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• W4311364102 startingPage "2682" @default.
• W4311364102 abstract "Anthropogenic climate change, global warming, environmental pollution, and fossil fuel depletion have been identified as critical current scenarios and future challenges. Cement plants are one of the most impressive zones, emitting 15% of the worldwide contaminations into the environment among various industries. These contaminants adversely affect human well-being, flora, and fauna. Meanwhile, the use of cement-based substances in various fields, such as civil engineering, medical applications, etc., is inevitable due to the continuous increment of population and urbanization. To cope with this challenge, numerous filtering methods, recycling techniques, and modeling approaches have been introduced. Among the various statistical, mathematical, and computational modeling solutions, Python has received tremendous attention because of the benefit of smart libraries, heterogeneous data integration, and meta-models. The Python-based models are able to optimize the raw material contents and monitor the released pollutants in cement complex outputs with intelligent predictions. Correspondingly, this paper aims to summarize the performed studies to illuminate the resultant emissions from the cement complexes, their treatment methods, and the crucial role of Python modeling toward the high-efficient production of cement via a green and eco-friendly procedure. This comprehensive review sheds light on applying smart modeling techniques rather than experimental analysis for fundamental and applied research and developing future opportunities." @default.
• W4311364102 created "2022-12-25" @default.
• W4311364102 creator A5013297039 @default.
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• W4311364102 date "2022-12-13" @default.
• W4311364102 modified "2023-10-14" @default.
• W4311364102 title "A Review on Pollution Treatment in Cement Industrial Areas: From Prevention Techniques to Python-Based Monitoring and Controlling Models" @default.
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• W4311364102 doi "https://doi.org/10.3390/pr10122682" @default.
• W4311364102 hasPublicationYear "2022" @default.
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