Месец: август 2021

Key Words: Heat integration; batch processes; stochastic optimization; flexibility index.

Abstract. By definition, sustainable development is a way of using natural resources that aims to meet human needs while maintaining the natural balance with the environment, so that these needs can be met both now and for future generations. The creation of highly efficient technological processes, energy efficiency in every sphere of the economy and society, the production of energy from renewable energy sources, the economy of materials, the use of renewable natural resources, the development of green and eco-technologies, prevention of harmful waste; effective governance of the economy, society and the environment are part of the most important policies underlying the European Union’s (EU) Sustainable Development Strategy. Creating energy efficient production systems involves less impact on the environment. One of the most powerful tools for creating this type of system is the integration of energy and mass processes. Process integration covers a wide range of system-oriented methods and approaches that are used in the design and reconstruction of industrial processes to obtain optimal use of resources. In recent years, the focus on energy integration of processes has shifted from the integration of processes in continuous systems to the integration in systems with batch processes. From the conducted researches it is clear that the production systems with batch processes have sufficient energy potential, which can be used to improve their energy efficiency. The recovery and use of this heat is complicated by the batch nature of the processes, and the task is further complicated by the impact of stochastically changing flow parameters such as temperature, volume, etc., overcoming which is a serious challenge to the sustainability of batch production systems. The aim of the present study is to propose a method for dealing with uncertainties and increasing resilience through heat integration of flows in periodic production systems. The method includes three main stages: 1. Uncertainty analysis and selection of a suitable scheme for energy integration of processes and its mathematical description; 2. Defining the problem of optimal redesign of an energy-integrated batch production system by incorporating the integration model within a stochastic optimization problem and its solution; 3. Assessment and decision making to choose the most appropriate solution, whereby the production system is sustainable of the impact of the uncertain parameters in the widest borders, by defining the flexibility index.

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Кey Words: Case-Base Reasoning (CBR); mathematical modeling; operational conditions; scheduling; suboptimal control; Thermal Treatment Process (TTP).

Abstract. An intelligent system for control of the thermal treatment process (TTP) of wood materials addressed toward manufacturing with necessity of often rescheduling is proposed via combination of model-based and data-driven approaches. Using First-principle mathematical model of TTP presented by Partial Differential Equations in 2D space with suboptimal model-based control algorithm and Case-Based Reasoning (CBR) approach an explicit suboptimal control system is investigated in different operational conditions. A set of virtual subspaces for feasible operational situations for variety of objective criteria of value assessment is created using traditional problem-decision representation. As the search spaces are well structured, the search procedure based on traditional K–NN algorithm is strongly simplified. In this way the complicated computer simulation of the TTP at each time step due to the plant’s parameter distribution, nonlinearity and operational or environmental disturbances are fulfilled off-line. On-line are accomplished relatively small part of the calculations connected with the traditional R4–operations in CBR, objective functions estimation, some databased and rule-based control parameter corrections and possible adaptation from charge to charge. Some results of the simulation experiments are presented and analyzed.

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Key Words: Information theory; entropy; hierarchy; energy mix; renewable energies.

Abstract. The dynamics of development and distribution of resources are key questions for characterizing the complexity of systems in a large number of areas. Traditionally, the natural sciences (physics, chemistry, computer science, telecommunications, and others) have used Shannon’s information theory and the concept of entropy to assess diversity, uncertainty, and chaos. Social sciences (economics, competition law, etc.) prefer to consider these issues from the opposite point of view – the concentration of resources, reflecting the dominance and hierarchy in competition interactions. Energy systems are a typical example of complex and dynamic systems and their study is of particular interest in theoretical and practical terms. Becoming the engine of innovation in energy technologies, renewable energy sources play an important role in the evolution and dynamics of energy balances at the international, national and local levels. The article presents the possibilities of the original method developed by the author for estimating the hierarchy of information, its advantages in comparison with the traditional methods of Shannon’s entropy and Herfindahl concentration and its application for improving the analysis of energy balances and the role of renewable energy sources.

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Key Words: Internet of Things; data transmission; communication protocols; big data.

