Radial Basis Function Algorithm Research Articles

Adaptive Control Using Radial Basis Function Neural Networks for Pneumatic Artificial Muscle Systems

This study introduces a novel adaptive controller employing neural networks, particularly radial basis function (RBF) algorithms, to enhance the control performance of pneumatic artificial muscle (PAM)-based systems. The proposed controller seeks to address the nonlinearities and hysteresis inherent in PAM-based systems by integrating neural approximation. Experimental testing and comparisons with conventional controllers are conducted using an antagonistic configuration of PAMs. The results illustrate the precision and reliability of the proposed controller, suggesting potential for future advancements in trajectory tracking control of PAM-based systems.

Using multiple-point geostatistics for geomodeling of a vein-type gold deposit

Geostatistical cascade modeling of Mineral Resources is challenging in vein-type gold deposits. The narrow shape and long-range features of these auriferous veins, coupled with the paucity of drill-hole data, can complicate the modeling process and make the use of two-point geostatistical algorithms impractical. Instead, multiple-point geostatistics techniques can be a suitable alternative. However, the most challenging part in implementing the MPS is to use a suitable training data set or training image (TI). In this paper, we suggest using the radial basis function algorithm to build a training image and the DeeSse algorithm, one of the multiple-point statistics (MPS) methods, to model two long-range veins in a gold deposit. It is demonstrated that DeeSse can replicate long-range vein features better than plurigaussian simulation techniques when there is a lack of conditioning data. This is shown by several validation processes, such as comparing simulation results with an interpretive geological block model and replicating geological proportions.

Research on the Teaching Model of English Teaching Quality Evaluation under the Background of MOOC

In the context of informatization, online teaching is increasingly favored by people for its convenience and resource richness. Through the Open CourseWar (MOOC) platform, people can easily access learning resources and achieve efficient line length learning. However, currently MOOC online learning cannot provide targeted evaluation for the learning effectiveness of English learners, and there is relatively little research on online English quality assessment. Propose an intelligent MOOC online English teaching evaluation technology. Firstly, analyze the existing English teaching factors and construct a quality evaluation system for online English teaching using Principal Component Analysis (PCA) and expert evaluation method. Considering that the quality of English teaching is influenced by many factors, the evaluation of English teaching quality belongs to a complex nonlinear solving problem. Therefore, the advanced GA-RBF (Genetic Algorithm Radial Basis Function Neural Network, GA-RBF) model is adopted to solve the English teaching quality evaluation model. In teaching quality evaluation, BP and RBF are selected to participate in comparative testing of teaching quality evaluation. In the training loss test of multiple models, the GA-RBF model has the best convergence speed and training performance in the oral sample test, tends to converge after 270 iterations, with a loss value of 0.24. In the evaluation of English proficiency indicators, the BP model has a significant error in testing, with an error of 13 in the evaluation of reading ability scores. The error of RBF in reading ability score evaluation is 4, and the GA-RBF model performs the best. The reading ability evaluation error is 1, and the overall evaluation performance is the best. Through the above research, an intelligent method for evaluating the quality of English teaching is proposed, which will provide important technical references for MOOC online education evaluation and English teaching improvement.

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.

A study of angle parametric optimization of the composite honeycomb on turbine blade tip

To reduce the secondary flow loss of the high-pressure turbine caused by the tip leakage flow, the design of the composite honeycomb tip has been experimentally and numerically examined in this study. The arrangement angle of the composite honeycomb is defined and optimized using the radial basis function and genetic algorithm. The tip flow field of the high-pressure turbine blade with a 2.128 mm clearance is simulated by CFX 18.0, and the arrangement angle of the composite honeycomb is optimized to obtain a lower loss. Moreover, the flat tip, basic honeycomb tip, and optimized honeycomb tip are tested in a low-speed cascade test facility. The results show that the vortices and radial velocity induced by the honeycomb structure cavity increase the kinetic energy loss of the leakage flow and reduce the leakage flow rate. Compared with the basic honeycomb, the optimized honeycomb has a supplemental blocking effect on the tip leakage flow by reducing the crosswise pressure gradient in the blade tip. The optimized honeycomb also changes the outlet angle of the leakage flow in the tip clearance, reduces the leakage vortex, and the total pressure loss is further reduced by 2.5%.

