Month: August 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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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: 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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Key Words: Jordan Canonical Form; algorithm of Kublanovskaya-Ruhe-Kågström; Weyr Canonical Form.

Abstract. The paper presents some comments about the development and implementation of numerical algorithms for finding the Jordan canonical form of a square matrix. A short history of the algorithm of Kublanovskaya-Ruhe-Kågström is given. This algorithm uses an orthogonal reduction to staircase form in order to find the Segre characteristic of the multiple eigenvalue (the dimensions of the Jordan blocks pertaining to this eigenvalue). It is noted that this algorithm actually finds the Weyr characteristic and Weyr canonical form of the original matrix. That is why the program of Kågström and Ruhe can determine reliably the numerical structure of the given matrix. It is argued that in most cases this program can produce an accurate result for the Jordan form although this is in contradiction with the opinion of many researchers working in the field of matrix computations. Three myths concerning the numerical determination of the Jordan form are discussed. An 8th order example is given which demonstrates that the program system MATLAB® (and LAPACK package) produce results for the multiple eigenvalues which contain large errors, while the Kågström-Ruhe algorithm finds these eigenvalues to full machine precision. In such cases the eigensystem problem is ill-conditioned and some regularization technic is necessary to use. It is insisted that the Kågström-Ruhe algorithm is well suited for such a purpose. The paper ends by the note of G. W. Stewart that“… as a mathematical probe the Jordan canonical form is still useful, and reports of its dead are greatly exaggerated”.

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Key Words: Smart Grid; distributed grid networks; intelligent management; energy storage; microgrid; ICT; smart home; smart sities.

Abstract. In the future, the methods and technical means for intelligent control of final energy consumption by economic criteria in real time, based on the integration of electricity and information networks, will become a priority for the construction and operation of Smart Energy Networks (SMART GRIDS), i.e. Energy Internet. Thus, energy and information processes in micro-networks must be considered as interconnected. Electricity storage is a key element of future smart distributed energy networks. For energy companies, the key pursued goals for the development of Smart Grid technologies are: reduction of energy losses; increasing the timeliness and completeness of payment for consumed energy resources; control of unevenness of the electric load schedule; improving the efficiency of asset management of energy companies; improving the quality of the integration of renewable and distributed generation facilities into the power system; improving the reliability of the energy system in the event of emergencies; improving the visualization of energy infrastructure facilities. The key tasks to be solved by energy consumers in the implementation of Smart Grid technologies are: improving consumer access to energy infrastructure; improving the reliability of power supply to all categories of consumers; improving the quality of energy resources; creation of a modern interface for interaction between energy consumers and its suppliers; the opportunity for the consumer to act as a full participant in the energy market; enhanced opportunities for consumers to manage energy consumption and reduce the level of payments for consumed energy resources. Governments and regulators of the energy industry are striving to achieve the following goals through the development of Smart Grid technologies: increasing the level of satisfaction of energy consumers with the quality and cost of energy supply; ensuring a stable economic position of enterprises in the energy industry; ensuring the modernization of fixed assets of the energy industry without a significant increase in tariffs. From the presented information it can be concluded that Smart-Grid is a system that is able to self-monitor and provide reports for all participants in the network (its status, needs, etc.) and complete information about the electricity generated and transmitted in every aspect: efficiency, losses or economic benefits; This is especially important for liberalized electricity markets, where trade is hour-ahead. In this way, the smart system builds a load profile of each user and can accurately redistribute prepaid energy from exchanges. The surplus can be accumulated in a storage battery module or in heat energy in the consumers’ boilers, depending on what the consumer or the consumer group has. In case of lack of (requested) energy, when the consumption has to be limited, the system has variants of strategy in which it either stops powerful consumers, without special significance (eg electric water heaters) or switches consumers to energy storage, until the next period/hour, thus the system includes as an energy generator the accumulator unit for storage of electricity and delivers in the network the insufficient amount of energy, ie. this user is active, i.e. it consumes and produces energy. This would reduce the need to maintain a cold reserve and make the energy produced cheaper. The authors study the behavior of a real SG system developed by them, have a lake of data on its operation for several years and prepare a patent solution for cheap home smart composite batteries. The concept of using smart controllers as perceptrons – elements of neural networks, in which SG can be trained and respond autonomously as effectively as possible, is also the author’s. The more modern and up-to-date perspective that the authors apply is to use neural network technology and machine learning to predict consumer behavior and energy generation in generating capacity, and to develop a strategy for the use of storage capacity (energy storage) as generating ones in order to balance the networks and use the cheapest source of electricity for a given period. It is also possible to apply purely economic approaches such as clearing, in the supplier-consumer relationship, consumer-consumer, many users to supplier. Thus, networks of pure distribution, if they have elements of smart grid, have energy storage capacity, can become highly efficient generating capacity to provide third parties (consumers, which can be entire networks) capacity, which far exceed their own consumption, but this will be the subject of a separate article by the authors.

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Кey Words: 2D model; atmospheric air temperature; beech logs; thermal treatment; model based control.

Abstract. This paper describes an approach for mathematical modeling and research of the 2D non-stationary temperature distribution and average mass temperature of logs stored in an open warehouse and influenced by the periodically changing atmospheric temperature near them. Mathematical descriptions of the thermo-physical properties (specific heat capacity, density, thermal conductivity and convective heat transfer coefficient in radial and longitudinal anatomical directions) of the non-frozen wood and also of the periodically changing during many days and nights temperature of the atmospheric air as a processing medium have been presented. They are introduced in the convective boundary conditions of our own 2D non-linear mathematical model of the logs’ heating and cooling processes. For the numerical solving of the model a software program has been prepared in the calculation environment of Visual FORTRAN Professional developed by Microsoft. Results from simulative investigation of the 2D non-stationary temperature distribution and average mass temperature of non-frozen beech logs with semi-industrial dimensions (diameter of 0.24 m and length of 0.48 m), moisture content of 0.6 kg·kg-1, and initial temperature of 20°C during their five days and nights continuous alternating heating and cooling at sinusoidal change of the air temperature with initial values of 20°C and different amplitudes are presented and analyzed. The presented approach for the computation of the 2D temperature field in logs and their average mass temperature at periodically changing ambient air temperature can help for the accurate determination of the initial temperature of the logs before their thermal treatment, depending on the duration of the logs’ storing in an open warehouse. This approach is suitable for application in the software of systems for optimized model based automatic control of the thermal treatment processes of logs and other wood materials. The obtained results can be used for development of energy saving technological regimes with an optimal duration depending on the precise determined initial temperature of the materials of each charge subjected to thermal treatment.

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