Utilizing machine learning to integrate silica-based production waste material in ceramic tiles manufacturing: Progressing toward sustainable solutions
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OpenAlex topics: Recycling and utilization of industrial and municipal waste in materials production · Building materials and conservation · Recycled Aggregate Concrete Performance
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References (76)
Show source-provided references
1. Suitability of some selected Ado-Ekiti (Nigeria) natural moulding sands’ properties for sand casting
2. Evaluation of chemical and physico-mechanical properties of ado-ekiti natural moulding sands for foundry applications
3. The importance of manufacturing in economic development: has this changed?10.1016/j.worlddev.2016.12.013 ↗
4. 2023
5. 2023
6. Determination of solar reflectance index of ceramic coatings for use in outside surfaces10.4028/www.scientific.net/MSF.881.251 ↗
7. 2023
8. The effects of fine fire clay sanitaryware wastes on ceramic wall tiles10.1016/j.ceramint.2016.07.222 ↗
9. Recycling of industrial wastes in ceramic manufacturing: state of art and glass case studies10.1016/j.ceramint.2016.05.205 ↗
10. Development and production of ceramic tiles from waste bottle powder (milled glass)10.3126/kuset.v12i2.21521 ↗
11. Recycling food, agricultural, and industrial wastes as pore-forming agents for sustainable porous ceramic production: a review10.1016/j.jclepro.2021.127264 ↗
12. Recycling of plastic waste into tiles with reduced flammability and improved tensile strength10.1016/j.psep.2019.02.018 ↗
13. Development of eco-ceramic wall tiles with bio-CaCO3 from eggshells waste10.1016/j.oceram.2022.100220 ↗
14. Characterization of sugarcane bagasse ash waste for its use in ceramic floor tile
15. Recycling of sludge from drinking water treatment as ceramic material for the manufacture of tiles10.1016/j.jclepro.2018.08.094 ↗
16. Preparation of ceramic tiles with black pigments using stainless steel plant dust as a raw material10.1016/j.ceramint.2014.02.050 ↗
17. Characterization of ceramic tiles containing LCD waste glass10.1016/j.ceramint.2016.01.172 ↗
18. Ceramic tiles obtained from clay mixtures with the addition of diverse metallurgical wastes
19. Rapporto integrato publication
20. Almeida · 2016
21. Paving blocks from ceramic tile production waste10.1016/j.jclepro.2009.08.008 ↗
22. Characterization of ceramic roof tile wastes as pozzolanic admixture
23. Hafiz · 2014 · 66
24. Recycling of the mixture resulted from sanitary ware waste, roller kiln waste and ceramic tiles sludge waste in the manufacturing of ceramic floor tiles
25. Bayesian network approach for ceramic shell deformation fault diagnosis in the investment casting process10.1007/s00170-016-8795-x ↗
26. Permafrost analysis methodology (PAM) for ceramic shell deformation in the firing process10.1007/s40962-019-00317-0 ↗
27. Sevinç
28. 2023
29. 1.6: particle size analysis: the hydrometer method
30. Relationship between the Atterberg limits and clay content10.3208/sandf.47.887 ↗
31. X-ray diffraction (XRD) basics and application
32. Mishra · 2021
33. Purification mechanism of quartz sand by combination of microwave heating and ultrasound assisted acid leaching treatment10.1007/s12633-020-00457-7 ↗
34. Waste recycling in ceramic tiles: a technological outlook10.1016/j.resconrec.2020.105289 ↗
35. Clays and bodies for ceramic tiles: reappraisal and technological classification
36. Real-time biscuit tile image segmentation method based on edge detection10.1016/j.isatra.2018.03.015 ↗
37. Firing clay: the lowdown on the ceramic firing process
38. Effect of firing temperature on sintering of porcelain stoneware tiles10.1016/j.ceramint.2007.06.006 ↗