A comparative study of different radial basis function interpolation algorithms in the reconstruction and path planning of γ radiation fields

Accurate reconstruction of radiation field and path planning are very important for the safety of operators in the process of dismantling nuclear facilities. Based on radial basis function (RBF) interpolation algorithm, this paper discussed the application of inverse multiquadric radial basis Function (IMRBF) interpolation method to the reconstruction of gamma radiation field, and proved the feasibility of reconstructing a radiation field with multiple γ sources. The average relative errors of IMRBF interpolation results were 4.28% and 8.76%, respectively, for the experimental scenarios with single and double gamma sources. After comparing the consistency between the simulated scene and the experimental scene, IMRBF method and Cubic Spline method were respectively used to reconstruct the gamma radiation field by Geant4 simulation data. The results showed that the interpolation accuracy of IMRBF method was superior to that of Cubic Spline method. Further, more RBF interpolation algorithms were used to reconstruct the multi-γ source radiation field, and then the Probabilistic Roadmap (PRM) algorithm was used to optimize the human walking path in the radiation field reconstructed by different interpolation methods. The optimal paths in radiation fields generated by multiple interpolation methods were compared. The results herein contribute to a comprehensive understanding of RBF interpolation methods in reconstructing γ radiation fields and their application in optimizing paths in radiation environments. The insights may provide valuable information for decision-making in radiation protection during the decommissioning of nuclear facilities.

Classification of cancer types based on microRNA expression using a hybrid radial basis function and particle swarm optimization algorithm.

The diagnosis and treatment of cancer is one of the most challenging aspects of the medical profession, despite advances in disease diagnosis. MicroRNAs are small noncoding RNA molecules involved in regulating gene expression and are associated with several cancer types. Therefore, the analysis of microRNA data has become one of the most important areas of cancer research in recent years. This paper presents an improved method for cancer-type classification based on microRNA expression data using a hybrid radial basis function (RBF) and particle swarm optimization (PSO) algorithm. Two datasets containing microRNA information were used, and preprocessing and normalization operations were performed on the raw data. Feature selection was carried out by using the PSO algorithm, which can identify the most relevant and informative features in the data along with helping to prioritize them. Using a PSO algorithm for feature selection is an effective approach to microRNA analysis. This enhances the accuracy and reliability of cancer-type classifications based on microRNA expression data. In the proposed method, we, respectively, achieved an accuracy of 0.95% and 0.91% on both datasets, with an average of 0.93%, using an improved RBF neural network classifier. These results demonstrate that the proposed method outperforms previous works. RESEARCH HIGHLIGHTS: To enhance the accuracy of cancer-type classifications based on microRNA expression data. We present a minimal feature selection method using particle swarm optimization to reduce computational load & radial basis function to improve accuracy.

Prediction method for response characteristics parameters of isolated-span overhead lines after ice-shedding based on finite element simulation and machine learning

In response to the problem of insufficient research on the prediction for response characteristics parameters of isolated-span transmission lines under various ice-shedding modes, study on the prediction for ice-shedding response parameters of isolated-span overhead lines is conducted in this paper based on finite element simulation and machine learning. Firstly, the finite element simulation model of isolated-span overhead line is established and verified. Seven model input parameters are determined by considering 12 typical ice-shedding modes and ice-shedding labels are introduced. Six key output parameters reflecting the electrical and mechanical characteristics of the ice-shedding transmission line are obtained by feature extraction. Secondly, the output parameters of the ice-shedding transmission line under different input parameter conditions are obtained by using the finite element simulation method. A total of 1400 sets sample data are constructed. Then, four typical regression prediction models of BP (Back Propagation Algorithm), RBF (Radial Basis Function Algorithm), SVM (Support Vector Machine Algorithm) and RF (Random Forest Algorithm) are established to predict the key output parameters of ice-shedding transmission lines. Finally, the optimal prediction model for ice-shedding response characteristics parameters of isolated-span transmission lines is selected and the application effect is analyzed by actual ice-shedding case. It is found that the RF prediction model is the optimal prediction model among the four regression prediction models. The prediction method for ice-shedding response characteristics parameters of isolated-span transmission line proposed in this paper can accurately predict the response characteristics parameters of ice-shedding transmission lines which can provide some references for the safe operation of isolated-span transmission lines in heavy ice areas.

Pantograph–Catenary Interaction Prediction Model Based on SCSA-RBF Network

As a traditional numerical simulation method for pantograph–catenary interaction research, the pantograph–catenary finite element model cannot be applied to the real-time monitoring of pantograph–catenary contact force, and the computational cost required for the multi-parameter joint optimization of the pantograph–catenary system with the finite element model is very high. In this paper, based on the selective crow search algorithm–radial basis function (SCSA-RBF) network, the time-domain signal of the panhead acceleration, which can be obtained in real-time through non-contact test technology, is taken as the boundary condition to directly solve the pantograph dynamic equation and a data-physics coupling model that can quickly predict the pantograph–catenary interaction is proposed. The prediction model is trained and verified using the dataset generated through the finite element model. Furthermore, the prediction model is applied to the multi-parameter joint optimization of six pantograph dynamic parameters and nine pantograph dynamic parameters, considering nonlinear panhead stiffness, and optimization suggestions under various speeds and filtering frequencies are given.

