Valorisation of waste galvanizing dross: Emphasis on recovery of zinc with zero effluent strategy.

Galvanizing dross-a waste product from steel industries but it can be a potential secondary resource for zinc through urban mining and recycling. In this concern, a novel and scalable recycling route with zero effluent strategy is developed for the recovery of zinc from galvanizing dross as high grade zinc salts along with value-added products through hydrometallurgical processing. In particular, as-such dross block was leached in 9% (v/v) sulphuric acid medium, wherein strong hydrogen gas effervescence results in alleviating the pulverization and stirring requirements; which are material and energy intensive. Leached zinc is purified and recovered as high purity ZnSO4.(H2O/7H2O) and Zn3(PO4)2.4H2O through controlled crystallization and phosphate precipitation respectively; which find application in fertilizers and anti-corrosive paints. Temperature difference method was opted for the crystallization of zinc sulfate salts, wherein 70 °C and 30 °C were found to be stability range of crystallization of ZnSO4.H2O and ZnSO4.7H2O respectively. ZnSO4-H2O phase diagram is developed using Factsage calculations to corroborate crystallization study. Moreover, exhaustive thermodynamic analysis of Zn2+-PO43--H2O system at 303 K on precipitation of zinc phosphate using di-ammonium hydrogen phosphate (80-240 g/L) is conducted and the results reveal that with increasing pH (3-6), intermediate hydrogen phosphate species (H3PO4, H2PO4- and H2PO42-) decompose to produce stable PO43- ions leading to zinc phosphate precipitation. Impurity like Iron and supernatant solution left after crystallization are recovered as hydrated iron-calcium sulfate mixture and ammonium sulfate salt respectively. This explored route is economical and easily adaptable with zero effluents, therefore, transcends serious challenges in terms of energy requirement, scale-up and effluent generation.

An integrated algorithm for text recognition: comparison with a cascaded algorithm.

The use of diverse knowledge sources in text recognition and in correction of letter substitution errors in words of text is considered. Three knowledge sources are defined: channel characteristics as probabilities that observed letters are corruptions of other letters, bottom-up context as letter conditional probabilities (when the previous letters of the word are known), and top-down context as a lexicon. Two algorithms, one based on integrating the knowledge sources in a single step and the other based on sequentially cascading bottom-up and top-down processes, are compared in terms of computational/storage requirements and results of experimentation.