Modeling Of Processes Research Articles

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

CAPILLARY PROCESSES DURING FLOODING OF LAYERED-HETEROMOGENEOUS PRODUCTIVE FORMATIONS

The paper presents an analysis of existing laboratory methods for determining capillary pressure functions used in mathematical and computer modeling of the processes of capillary impregnation of low-permeability layers of layered heterogeneous hydrophilic reservoirs during their flooding.It is shown that in existing methods for determining capillary pressure functions in rock samples using capillarimeters and high-speed centrifuges, the factor of the formation of residual oil in the pore space of the rock is not taken into account. At the same time, the processes of displacement of oil by water from productive formations and, in particular, the processes of capillary impregnation of the rock, are characterized by the processes of formation of microvolumes precisely of immobile residual oil in its pore space. Meanwhile, when conducting laboratory studies of rock samples using existing methods, another process is physically reproduced — the process of displacement of water by oil, during which microvolumes of residual water, rather than oil, are formed.This difference in the structure of the oil-water medium leads to a significant difference between the capillary pressure functions determined by traditional methods and the capillary pressure functions that control the distribution of oil and water phases in the pore space of rocks in the processes of displacement of oil from them by water pumped into the reservoir.This difference is especially significant when flooding low-permeability rocks characterized by high capillary pressure values.The work suggests possible approaches to conducting laboratory studies of rock cores that are more consistent with the structural features of the oil-water environment during flooding of productive formations.For the most complete capillary impregnation of low-permeable layers of a layered heterogeneous formation with water from quickly watered high-permeability layers, a method is proposed that consists in the fact that the water pumped into the formation must contain the minimum required amount of oil, ensuring the mobility (connectivity) of the oil phase in the watered high-permeability layers.For this purpose, water from production wells containing a certain amount of residual oil can be used for injection into the reservoir. The economic feasibility of the proposed method should be justified by the results of computer modeling of capillary impregnation processes, taking into account the described features of capillary pressure functions characteristic ofprocesses of oil displacement from productive formations during their waterflooding.

Two Old Wild-Type Strains of Drosophila melanogaster Can Serve as an Animal Model of Faster and Slower Aging Processes

Background: Drosophila melanogaster provides a powerful platform to study the physiology and genetics of aging, i.e., the mechanisms underpinnings healthy aging, age-associated disorders, and acceleration of the aging process under adverse environmental conditions. Here, we tested the responses of daily rhythms to age-accelerated factors in two wild-type laboratory-adapted strains, Canton-S and Harwich. Methods: On the example of the 24 h patterns of locomotor activity and sleep, we documented the responses of these two strains to such factors as aging, high temperature, carbohydrate diet, and diet with different doses of caffeine-benzoate sodium. Results: The strains demonstrated differential responses to these factors. Moreover, compared to Canton-S, Harwich showed a reduced locomotor activity, larger amount of sleep, faster rate of development, smaller body weight, lower concentrations of main sugars, lower fecundity, and shorter lifespan. Conclusions: It might be recommended to use at least two strains, one with a relatively fast and another with a relatively slow aging process, for the experimental elaboration of relationships between genes, environment, behavior, physiology, and health.

Computable phenotype for diagnostic error: developing the data schema for application of symptom-disease pair analysis of diagnostic error (SPADE).

Diagnostic errors are the leading cause of preventable harm in clinical practice. Implementable tools to quantify and target this problem are needed. To address this gap, we aimed to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing its computable phenotype and then demonstrated how that schema could be applied in multiple clinical contexts. We created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility. We mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms. Data for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.