Design of image intelligent focusing system based on improved SMD function and RBF algorithm.

The utilization of digital statistical processes in images and videos can effectively tackle numerous challenges encountered in optical sensors. This research endeavors to overcome the limitations inherent in traditional focus models, particularly their inadequate accuracy. It aims to bolster the precision of real-time perception and dynamic control by employing enhanced data fusion methodologies. The ultimate objective is to facilitate information services that enable seamless interaction and profound integration between computational and physical processes within an open environment. To achieve this, an enhanced sum-modulus difference (SMD) evaluation function has been proposed. This innovation is founded on the concept of threshold value evaluation, aimed at rectifying the accuracy shortcomings of traditional focusing models. Through the computation of each gray value after threshold segmentation, the method identifies the most suitable threshold for image segmentation. This identified threshold is then applied to the focus search strategy employing the radial basis function (RBF) algorithm. Furthermore, an intelligent focusing system has been developed on the Zynq development platform, encompassing both hardware design and software program development. The test results affirm that the focusing model based on the improved SMD evaluation function rapidly identifies the peak point of the gray variance curve, ascertains the optimal focal plane position, and notably enhances the sensitivity of the focusing model.

Face orientation recognition based on Kinectv2 and an improved ELM model

Abstract Face orientation recognition is a crucial component of human behavior analysis. Challenges such as face occlusion and varying lighting conditions frequently degrade recognition accuracy. This study introduces an innovative approach using the Kinect sensor’s skeleton tracking technology to capture face orientation images across different lighting scenarios. To enhance image quality, techniques such as light attenuation and compensation are applied. Furthermore, multiple feature vectors based on face orientation are extracted and fused, followed by the optimization of the Extreme Learning Machine (ELM) model through the Archimedean Optimization Algorithm (AOA). The developed AOA-ELM model for face orientation recognition is compared with traditional models, including Back Propagation (BP), Radial Basis Function (RBF), standard ELM, and Genetic Algorithm enhanced ELM (GA-ELM). The AOA-ELM model exhibits superior performance in estimating face orientation amidst lighting disturbances, achieving an accuracy exceeding 95% in varied lighting conditions, thereby outperforming other classification models.

Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling

For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (φ) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap between experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.

Agile and integrated workflow proposal for optimising energy use, solar and wind energy potential, and structural stability of high-rise buildings in early design decisions

Postponing performance analysis of buildings until the final stage of the design process often leads to unexpected results, introducing errors and disrupting the architect's workflow. The literature review identifies prior research limitations, motivating this study to develop a high-rise design optimisation workflow, automate design preparation, explore alternative airflow simulation methods, and compare Optimisation (Opt) algorithms with Genetic Algorithms (GAs) for broader design considerations. We thus propose an integrated computational workflow for sustainable building design optimisation with four objectives. This study applied it in the early design phase of a practical energy efficiency-oriented redevelopment project involving a cluster of seven high-rise office buildings in Nanjing. The project aimed to decrease annual energy use, increase solar and wind energy potential, and reduce structural displacement. Our methodology primarily utilises the surrogate-model algorithm Radial Basis Function Muti-objective Optimisation (RBFMOpt) algorithm, enabling quick identification of self- and new- energy demands and cost-effective structural solutions for tall buildings while optimising energy performance. Different from traditional genetic algorithm-driven optimisation procedures or with a separated post-optimisation, our workflow follows a pre-design approach. Test results are automatically optimised in a loop without post-processing, aligning with the architect's research objectives and improving the decision-making process. Our study yielded a 12% increase in energy use efficiency and a 23% improvement in structural stability. However, achieving these gains resulted in a 32% decrease in solar energy potential. Furthermore, we provide a comparative study between RBFMOpt and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), offering critical guidance for designers when selecting optimisation algorithms to address efficiency and precision challenges in energy-related design. These findings contribute to an integrated research-for-design workflow, providing data-driven evidence to support early design decisions.

