Diffraction (XRD), indicated a maximum value of 65.77%. Thermo-gravimetric analysis (TGA) exhibited a degradation temperature of 365.24 °C and a material loss of 63.11%. Potassium Permanganate treatment at 4 Wt.% and 3 h of solutionizing time has yielded the best results. Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) has been successfully applied to accurately predict the output physical characteristics of chemically treated HRS fibers using experimental data. The results are in close alignment with the literature. Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) analyses have provided valuable insights into the microstructure and constituents of the chemically treated HRS fibers. This research emphasises on the effectiveness of the chemical treatment process in enhancing the properties of HRS plant fibers for potential composite applications.

Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean

Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its underlying factors at a regional scale. In this context, the present study aimed to evaluate soil health in the eastern Mediterranean region by using the Sodium Adsorption Ratio (SAR) as an indicator of soil salinity in three distinct soil horizons. The main objective of the research was to evaluate the performance of four machine learning (ML) models, including Random Forest (RF), Nu Support Vector Regression (NuSVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Gradient Boosting Regression (GBR), for accurate prediction of SAR following the Recursive Feature Elimination (RFE) as a feature selection method. Moreover, SHapely Additive exPlanations (SHAP) was applied as sensitivity analysis to identify the most influential covariates. Main findings of the research revealed that the average clay content in the surface horizon (H10-25cm) was 50.5% ± 10.4, which significantly increased to 57.5% ± 8.7 (p < 0.05). No significant mean differences were detected between the studied horizons for SAR and Na+. ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is ranked as NuSVR > GBR > ANN-MLP > RF. Lastly, SHAP sensitivity analysis identified CEC, Ca+2, Mg+2, and Na+ as the most influential variables for SAR prediction in both the training and testing stages. Hence, the research yielded valuable insights for efficient agricultural soil management at a regional level using state-of-the-art technology.

Spatiotemporal analysis of land use and land cover changes, LST and NDVI in Thatta district, Sindh, Pakistan

The purpose of this work is to determine land-use and land-cover (LULC) patterns, land surface temperature (LST), and normalized difference vegetation index (NDVI) changes in Thatta district using Landsat data from 1991 to 2021 and evaluate the relationship between LST and NDVI. The research process employed the selection of the study area, data acquisition, preprocessing, and classification of remotely sensed images for the estimation of the land use land cover change (LULC), vegetation index (NDVI), and evaluation of LST using thermal bands in the Landsat dataset. The study revealed the area under built-up structures has increased from 1991 to 2021. Although the vegetation cover showed an increase, the bare soil showed a decreasing pattern, indicating a constant change in the LULC patterns in the region. The confusion matrix method for accuracy valuation of LULC data of 2021 revealed an overall accuracy of 88.24%, with a Kappa coefficient of 84.22%, while the Artificial Neural Network Multilayer Perceptron (ANN-MLP) model had a Kappa validation of 0.95 for 2021. The highest maximum temperature is observed for 2021, indicating a positive relationship between LST and built-up structures, while regression analysis found a negative correlation between LST and NDVI. This study provides a valuable monitoring framework to help resource managers develop strategies to manage land resources.

The Forecasting of TheNumber Tourists Arriving in Turkey with Intuitionistic Fuzzy Regression Functions Approach

The impacts of the tourism sector on countries are felt in various areas such as economy, cultural heritage and social development. Tourism contributes significantly to a country's foreign exchange earnings and positively affects the trade network. Tourists' spending boosts local economies and increases employment. These effects are particularly important for Turkey. Tourist visits can be used as a tool for regional promotion. Therefore, tourism demand forecasting is necessary to make the best use of these positive effects on Turkey's economic development and to plan tourism activities. Artificial neural network methods and fuzzy systems for time series forecasting problem are frequently used analysis methods in recent years. In this study, the time series of the total number of tourists visiting Turkey on a monthly basis is analyzed with the intuitionistic fuzzy regression functions approach, which is a generalization of the fuzzy regression functions approach. The analysis performance of the intuitionistic fuzzy regression functions approach is evaluated using fuzzy regression functions approach, multilayer perceptron artificial neural network and multiplicative neuron model artificial neural networks. As a result of the analysis, it is concluded that the intuitionistic fuzzy regression approach produces better forecasting results than both some artificial neural network models and the fuzzy regression functions approach. Since this is the first time that the intuitionistic fuzzy regression functions approach has been used in forecasting the number of tourists, the study aims to contribute to the literature and to help tourism industry employees to be more efficient and successful by providing them with the opportunity to make better future planning.

