Development and Testing of Low-Energy-Intensive Technology of Receiving Sulphate-Resistant and Road Portlandcement

Taimasov, B. T.; Sarsenbayev, B. K.; Khudyakova, T. M.; Kolesnikov, Alexandr; Zhanikulov, N. N.

doi:10.18321/ectj683

Cited by 26 publications

(13 citation statements)

References 2 publications

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“…Non-standard reaction (10) proceeded in the entire investigated temperature range, having negative Gibbs energies from −898.9 kJ to −20,066.2 kJ, respectively, having a significant advantage over the standard reaction (6). The data obtained were consistent with the research carried out by a number of scientists [ 25 , 26 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ] and complement them. Regarding the distillation of non-ferrous metals, in particular, zinc, in the simulated reactions, it was shown that non-ferrous metals in the form of zinc are able to pass into the gas phase and be captured in bag filters.…”

Section: Discussionsupporting

confidence: 89%

Processing of Waste from Enrichment with the Production of Cement Clinker and the Extraction of Zinc

et al. 2022

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This paper presents studies on the processing of enrichment tailings as a component of a raw mixture in order to obtain cement clinker, with simultaneous distillation of zinc. Thermodynamic studies were carried out in the temperature range of 600–1600 °C using the software application “HSC Chemistry 6” developed by the metallurgical company Outokumpu (Finland). As a result of the conducted studies, we found that zinc contributes to the intensification of mineral formation of cement clinker. In particular, it was found that the formation of belite is possible in the temperature range from 990.7 to 1500 °C with Gibbs energy values of −0.01 and −323.8 kJ (which is better than the standard process by −11.4 kJ), respectively; the formation of alite is possible in the temperature range from 982.9 to 1500 °C with Gibbs energy values of −0.05 and −402.1 kJ (better than the standard process by −11.4 kJ), respectively; the formation of tricalcium aluminate is thermodynamically possible in the temperature range from 600 °C at ΔGTo = −893.8 kJ to 1500 °C at ΔGTo = −1899.3 kJ (better than the standard process by −1570.1 kJ), respectively; and the formation of four calcium aluminoferrite is possible in the temperature range from 600 °C at ΔGTo = −898.9 kJ to 1500 °C at ΔGTo = −1959.3 kJ (better than the standard process by −1570.2 kJ), respectively, with simultaneous distillation of zinc into a gaseous state for its further capture.

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Section: Discussionsupporting

confidence: 89%

Processing of Waste from Enrichment with the Production of Cement Clinker and the Extraction of Zinc

et al. 2022

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“…The essence of regularization is to impose additional constraints on various parameters or to add a priori information, thus reducing the model error as its complexity increases [19,20].…”

Section: Using Big Data To Develop Multi-dimensional and Regularized Regression Modelsmentioning

confidence: 99%

Big Data as a Tool for Building a Predictive Model of Mill Roll Wear

2021

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Big data analysis is becoming a daily task for companies all over the world as well as for Russian companies. With advances in technology and reduced storage costs, companies today can collect and store large amounts of heterogeneous data. The important step of extracting knowledge and value from such data is a challenge that will ultimately be faced by all companies seeking to maintain their competitiveness and place in the market. An approach to the study of metallurgical processes using the analysis of a large array of operational control data is considered. Using the example of steel rolling production, the development of a predictive model based on processing a large array of operational control data is considered. The aim of the work is to develop a predictive model of rolling mill roll wear based on a large array of operational control data containing information about the time of filling and unloading of rolls, rolled assortment, roll material, and time during which the roll is in operation. Preliminary preparation of data for modeling was carried out, which includes the removal of outliers, uncharacteristic and random measurement results (misses), as well as data gaps. Correlation analysis of the data showed that the dimensions and grades of rolled steel sheets, as well as the material from which the rolls are made, have the greatest influence on the wear of rolling mill rolls. Based on the processing of a large array of operational control data, various predictive models of the technological process were designed. The adequacy of the models was assessed by the value of the mean square error (MSE), the coefficient of determination (R2), and the value of the Pearson correlation coefficient (R) between the calculated and experimental values of the mill roll wear. In addition, the adequacy of the models was assessed by the symmetry of the values predicted by the model relative to the straight line Ypredicted = Yactual. Linear models constructed using the least squares method and cross-validation turned out to be inadequate (the coefficient of determination R2 does not exceed 0.3) to the research object. The following regressions were built on the basis of the same operational control database: Linear Regression multivariate, Lasso multivariate, Ridge multivariate, and ElasticNet multivariate. However, these models also turned out to be inadequate to the object of the research. Testing these models for symmetry showed that, in all cases, there is an underestimation of the predicted values. Models using algorithm composition have also been built. The methods of random forest and gradient boosting are considered. Both methods were found to be adequate for the object of the research (for the random forest model, the coefficient of determination is R2 = 0.798; for the gradient boosting model, the coefficient of determination is R2 = 0.847). However, the gradient boosting algorithm is recognized as preferable thanks to its high accuracy compared with the random forest algorithm. Control data for symmetry in reference to the straight line Ypredicted = Yactual showed that, in the case of developing the random forest model, there is a tendency to underestimate the predicted values (the calculated values are located below the straight line). In the case of developing a gradient boosting model, the predicted values are located symmetrically regarding the straight line Ypredicted = Yactual. Therefore, the gradient boosting model is preferred. The predictive model of mill roll wear will allow rational use of rolls in terms of minimizing overall roll wear. Thus, the proposed model will make it possible to redistribute the existing work rolls between the stands in order to reduce the total wear of the rolls.

