Abstract
The paper presents the method and instrumental system for modeling and optimizing technological modes of direct metal reduction processes in a jet-emulsion aggregate (JER). Stages of the method are considered. The first one is the problem statement: formation of target conditions, choice of the process type, the task and system of optimization criteria. The second stage includes selection of the object of study: setting parameters of input and output flows, process parameters, stages and subprocesses. The third one includes thermodynamic modeling to assess the final equilibrium state in which optimization problem is solved to determine the best conditions for implementation of the processes of metal reduction from oxides in model systems. The fourth stage is development of metallurgical technology (finding the optimum modes and ways for achieving these modes by specified output stream parameters). And the final one is process optimization in technical and economic indicators. As part of the fourth stage, the complex of mathematical models has been developed that reflects relationship of flows and processes in a metallurgical unit. The structure of instrumental system is presented, in which mathematical models and an algorithm for determining optimal technological modes are implemented. A set of optimization criteria has been developed and a scheme for solving two types of optimization problems are presented: finding optimal conditions for reduction processes in thermodynamic systems and determining optimal modes of direct metal reduction. Application of the method to develop optimal technological modes of direct metal production in a JER-type aggregate is shown: metal production from cast iron and mill scale; direct reduction of metal from dusty ores and iron-containing man-made materials; obtaining manganese alloys from carbonate and oxide ores; processing titanium-magnetite concentrates with an almost complete separation of iron-containing and titaniumcontaining component; and direct reduction of iron with associated production of high-calorie synthesis gas.
Highlights
Deployment of new service assets in operational IT environment is associated with the risk of disruption of the assets of its “basic”
To reduce the risk of disruption, the deploying service assets are divided by releases – sub-sets of service assets that will be embedded in the IT environment in one period
The traditional approach to formation and deployment of releases uses information on services structural properties to predict the number of failures due to the deployment of each service asset, each release and each application for deployment
Summary
Обозначим через множество заявок на обновление ИТ-среды, где и aij – обновляемые конфигурационные элементы. Обозначим через mi = m(Ai ) среднее прогнозируемое число отказов сервисов в результате развертывания активов отдельной заявки. Отказов сервисов от всего множества заявок будет определяться соотношением [8]:. T pT) – последовательность, описывающая дни регламентного развертывания релизов в этом периоде; pТ –. Mi количество ожидаемых отказов сервисов в интервале вследствие развертывания активов заявки Ai. Тогда последовательность mi , описывающая прогнозируемые отказы от заявки Ai на интервале (0, Т), будет (3). Помимо отказов от развертывания заявок множества A в плановом периоде реализуется множество отказов, обусловленных развертыванием релизов, выполненных в периоды времени, предшествующие плановому периоду (0, Т), то есть развернутыми в интервале времени (∞, T). Обозначим через m–0 количество отказов, обусловленных такими релизами и приходящееся на интервал. Последовательность, описывающая прогнозируемые количества таких отказов, приходящихся на соот ветствующие периоды развертывания в плановом пе риоде (0, Т)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