39. Influence of firing temperature on the ceramic properties of illite-chlorite-calcitic clays10.1016/j.ceramint.2022.11.077 ↗
40. Ceramic Tiles; Part-2: Determination of dimensions and surface quality (ISO Standard No. 10545-2) · 2018
41. 2023
42. Ceramic Tiles; Part-3: Determination of water absorption, apparent porosity, apparent relative density and bulk density (ISO Standard No. 10545-3) · 2018
43. 2018
44. Chemical reactions of clay minerals and their utilization
45. Recycling of ceramic industry wastes in floor tiles recipes
46. Ceramic properties of a Spanish clay containing illite, chlorite, and quartz
47. Characterization of porcelain tile bodies with colemanite waste added as a new sintering agent
48. Densification behaviour of a red firing Tunisian triassic clay
49. Preparation and characterization of dry pressed ceramic tiles incorporating ceramic sludge waste
50. New body mixes for porcelain stoneware tiles with improved mechanical characteristics
51. Effect of marble and granite sludge in clay materials10.1016/j.msea.2006.01.021 ↗
52. Effect of polishing waste additive on microstructure and foaming property of porcelain tile and kinetics of sinter-crystallization10.1007/s10973-015-4796-5 ↗
53. Comparison and low-temperature sintering mechanism of “K2O–Na2O” and “Li2O–K2O–Na2O” fluxes on the porcelain building tiles10.2109/jcersj2.19235 ↗
54. Utilization of muscovite granite waste in the manufacture of ceramic tiles10.1016/j.ceramint.2010.10.032 ↗
55. The non-isothermal kinetics of mullite formation in boehmite–zircon mixtures10.1007/s10973-013-3601-6 ↗
56. Recycling of ceramic dust waste in ceramic tiles manufacture
57. Anticipated Overfiring in Porcelain Tiles: effects of the firing cycle and green bulk density10.1016/j.bsecv.2018.07.001 ↗
58. Variation in evenness after the rapid cycle firing of various kinds of tiles
59. Elmahgary · 2018
60. The use of sewage sludge in the production of ceramic floor tiles10.1016/j.hbrcj.2017.02.002 ↗
61. 2024
62. The prediction of fire performance of concrete-filled steel tubes (CFST) using artificial neural network10.1016/j.tws.2021.107499 ↗
63. Simulation modelling practice and theory
64. Training feedforward networks with the Marquardt algorithm10.1109/72.329697 ↗
65. Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate10.1016/j.jclepro.2023.138673 ↗
66. Multicollinearity in regression analysis: the problem revisited
67. Experimental investigation and AI prediction modelling of ceramic waste powder concrete – an approach towards sustainable construction10.1016/j.jmrt.2023.02.024 ↗
68. Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach10.1016/j.conbuildmat.2019.117000 ↗
69. Empirical approach for prediction of bearing pressure of spread footings on clayey soil using artificial intelligence (AI) techniques10.1016/j.rineng.2022.100489 ↗
70. baeldung, Normalizing inputs of neural networks, Baeldung on Computer Science (2020) July 30.Science, B. O. C., & Science, B. O. C. (2023, August 18). Normalizing inputs of neural networks | Baeldung on Computer Science. Baeldung on Computer Science.Retrieved from https://www.baeldung.com/cs/normalizing-inputs-artificial-neural-network.
71. Predicting the compressive strength of concrete containing metakaolin with different properties using ANN10.1016/j.measurement.2021.109790 ↗
72. An artificial neural network-based mathematical model for the prediction of blast-induced ground vibration in granite quarries in Ibadan, Oyo State, Nigeria10.1016/j.sciaf.2020.e00413 ↗
73. Ganesh · 2022
74. Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods. 2024. https://www.hindawi.com/journals/ace/2019/3069046/. Accessed January 15.10.1155/2019/3069046 ↗
75. Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression
76. Environmental impact estimation of ceramic tile industry using modeling with neural networks