Розширення функціональних можливостей автоматизованих систем керування технологічними об’єктами
Abstract
EXPANSION of FUNCTIONAL POSSIBILITIES ofcontrol systems of technological objects
V.Sidletskyy, I.Elperin
Control system are constructed as a hierarchical system where the lower level is an automated process control systems, and at the top level management system business processes, in accordance with existing standards and practices implementations of automation systems for the food industry
Each level has its own system and subsystem responsible for carrying out certain tasks such as: ERP - planning and management, MES - process control system or SCADA - supervisory control system production. Creation and transfer of information and data between control levels for decision-making is the main problems in the control of such systems
The initial "lower" level is the level of direct process control. It includes sensors, actuators, devices of microprocessor technology. In general, this level is characterized by a control system - real-time systems: supervisory control and data acquisition system SCADA, distributed control system DCS, advanced control system APC. Almost all of the operating system is work on regulation algorithms (PID controller, control of periodic processes, management of carriage systems).
But approaches in the control of production processes remain traditional despite the considered approaches.
It can be concluded in general. Each controlled process can be converted (requires conversion) with a current state to another of the management of process parameters (control devices, sites, production lines or the whole enterprise) at any time. That is not important whether it be upgrading (calculation of necessary equipment or calculation methods for scaling the system), process control or technological apparatus. Control task will be to go back to one point (current state) in a given. It is also necessary to take into account that the processes in the food industry are complex, inertial, with significant internal connections and production lines can very vary - adding or deleting device for work or further processing of the product.
Also the environment are affected the process (state of other parameters). Therefore system (point) begins to deviate from the calculated path to the specified value. Therefore in accordance with the conditions necessary is return or continue driving to the calculated state. Therefore in this case the main task is to choose the method (way) of the situation that has arisen.
You need to use the apparatus of tensor analysis for distributed control in automated installations and facilities of food industry. This approach allows us to consider the entire domain process another level of hierarchy and made it possible to move from one space to another state. It is also possible to combine such things as temperature and the amount of raw materials in stock, mathematical relationships and work history data.
Tensors can be formed as the coefficients of a system of equations of mathematical model, with experimental values or of historical trends, vehicle, land, business. Besides that using tensor can ask any plane with them can do all the math, you can add, subtract, multiply, divide, and both scalar and the vector. In this case, the use of tensor will help navigate hierarchical levels of control system for optimal control search, find the shortest path between the transition from the current state to the new technological system, which is set by regulation.
One of the most important tasks in process control is the choice of control actions that will satisfy the work as a single unit and not contradictory to technological areas and enterprises in general, and the management as device and technology department, cannot find a solution in which all indicators of (criteria), adopt both the best value.
Control system one of the technology areas of production can be represented as a block diagram of the automation system to maintain process parameters within specified limits and add additional modules that extend the functionality of the control system.
The system performs: analysis of the process, rating situations in the workplace, and it is supplemented by modules. Module "Forecasting occurrence Troubleshooting", responsible for analysis of areas including the control system, then use the data analysis, simulated and tested the possibility of emergency situations. Counting all the possible variations in emergency situations selected those situations that will cause rejection of the equipment. That is why a module "Forecasting equipment failure" calculation of possible equipment failures, this module receives data from control systems and the emergence of the module of emergency situations. As a result this leads to the need to find duplicate versions of system elements, and in cases build some technology areas need to scale these systems function modules can be executed "calculation of necessary equipment" and "Calculation methods of scaling system".
References
Intehratsiia system upravlinnia pidpryiemstvom i tekhnolohichnym protsesom»: Standart ISA-95. Available at http://isa-95.com/.
Kadane, J. (2013). Planning for Value: Setting Priorities for Manufacturing Execution System Functionality for Food and Beverage Companies. An Industry White Paper. Aspen Technology, Inc. Available at: www.aspentech.com/MES_Food_Bev_White_Paper.pdf.
Takha, Khemdi А. (2005). Vvedeniye v issledovaniye operatsiy [Introduction to Operations Research]. Moskva: Izdatel’skiy dom «Vil’yams», 912.
Peifeng, Niu, Guoqiang, Li, Mizhe, Zhang. (2011). Design Research of an Adap-tive-Fuzzy-Neural Controller. Journal of Advances in Information Technology, 2 (2), 122–127.
Galzina, V., Saric, T., Lujic R. (2011). Application of fuzzy logic in boiler control. Technical Gazette, 15 (15), 4–21.
Petrov, А. E. (1985). Tenzornaya metodologiya v teorii sistem [Tensor methodology of systems theory]. Moskow: Radio i svyaz’, 152.
Tensor Toolbox version 2.6 by Brett W. Bader, Tamara G. Kolda, Jimeng Sun, Evrim Acar, Daniel M. Dunlavy, Eric C. Chi, Jackson Mayo, et al. (2015). Sandia National Laboratories.
TDALAB Laboratory for Tensor Decomposition and Analysis by Guoxu Zhou, Andrzej Cichocki (2012). Cichocki Laboratory for Advanced Brain Signal Processing.
Downloads
Published
Issue
Section
License
Relationship between right holders and users shall be governed by the terms of the license Creative Commons Attribution – non-commercial – Distribution On Same Conditions 4.0 international (CC BY-NC-SA 4.0):https://creativecommons.org/licenses/by-nc-sa/4.0/deed.uk
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).