Input Variables Research Articles

PERSONALITY PREDICTION SYSTEM

Personality prediction is a challenging yet crucial task in various fields such as psychology, human resources, and marketing. In this study, we propose a questionnaire-based approach using the random forest algorithm to predict personality traits. The questionnaire is designed to gather information related to the personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The dataset used for this study consists of responses from individuals who completed the questionnaire. Responses to specific questions are used as input variables for the random forest algorithm. The algorithm is trained on a portion of the dataset and then tested on the remaining portion to evaluate its performance in predicting personality traits. Our results show that the random forest algorithm achieves high accuracy in predicting personality traits, outperforming other machine learning algorithms such as logistic regression and support vector machines. This approach has the potential to be used in various applications, such as personalized marketing, recommendation systems, and mental health assessment. Key Words: Personality prediction, Random forest algorithm, personality traits, Questionnaire-based approach.

Experimental Investigation on the 3D Printing of Nylon Reinforced by Carbon Fiber through Fused Filament Fabrication Process, Effects of Extruder Temperature, and Printing Speed

This study investigated how the extruder temperature, printing speed, and specimen geometry interact during a tensile test of continuous carbon fiber-reinforced nylon matrix composites produced by the fused deposition modelling (FDM) process. The investigation utilized statistical techniques. For this purpose, tensile examinations were done on manufactured samples using a testing apparatus. The study’s objective is to identify the most efficient specimen geometry for tensile testing result optimization and to maximize the 3D printing process’s capability for producing complex, freeform patterns in these composites. In this study, the input parameters required for the response surface methodology (RSM) were varying extruder temperature (240-255°C) and printing speed (60-80 mm/s), and experimental responses included modulus, elongation at break, and weight. The findings of the regression analysis showed output responses are influenced by both input variables. The results showed that the strength of the samples was significantly influenced by the input parameters. To draw the surface and residual plots, the software of design expert software was used. The interaction between the two input variables suggests raising the extruder temperature and decreasing printing speed, which leads to printing heavier samples. Inversely, the diversity between the forecasted and real responses for the optimal specimens is less than 10% which is assumed to be acceptable for the design of experiments (DOE). The analysis took into account the lower and upper ranges of the input variable with the goal of enhancing both the most modulus and fracture elongation while simultaneously degrading the weight of the specimens. To achieve this objective, the extruder temperature and printing speed are between 240 and 250°C and 65 and 75 mm/s, respectively.

Optimizing properties of clayey soil using lime and waste marble powder: a sustainable approach for engineering applications

Several studies have explored the potential of waste marble powder (WMP) and lime (LM) as solutions for issues associated with clayey soils. While WMP enhances mechanical properties and addresses environmental concerns, LM effectively improves soil characteristics. This research investigates the efficacy of LM and WMP, both individually and in combination, in addressing challenges specific to clayey soils in Bouzaroura El Bouni, Algeria. These soils typically exhibit low load-bearing capacity, poor permeability, and erosion susceptibility. LM demonstrates promise in enhancing soil properties, while WMP not only addresses environmental concerns but also enhances mechanical characteristics, providing a dual benefit. The study utilizes a three-variable experiment employing Response Surface Methodology (RSM) Box-Behnken Design, with variations in clay content (88%–100%), LM treatment (1.5%–9%), and WMP inclusion (1.5%–9%). Statistical analysis, including ANOVA, reveals significant patterns with p-values <5%. Functional relationships between input variables (clay, LM, and WMP) and output variables (cohesion, friction angle, and unconfined compressive strength) are expressed through high determination coefficients (R2 = 99.84%, 77.83%, and 96.78%, respectively). Numerical optimization identifies optimal mixtures with desirability close to one (0.899–0.908), indicating successful achievement of the objective with 88% clay content, 3% LM, and 6% WMP. This study provides valuable insights into optimizing clay soil behavior for environmental sustainability and engineering applications, emphasizing the potential of LM and WMP as strategic additives.

