Abstract
This paper proposes multilevel architecture for an intelligent control system for the complex chemical energy technological process of yellow phosphorus production from apatite-nepheline ore processing waste. The research revealed that, when controlling this process, one has to deal with large amounts of multiformat and polymodal information, and control goals differ at different levels not only in effectiveness criteria, but also in the structuredness of the level problems. On this basis, it is proposed that intelligent methods be used for the implementation of information processes and control goals at individual levels and the whole system. The artificial intelligence methods underlying the informational model of a control system offer solutions to problems of analyzing control processes at different hierarchy levels, namely the initial level of sensing devices, the levels of programmable logic controllers, dispatching of control and production processes, enterprise management and strategic planning. Besides, the intelligent control system architecture includes analytical and simulation models of processes occurring in the multistage procedure of ore waste processing by a plant consisting of a granulating machine, a conveyor-type multichambercalcining machine, and an ore thermal furnace. The architecture of information support for the control system comprises a knowledge-based inference block intended for implementing the self-refinement of neural network and simulation models. Fuzzy logic methods are proposed for constructing this block. The paper considers the deployment of control algorithms for a phosphorus production system using the Matlab software environment on the basis of a modern complex system development paradigm known as the model-oriented design concept.
Highlights
The implementation of projects in the field of new flow sheets for the recycling of wastes from various industries is currently one of the ways of reducing the adverse effects of human industrial activity on the environment [1,2,3]
The procedure of control of the complex chemical and energy technology system (CETS) for producing phosphorus from apatitenepheline ore waste is characterized by multilevel architecture; each level engaged in solving the general problem, i.e., ensuring maximum phosphorus production volume, and takes into account the specificity of the problems of each level, starting from actuators and ending with the level of decision making by enterprise management
In order to develop the elements of the intelligent control system for the CETS, we chose the Matlab software environment for scientific and technical calculations and simulation, which has a rich set of data analysis tools, including those for the application of fuzzy logics (Fuzzy Logic Toolbox), deep machine learning (Deep Learning Toolbox), dynamic system simulation and simulation modeling (Simulink)
Summary
The implementation of projects in the field of new flow sheets for the recycling of wastes from various industries is currently one of the ways of reducing the adverse effects of human industrial activity on the environment [1,2,3]. A special contribution to performing these operations is made by their information support solving a complex of satellite control and optimization problems, including those aimed at increasing the energy and resource efficiency of processing and its environmental safety [6,7] Large processing plants, such as mining-and-processing integrated works, were designed and constructed tens of years ago, when less attention than was paid to the tasks of reducing energy and resource consumption and decreasing the adverse effect on the environment. These tasks can be solved along two lines: (i) implementation of novel processes with entire re-equipment; (ii) optimization of existing technological concepts by implementing industrial waste recycling, upgrading information support, and improving control schemes. In terms of minimization of material costs, the latter line can be a reasonable alternative as a short-term solution (before general modernization of production), which implies an integrated approach due to complementing existing procedures by waste recycling, as well as optimization of control schemes and information support of the process by means of advanced digital solutions
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