Precision forecasting of spray-dry desulfurization using Gaussian noise data augmentation and k-fold cross-validation optimized neural computing-Reference-Cited by-同舟云学术

Precision forecasting of spray-dry desulfurization using Gaussian noise data augmentation and k-fold cross-validation optimized neural computing

Published:2024-01-02 Issue:1 Volume:59 Page:1-14
ISSN:1093-4529
Container-title:Journal of Environmental Science and Health, Part A
language:en
Short-container-title:Journal of Environmental Science and Health, Part A

Author:

Makomere Robert Someo¹^ORCID,Koech Lawrence¹,Rutto Hilary Limo¹,Kiambi Sammy¹

Affiliation:

1. Clean Technology and Applied Materials Research Group, Department of Chemical and Metallurgical Engineering, Vaal University of Technology, Vanderbijlpark, Gauteng, South Africa

Funder

Eskom

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10934529.2024.2317670

Reference46 articles.

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3. Progress in CFD Simulations of Fluidized Beds for Chemical and Energy Process Engineering

4. CFD modeling of the Dry-Sorbent-Injection process for flue gas desulfurization using hydrated lime

5. Tamrakar, A.; Devarampally, D. R.; Ramachandran, R. Advanced Multiphase Hybrid Model Development of Fluidized Bed Wet Granulation Processes. In Computer Aided Chemical Engineering; Elsevier: Amsterdam, 2018; Vol. 41, pp. 159–187.

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