Category: Journal Automatica and Informatics

Key Words: Adaptation; integration; Machine Learning; modeling; optimization; Reinforcement Learning; tuning.

Abstract. The article examines the main directions for integrating the Reinforcement Machine Learning (RL) as an extension of the already existing advanced industrial automation systems based on the classical PID and MPC regulators. The appropriate areas of application, functionality, advantages and disadvantages of the application of RL in direct process control, hybrid regulation, as well as in local coordination of complex multidimensional, nonlinear, with parametric variability and large number of constraints objects with frequent changes of the control objectives are analyzed. The goal of integrating RL is to achieve optimality, adaptability and security in all possible operational situations from technological and business aspects that are unattainable for traditional systems with PID and MPC regulators. An analysis was made and the prospects for the gradual transformation of RL into the third main pillar of industrial automation along with PID and MPC were assessed.

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Key Words: Neural PID control; adaptive control; electro-hydraulic system; nonlinear systems; MATLAB/Simulink; robustness.

Abstract. This article presents an adaptive Neural PID Optimizer for control of a nonlinear electro-hydraulic servo system. Conventional PID controllers with fixed parameters ensure satisfactory performance only in a limited operating range and exhibit degraded behavior at low rotational speeds due to dead-zone effects and at high speeds due to actuator saturation. To address these limitations, a neural network is employed to adapt the PID gains Kp, Ki, and Kd in real time based on the system state. Simulation studies conducted in MATLAB/Simulink for different operating regimes and load variations demonstrate that the proposed adaptive controller improves dynamic performance, reduces settling time, eliminates steady-state error, and enhances robustness to disturbances. The time evolution of the controller parameters confirms the effective operation of the neural adaptation mechanism. The results validate the suitability of the proposed approach for control of nonlinear electrohydraulic systems with variable operating conditions.

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Key Words: Signal sources; PLC; Arduino; Raspberry Pi; Data Base; data server; programming software.

Abstract. In this article are considered several variants of systems for capturing data from measuring devices, their processing and their transmission for further storage in a database. Databases are considered, also the formation of records in them. In the modern digital world, automation acquires new dimensions and new qualities of receiving, processing and storing results. This article considers: measuring devices – sources of digital signals; devices/controllers for signal processing; methods and means for storing measured information; software tools for processing and storing data. As an example, elements of a program for receiving, processing and storing data received via the Modbus protocol are shown.

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Key Words: Bit manipulations; hardware optimizations; Edge devices; lookup tables (LUT); CORDIC; fast inverse square root; embedded machine learning.

Abstract. Fast and energy-efficient evaluation of transcendental functions (for example, logarithm, exponential, square root, and normalization) is critical for real-time signal processing and on-device AI inference in Edge and IoT systems. This paper surveys and experimentally validates a set of lightweight techniques – primarily IEEE-754 based bit-level manipulations (bit tricks) – alongside code- and hardware-level optimizations (loop unrolling, DMA usage, and other low-level optimizations) that dramatically accelerate these functions on resource-constrained microcontrollers. We describe simple but effective bit hacks for fast approximations of log₂(x), 2ˣ, √x and 1/√x (including the well-known “Quake” inverse square root), explain their theoretical basis in floating-point representation, and show how modest corrections to the mantissa (linear and low-order polynomial) can substantially improve accuracy while retaining most performance gains. Benchmarking across representative platforms (AVR, Cortex-M variants with/without FPU, ESP32, and RISC-V boards) demonstrates up to 10× speedups relative to standard library implementations, with typical accuracy in the range of 5-6 bits for raw bit-trick estimates and much improved error after correction terms. We discuss practical integration patterns, trade-offs between latency, memory footprint, and numerical fidelity, and recommend when to combine bit tricks with LUTs or CORDIC for broader function coverage. Finally, we outline use cases (embedded ML feature scaling, entropy computation, normalization in DSP and robotics) and caveats (unsuitability for high-precision scientific/financial calculations and for periodic functions like sin/cos), providing guidelines for safe deployment in production Edge/IoT projects.

