Категория: АВТОМАТИКА И ИНФОРМАТИКА 1, 2025 г.

На 22 февруари 2025 г. акад. Кирил Любенов Боянов навърши 90 години! Изявен български инженер, учен в областта на електротехниката, изчислителната техника и информационните технологии, академик на БАН, добър наш колега и приятел, достоен човек, той е и един от най-авторитетните членове на Съюза по автоматика и информатика „Джон Атанасов“ с голям принос за утвърждаването и развитието на САИ през годините. Спомогнал е съществено за интегрирането на двете съставящи професионални групи от специалисти в САИ – по автоматика и информатика. Няколко поколения от нашата общност черпят познание, мъдрост, ум и вдъхновение от него.

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Клуб САИ продължава да е едно от най-привлекателните и популярни мероприятия на САИ. Наред с чисто човешките, колегиални и професионални контакти представянето на фирми и постижения на колеги в областта на автоматиката и информатиката позволява членовете на САИ да се запознаят с актуални теми в науката и реалната икономика в предметната област на Съюза.

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На 12-13 ноември 2024 г. в зала 3 на Дома на науката и техниката, ФНТС, ул. Г. С. Раковски 108 се проведе 32-то издание на традиционния ежегоден международен симпозиум „Управление на енергийни, индустриални и екологични системи“, организиран от Съюза по автоматика и информатика „Джон Атанасов“ със съдействието на ФНТС. В 32-годишното си съществуване симпозиумът поддържа неизменно линията за връзка между специалистите по автоматика и информатика от изследователските групи и индустрията.

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Ежегодната международна конференция IEEE International Conference Automatics and Informatics’24 (ICAI’24), продължение на традиционната Международна конференция „Автоматика и информатика“, се проведе на 10-12 октомври 2024 г. в Техническия университет – Варна. Тя е едно от основните мероприятия в Дните на Джон Атанасов, провеждани традиционно под патронажа на президента на Република България с цел представяне и популяризиране на резултатите от научните изследвания в областта на автоматиката и информатиката.

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Престижната Международна конференция по интелигентни системи се проведе от 29 до 31 август 2024 г. във Варна. Това е нейното 12-то издание, като шест от тях са проведени в България.

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CompSysTech’24 е двадесет и петото издание на Международната конференция по компютърни системи и технологии. Конференцията се проведе на 14-15 юни 2024 г. в Русенския университет „Ангел Кънчев“, Русе.

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Key Words: Artificial neural network; approximation; Laguerre functions.

Abstract. The present article examines a well-known procedure used in various scientific fields – approximation. Specifically, it analyzes the application of Laguerre orthonormal functions in the approximation of transient and impulse responses of dynamic systems. Particular attention is given to the challenges associated with using these functions in the context of model predictive control (MPC) synthesis. These challenges include determining the scaling factor, the number of orthonormal functions, and the calculation of decomposition coefficients, which create additional difficulties when modeling systems with parametric uncertainties. To overcome these challenges, the use of artificial neural networks (ANN) is proposed for the automatic generation of the decomposition coefficients of Laguerre functions. Instead of directly approximating the impulse responses of systems, which involves significant computational complexity, ANN is employed to predict the optimal decomposition coefficients, thereby reducing computational time and improving accuracy. As part of the study, simulations were conducted for the approximation of both low- and high-order systems with different levels of parametric uncertainty. The results demonstrate a significant improvement in approximation accuracy when using neural networks compared to standard coefficient computation methods. Graphical representations of the approximation results for different values of the scaling factor and the number of orthonormal functions are provided, analyzing their effects on approximation error. It has been established that, with proper training of neural networks, significant improvements in the approximation process can be achieved, making them suitable for implementation in real-time control systems. The proposed methodology extends the applicability of Laguerre functions in model predictive control and provides an efficient approach for handling systems with uncertainties.

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Key Words: Anaerobic co-digestion; complex mixtures; two-stage anaerobic co-digestion; Extremum Seeking Control; combination of AD with photovoltaic energy.

Abstract. Anaerobic digestion is a biotechnological method by which various organic wastes can be used as feedstock for biogas production. Anaerobic co-digestion can be considered as the simultaneous anaerobic digestion of a mixture of two or more waste types as substrate and co-substrate. Over the past two decades, our multidisciplinary team has performed many experiments on anaerobic co-digestion of various organic wastes. Different ratios of organic waste mixtures, in binary and ternary mixtures, have been investigated in order to maximize the resulting methane. Experimental studies confirm that biogas yields and process stability can be significantly increased when using two or three-component mixtures depending on the feedstock types and the ratio between them. Another option for increasing energy yields is the two-stage process, in which hydrogen and methane are produced simultaneously. A very good opportunity in this direction theoretically provides also the so-called extremum seeking control, on which our team has achieved significant results. Our research group is currently working on a project exploring the possibilities of combining solar energy with photovoltaic energy for improvement of energy balance of model facilities representing livestock farms.

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Key Words: Artificial intelligence; control theory; data driven; decision making; industry; industrial automation; integration; learning; modeling; operation control; process control; security; system theory.

Abstract. Industrial automation has entered a new stage in its long historical development by integrating the remarkable achievements of AI, accelerating exponentially in the last decade. The present work aims to summarize the prospects and expected main directions of the evolution of industrial automation in the era of artificial intelligence. It is based on the realistic assumption that innovations in industrial automation will be implemented primarily by upgrading existing and proven industrial facility management systems. One of the main directions is the multifaceted expansion of modern capabilities of industrial automation through the adaptation and further development of leading artificial intelligence technologies. The functional scope of industrial automation systems is expanding through “end-to-end automation” of industrial systems with a focus on operational management of the final production stages; the increasing volume of automation of particularly important procedures such as decision-making, dynamic optimization, coordination in unforeseen and extreme conditions (damages, accidents, disasters); predictive maintenance of the technical condition of facilities. Modern industrial automation, integrating elements of artificial intelligence, will implement a new approach in the strategy of industrial management through the transformation of approaches to achieving high efficiency, production quality, energy efficiency, environmental friendliness, and cybersecurity. Artificial intelligence methods will expand the capabilities of modern management of complex objects with large dimensions, nonlinearity, uncertainty, non-stationary variability of structures, models, parameters, and disturbances. The learning methods and generative capabilities of artificial intelligence systems will allow the implementation of complex hierarchical, networked and multi-connected structures with autonomous agents, necessary for a number of critical industrial infrastructure systems. Integrating elements of artificial intelligence into industrial automation systems still poses a major challenge for researchers, designers, operators and managers. Therefore, any implementation of artificial intelligence technologies so far has a unique character, represents an innovative solution and requires significant efforts, a holistic approach and conviction. The undoubted rapid development of artificial intelligence in the coming years and the increased needs of industrial systems will simulate rapid progress in industrial automation systems.

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