Industrial radioactive barite scale: suppression of radium uptake by introduction of competing ions

Incorporation of radioactive isotopes during the formation of barite mineral scale is a widespread phenomenon occurring within the oil, mining and process industries. In a series of experiments radioactive barite/celestite solid solutions (SSBarite-Celcstite) have been synthesized under controlled conditions by the counter diffusion of Ra-226, Ba2+, Sr24+ and SO42- ions through a porous medium (silica gel), to investigate inhibiting effects in Ra uptake associated with the introduction of a competing ion (Sr2+). From characterization studies, the particle size and the morphology of the crystals appear to be related to the initial [Sr]/[Ba] molar ratio of the starting solution. Typically, systems richer in Sr produce smaller sized crystals and clusters characterized by a lower degree of order. The activity introduced to the system is mainly incorporated in the crystals generated from the barite/celestite solid solution as suggested by the activity profiles of the hydrogel columns analysed by gamma-spectrometry. There is a relationship between the initial [Sr]/[Ba] molar ratio of the starting solution and the activity exhibited by the synthesized crystals. An effective inhibition of the Ra-226 uptake during formation of the crystals (SSBarite-Celestite) was obtained through the introduction of a competing ion (Sr2+): the higher the initial [Sr]/[Ba] molar ratio of the starting solution, the lower the intensity of the activity peak in the crystals. (C) 2003 Published by Elsevier Ltd.

Predicting fault inflow in iterative software development processes: an industrial evaluation

This paper addresses the need for accurate predictions on the fault inflow, i.e. the number of faults found in the consecutive project weeks, in highly iterative processes. In such processes, in contrast to waterfall-like processes, fault repair and development of new features run almost in parallel. Given accurate predictions on fault inflow, managers could dynamically re-allocate resources between these different tasks in a more adequate way. Furthermore, managers could react with process improvements when the expected fault inflow is higher than desired. This study suggests software reliability growth models (SRGMs) for predicting fault inflow. Originally developed for traditional processes, the performance of these models in highly iterative processes is investigated. Additionally, a simple linear model is developed and compared to the SRGMs. The paper provides results from applying these models on fault data from three different industrial projects. One of the key findings of this study is that some SRGMs are applicable for predicting fault inflow in highly iterative processes. Moreover, the results show that the simple linear model represents a valid alternative to the SRGMs, as it provides reasonably accurate predictions and performs better in many cases.

Knowledge-elicitation and data-mining: fusing human and industrial plant information

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process. (C) 2005 Elsevier Ltd. All rights reserved.