457 Prediction of methane emissions using rumination time and milk mid-infrared spectral data via artificial neural networks

Abstract Cattle methane emissions (ME) account for approximately 6% of global anthropogenic greenhouse gas emissions. Given the challenges in measuring ME directly from individual animals, there is a need for the development of novel indirect methods. Rumination time (RT) and milk mid-infrared spectral data (MIR) show promise for the indirect assessment of ME in dairy cows. Both traits have been used as indicators of reproduction, production, and gas emission traits. Methodologies combining the use of MIR and machine learning algorithms such as artificial neural networks (ANN) for the prediction of ME have been successful; however, the inclusion of RT has not been assessed. This study aimed to evaluate the impact of RT on milk MIR-based models using ANN for the prediction of ME. One-week averages for RT, ME, and MIR from first-lactation Canadian Holstein cows (n = 412) were calculated. Six data sets were evaluated using a multilayer perceptron ANN. All sets included age at calving, season of calving and days in milk as model factors, but varied in using milk MIR data points (1,060 or 235) and including or not including RT. The ANN architecture consisted of one input layer, one hidden layer with one or more neurons, and one output layer. Results showed that sets using both RT and milk MIR data achieved correlations from 0.5 to 0.6 between predicted and observed ME. Notably, the inclusion of RT did not improve the performance of the models. Predictions may be improved through the use of larger data sets, the use of daily records, and inclusion of data across herds and lactations. Optimizing parameters of the ANN could also improve predictions. Further research is needed to fully assess the potential of RT as a predictor of ME in dairy cows.

Designing a reliable machine learning system for accurately estimating the ultimate condition of FRP-confined concrete

Precisely forecasting how concrete reinforced with fiber-reinforced polymers (FRP) responds under compression is essential for fine-tuning structural designs, ensuring constructions fulfill safety criteria, avoiding overdesigning, and consequently minimizing material expenses and environmental impact. Therefore, this study explores the viability of gradient boosting regression tree (GBRT), random forest (RF), artificial neural network-multilayer perceptron (ANNMLP) and artificial neural network-radial basis function (ANNRBF) in predicting the compressive behavior of fiber-reinforced polymer (FRP)-confined concrete at ultimate. The accuracy of the proposed machine learning approaches was evaluated by comparing them with several empirical models concerning three different measures, including root mean square errors (RMSE), mean absolute errors (MAE), and determination coefficient (R2). In this study, the evaluations were conducted using a substantial collection of axial compression test data involving 765 circular specimens of FRP-confined concrete assembled from published sources. The results indicate that the proposed GBRT algorithm considerably enhances the performance of machine learning models and empirical approaches for predicting strength ratio of confinement (f′cc/f′co) by an average improvement in RMSE as 17.3%, 0.65%, 66.81%, 46.12%, 46.31%, 46.87% and 69.94% compared to RF, ANNMLP, ANNRBF, and four applied empirical models, respectively. It is also found that the proposed ANNMLP algorithm exhibits notable superiority compared to other models in terms of reducing RMSE values as 9.67%, 11.29%, 75.11%, 68.83%, 73.64%, 69.49% and 83.74% compared to GBRT, RF, ANNRBF and four applied empirical models for predicting strain ratio of confinement (εcc/εco), respectively. The superior performance of the GBRT and ANNMLP compared to other methods in predicting the strength and strain ratio confinements is important in evaluating structural integrity, guaranteeing secure functionality, and streamlining engineering plans for effective utilization of FRP confinement in building projects.

Comparison of appearance quality, cooking quality, and nutritional quality of geographical indication rice and their application in geographical indication discrimination

Authentication of geographical indication (GI) is important for the protection of GI rice due to its unique quality characteristics and high economic value in the market. This paper compared the appearance quality, cooking quality and nutritional quality of four types of GI rice products (Luoding rice, Zhefang Tribute rice, Xijiang rice and Wanbaozhen rice) in China. Luoding rice was found to have the highest nutritional value with respect to protein and amino acid profile. The vitamin content of Xijiang rice was significantly higher than that of other types of GI rice. Principal component analysis showed that Luoding rice had a quite different protein, aspect ratio and amino acid profile from other GI rice samples. Zhefang Tribute rice had quite different cooking quality, fatty acid and sugar profile from Xijiang rice and Wanbaozhen rice. Geographical origin was predicted by using a multilayer perceptron artificial neural network model with quality characteristics as the input variables. It was found that all the samples were accurately predicted in both training and test sets, indicating the potential of using quality characteristics to discriminate GI rice samples from different geographical origins.