Robust prediction of workability properties for 3D printing with steel slag aggregate using bayesian regularization and evolution algorithm

This study addresses the growing interest in utilizing steel slag as a sustainable alternative to river sand in additive manufacturing of concrete, driven by the increasing scarcity of natural resources. The rheological properties of fresh material significantly impact the quality of 3D-printed filament, necessitating suitable workability for printability. The research focuses on evaluating the influence of steel slag aggregate and key admixtures, such as silica fume and superplasticizer, on the workability properties of fresh concrete. Through an extensive series of 90 slump tests, optimal combinations ensuring the desired workability for 3D printing applications were identified. To enhance the manufacturing of fresh concrete and develop user-friendly tools, a novel soft computing approach is introduced—Adaptive Elitist Differential Evolution coupled with Bayesian Regularization Artificial Neural Network (aeDE-BRANN). This advanced model considers five critical input parameters: silica fume to Portland cement ratio, steel slag to cement ratio, water to cement ratio, cement content, and superplasticizer dosage. The model outputs crucial workability metrics, including slump flow and slump. In a comprehensive comparative study, the aeDE-BRANN framework demonstrates superior performance in terms of both simplicity and efficiency when compared to other forecasting models. Feature analysis techniques, including Shapley values from game theory and partial dependence plots, provide valuable insights into the intricate relationships between input variables and workability properties. The findings underscore the water to cement ratio as the most influential factor on workability, followed by silica fume to Portland cement ratio and steel slag to cement ratio. This study contributes a reliable tool for predicting workability properties, aiding in the selection of suitable steel slag aggregate and admixtures. Ultimately, it facilitates the sustainable and innovative evolution of 3D concrete printing practices.

Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter

Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.

Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the “trilateral” parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0–2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the “trilateral” parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0–2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.

Spatial differentiation and influencing factors of red tourism resources transformation efficiency in China based on RMP-IO analysis

This study employs modified data envelopment analysis (DEA) models and spatial autocorrelation methods to analyze the characteristics of red tourism transformation efficiency and categorize them into efficiency zones. By utilizing geographic detector models, the interactive driving mechanisms behind spatial differentiation are revealed, providing valuable insights for the high-quality transformation and development of China's red tourism economy. The application of modified DEA models facilitates the evaluation of red tourism resource transformation efficiency by decomposing comprehensive efficiency into single-factor efficiency for individual input and output variables. The results indicate that: (1) Expansion of tourism factors is crucial for achieving red tourism resource transformation in China, with low efficiency in resource endowment investment acting as the primary constraint. (2) Local spatial correlation between production efficiency and resource transformation efficiency demonstrates a decreasing trend from east to west, leading to the classification of China’s red tourism resources into five types of efficiency zones. (3) Endogenous ability factors predominantly affect red tourism resource transformation efficiency, with interaction between internal and external factors driving spatial differentiation.

Multivariate Bayesian Optimization of CoO Nanoparticles for CO2 Hydrogenation Catalysis.

The hydrogenation of CO2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO2 and assessed for thermocatalytic CO2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO2 and a conventionally prepared catalyst. The optimized CoO/SiO2 catalyst consistently exhibited higher activity and CH4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added.

Application of hybrid modeling to predict California bearing ratio of soil

California Bearing Ratio (CBR) is used to assess bearing capacity, deformation characteristics of roadbed soil, and base layer material in pavement structure. In general, CBR is often determined by laboratory or in-situ tests. However, it is time- and cost-consuming to conduct this experiment because this test requires cumbersome equipment such as a compressor. In this study, two Artificial Intelligence models are developed, including a simple model (Decision Tree Regression, DT) and a hybrid model (AdaBoost - Decision Tree, AB-DT). Using 214 data samples from Van Don - Mong Cai expressway, Vietnam, 10 input variables of the model were considered namely particle composition (content of gravel (X1), coarse sand (X2), fine sand (X3), silt clay (X4), organic (X5)), Atterberg limits (Liquid limit (X6), Plastic limit (X7), Plastic index (X8)), and compaction curve (optimum water content (X9) and maximum dry density (X10)). The developed models were evaluated by using a variety of statistical indicators, including coefficient of determination (R2­­), Root mean square error (RMSE), and Mean absolute error (MAE). The results show that AB-DT model has higher accuracy than the DT model. Moreover, the SHAP value analysis shows that the variable X10 influences the CBR value the most. Thus, it implies that applying the AB-DT model to effectively predict the CBR of the roadbed soil saves time and money for experiments.

Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?

ABSTRACT Objectives To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. Materials and Methods Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. Results In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. Conclusions AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Production and testing of mahua oil-based biodiesel synthesized through heterogeneous catalyst using experimental and numerical method.

A two-step treatment of mahua oil was conducted to synthesize mahua biodiesel using heterogeneous biomass-based catalyst derived from mahua shell. Mahua oil having higher free fatty acid (FFA) content (about 19%) was esterified to reduce the FFA content up to 1%. The esterification process was carried out using 200mL mahua oil, 5:1 molar ratio (methanol:oil), and 2.25 weight% of H2SO4 at a temperature of 60°C for 3h. Post esterification, a set of 16 experiments were created using a Box-Behnken design (BBD)-based response surface methodology (RSM) approach to conduct the transesterification of the esterified oil. Molar ratio, catalyst loading, reaction temperature, and reaction time were the four input variables chosen for the design of experiments. The optimized conditions for maximum biodiesel yield (87.7%) were found to be 14.88 molar ratio, 3.578% catalyst loading, 69.7°C reaction temperature, and 81.9min reaction time. The Diesel RK engine simulation tool which was experimentally validated for baseline diesel fuel was used for numerical simulation of mahua biodiesel. The performance, combustion, and emission behavior of mahua biodiesel analyzed using numerical simulation presented the sustainability of mahua biodiesel as an alternate fuel.

Evolutionary Algorithms for Strength Prediction of Geopolymer Concrete

Geopolymer concrete (GPC) serves as a sustainable substitute for conventional concrete by employing alternative cementitious materials such as fly ash (FA) instead of ordinary Portland cement (OPC), contributing to environmental and durability benefits. To increase the rate of utilization of FA in the construction industry, distinctive characteristics of two machine learning (ML) methods, namely, gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to propose precise prediction models for the compressive strength and split tensile strength of GPC comprising FA as a binder. A comprehensive database was collated, which comprised 301 compressive strength and 96 split tensile strength results. Seven distinct input variables were employed for the modeling purpose, i.e., FA, sodium hydroxide, sodium silicate, water, superplasticizer, and fine and coarse aggregates contents. The performance of the developed models was assessed via numerous statistical metrics and absolute error plots. In addition, a parametric analysis of the finalized models was performed to validate the prediction ability and accuracy of the finalized models. The GEP-based prediction models exhibited better performance, accuracy, and generalization capability compared with the MEP-based models in this study. The GEP-based models demonstrated higher correlation coefficients (R) for predicting the compressive and split tensile strengths, with the values of 0.89 and 0.87, respectively, compared with the MEP-based models, which yielded the R values of 0.76 and 0.73, respectively. The mean absolute errors for the GEP- and MEP-based models for predicting the compressive strength were 5.09 MPa and 6.78 MPa, respectively, while those for the split tensile strengths were 0.42 MPa and 0.51 MPa, respectively. The finalized models offered simple mathematical formulations using the GEP and Python code-based formulations from MEP for predicting the compressive and tensile strengths of GPC. The developed models indicated practical application potential in optimizing geopolymer mix designs. This research work contributes to the ongoing efforts in advancing ML applications in the construction industry, highlighting the importance of sustainable materials for the future.

