MTW European Type Trapezium Mill
Input size:30-50mm
Capacity: 3-50t/h
LM Vertical Roller Mill
Input size:38-65mm
Capacity: 13-70t/h
Raymond Mill
Input size:20-30mm
Capacity: 0.8-9.5t/h
Sand powder vertical mill
Input size:30-55mm
Capacity: 30-900t/h
LUM series superfine vertical roller grinding mill
Input size:10-20mm
Capacity: 5-18t/h
MW Micro Powder Mill
Input size:≤20mm
Capacity: 0.5-12t/h
LM Vertical Slag Mill
Input size:38-65mm
Capacity: 7-100t/h
LM Vertical Coal Mill
Input size:≤50mm
Capacity: 5-100t/h
TGM Trapezium Mill
Input size:25-40mm
Capacity: 3-36t/h
MB5X Pendulum Roller Grinding Mill
Input size:25-55mm
Capacity: 4-100t/h
Straight-Through Centrifugal Mill
Input size:30-40mm
Capacity: 15-45t/h
Slag machine model
Slag foaming estimation in the electric arc furnace using machine
2021年5月1日 In this study, a soft sensor model, which correlates the influential process variables with the slag foaming height, was developed by using machine learning based long 2023年11月1日 The trained model described the relationships between multiple design parameters, including c/t ratio, solids content, and curing time and UCS of OPCCPB These A machine learning model to predict unconfined compressive 2024年4月1日 This study aims to mitigate this deficiency by introducing a DT algorithm to establish a model for predicting slag eyes and entrapment Due to challenges in onsite data Metallurgical Mechanism Guided Machine Learning to Predict Slag 2023年10月4日 Sulfide capacity is one of the essential properties of molten slag, which determines the sulfur content in the hot metal during the smelting process In the present Sulfide Capacity Model for Multicomponent Molten Slag Based on
Prediction of Slag Viscosity Based on Machine Learning
2022年11月28日 Experimental data from viscosity measurements of 124 glassy slags were used to drive and develop machine learning models that could be used for direct or indirect viscosity prediction Samples were categorized the new model and database, reliable calculations of the slag viscosity are possible for not only selected model systems but also many multicomponent industrial and geological applications Accurate Viscosity Prediction for Molten Slags: A New Model and 2021年3月1日 In this work, experimental data were collected from the literature and a regularized extreme learning machine (RELM) model was established to predict the sulphide capacity of the CaO–SiO 2 –MgO–Al 2 O 3 slag system Sulphide capacity prediction of CaO–SiO2–MgO–Al2O3 mentation of a machine learning– based online model for slag conditioning in ladle furnaces The model calculates the lime and aluminum additions in order to achieve a slag target chemical Machine Learning–Based OnLine Model for Slag Conditioning in
Mathematical modelling and plant trial on slagging
2021年10月1日 In this study, a calculation model of slagmaking materials was established based on the actual LF refining conditions, the metallurgical mechanism model and the production data model of the sulphur distribution ratio2022年11月28日 shows the indirect prediction of slag viscosity compared with the experimental results Experiments 1, 2, and 3 shown in Figure 10a were the measurement results of the same sample at different Prediction of Slag Viscosity Based on Machine Learning for The majority of the world’s population (about 4 billion people) now uses social media such as Facebook, Twitter, Instagram, and others Social media has evolved into a vital form of communication, allowing individuals to interact with each other and share their knowledge and experiences On the other hand, social media can be a source of malevolent conduct In fact, Machine Learning Model for Offensive Speech Detection in 2024年2月26日 This study rigorously examines the impact of various data preprocessing techniques on the accuracy of machine learning models in predicting concrete's compressive strength It develops ten regression models under nine distinct preprocessing scenarios, including normalization, standardization, principal component analysis (PCA), and polynomial features, Evaluating the Sensitivity of Machine Learning Models to Data
Effect of composition and curing on alkali activated fly ashslag
2023年2月22日 A random forest (RF) model was developed on these datasets to predict FST and UCS of AAMs The hyperparameters of the RF model were optimized using the Genetic Algorithm (GA) Results show that the hybrid GARF model achieved the highest prediction accuracy on the test set of UCS (0932) and FST (0997), compared to other machine learning 2022年11月28日 Experimental data from viscosity measurements of 124 glassy slags were used to drive and develop machine learning models that could be used for direct or indirect viscosity prediction Samples were categorized according to the content of chemical components or general competitive neural network The direct viscosity prediction using artificial neural Prediction of Slag Viscosity Based on Machine Learning for Deburring and Slag Removal of Hot Rolled Steel Plate This video demonstrates deburring hot rolled steel plates with an Apex Machine Group Model 2037MD 37″ single head slag grinder This machine is designed to quickly grind plasma slag or laser Wide Belt Sander Demonstration Videos Apex Machine GroupGTL drumtype cinder cooling machine is designed and developed to cater for the development trend of largecapacity fluidized bed boiler The machine is composed of a shuttertype heat transfer drum, a slag inlet device, a slag outlet device, a rotating mechanism, a cooling water system, an electric control device, etcGLJn series drum cold slag machineZdz
