TOWARDS A DIAMEX PROCESS FLOWSHEET USING HIGH ACTIVE CONCENTRATE (HAC)
The efficiency of MA recovery in the DIAMEX process has already been demonstrated using HAR. The next step aims at the demonstration of reprocessing from HAC as feed, in view of an industrial application. The volume reduction would reduce the size of the installation and thereby the costs of the process. The first step towards the demonstration of a DIAMEX process using HAC is the production of the genuine solutions. In ITU about 9 L HAR raffinate has been prepared, from PUREX reprocessing of 1.4 kg of MOX fuel, and characterised in order to produce HAC for the upcoming extraction tests. For this purpose, the HAR is subjected to a concentration/denitration process to obtain HAC with a final acidity compatible with the DIAMEX operating conditions, i.e. around 3 M HNO3. This process has been tested and optimised using HAR-simulate. Promising results were found for concentration factors (CF) of 6 to 8. At CF of 39 a significant co-precipitation of lanthanides occurred.
Chemical Reactions Involved in the Denitration Process with HCOOH and HCHO
Denitration reactions with HCOOH and HCHO proceed according to several reactions depending on the acidity and/or the redox potential of the reacting mixture. The parameters acting on the induction time — Inherent to these reactions — and the denitration rate have been identified and their possible effects discussed. These mainly deal with the concentration of the reacting mixture components, the pressure in the reactor and the nitrous acid concentration. The basic outcome of this study is that gaseous compounds in equilibrium with nitrous acid catalyze the denitration reaction. As a consequence, every action which could remove these products from the reacting mixture extends the induction time and are detrimental to the denitration rate.
An automated retinal image quality grading algorithm
This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.