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Key Words: Concrete pits; veneer logs; steaming; thermal balance; energy efficiency.

Abstract. A methodology for computing the thermal balance and energy efficiency of concrete pits when steaming veneer logs in them has been presented. The methodology is based on the use of two personal mathematical models: 1D non-linear model of the unsteady temperature distribution along the radius in the central cross section of non-frozen logs at conductive boundary conditions, and a stationary model of the thermal balance of concrete pits when steaming wood materials in them. For numerical solving of the models and practical application of the suggested methodology, a software packages were prepared in the calculation environment of Visual FORTRAN Professional and in Excel respectively. With the help of the first model, the steaming times of beech logs with a diameter of 0.4 m, initial temperature of 0, 10, and 20 °C and moisture content of 0.4, 0.6, and 0.8 kg∙kg-1 were determined at operating temperatures in the pit equal to 70, 80, and 90 °C. Using the determined logs’ steaming durations, with the help of the second model the change in the energy required for the entire steaming process and that for each of the components of the pit’s thermal balance was calculated. The thermal efficiency of a concrete pit was also determined under different combinations of factors affecting the steaming of the logs. Computer simulations were performed for a well-insulated pit with working volume of 20 m3 and degree of filling it with logs 45%, 60%, and 75%. It has been established that the energy consumption of the pit decreases from 122.8 to 106.3 kWh·m-3 when the initial temperature of logs with a moisture content of 0.6 kg∙kg-1 is increased from 0 °C to 20 °C, and their logs heating is realized at a temperature of the steaming medium of 80 °C and a maximum possible in practice degree of filling the pit with logs of 75%. The increase in the temperature in the pit from 70 to 90 °C at its 75% filling with logs with an initial temperature of 10 °C and a moisture content of 0.6 kg∙kg-1 causes an increase in its energy consumption from 103.4 to 127.0 kWh·m-3. In these two cases, the thermal efficiency of the pit decreases – in the first case from 41.8% to 35.7% and in the second case – from 39.1% to 38.9%. The increase in the moisture content of the logs from 0.4 to 0.8 kg·kg-1 causes an increase in the energy consumption of the pit from 104.3 to 125.2 kWh∙m-3 at its 75% filling and steaming at 80 °C of logs with an initial temperature of 10 °C. In this case, the thermal efficiency of the pit increases from 34.3% to 42.9%. The presented methodology can be used to calculate the thermal balance and efficiency of concrete pits with different geometric and thermophysical parameters when steaming logs of different wood species to any desired degree of heating and plasticization in the production of veneer

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Key Words: EBSI; EUROPEUM-EDIC; EBSI-NE; VeloxWallet; EBSI OnePass.

Abstract. The aim of the article is to present the challenges, experience and solutions obtained by the teams in which the authors work for the architecture of EBSI nodes, processes and roles in verification and financing of start-ups and SMEs in the European projects EBSI-NE and EBSI OnePass. The European Blockchain Infrastructure for Services (EBSI) is presented as a common cross-border framework in Europe – need and opportunities, technologies and a single legal framework to support growth and business. The architectures and basic functionalities of the EBSI-NE projects, which develop the EBSI network by adding 18 new validator nodes to the production network and providing comprehensive support services to all stakeholders, and the EBSI OnePass project, which develops decentralized services for the processes of innovative financing of SMEs, are described. The developed digital wallet VeloxWallet, which offers various functionalities, such as KYC (Know Your Customer) and KYB (Know Your Business) schemes for verifiable certificates and the verification process of the participants in the OnePass project, are examined. Empirical results from the work on the two projects are also discussed: a developed environment for implementation in the EBSI pre-production stage, ecommendations for testing the OnePass project, and future directions for the application and development of the OnePass platform.

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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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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: 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: 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: Applied Artificial Intelligence; Generative Artificial Intelligence (GenAI); Large Language Models; Digital transformation.

