English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Droplet size is a fundamental parameter for Venturi scrubber performance. For many years, the correlations proposed by Nukiyama and Tanasawa (1938) and Boll et al. (1974) were used for calculating mean droplet size in Venturi scrubbers with limited operating parameters. This study proposes an alternative approach on the basis of artificial neural networks (ANNs) to determine the mean droplet size in Venturi scrubbers, in a wide range of operating parameters. Experimental data were used to design the ANNs. A neural network was trained based on the liquid to gas ratio (L/G) and throat gas velocity (Vgth), as input parameters, and the Sauter mean diameter (D32) as the desired parameter. The back-propagation learning algorithms were used in the network and the best approach was found. A new formula for the prediction of D32 using the weights of the network was then generated. This formula predicts mean droplet size in Venturi scrubbers more accurately than the correlations of Boll et al. (1974) and Nukiyama and Tanasawa (1938). The Average Absolute Percent Deviation (AAPD) of our formula and the Boll et al. and Nukiyama and Tanasawa correlations for the full ranges of experimental data are 26.04%, 40.19% and 32.99%, respectively

Introducing a new formula based on an artificial neural network for prediction of droplet size in venturi scrubbers