Abstract. Communication models and protocols in Internet of Things (IoT) are relatively new field of research and use due to the fact, that connecting objects by digital means in Internet has occurred only in the last decade. However, the rapid grow of applications of this paradigm in almost all areas of human activity has led to enormous volume of data generated from all kind of sources (things) and transmitted all over the global digital network. This, in turn, has led to the creation of new models and protocols for data transmission in IoT. The paper gives an overview of the main approaches / models for communication in IoT. The requirement for scalability and simplicity is amongst the most important when IoT and Big data are involved. Four models are presented – the Push, Request/Response, Subscribe/Notify and Publish/Subscribe. A classification and layered approach is made for protocols used in IoT. Three of the most widely used for data transmission in IoT are taken for further investigation – MQTT, CoAP and AMQP. They are compared in terms of bandwidth, overhead, size of transmitted data, reliability and security. Then are compared with HTTP, which is well known and widely used on the Internet. The conclusions are that the most popular model for IoT and Big data communication is the Publish/Subscribe one, whether in regard to the use and recommendation for data protocol, one cannot point a clear leader at present (unlike the protocols in the TCP/IP stack for Internet), so there is the tendency that for the time being, different data protocols will be used in different IoT and Big data applications.

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Key Words: G Code; vectorization; colors reduction; plotting.

Abstract. G Code instructions are used for CNC machines. In order, such machines to produce objects or drawings visual information should be vectorized. Transformation of full-color image to set of 16 colors is achieved by color reduction algorithm. Pixels in the image are replaced with simple shapes (strokes) in such a way that these simple graphic primitives to be drawn by CNC driven 2D plotter. Each stroke should be drawn by a single loading of oil paint. This goal cannot be achieved by standard G Code generation and this research.

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Key Words: Particulate matter PM10 and PM2.5; sensors for РМ; temperature and light; Arduino Nano 3.0.

Abstract. Air pollution causes damage to human health and ecosystems. Large parts of the population do not live in a healthy environment in accordance with current standards. Today, almost every city has problems with particulate matter concentration, especially in time of temperature inversions. This article introduces an automated system for monitoring and analyzing fine particulates in ambient air, temperature and light. It can be used in the workplace, at home, and in particular in student classrooms and laboratories, not only for monitoring, but also for training and refinement involving students, in various disciplines or informal аactivities.

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Кеу Words: Cybersecurity; OPC-UA applications; security; formal verification; information modeling; timed automata models; temporal logic.

Abstract. Cybersecurity protect the confidentiality, integrity and availability of computing devices and networks, hardware and software, and most importantly, data and information. The main aim of the paper is to analyze the capabilities and to propose an approach for increasing the security of OPC-UA applications based on the use of formal verification, and its integration into the development lifecycle model and information modeling capabilities provided by OPC-UA. The formal verification is based on using model checking based on timed automata models of the system and cybersecurity properties, defined by using temporal logic.

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На 18 и 19 юни 2021 г. се състоя двадесет и второто издание на Международната научна конференция CompSysTech’21.
Международната конференция CompSysTech’21 се организира от българската Академична общност по компютърни системи и информационни технологии (АОКСИТ) и нейната цел е да засили информационния обмен на резултати в теоретичните изследвания и практическите разработки в областта.

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Кey Words: Case-Base Reasoning (CBR); mathematical modeling; operational conditions; scheduling; suboptimal control; Thermal Treatment Process (TTP).

Abstract. An intelligent system for control of the thermal treatment process (TTP) of wood materials addressed toward manufacturing with necessity of often rescheduling is proposed via combination of model-based and data-driven approaches. Using First-principle mathematical model of TTP presented by Partial Differential Equations in 2D space with suboptimal model-based control algorithm and Case-Based Reasoning (CBR) approach an explicit suboptimal control system is investigated in different operational conditions. A set of virtual subspaces for feasible operational situations for variety of objective criteria of value assessment is created using traditional problem-decision representation. As the search spaces are well structured, the search procedure based on traditional K–NN algorithm is strongly simplified. In this way the complicated computer simulation of the TTP at each time step due to the plant’s parameter distribution, nonlinearity and operational or environmental disturbances are fulfilled off-line. On-line are accomplished relatively small part of the calculations connected with the traditional R4–operations in CBR, objective functions estimation, some databased and rule-based control parameter corrections and possible adaptation from charge to charge. Some results of the simulation experiments are presented and analyzed.

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Key Words: Empirical MIMO model; biological wastewater treatment; Wiener model structure; Principal Component Analysis; polynomial approximation; neural networks.