Adaptive soft sensor modeling of chemical processes based on an improved just‐in‐time learning and random mapping partial least squares

AbstractThe just‐in‐time learning‐based partial least squares (JIT‐PLS) has been extensively applied to adaptive soft sensor modeling of complex nonlinear processes. However, it still has the problems of unreasonable relevant samples selection and unsatisfactory local modeling. Aiming at these problems, this paper proposes an improved just‐in‐time learning‐based random mapping partial least squares (IJIT‐RMPLS), including an improved relevant samples selection strategy and a random mapping PLS (RMPLS) model. On the one hand, considering the different correlation degrees between input variables and output variable, this method applies mutual information to evaluate the importance of each input variable and designs a variable‐weighted Euclidean distance to select relevant samples for local modeling. On the other hand, in order to prompt the prediction precision of local soft sensor models, this method combines the idea of nonlinear random mapping in extreme learning machines with PLS and builds a RMPLS with multiple activation functions. Applications on a numerical example and a real chemical process show that the proposed IJIT‐RMPLS has smaller prediction error compared with traditional JIT‐PLS.

Predefined‐time tracking control of multiplicative systems

ABSTRACTThis article describes a control approach for obtaining predefined‐time robust tracking in multiplicative systems despite positive, bounded, and unknown multiplicative disturbances. The proposed approach is distinguished by imposing predefined‐time convergence, a topic previously studied in conventional calculus in the context of multiplicative systems. Multiplicative calculus is recognized as a beneficial tool that complements standard calculus by simplifying the modeling and comprehension of numerous processes. Simulations are carried out to illustrate that the given control strategy enforces convergence before a predefined time instant and, while inducing robustness against system uncertainties. The findings of this article pave the way for further research into predefined‐time synchronization of multiplicative oscillator systems, which would bring promising implications for data encryption and secure communication.

MALTA: A Zonally Averaged Global Atmospheric Transport Model for Long‐Lived Trace Gases

AbstractWe present a two‐dimensional, zonally averaged global model of atmospheric transport named MALTA: Model of Averaged in Longitude Transport in the Atmosphere. It aims to be accessible to a broad community of users, with the primary function of quantifying emissions of greenhouse gases and ozone depleting substances. The model transport is derived from meteorological reanalysis data and flux‐gradient experiments using a three‐dimensional transport model. Atmospheric sinks are prescribed loss frequency fields. The zonally averaged model simulates important large‐scale transport features such as the influence on trace gas concentrations of the quasi‐biennial oscillation and variations in inter‐hemispheric transport rates. Stratosphere‐troposphere exchange is comparable to a three‐dimensional model and inter‐hemispheric transport is faster by up to 0.3 years than typical transport times of three‐dimensional models, depending on the metric used. Validation of the model shows that it can estimate emissions of CFC‐11 from an incorrect a priori emissions field well using three‐dimensional (3D) mole fraction fields generated using a different 3D model than which the flux gradient relationships were derived. The model is open source and is expected to be applicable to a wide range of studies requiring a fast, simple model of atmospheric transport and chemical processes for estimating associated emissions or mole fractions.

THE PROBLEM OF PROFESSIONAL TRAINING OF FUTURE IT SPECIALISTS IN THE RECEPTION ROOMS OF DOMESTIC SCIENTISTS OF THE LAST DECADE

In the article was analyzed the problem of professional training of future IT specialists in the receptions of domestic scientists of the last decade. It is found that the modern system of training specialists in higher education in Ukraine is focused on global trends in the development of higher education, such as globalization, integration, democratization, continuity, humanization, openness of education, academic freedom, autonomy, digitalization, etc., which leads to the transformation of training of highly qualified specialists, including IT specialists. It is proved that reception as a new methodological technology involves interpretation – one's own interpretation, addition to the previously created. Based on the analysis, it is determined that the professional training of future IT specialists involves the formation of their readiness to work and acquire relevant competencies in information technology, mathematical and computer modeling of processes and systems of various nature, forecasting, optimization, system analysis and decision-making, etc. The article characterizes the research of Ukrainian scientists related to the professional training of IT specialists in its multi-vector dimension over the past decade, such as I. Gerasymenko, A. Stryuk, A, M. Rassovutska, S. Semerikov, I. Tarasov, Y. Sikora, O. Glazunova, A. Kocharyan, S. Nikolayenko, V. Shynkaruk, V. Kovalchuk Y. Vasylenko, L. Dmytritsa, T. Bodnenko, V. Vlasenko, V. Bykov, A. Gurzhiy, M. Shyshkina, T. Voloshyna, T. Vakaliuk, T. Potapchuk, O. Malykhin, T. Yarmolchuk, S. Berezenska, N. Oliynyk, I. Varava, A. Kapiton, A. Schlichta, and others. It has been established that in the last decade, Ukrainian researchers have considered the professional training of future IT specialists in the context of their professional activities in a rather different way and have drawn certain analogies with the concept of competence as a person's ability to perform a certain type of activity