Multi‐response optimization of characteristics for graphite reinforced biodegradable PVA‐fumaric acid cross‐linked composite: A gray‐fuzzy logic‐based hybrid approach

AbstractIn the current investigation, enhancement in the mechanical properties, thermal stability, and decrement in the moisture absorption capabilities of polyvinyl alcohol was observed by cross‐linking it with different weight percentages (wt. %) of fumaric acid and reinforcing them along with sulfuric acid‐modified graphite particles. The effects of altering the cross‐linking duration and fumaric acid wt. % were investigated in the resulting composite using the Taguchi L16 experimental design method. As in this contemporary era of rapid industrial expansion and fierce competition, the primary goals of manufacturers revolve around the production of high‐quality goods at optimal costs within a specified timeframe. Therefore, to save both cost and time, the authors have utilized a technique known as “gray‐fuzzy optimization” to balance competing goals between mechanical (toughness, elongation and strength) and physical (water intake capacity) attributes. The present approach aimed to reduce the experimentation expenses and increase the effectiveness of composite manufacturing by considerably reducing the intricacy of practical multi‐response optimization issues. Response graphs and analysis of variance tests were also utilized to calculate the percentage contribution of the input variables and provide model significance.Highlights Polyvinyl alcohol is cross‐linked with different weight percentages (wt. %) of fumaric acid, and reinforced them with sulfuric acid‐modified graphite particles. Enhancement in mechanical properties, thermal stability, and decrease in moisture absorption capabilities were perceived for the fabricated composite. Effects of altering the cross‐linking duration and fumaric acid wt. % were investigated in the resulting composite using the Taguchi L16 experimental design. Gray‐fuzzy optimization technique was utilized to balance competing goals between water intake and mechanical attributes like toughness, elongation and strength. Response graphs and analysis of variance (ANOVA) tests were also utilized to evaluate the percentage contribution of input variables and provide model significance.

Artificial intelligence modeling and simulation of membrane-based separation of water pollutants via ozone Process: Evaluation of separation

The present study offers a comparative examination of regression models that are utilized for the prediction of concentration (C) in a new hybrid ozone-membrane process for removal of water pollutants. The main focus is on the tracking ozone concentration in the feed side of a membrane contactor system. Computational fluid dynamics (CFD) was carried out to obtain data of ozone concentration (C) for developing some machine learning (ML) models. The models are based on input variables r (m) and z (m). The dataset comprises over 10,000 data, and three different models, namely Convolutional Neural Network (CNN), Support Vector Regression (SVR), and Orthogonal Matching Pursuit (OMP), are evaluated. The hyperparameters of these models are optimized using the Glowworm Swarm Optimization (GSO) technique. Prior to model training, preprocessing steps are applied. The findings suggest that SVR exhibited a noteworthy R2 score of 0.99698, surpassing CNN which obtained a R2 score of 0.98073, and OMP which obtained a R2 score of 0.8748. The aforementioned discoveries offer significant perspectives on the utilization of diverse machine learning models in the prognostication of C, demonstrating their efficacy and proficiency in this particular realm.

Conceptualization of Intelligent Control System for Humanitarian Demining Robotic Complexes Based on Verbal Methods

Introduction. Humanitarian demining is characterized by growing attention to the problems of creating and using robotic systems. The most important aspect of their use is the control method.Problem Statement. At present, the fi rst generation robotic complexes (controlled devices) have been the mostcommon, the second generation complexes (semi-autonomous devices) have been improving. To switch to the third generation complexes (autonomous devices), it is necessary to develop intelligent control systems based on artificial intelligence technologies. The most used for system development are quantitative methods, but such models are “black box models” that do not provide complete clarity about their behavior.Purpose. To develop conceptual model of intelligent control systems for humanitarian demining robotic complexes based on verbal methods.Materials and Methods. Formal logic methods, verbal analysis qualitative methods, and BPMN. Results. Conceptual model of intelligent control systems for humanitarian demining robotic complexes and sym bolic models of representing relevant declarative and procedural knowledge based on verbal methods have been developed.Conclusions. The conceptual model makes it possible to formulate Symbolic Models in the notations of selected verbal methods. At the decision-making level, the ordinary classifi cation method by denotation of the terms used by experts in the selected subject area has been chosen. At the executive level, the BPMN method has been chosen to create process diagrams in graphical notation. The verbal methods make it possible to create “white-box models” that unambiguously interpret the dependence of output and input variables and to explain the model behavior. Symbolic models in the notations of selected verbal methods allow the implementation of an intelligent control systems for a specifi c humanitarian demining robotic complex.