Abstract. The aim of the present work is to develop data based MIMO mathematical model for biological wastewater treatment, designed for real-time work, and a procedure for creating mathematical models of this class. An analysis of the processes of biological wastewater treatment for the purposes of their mathematical modelling is made. The study includes variables that are known to have sensors worldwide or to have software sensors developed. In conducting the research published in the present work, a combination of real and synthetic data is used. The constructive parameters of the considered installation correspond to settlements with an average number of equivalent inhabitants for the country. To develop a MIMO nonlinear dynamic mathematical model, the structure of Wiener model was chosen – series-connected linear dynamic and nonlinear static parts. The procedure for creating the mathematical model includes: processing of incoming data by the principal components method (PCA); to form the nonlinear static part of the model and to compare the predictive abilities, polynomial dependences for each of the intermediate and target variables are derived as a function of the normalized values of the three principal components and two types of neural networks for each variable are trained. In one case the independent variables are the normalized values of the principal components and in the other – the natural values of principal components. In some cases, higher accuracy of approximation is obtained in polynomial dependencies, in others in neural networks. In neural networks, the same approximation accuracy with polynomial models is obtained with a larger number of parameters. Based on simulation studies, the dynamic characteristics of an installation for biological wastewater treatment are derived. A block diagram of the mathematical model for is presented. The created mathematical model can be used on a modular basis with respect to the target variables of interest, regardless of the other target variables.

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Key Words: Model-based diagnostics; pneumatic systems; pressure drops; time series; coefficient of determination; Pearson correlation coefficient.

Abstract. Faults are adverse events in any industrial production system. Their occurrence affects the efficiency of the system and reduces the competitiveness of production. Early detection and diagnosis of faults in automated systems is important to prevent equipment damage and loss of performance. For this purpose, more and more sophisticated systems for observation and monitoring of basic characteristics in automated processes are being built. A prerequisite for increasing their efficiency is the use of additional sensory information, modeling and intelligent information analysis to detect faults. The paper explores the possibility of diagnosis of unwanted pressure drops in pneumatic systems. These model-based diagnostic methods aim to distinguish the causes of their occurrence or location. The objects of diagnosis are pressure drops in the supply line or those in the branch, main lines. The presented formulation of the problem and task are dictated as a result of inspection and analysis of operating pneumatic systems of industrial enterprises in the country. It is the pressure drops that are defined as the main and most frequently occurring problem in the operation of the system, and for the resource optimization of the distribution network their localization is of special importance. The paper proposes an approach for the use of load diagrams (time series) with two measurable variables – instantaneous flow and pressure. Based on continuous monitoring and a known model relationship between the two quantities, indicators for detection and localization of pressure drops are determined, reducing the efficiency in the components of the pneumatic system – the main line, local stations or the compressor installation. For the purposes of verification of the proposed approach and the performed analysis – in general, real system data from 13 specific production machines were used.

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Key Words: IEC-62541 (OPC-UA) standard; Industry 4.0 reference architecture; cyber-attacks; security recommendations; OPC-UA applications.

Abstract. The IEC-62541 (OPC-UA) standard is an important part of the Industry 4.0 reference architecture and is recommended as the only possible communication standard. A particularly important issue that is being addressed is the issue of security. The paper analyzes the vulnerability of cyber-attacks and the main threats that threaten the security of OPC-UA-based applications and defines established and sustainable recommendations for increasing the security of these applications.

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Key Words: Particulate matter PM10 and PM2.5; sensors for РМ; temperature and light; Arduino Nano 3.0.

Abstract. Air pollution causes damage to human health and ecosystems. Large parts of the population do not live in a healthy environment in accordance with current standards. Today, almost every city has problems with particulate matter concentration, especially in time of temperature inversions. This article introduces an automated system for monitoring and analyzing fine particulates in ambient air, temperature and light. It can be used in the workplace, at home, and in particular in student classrooms and laboratories, not only for monitoring, but also for training and refinement involving students, in various disciplines or informal activities.

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Key Words: Internet of Things (IoT); big data; big data processing tools; Hadoop.

Abstract. Internet of Things (IoT) is a modern paradigm referring to interconnected things/objects in the global digital network Internet. This model differs significantly from the traditional approach of connecting computers, laptops and servers to Internet. There is a huge variety of connected devices – ranging from sensors and RFID tags and mobile phones to data centres and supercomputers. They all create, transmit and process digital/digital data in a quantity, variety and unimaginable until recently. All this leads to new requirements for the means and environment for data processing. The paper presents a classification of architectural model, used for data from IoT. They are placed in four groups – such of standardization organization, of commercial organizations, in respect of Industrial Internet of Things and of researchers. A well-known architecture, that distinguishes the data path according to the speed of data processing – Lambda Architecture is also presented. The paper also looks at the most popular products, programs and software libraries for big data processing. A particular attention is given to the Hadoop software library, which allows processing of big sets of data. Other products and tools for ETL (Extract, Transform and Load), distributed event streaming, data storage, data processing and analytics are also presented. The paper describes a simplified architecture, which has been implemented and demonstrated to work on a 40-node cluster. Its software comes from the open source Hadoop environment. The next tasks and future work on this architecture are presented.