Research on prediction of compressive strength of fly ash and slag
2022年12月27日 Every year, a large amount of solid waste such as fly ash and slag is generated worldwide If these solid wastes are used in concrete mixes to make concrete, it can effectively save resources and protect the environment The compressive strength of concrete is an essential indicator for testing its quality, and its prediction is affected by many factors It is 2023年4月1日 Blast Furnace Slag (BFS) could be defined as a mixture of poorly crystalline phases with composites similar to gehlenite (2CaOAl 2 O 3SiO 2) This research analyzes the compressive strength of concrete containing GGBFS Compressive strength of concrete containing furnace blast slag 2024年1月28日 The present research employs new boostingbased ensemble machine learning models ie, gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS Boostingbased ensemble machine learning models for 2024年7月5日 This study applies machine learning models to predict the slag eye area, using a comprehensive 13feature dataset from literature spanning 25 years Various machine learning models were tested, including Linear Regression, Support Vector Machines, and Machine Learning Approach for Accurate Slag Eye Predictions in
Slag Crushing Machine at Best Price in India India
Find here online price details of companies selling Slag Crushing Machine Get info of suppliers, manufacturers, exporters, traders of Slag Crushing Machine for buying in India 8mm Till 32mm Plate Rolling Machine Rental, 100 % 2021年1月1日 The proposed Machine Learningbased approach consisted of first applying data filtering techniques and selecting the predictive variables The slag prediction model was addressed using three different focuses: 1) a weekly model of the generated slag; 2) a calculated slag model per casting; 3) different slag composition models 31A slag prediction model in an electric arc furnace process for A whitebox machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of ecofriendly concrete The model was compared with the other two machine learning models, where one is also a whitebox machine leaCompressive strength of concrete containing furnace blast slag 2022年9月21日 Due to the high cost and low accuracy of hightemperature tests, the viscosity data for multicomponent slag systems is difficult to be obtained precisely Therefore, it is important to fulfill the viscosity database of the multicomponent slag systems via reasonable methods with lower costs In this study, a viscosity prediction method based on the machine learning method Composition Engineering on the Local Structure and Viscosity of
A Creep Model of Steel Slag–Asphalt Mixture Based on Neural
2024年7月3日 To characterize the complex creep behavior of steel slag–asphalt mixture influenced by both stress and temperature, predictive models employing Back Propagation (BP) and Long ShortTerm Memory (LSTM) neural networks are described and compared in this paper Multiple stress repeated creep recovery tests on AC13 grade steel slag–asphalt mix samples 2022年11月28日 By applying these machine learning techniques, suitable methods can be screened out by comparing the results The purpose of this research is to develop and validate viscosity prediction models for glassy slag by machine learning technology The viscosity data measured in a mildly reducing atmosphere were used in the model development processPrediction of Slag Viscosity Based on Machine Learning for 2024年7月24日 The waste products phosphogypsum (PG) and ground granulated blast furnace slag (GGBS) contained alumina and silica which were helpful in the geopolymerisation This research focused on the mechanical properties of geopolymer concrete by substituting different proportions of PG with GGBS at varying concentrations of sodium hydroxide (NaOH) The Predictive Modeling of Geopolymer Concrete Properties The 42 Series Rotary Brush dry machine is the ultimate machine for deburring, finishing, edge radiusing, laser oxide removal, and heavy slag removal This machine offers the perfect solution for manufacturers who supply premium quality productsMetal Wood Finishing Products Timesavers, LLC
Metaheuristic optimization of machine learning models for
2024年3月4日 The present study focuses on producing highperformance ecoefficient alternatives to conventional cementbased composites The study is divided into two parts The first part comprises of production of highstrength selfcompacting alkaliactivated slag concrete (SCAASC) with GGBFS as a primary binder The second part deals with the development of 2024年7月11日 41 Model building for Random Forest and AdaBoost regression The machine learning (ML) model development approach used in this study is shown in Figure 5 A dataset was imported and the training and test Full article: Machine learning prediction and The main design parameters of the combined drum cold slag machine are shown in Table 1 Table 1 main design parameters of combined type roller slag cooler Design Parameters value unit Number of combined series 4 group Effective calculation length of slag machine 32 m Slag inlet temperature 1000 ℃ Slag outlet temperature ≤170 ℃Study on Heat Transfer Performance of a Combined Type Roller Cold Slag With over 50 years of combined experience and a wide range of machine models, Apex provides innovative allinone deburring and finishing equipment tailored to your application, production level, and budget Send Us Your Slag grinding IndustryLeading Machines for Metal Processing and