Sensor for Real-Time Glucose Measurement in Aqueous Media based on Nanomaterials Incorporating an Artificial Neural Network Algorithm on a System-On-Chip

The aim of this paper is to present the development of a real-time measurement system for glucose in aqueous media. The proposed system incorporates two lines of research: i) design, synthesis, and implementation of a non-enzymatic electrochemical sensor of Multi-Walled Carbon Nanotubes with Copper nanoparticles (MWCNT-Cu) and ii) design and implementation of a machine learning algorithm based on an Artificial Neural Network Multilayer Perceptron (ANN-MLP), which is embedded in an ESP32 SoC (System on Chip). From the current data that is extracted in real-time during the oxidation-reduction process to which an aqueous medium is subjected, it feeds the algorithm embedded in the ESP32 SoC to estimate the glucose value. The experimental results show that the nanostructured sensor improves the resolution in the amperometric response by identifying an ideal place for data collection. For its part, the incorporation of the algorithm based on an ANN embedded in a SoC provides a level of 97.8 % accuracy in the measurements. It is concluded that incorporating machine learning algorithms embedded in low-cost SoC in complex experimental processes improves data manipulation, increases the reliability of results, and adds portability.

Application of Multilayer Perceptron Artificial Neural Network (MLP-ANN) Algorithm for PM2.5 Mass Concentration Estimation during Open Biomass Burning Episodes in Thailand

Application of Multilayer Perceptron Artificial Neural Network (MLP-ANN) Algorithm for PM2.5 Mass Concentration Estimation during Open Biomass Burning Episodes in Thailand

Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms

Artificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimental trials to attain specific properties such as compressive strength (CS). In this context, linear regression (LR), support vector regression (SVR), a multilayer-perceptron artificial neural network (MLP-ANN), and Gaussian process regression (GPR) algorithms, were used to predict the CS of FC. 261 experimental results were utilized, incorporating input variables such as density, water-to-cement ratio, and fine aggregate-to-cement ratio. During the training phase, 75 % of the experimental dataset was utilized. The experimental data is then validated using metrics such as coefficient of determination (R2), root mean square error, and root mean error. In comparison, the GPR algorithm reveals high-accuracy towards the estimation of CS, as proved by its high R2-value, which equals 0.98, while the R2 for ANN, SVR, and LR are 0.97, 0.90, and 0.89, respectively. Additionally, parametric and sensitivity analyses were used to assess the performance of the GPR and LR algorithms. Results revealed that density exerted the most significant influence on CS, with the GPR model showing a pronounced negative impact of fine aggregate-to-cement ratio on CS, particularly in low-density FC, contrasting with the LR model. This study confirmed that the GPR algorithm provided reliable accuracy in predicting the CS of FC. Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results.

Predicting Land Use and Land Cover Changes in the Chindwin River Watershed of Myanmar Using Multilayer Perceptron-Artificial Neural Networks

Predicting Land Use and Land Cover Changes in the Chindwin River Watershed of Myanmar Using Multilayer Perceptron-Artificial Neural Networks

Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance.

This study aimed to examine the satisfaction of dental professionals, including dental students, dentists, and dental technicians, with computer-aided design (CAD) software performance using deep learning (DL) and explainable artificial intelligence (XAI)-based behavioral analysis concepts. This study involved 436 dental professionals with diverse CAD experiences to assess their satisfaction with various dental CAD software programs. Through exploratory factor analysis, latent factors affecting user satisfaction were extracted from the observed variables. A multilayer perceptron artificial neural network (MLP-ANN) model was developed along with permutation feature importance analysis (PFIA) and the Shapley additive explanation (Shapley) method to gain XAI-based insights into individual factors' significance and contributions. The MLP-ANN model outperformed a standard logistic linear regression model, demonstrating high accuracy (95%), precision (84%), and recall rates (84%) in capturing complex psychological problems related to human attitudes. PFIA revealed that design adjustability was the most important factor impacting dental CAD software users' satisfaction. XAI analysis highlighted the positive impacts of features supporting the finish line and crown design, while the number of design steps and installation time had negative impacts. Notably, finish-line design-related features and the number of design steps emerged as the most significant factors. This study sheds light on the factors influencing dental professionals' decisions in using and selecting CAD software. This approach can serve as a proof-of-concept for applying DL-XAI-based behavioral analysis in dentistry and medicine, facilitating informed software selection and development.

