Категория: АВТОМАТИКА И ИНФОРМАТИКА 3, 2025 г.

Key Words: Waste valorisation; bioenergy production; containerised biogas system; climate change mitigation.

Abstract. The EU is fostering new set of policies in all sectors, so as to achieve climate neutrality in 2050. The generation of new practices for improved exploitation of available waste streams in the agricultural sector holds significant potential for minimization of the generated greenhouse gases. This requires innovations towards more efficient waste valorisation systems for the production of bioenergy. To tackle the issue, a containerised biogas system is proposed to prevent the free release of methane and other GHG, while producing renewable energy. The experience from the development of containerised biogas system in Dimitrievo, Bulgaria is described.

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Key Words: Generalized predictive control; disturbance compensation; feedforward control; time-delay; nitrogen oxides (NOx).

Abstract. This paper describes a NOx control system in a combined cycle power plant. The aim is to increase the control performance of a NOx decomposition process by improving measurable disturbance compensation. Extended Generalized Predictive Control algorithm is used. The typical predictive model in GPC algorithms (ARIMAX) is extended by a measurable disturbance vector.

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Key Words: Artificial Intelligence; business analytics; Machine Learning; optimization; Decision Making.

Abstract. The paper discusses the development and implementation of artificial intelligence algorithms for business analytics. The study focuses on the potential of AI in predictive analytics, decision making, and optimization of business processes. Key technologies such as machine learning, natural language processing, and data mining are analyzed. Practical applications in sectors like finance, marketing, and supply chain management are explored. The impact of AI-driven solutions on improving business efficiency and decision accuracy is highlighted. The results from real-world case studies are presented, confirming the effectiveness of AI in complex business environments.

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Key Words: Artificial intelligence; expert systems; industrial automation; operation control; predictive maintenance.

Abstract. Expert systems were the first field at the dawn of artificial intelligence, in the mid-20th century. They also represent the first real practical applications in areas such as the synthesis of chemical compounds, healthcare, and computer system design, thanks to their remarkable ability to mimic human abilities for logical inference in the presence of in-depth expert knowledge. Industrial automation, in turn, reached a high level of development in the 1980s, but technological innovations posed a number of new problems, the solution of which by applying expert systems proved to be a suitable alternative in combination with the significant achievements of computing and control theory. This synergy of rapidly developing fields is particularly productive currently in the era of exponentially advancing artificial intelligence technologies, part of which are expert systems. The paper examines the features and capabilities of a large number of modern industrial automation systems with the integration of expert systems and artificial intelligence modules. Particular emphasis is placed on expanding the scope of industrial automation with the inclusion of predictive maintenance of the technical condition of technological objects as an important part of the operational management of objects from “end to end”. The main challenges and unresolved problems of the application of expert systems in industrial automation are examined with confidence in the possibilities for realizing their potential.

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