Basics of calculation of a two-circuit air purification system for polydisperse dust

Basics of calculation of a two-circuit air purification system for polydisperse dust

Review of Advanced Digital Technologies, Modeling and Control Applied in Various Processes

This special issue reviews advanced digital technologies in modeling and control of technological processes [...]

Overview of the Current State of Physical-Mathematical Modelling of Solid Fuel Combustion Processes

This study provides a comprehensive introduction to the combustion of solid propellants. We conduct a thorough review of existing physical-mathematical models related to solid fuel combustion processes. Additionally, we explore the heat propagation within the solid phase, examine the physical chemical processes in the k–phase, and investigate the fundamental solution of the heat conduction equation in a multiphase (k–phase) medium. The heat conduction equation plays a crucial role in governing the temperature distribution within a given material, and a profound understanding of its fundamental solution is essential for various engineering and scientific applications. In this research, we expand our analysis to a multiphase scenario, considering different phases with distinct thermal properties coexisting.

Revealing Interface Polarization Effects on the Electrical Double Layer with Efficient Open Boundary Simulations under Potential Control.

A major challenge in modeling interfacial processes in electrochemical (EC) devices is performing simulations at constant potential. This requires an open-boundary description of the electrons, so that they can enter and leave the computational cell. To enable realistic modeling of EC processes under potential control we have interfaced density functional theory with the hairy probe method in the weak coupling limit (Zauchner et al. Phys. Rev. B 2018, 97, 045116). Our implementation was systematically tested using simple parallel-plate capacitor models with pristine surfaces and a single layer of adsorbed water molecules. Remarkably, our code's efficiency is comparable with a standard DFT calculation. We reveal that local field effects at the electrical double layer induced by the change of applied potential can significantly affect the energies of chemical steps in heterogeneous electrocatalysis. Our results demonstrate the importance of an explicit modeling of the applied potential in a simulation and provide an efficient tool to control this critical parameter.

An information flow field model for human–machine interaction

Purpose About 70% of all aircraft accidents are caused by human–machine interaction, thus identifying and quantifying performance shaping factors is a significant challenge in the study of human reliability. An information flow field model of human–machine interaction is put forward to help better pinpoint the factors influencing performance and to make up for the lack of a model of information flow and feedback processes in the aircraft cockpit. To enhance the efficacy of the human–machine interaction, this paper aims to examine the important coupling factors in the system using the findings of the simulation. Design/methodology/approach The performance-shaping factors were retrieved from the model, which was created to thoroughly describe the information flow. The coupling degree between the performance shaping factors was calculated, and simulation and sensitivity analysis are based on system dynamics. Findings The results show that the efficacy of human–computer interaction is significantly influenced by individual important factors and coupling factors. To decrease the frequency of accidents after seven hours, attention should be paid to these factors. Originality/value The novelty of this work lies in proposing a theoretical model of cockpit information flow and using system dynamics to analyse the effect of the factors in the human–machine loop on human–machine efficacy.

Generalized Linear Driving Force Formulas for Diffusion and Reaction in Porous Catalysts

Approximate models are a fast and most often precise tool for determining the effectiveness factor for heterogeneous catalysis processes that are realized in the real world. They are also frequently applied as robust transient models describing the work of a single catalyst pellet or as a part of a more complex model, for example, a reactor model, where mass balances for the gas phase and solid phase are necessary. So far, approximate models for diffusion and reaction processes have been presented for processes described by a single balance equation. In the present work, approximate models without the mentioned limitation are presented and discussed. In addition, simple rules are shown for the development of other complex approximate models without tedious derivation in the complex domain. The formulas considered in this work are typical long-time approximations of the transient process. The accuracy is good, especially in the range of small and intermediate Thiele modulus values.

