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

A quantitative structure-property relationship (QSPR) study based on partial  least squares (PLS) and artificial neural network (ANN) was developed for the  prediction of ferric iron precipitation in bioleaching process. The leaching  temperature, initial pH, oxidation/reduction potential (ORP), ferrous  concentration and particle size of ore were used as inputs to the network.  The output of the model was ferric iron precipitation. The optimal condition  of the neural network was obtained by adjusting various parameters by  trial-and-error. After optimization and training of the network according to  back-propagation algorithm, a 5-5-1 neural network was generated for  prediction of ferric iron precipitation. The root mean square error for the  neural network calculated ferric iron precipitation for training, prediction  and validation set are 32.860, 40.739 and 35.890, respectively, which are  smaller than those obtained by PLS model (180.972, 165.047 and 149.950,  respectively). Results obtained reveal the reliability and good predictivity  of neural network model for the prediction of ferric iron precipitation in  bioleaching process

Prediction of ferric iron precipitation in bioleaching process using partial least squares and artificial neural network