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“…Industrial waste located in dumps and containing in its chemical composition heavy non-ferrous metals and various compounds of silicon, calcium, aluminum and iron has a negative impact on the environment from the point of view of ecology, in particular on public health, flora and fauna [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. However, based on its chemical composition, it is valuable and can serve as a secondary technogenic raw material for various kinds of industries, for example in the metallurgical, construction, chemical and other industries [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 …”

Section: Introductionmentioning

confidence: 99%

“…However, based on its chemical composition, it is valuable and can serve as a secondary technogenic raw material for various kinds of industries, for example in the metallurgical, construction, chemical and other industries [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. Thus, in Kazakhstan, in the process of extracting non-ferrous metals at a number of plants, since the 1920s and up to the present, in a number of areas, in particular, in the east Kazakhstan and Turkestan regions, there is a significant amount of clinker waste from the cultivation of various raw materials, which is now stored in dumps, occupying fertile lands and polluting the soil, surface and groundwater, the atmosphere, penetrating animal and human organisms through the migration of heavy metals along the food chain [ 9 ,…”

Section: Introductionmentioning

confidence: 99%

Modeling of Non-Ferrous Metallurgy Waste Disposal with the Production of Iron Silicides and Zinc Distillation

et al. 2022

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This paper presents studies on the possibility of utilization of technogenic waste from the metallurgical industry by the method of complex processing in order to reduce the anthropogenic load on the environment of the region with the example of the zinc silicate-magnetite-carbon system. The selected sample of clinker dump from welting was subjected to chemical and scanning electron microscopic analyses and thermodynamic modeling. Thermodynamic studies were carried out in the temperature range 1600–2200 K and pressure p = 0.1 MPa, modeling the process of electric melting of clinker from welting in an arc furnace using the software application Astra 4 developed at the Bauman Moscow State Technical University (Moscow, Russian Federation). As a result of the thermodynamic modeling, the optimal temperature range was established, which was 1800–1900 K. Thermodynamic studies established that it is possible to drive away zinc from the system under study by 99–100% in the entire temperature range under study. The maximum degree of silicon extraction (αSi) in the alloy is up to 69.44% at T = 1900 K, and the degree of iron extraction (αFe) in the alloy is up to 99.996%. In particular, it was determined and proved that clinker waste from welting can act as a secondary technogenic raw material when it is processed as a mono mixture to produce iron silicides with a silicon content of 18 to 28%.

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Development and Testing of Low-Energy-Intensive Technology of Receiving Sulphate-Resistant and Road Portlandcement

Cited by 26 publications

References 2 publications

Processing of Waste from Enrichment with the Production of Cement Clinker and the Extraction of Zinc

Processing of Waste from Enrichment with the Production of Cement Clinker and the Extraction of Zinc

Big Data as a Tool for Building a Predictive Model of Mill Roll Wear

Modeling of Non-Ferrous Metallurgy Waste Disposal with the Production of Iron Silicides and Zinc Distillation

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