Abstract. The paper gives a condensed overview of the industry’s current state-of-the-art artificial intelligence (AI) applications. From a business perspective, the critical issue of understanding the differences between basic AI and the new popular Generative AI (GenAI) is emphasized. Special attention is given to current and future technology trends, such as Causal AI, automated reasoning with knowledge graphs, Large Language Model (LLM)-based agents, Agentic AI, and their value-creation potential. The application landscape, defined mainly by the impressive record of basic AI after 2010 and the potential for mass-scale use of GenAI, is discussed. The paper also suggests a roadmap for big and small businesses to introduce an appropriate form of basic AI or GenAI.

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Key Words: Case-Base Reasoning; Risk Management; Fault Tree; System Control; Power Plant.

Abstract. The management of Energy Power Plants (PP) is a multifaceted technological process influenced by various factors, including environmental changes, the state of system control elements, and potential faults in sensors and other critical components. During the control of Power Plants, some failures in system devices (such as sensors, valves, and other essential components) can occur. These failures can lead to damage and malfunctions within the system control loops, ultimately causing the system to work unstably and rise risk situations. Early diagnosis and prediction of these failures are crucial for maintaining the safe and efficient operation of Energy Power Plants. The research introduces a new approach for diagnosing the state of the system and presents a mechanism for predicting some failures of control loop elements at an early stage. The proposed approach combines Fault Tree (FT) analysis with Case-Based Reasoning (CBR) in a way to provide a comprehensive system for prediction of fault devices detection. Using the previous knowledge for the technological characteristics of control devices elements – such as material wear, working time under high load, and contamination of sensors and photocells is used for forming of knowledge base-oriented system (CBR).

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Key Words: Steaming autoclaves; thermal balance; wooden prisms; energy consumption; energy efficiency.

Abstract. An approach for computing the energy consumption and energy efficiency of autoclaves during steaming of non-frozen wooden prisms intended for veneer production has been presented. The approach is based on the use of two personal mathematical models: 2D non-linear model of the unsteady distribution of the temperature in the central cross section of non-frozen prisms subjected to steaming at conductive boundary conditions, and stationary model of the thermal balance of autoclaves during steaming of wood materials in them. For numerical solving of the models and practical application of the suggested approach, a software packages were prepared in the calculation environment of Visual FORTRAN Professional and in Excel respectively. With the help of the first model, the durations of all of the 5 stages of the regimes for steaming beech prisms with cross section dimensions of 0.3 × 0.3 m, 0.4 × 0.4 m, 0.5 × 0.5 m, initial temperature of 0 °C, 10 °C, 20 °C and moisture content of 0.6 kg∙kg-1 were determined at maximum temperatures of the steaming medium equal to 130 °C.

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Key Words: Generalized predictive controller; multi-objective optimization; constant step scanning; tuning parameters.

Abstract. A multi-objective and multi-parametric optimization problem with interval constraints, linear inequality constraints and constraints on the values of the objective functions is formulated to determination of the tuning parameters in generalized predictive controller design. Quality indicators of the desired control system are optimization objectives. The problem is solved by modified methods of constant step scanning. The tunable parameters optimization problem is transformed into a single-objective constraint problem by methods of the vector criterion scalarization.

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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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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 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: Artificial Intelligence; integration; industrial automation; industrial control systems; Machine Learning; operational control; technological control.

Abstract. The newly emerging challenges of interpenetration and multifaceted integration of modern artificial intelligence technologies and leading computer systems of industrial automation are considered in the article. The relatively independent development of industrial automation and artificial intelligence has reached in the last one or two decades such a high technological level of openness, flexibility and compatibility, creating prerequisites to build integrated systems based on a holistic synthesis of a completely new type for solving tasks which until recently were considered intractable. The new conditions of competition are significantly complicated due to the extremely accelerated dynamics and depth of the various stages of industrial processes – supply chains, transportation problems, technological breakthroughs in the basic facilities and processes, malicious cyber attacks, situations resulting from global geopolitical conflicts. The main achievements that give rise to huge expectations for overcoming these and the expected future challenges are: the fundamentally new level of information treatment (Big Data), the modern achievements of computing technology (Industrial Internet of Things – IIoT, cloud technologies, computing architectures based on of graph models) and the explosive development of artificial intelligence (multidimensional and multi-aspect models, automatic prediction, decision-making and planning, revealing implicit relationships and dependencies, learning based on historical, current and simulation data, human-machine interaction of a new type ). Under the new highly complicated conditions and increased requirements, industrial automation is steadily following its trajectory of expanding its scope of action – from primarily technological control to gradually including all sides of the operational control and management (planning, production schedules, predictive maintenance of the facilities, adaptation to the goals and situations in the short and long term). The realistic approach requires strategic decisions in the development of industrial automation to be in the direction of „end-to-end automation“ by upgrading existing systems while uncompromisingly evaluating the advantages and disadvantages of the relevant artificial intelligence technologies that we have now.