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Key Words: Online learning; deep neural networks; face recognition; facial emotions recognition; python.

Abstract. This paper presents the application of Deep Convolutional Neural Networks (DNN) in the process of online learning of students, which become very important in the time of Covid pandemic. The pre-trained DNN are analyzed and selected one applied for students’ face recognition and for facial emotions recognition. On the base of face recognition students are admitted to the online lectures, exercises and exams. As well, face recognition used to control students whether they regularly visit the lectures and the exercises. The analysis of students’ facial emotions (positive, neutral or negative) was used for personalization of the study process and for adaptation of the lecture material.

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На 1-3 октомври 2020 г. във Варна се проведе IEEE International Conference Automatics and Informatics’20 (ICAI’20), продължение на традиционната Международна конференция „Автоматика и информатика“:
https://conferences.ieee.org/conferences_events/conferences/conferencedetails/50593
http://www.tu-varna.bg/icai/.
За първи път конференцията се провежда като IEEE конференция. Изнесените доклади се публикуват в IEEE Xplore Digital Library. Всички доклади, включени в IEEE Xplore, се индексират в базата данни на SCOPUS. IEEE притежава авторските права върху материалите, представени на конференцията.

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На 26-28 август 2020 г. във Варна се проведе 10-тата IEEE международна конференция по интелигентни системи, https://www.ieee-is.org/. Конференцията е организирана от САИ „Джон Ата-
насов“, IEEE IM/CS/SMC Joint Chapter of Bulgaria, IEEE Computational Intelligence Chapter of Bulgaria, Университета по библиотекознание и информационни технологии – София, и Института по информационни и комуникационни технологии – БАН, със съдействието на ФНТС.

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На 5-6 ноември 2020 г. във виртуална среда се проведе традиционният ежегоден 28-и международен симпозиум „Управление на енергийни, индустриални и екологични системи“, организиран от Съюза по автоматика и информатика „Джон Атанасов“ със съдействието на ФНТС. В повече от четвъртвековното си съществуване симпозиумът е поддържал неизменно линията за връзка между специалистите по автоматика и информатика от изследователските групи и индустрията.

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Key Words: Industrial automation; artificial intelligence; trends; distributed control systems; cyber security.

Abstract. In the paper is presented analysis of the main now days challenges in the field of business, basic industrial technologies and ecology and the potential of the advanced control technologies as an important component for solving them. As dominant are considered the Industrial Automation (IA) methods and implementation of Artificial Intelligence (AI) achievements in industrial automation in order to meet automatic and operational management as well equipment reliability and cybersecurity. The historical development of industrial automation at different levels in modern Distributed Control Systems (DCS) is considered. Special attention is paid to the rapid development of the basic control level through PLC, PAC and EPIC controllers and expansion of their technological capabilities for control and communication with the higher hierarchical levels of DCS. The reasons why AI is becoming a leading paradigm in modern times are analyzed. The historically formed connections and mutual influence between the control theory and artificial intelligence are discussed. The main directions in which the fastest and most effective ways of introduction the AI’s methods and techniques in industrial automation are under consideration. The problems of suitability for solving the tasks of industrial automation with the methods of AI depending on the amount of available data are treated. It is specifically focused on one of the key points of advanced industrial automation – creation of mathematical models and their maintenance with the necessary accuracy due to the evolution of the environment and the elements of the control system itself. The integration of classical control methods and AI-based approaches are considered in two case studies: (i) process control of cement production with emphasis on the clinker kiln and (ii) control of the regime of heat treatment of wood in an autoclave with a focus on combining analytical modeling of heat transfer processes and data-driven sub-optimal control under conditions of parametric uncertainties. The study examines the effectiveness of the application of artificial intelligence methods to expand the scope of traditional industrial automation to include subsystems for reliability and cybersecurity. The reliability of the technological equipment is ensured by modules for achieving fault tolerance model-based diagnostics and technical maintenance based on the assessment of the state of the system. Cyber security is guaranteed by elements that provide protection against cyberattacks and reduce operational uncertainty. As an example for condition-based maintenance is considered an integrated control system of Peirce-Smith converter from the metallurgical industry. It is concluded that the methods of artificial intelligence give a new inspiration to the future development of industrial automation. These methods allow to achieve new functional capabilities for technological and operational control, reliability and cyber security compared to traditional means of industrial automation. The integration of artificial intelligence in industrial control systems can be successful only if the combination of domain knowledge with the achievements of advanced industrial automation and the new methods, techniques and tools of artificial intelligence will be realized in full degree.

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