A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting

Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-MEC) system with energy harvesting devices is a critical challenge due to the dynamic load level at edge nodes and the variability in harvested energy. In this paper, we propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in a D2D-MEC system, taking into account the intricacies of multi-slot tasks, characterized by diverse processing speeds and data transmission rates. Our methodology involves meticulous modeling of task arrival and service processes using queuing systems, coupled with the strategic utilization of D2D communication to alleviate edge server load and prevent network congestion effectively. Central to our solution is the formulation of average task delay optimization as a challenging nonlinear integer programming problem, requiring intelligent decision making regarding task offloading for each generated task at active mobile devices and CPU frequency adjustments at discrete time slots. To navigate the intricate landscape of the extensive discrete action space, we design an efficient multi-agent DRL learning algorithm named MAOC, which is based on MAPPO, to minimize the average task delay by dynamically determining task-offloading decisions and CPU frequencies. MAOC operates within a centralized training with decentralized execution (CTDE) framework, empowering individual mobile devices to make decisions autonomously based on their unique system states. Experimental results demonstrate its swift convergence and operational efficiency, and it outperforms other baseline algorithms.

Моделирование установки замедленного коксования в программе MatLab

In order to prevent the occurrence of an emergency situation in the oil and gas industry, it is necessary to use control, automation and alarm systems. In this regard, artificial intelligence technologies have recently become in-creasingly popular. Neural networks are of particular interest. To implement the task of regulating, automating and predicting technological processes in the oil industry, it is possible to use neural network modeling of chemical and technological processes. Examples of using neural network modeling in practice are given. The results of neural network modeling of a delayed coking plant of one of the operating enterprises are presented. A delayed coking installation was modeled in the UniSim Design software package, which allowed us to obtain an initial data array for a neural network. The neural network was built in the MatLab program, and the program code was created. Graphs of error and regression are presented. The analysis of the results presented on the regression and error graphs is given. As a result of testing the model, minimal discrepancies between experimental and predicted data were obtained, which indicates the adequacy of the neural network model. An additional test of the program was also performed. The results of training and testing of the model are presented. The results obtained can later be used to create programs at different levels of control, since the model allows you to estimate the amount of losses during the operation of the installation at a certain flow rate of feed water supplied to the installation as a turbocharger.

Catalytic hydrogenation reaction micro-kinetic model for dibenzyltoluene as liquid organic hydrogen carrier

The implementation of the liquid organic hydrogen carrier (LOHC) technology for efficient energy storage requires the development of a reliable kinetic model for both hydrogenation and dehydrogenation processes. In this research study, the catalytic hydrocarbon saturation for a dibenzyltoluene (DBT) mixture solution, containing dibenzylbenzene (DBB), dibenzylethylbenzene (DBEB) and impurities has been performed in the presence of Ru/Al2O3 particles. The influence of different reaction conditions, such as temperature, pressure, initial reactant concentration, catalyst amount and stirring speed has been examined. A measurement-based system micro-kinetics, based on the Langmuir–Hinshelwood mechanism with dissociative H2 surface adsorption, has been derived. H2 thermodynamic solubility equilibrium was defined through Henry's law. The adsorbing, desorption and reactivity of inert solvent molecules was not considered to be relevant. The mass transfer resistance over 1000 rpm stirring speed was negligible. Relative- and mean squared error of representation were 40.9% and 1.00×10−4, respectively. Expressions gave an excellent data prediction for the profile period trends with a relatively accurate estimation of H2 intermediates' rate selectivity, H2-covered area approximation and pathway rate-determining steps. Due to the lack of commercially available standard chemical compounds for quantitative analysis techniques, a novel experiment-based numerical calibration method was developed. Mean field (micro)kinetics represent an advancement in the mesoscale mechanistic understanding of physical interface phenomena. This also enables catalysis structure–activity relationships, unlocking the methodology for new LOHC reaching beyond traditional, such as ammonia, methanol and formate, which do not release H2 alone. Integrated multiscale simulations could include fluidics later on.