A rapid method to predict biaxial fatigue life of automotive wheels using proper orthogonal decomposition and radial basis function algorithm

This paper presents a rapid method for predicting the biaxial fatigue life of automotive wheels using a combination of proper orthogonal decomposition and radial basis function algorithm. Currently, numerical simulations of biaxial fatigue tests are being developed to evaluate wheel performance. However, these simulations are computationally expensive due to the need to simulate multiple discrete loading cases within a given biaxial spectrum. To address this issue, we propose a novel approach that utilizes proper orthogonal decomposition and radial basis function algorithm to improve computational efficiency. By leveraging high-fidelity simulation results from a small number of loading cases, a reduced order model is constructed to accurately predict the tire-rim interface force fields required for wheel strength calculations. The reduced order model significantly reduces the computational time by 65.4% for simulating all loading cases, while maintaining a maximum predicted error of less than 2% compared to the high-fidelity model. Subsequently, the predicted interface forces are mapped onto the rim surface for strength calculation, and the wheel fatigue life is determined using the Brown-Miller multiaxial damage criterion. Comparative analysis with experimental results demonstrates the desirable accuracy of our method in simulating the stress-strain history, crack initiation position, and minimum fatigue life of the wheel. Overall, the proposed method offers a powerful tool for the rapid fatigue analysis of spectrum-loaded wheels, providing an efficient and accurate means of predicting biaxial fatigue life.

Artificial neural network based prediction and optimization of centelloside content in Centella asiatica: A comparison between multilayer perceptron (MLP) and radial basis function (RBF) algorithms for soil and climatic parameter

Artificial neural network based prediction and optimization of centelloside content in Centella asiatica: A comparison between multilayer perceptron (MLP) and radial basis function (RBF) algorithms for soil and climatic parameter

Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data

Photon-counting light detection and ranging (LiDAR) emits and receives weak photon signals, which are easily mixed with background noise caused by the sun, the atmosphere, etc., and is thus difficult to distinguish. Therefore, point-cloud denoising is a key step in point-cloud data processing of photon-counting LiDAR. To explore the adaptability of different denoising algorithms for photon-counting LiDAR data in different times and spaces, in this paper, NASA’s official differential, regressive and Gaussian adaptive nearest neighbor (DRAGANN) algorithm; Herzfeld’s radial basis function (RBF) denoising algorithm; and the density-based spatial clustering of applications with noise (DBSCAN) algorithm based on density clustering are used to denoise the ICESat-2 ATL03 photon point-cloud data. Airborne LiDAR data are used to verify the denoising accuracy, and then the adaptability of the three algorithms is discussed. The results show that the DRAGANN algorithm is suitable for data with moderate Fraction Vegetation Coverage (FVC) (45–75%) at night and is less affected by slope; therefore, it is not limited to terrain slope. The denoising accuracy of the RBF algorithm decreases with increasing FVC and decreases with increasing slope. It is suitable for data with low terrain slope (0~55°) and low FVC (0~220°), which is less affected by observation time; therefore, it is suitable for all-day data. The DBSCAN algorithm is suitable for data with moderate FVC (45~75%) at night, regardless of terrain slope. Unlike the DRAGANN algorithm, the DBSCAN algorithm is greatly affected by solar noise photons, but at night, its denoising accuracy is higher than that of the DRAGANN algorithm. The research results have certain reference significance for the subsequent processing and application of ICESat-2 data.

New indices to quantify patterns of relative errors produced by spatial interpolation models – A comparative study by modelling soil properties

Spatial interpolation has been applied for mapping various variables in a wide range of environmental disciplines. This study aims to develop novel tools for examining the relative performance of different interpolation methods. We shall quantify and compare the quality of interpolation models by applying, among others, some inequality indices of error distributions. Such indices can generally be classified as non-dimensional and global. The performance measures explored here provide a valuable supplement to the conventional accuracy assessment, and have so far received only scant attention in the relevant literature. Given a wide range of potential applications for the methods discussed here, the main focus of the paper will be on empirical research concerning variability of soil properties. The eight interpolation methods, i.e., Inverse Distance Weighting (IDW), Modified Shepard’s Method (MS), Radial Basis Function (RBF), Natural Neighbour (NaN), Nearest Neighbour (NeN), Triangulation with Linear Interpolation (TIN), Local Polynomial (LP) and Ordinary Kriging (OK) were applied to estimate spatial distribution of soil pH, nitrogen, potassium and phosphorus content. Biplot methods were applied to visually examine the numerical results on the assessment of prediction quality. The ordinary kriging showed superior performance compared to the competing methods in majority of the cases. Significantly, predictions by kriging approaches revealed substantial improvement by considering data transformations. As concerns the other tested methods, the IDW and the LP algorithms tend to share similar characteristics. In turn, the NeN, RBF and MS algorithms scored relatively small inequality indices, when compared to the other methods. The use of new proposed measures will enable practitioners to gain more insightful and comprehensive evaluations of spatial interpolation techniques.