Minimizing Temperature Deviations in Rubber Mixing Process using Artificial Neural Networks

ABSTRACT Rubber mixing is a complex manufacturing process that poses challenges for process control due to the high number of control variables including mixing parameter settings, rheological behaviour, compound viscosity and batch-dependent material variations. Already small deviations from the control variables can influence the compound properties, leading to increased scrap rates. To address these challenges, this paper introduces an Artificial Intelligence (AI)-based approach to enhance process control in rubber mixing by predicting mixing temperatures from input variables. The proposed method utilizes Feedforward Neural Networks (FFN) to enable early identification of batch-specific temperature deviations, enabling systematic improvements with each new application. The FFN was trained on a diverse dataset encompassing various rubber recipes and batches. Post-training, the FFN demonstrated remarkable accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 1.00% on the training dataset and 1.44% on the validation dataset, thereby showcasing its efficacy in predicting temperature fluctuations within the mixing process. Consequently, the FFN can determine the relevant input variables necessary to achieve specific mixing temperatures, providing a foundation for an automated control system in rubber mixing process. This paper outlines the system architecture of the FFN tailored for rubber mixing and provides a comprehensive overview of the experimental results.

Microbial Fuel Cells Performance Evaluation through the Application of Fuzzy Logic

Objective: This work aimed to employ fuzzy logic combined with the design of experiments technique to statistically evaluate how the microbial fuel cells operating parameters influence their performance. Theoretical Framework: Microbial fuel cells (MFC) are a technology of interest in the current scenario as they allow simultaneously promoting the biotreatment of waste and the bio generation of electrical energy. Methodology: Through a bibliographical search based on publications on Google Scholar platform over the last 10 years, it was noticed that anode area, external electrical resistance and reactor volume are the most reported input parameters in MFC research and current density and power density are the output parameters most frequently portrayed in these studies, which is why these variables were selected for statistical investigation. Results and Discussion: The results showed that, for both outputs studied, reactor volume and anode area showed a positive effect, while the external electrical resistance showed a negative effect. It was also possible to develop mathematical models that indicated the relationship between the input and output variables studied, with statistical significance for power density model (R2 = 86%). Originality: Applying computational simulations and subsequent design of experiments to obtain results in accordance with those obtained experimentally in the laboratory.

Predicting hourly heating load in residential buildings using a hybrid SSA–CNN–SVM approach

This study proposes a hybrid prediction model using sparrow search algorithm (SSA) to optimize the convolutional neural network (CNN) and support vector machine (SVM), in order to perform accurate prediction of secondary supply temperature (Ts2). The historical operation data of Weifang residential building thermal station was adopted and reasonable data preprocessing was performed to suppress the interference of abnormal data. The input variables of the prediction model were screened using the correlation analysis method, taking the influence of the hysteresis effect into consideration. The SSA–CNN–SVM model was then developed for prediction. The performance of the model was evaluated by the root mean square error, mean absolute error, mean absolute percentage error (MAPE), and absolute value of relative error of each time step. The results obtained demonstrated that the SSA–CNN–SVM model has high prediction accuracy. The MAPE values of the two heat exchange stations were between 2.28 % and 2.4 %. The indoor temperature significantly affected the prediction accuracy of Ts2. After the introduction of the indoor temperature, the MAPE of the predicted values of the hybrid model was reduced by 0.35 %. The maximum reduction in MAPE of the SSA–CNN–SVM model was 1.5 % compared with other prediction models.