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts

Pesticide pollution has been posing a significant risk to human and ecosystems, and photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) emerges as a powerful method for modeling complex water treatment processes. For the first time, this study developed novel ML models that improved the estimation of the photocatalytic degradation of various pesticides using ZnO-based photocatalysts. The input parameters encompassed the source of light, mass proportion of dopants to Zn, initial pesticide concentration (C0), pH of the solution, catalyst dosage and irradiation time. Additionally, physicochemical properties such as the molecular weight of the dopants and pesticides, as well as the water solubility of both dopants and pesticides, were considered. Notably, the numerical data were extracted from the literature via relevant tables (directly) or graphs (indirectly) using the web-based tool WebPlotDigitizer. Four ML models including multi-layer perceptron artificial neural network (MLP-ANN), particle swarm optimization-adaptive neuro fuzzy inference system (PSO-ANFIS), radial basis function (RBF), and coupled simulated annealing-least squares support vector machine (CSA-LSSVM) were developed. In comparison, RBF showed the best accuracy of modeling among all models, with the highest determination coefficient (R2) of 0.978 and average absolute relative deviation (AARD) of 4.80%. RBF model was effective in estimating the photocatalytic degradation of pesticides except for 2-chlorophenol, triclopyr and lambda-cyhalothrin, where CSA-LSSVM model demonstrated superior performance. Dichlorvos was completely degraded by ZnO photocatalyst under visible light. The sensitivity analysis by relevancy factor exhibited that light irradiation time and initial pesticide concentration were the most important parameters influencing photocatalytic degradation of pesticides positively and negatively, respectively. The new ML models provide a powerful tool for predicting pesticide degradation in wastewater treatment, which will reduce photochemical experiments and promote sustainable development.

Quantitative and deep learning based fourier transform infrared radiation and tensile characteristics study on chemically treated hibiscus rosa-sinensis plant fibers

Incorporation of natural fibers with Fiber Reinforced Polymers (FRPs) is a promising avenue for sustainable and high-performance composite materials. The fibers, derived from outer bark portion of plants, offer significant merits such as renewability, low cost, and eco-friendly. Unique mechanical and physical properties, and widespread availability of Hibiscus Rosa-sinensis have made them subject of intense research interest. The present study investigated the chemically treatment of HRS Fibers using Sodium Hydroxide (NaOH), Potassium Permanganate (KMnO4), and Acetic Acid (CH3COOH) bat varying weight percentages (3, 4, 5 Wt%) and solutionizing times (1, 2, 3 h) based on Taguchi’s L27 orthogonal array. Fourier Transform Infrared (FT-IR) analysis revealed significant changes in O–H, C–H stretching, C=O moiety, aromatic ring, and C–O/C–C stretching. Potassium Permanganate treatment at 4 Wt% and 3 h of solutionizing time has yielded the best results. Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) has been successfully applied to accurately predict the output physical characteristics of chemically treated HRS fibers using experimental data. Further Single Fiber Pull-out test results in Potassium Permanganate at 4 Wt% and 3 h solutionizing time as best sample with highest Tensile Strength and Modulus. This research underscores the effectiveness of the chemical treatment process in enhancing the properties of HRS plant fibers for potential composite applications.

PRICE DISCOVERY EFFICIENCY IN BIST LIQUIDITY BANK INDEX USING DEFERRED FUTURES: A MULTILAYER PERCEPTRON NEURAL NETWORK APPROACH

The literature indicates that the process of price discovery in spot and futures can be bidirectional. This study novelty lies in its analysis of the spot data of the BIST liquid bank index, a relatively new index in Turkey, using futures contracts of different maturities with a multi-layer perceptron (MLP) artificial neural network model. The efficacy of the models is evaluated by examining the capacity of futures prices to inform spot price discovery. The effectiveness of the MLP models is measured by low mean squared error (MSE) ratios relative to the out-of-samples test series results. The findings indicate that the one- and two-next futures contracts of the liquid bank index are more effective than the nearest futures contracts in explaining spot prices. Additionally, the nearest expiry contracts are observed to exhibit higher variances than the others. The most efficient pricing model including both spot and futures as explaining variables, is autoregression with three lags for spot and two lags for the two next futures contracts. These results must be considered when implementing risk management strategies for individuals engaged in spot and futures transactions.

Predictive study of drying process for limonite pellets using MLP artificial neural network model

Due to the decline in high-grade iron ore production, the utilization of low-grade iron ore, such as limonite, has become necessary. Limonite contains a significant amount of bound water, which requires a drying process prior to use. Excessive heat stress caused by the evaporation of bound and free water during the drying of limonite pellets can lead to pellet disintegration and adversely affect gas-solid reactions. In recent years, artificial neural network (ANN) has been developing continuously in the fields of modeling and intelligent control, and has been widely used. Many predecessors used artificial neural network model to study the drying process of natural organic matter, and analyzed the factors affecting the drying rate of organic matter. In this study, we employed big data analysis, specifically Multilayer Perceptron (MLP) artificial neural networks, to analyze the drying process of limonite pellets and successfully established a predictive drying model applicable to limonite pellets. The MLP artificial neural network demonstrated excellent fitting between predicted and experimental values, with a maxi-mum R2 value of 0.999. The artificial neural network for drying developed in this study provides technical guidance for industrial material drying, reduces the workload of manual measurements, and minimizes energy consumption.