Sulfide Capacity Model for Multicomponent Molten Slag Based
2023年10月4日 Sulfide capacity is one of the essential properties of molten slag, which determines the sulfur content in the hot metal during the smelting process In the present study, an artificial neural network model was developed to predict the sulfide capacity of molten slag over a wide range of components and temperature A Apex Machine Group4700 Olson Memorial Highway Golden Valley, MN 55422; TollFree: 855500APEX (2739) Local Phone: 9528951518 : info@apexmachinegroup52" Deburring Finishing Machine Apex Model 2052MDSSDc2024年2月5日 This study aims to identify the most suitable machine learning model for predicting the permeability and halfcell potentiometer test readings of hybrid concrete containing varying percentages of blast furnace slag and fly ash when exposed to a chloriderich environment The mix design adhered to IS 10262:2019 standards, and hybrid concrete beam Datadriven machine learning approaches for predicting 2023年2月1日 In this study, hence, machine learning (ML)based models are proposed for predicting fragility parameters of structures namely dispersion, β, and μ (log (PGA)), based on a reduced set of Compressive strength of concrete containing furnace blast slag
Slag foaming estimation in the electric arc furnace using machine
2021年5月1日 The reference data of the slag foaming height were measured by indirect measurement method using vibration sensors Slag foaming height is nonlinear and sequential; thus, LSTM networks are used The estimation model is evaluated by performance metrics; RMSE, R 2 and correlation coefficientPDF On Feb 1, 2023, Mo Zhang and others published Effect of composition and curing on alkali activated fly ashslag binders: Machine learning prediction with a random forestgenetic algorithm Effect of composition and curing on alkali activated fly ashslag 2024年2月16日 With the development of “Industry 40”, artificial intelligence technology is gradually being applied in the steel industry In actual production, there is a serious lag in viscosity measurement methods First, this paper used big data technology to analyze and process the viscosity test data and then established prediction models based on the CatBoost model and Prediction Model for Viscosity of TitaniumBearing Slag Based on 2022年7月25日 Also, for steel slag models, Bian et al [15] reached results with deviations around 25–5% Systematic comparison of five machinelearning models in classification and interpolation of soil particle size fractions using different transformed data Hydrol Earth Syst Sci, 24 (5) (2020) Predicting the compressive strength of steelmaking slag concrete
Machine learning and interactive GUI for concrete compressive
2024年7月19日 Similarly, a study by Nguyen et al 32 introduced four distinct machinelearning models to anticipate the compressive and tensile strength of HPC, highlighting the superior output accuracy of 2023年7月15日 This paper aims to construct the autogenous shrinkage prediction model of alkaliactivated slagfly ash geopolymer paste and mortar considering slagtobinder GUI of autogenous shrinkage prediction of alkaliactivated slagfly ash geopolymer mortar through machine learning model based on DatabasePM: (a) 1d; (b) 3d; (c) 7d; (d Development of autogenous shrinkage prediction model of alkali 2024年6月14日 Numerical models of reinforced embankments using different nickel–iron slagmodified soils were established to analyze the influence of fill height and slope ratio on settlementthickness ratio and settlement factor Finally, a prediction model for settlementthickness ratio was developed based on the numerical simulation resultsMachine learningbased study on the mechanical properties and 2021年2月1日 In this study, a soft sensor model, which correlates the influential process variables with the slag foaming height, was developed by using machine learning based long shortterm memory (LSTM Slag Foaming Estimation in the Electric Arc Furnace using Machine
(PDF) Composition Engineering on the Local Structure and
2022年9月21日 Different machine learning models were also developed The results showed that the prediction results from the gradient boosting decision tree method were the most accurate for the CaOSiO2FeO 2024年4月1日 This study analyzed the impact of gas flow rate, oil layer thickness, and purging plug position on oil eye area and oil entrapment depth during ladle refining To this end, a singleplug stirred water model system was used to experimentally investigate the dynamics of slag entrapment, which plays a vital role in purifying molten steel Highdefinition cameras captured Metallurgical Mechanism Guided Machine Learning to Predict Slag 2022年12月27日 Therefore, based on the current popular machine learning supervised learning algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVR), three models Research on prediction of compressive strength of fly ash and slag The Slagbuster utilizes a set of two rotating blades which clean both sides of the slat simultaneously As the machine moves down the slat, the upward rotation of the blades scrapes slag from the surface and deposits it underneath for removal OPTIONS AVAILABLE The Slagbuster is designed for CO2 and Fiber lasers and comes in two modelsSlagbuster – Slagger
Predicting Compressive Strength of Blast Furnace Slag and Fly Ash
2022年6月29日 Hence, developing mathematical prediction models by machine learning (ML) methods has gained interest as it depicts significant performance in making predictive models Khan et al, 2021;Zheng et