A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms

Due to the significant effects of the human anatomical characteristics on the injury mechanism of passenger in traffic accidents, it is necessary to develop human body FEM (Finite Element Model) with detailed anatomical characteristics. However, traditional development of a human body FEM is an extremely complicated process. In particular, the meshing of human body is a huge and time-consuming project. In this paper, a new fast methodology based on CPD (Coherent Point Drift) and RBF (Radial Basis Function) was proposed to achieve the rapid developing the FEM of human bone with detailed anatomical characteristics. In this methodology, the mesh morphing technology based the RBF was used to generate FEM mesh in the geometry extracted from the target CT (Computed Tomography) data. In order to further improve the accuracy and speed of mesh morphing, the target geometric feature points required in the mesh morphing process were realized via the rapid and automatic generation based on the point-cloud registration technology of the CPD algorithm. Finally, this new methodology was used to generate a 3-year-old ribcage FEM consisting of a total of 27,728 elements with mesh size 3–5 mm based on the THUMS (Total Human Model for Safety) adult model. In the entire process of generating this new ribcage model, it only took about 2.7 s. The average error between the new FEM and target geometries was only about 2.7 mm. This indicated that the new FEM well described the detailed anatomical characteristics of target geometry, thus importantly revealing that the mesh quality of the new FEM was basically similar to that of source FEM.

Renovating buildings by modelling energy–CO2 emissions using particle swarm optimization and artificial neural network (case study: Iran)

Climate change is known as a serious threat to the human species, and its significance should be considered in building design. This study aims to investigate the relationship between energy consumption and CO2 emission in Iran during the years 2018–2019 using artificial neural networks (ANNs) and regression methods. The input data were gathered and optimized by the particle swarm optimization (PSO) algorithm. Lighting, equipment load rate, wall U-value, roof U-value and people density were deliberated as effective parameters. Afterwards, the ANN was created, trained and tested by the radial basis function (RBF) algorithm; also, the data were evaluated based on statistical analysis in SPSS software. The results demonstrated R2 = 0.99 and the 45-degree line for the predicted value. Energy consumption and CO2 were reduced to 35% and 73.21%, respectively. Furthermore, CO2 emissions and energy consumption had an inverse relationship with infiltration rates (−0.201) and (−0.098). Furthermore, CO2 emission and energy consumption had a linear relation in Iran with the equation of y = 1.63x + 0.52. Moreover, based on ANOVA test, R2 linear was 0.985 and R = 0.993, illustrating significant accuracy. Architects and designers could enjoy these findings as guidelines for renovation and designing purposes so as to alleviate the negative environmental impacts of construction.

Machine Learning-Based Tap Temperature Prediction and Control for Optimized Power Consumption in Stainless Electric Arc Furnaces (EAF) of Steel Plants

The steel industry has been forced to switch from the traditional blast furnace to the electric arc furnace (EAF) process to reduce carbon emissions. However, EAF still relies entirely on the operators’ proficiency to determine the electrical power input. This study aims to enhance the efficiency of the EAF process by predicting the tap temperature in real time through a data-driven approach and by applying a system that automatically sets the input amount of power to the production site. We developed a tap temperature prediction model (TTPM) with a machine learning (ML)-based support vector regression (SVR) algorithm. The operation data of the stainless EAF, where the actual production work was carried out, were extracted, and the models using six ML algorithms were trained. The model validation results show that the model with an SVR radial basis function (RBF) algorithm resulted in the best performance with a root mean square error (RMSE) of 20.14. The SVR algorithm performed better than the others for features such as noise. As a result of a five-month analysis of the operating performance of the developed TTPM for the stainless EAF, the tap temperature deviation decreased by 17% and the average power consumption decreased by 282 kWh/heat compared with the operation that depended on the operator’s skill. In the results of the economic evaluation of the facility investment, the economic feasibility was found to be sufficient, with an internal rate of return (IRR) of 35.8%. Applying the developed TTPM to the stainless EAF and successfully operating it for ten months verified the system’s reliability. In terms of the increasing proportion of EAF production used to decarbonize the steel industry, it is expected that various studies will be conducted more actively to improve the efficiency of the EAF process in the future. This study contributes to the improvement of steel companies’ manufacturing competitiveness and the carbon neutrality of the steel industry by achieving the energy and production efficiency improvements associated with the EAF process.