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Key Words: Calculation of energy; wood containing ice; melting frozen water; thermal treatment; Table Curve 2D.

Abstract. A mathematical description of the thermal energy consumption Qice required to melt the ice formed in the wood by the natural freezing of both free and bound water in it, using the software package Table Curve 2D v.5.01 has been presented. This package allows for the selection of equations, which provide the best similarity between the calculated with them values of Qice during thermal treatment of wood containing ice and the respective numerical data obtained experimentally or using an adequate temperature-energy model of Qice. For the determination of Qice, the classical approach from thermodynamics was used to calculate the energy required to heat a solid body from any initial temperature to a certain final temperature. In our case, the energy Qice is represented as a sum of the energy Qice-bw required to melt the initial temperature-dependent frozen portion of the bound water in the wood, and the energy Qice-fw required to melt the ice formed by freezing all the free water in the wood. The energies Qice-bw and Qice-fw are represented as a product of the ice density with the specific heat capacities of the frozen bound and free water in the wood and the temperature ranges in which the melting of the two types of frozen water takes place. Mathematical descriptions of the specific heat capacities of frozen bound and free water in wood are also presented, taking into account the latent heat of the phase transition of water. The calculations of the energies Qice-bw, Qice-fw, and their sum Qice were made for the case of thermal treatment of frozen beech wood with an initial temperature of −1 °C, −10 °C, −20 °C, −30 °C, −40 °C and moisture content of 0.4 kg·kg-1, 0.6 kg·kg-1, and 0.8 kg·kg-1. The obtained results were processed using the Table Curve 2D software package. Three logarithmic equations of the same type were selected, which mathematically describe the dependence of Qice on the initial temperature of the frozen wood for each of the investigated values of its moisture content. It was found that there is a very small error (within the limits of only 3.5%) between the equations calculated with Table Curve and the approximate values of the Qice. The equations obtained can be used for development and automatic implementation of scientifically based energy-efficient regimes and technologies for steaming or boiling of frozen wood materials with different properties and purposes.

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Key Words: Human-Computer Interaction; smart home systems; security; human-computer interface.

Abstract. Human-Computer Interaction (HCI) is a critical field of study that has gained increasing importance over time. Today, smart home systems are among the most common application areas of human-computer interaction. These systems offer efficiency and end-to-end connectivity to users. Moreover, IoT frameworks and other technological integrations are widespread and indispensable components of these systems. However, significant security concerns and vulnerabilities associated with these systems are also on the rise. This study examines the relationship between HCI and smart home systems, focusing on the requirements for intuitive and secure interface designs. The research highlights the need for the convergence of HCI interface designs with smart home systems, emphasizing the necessity for continuous innovation and improved security mechanisms. It also underscores that future advancements in cutting-edge technologies, such as artificial intelligence, 5G/6G, blockchain, and quantum computing, are expected to present both opportunities and challenges to the design and operation of smart home systems. In conclusion, as interactions between humans, advanced technologies, and smart systems become more frequent, the application of HCI principles in the design of smart home systems will become increasingly crucial. Ensuring that these systems are not only usable but also secure is vital, making them safe and accessible for users with low digital literacy. Integrating both technical and human-centered perspectives in the holistic development of these systems is imperative.

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