Partial melting mechanisms of peraluminous felsic magmatism in a collisional orogen: An example from the Khondalite belt, North China craton

AbstractSedimentary‐derived (S‐type) granites are an important product of orogenic metamorphism, and a range of subtypes can be recognized by differences in field occurrence, mineralogy and geochemistry. These subtypes can reflect variations of initial protolith composition, partial melting reactions, pressure and temperature of anatexis, or magmatic processes that occur during ascent through the crust (e.g. mineral fractional crystallization or crustal assimilation). Together, these diverse factors complicate geological interpretation of the history of peraluminous felsic melt fractions in orogenic settings. To assess the influence of these factors, we performed integrated field investigation, petrology, geochemistry, geochronology and phase equilibrium modelling on a series of leucosomes within migmatite associated with different S‐type granites within the Khondalite belt, North China craton (NCC), which is an archetypal collisional orogen. Three types of leucosome are recognized in the east Khondalite belt: leucogranitic leucosome, K‐feldspar (Kfs)‐rich granitic leucosome and garnet (Grt)‐rich granitic leucosome. Phase equilibrium modelling of partial melting and fractional crystallization processes indicate that the leucogranitic leucosomes were mostly produced through fluid‐present melting, Kfs‐rich granitic leucosomes are produced through muscovite dehydration melting with 3 vol.% garnet fractional crystallization, and Grt‐rich granitic leucosomes are produced through biotite dehydration melting with 20–40 vol.% K‐feldspar fractional crystallization and up to 20 vol.% peritectic garnet entrainment. Mineral fractional crystallization and peritectic mineral entrainment occur in the source during melting, and play equally important roles in partial melting mechanisms in terms of affecting the geochemical compositions of granitic melts. Thus, we suggest that peraluminous felsic magmas preserved in collisional orogens are dominantly produced by fluid‐absent melting in the middle to deep continental crust, although extraction of low‐volume melt fractions from an anatectic source region at shallower depths during fluid‐present melting can also generate small amounts of S‐type granites that subsequently crystallize at high structural levels in the crust.

Multi-objective optimization for sustainable and economical polycarbonate production with reaction kinetics inference for real-world industrial process

Polycarbonate has a wide range of applications in electronic devices and medicine because of its remarkable properties such as transparency and mechanical strength. However, the polycarbonate production process using diphenyl carbonate leads to the inevitable generation of nitrogen oxide (NOx) from byproduct incineration. Therefore, it is essential to optimize the process conditions while simultaneously considering the production costs and NOx emissions. Herein, we propose a multi-objective optimization model for sustainable and economical polycarbonate production using a high-fidelity reaction model for real-world industrial processes. Additionally, a data-driven model was developed using industrial data to identify the operating conditions that minimize NOx emissions and maximize profitability. Four of the ten reaction parameters were selected through variance-based global sensitivity analysis, and the posterior distribution of each parameter was determined through Bayesian inference. In addition, NOx emissions were predicted using the data-driven model developed from industrial data. A process model with coefficients of determination above 0.7 and a data-driven model with R-squared value above 0.99 for pilot-scale experimental and industrial data were generated using our approach. The Pareto optimality, identified based on the developed models, suggested operating conditions that optimize both economic and environmental benefits. Consequently, NOx emissions could be reduced by 27.2%–27.4% and the unit costs of production could be reduced by 0.8%–1.6% compared with the base case.

Investigation of electromagnetic processes in the case of static eccentricity of a two-pole induction motor with a short-circuited rotor

Purpose. Correction of the mathematical model of electromagnetic processes in a two-pole induction motor with a short-circuited rotor, taking into account static rotor eccentricity to identify diagnostic correlations. Methodology. Analytical modeling using the method of specific magnetic conductivity, mathematical modeling of electromagnetic fields in a three-phase induction motor with a short-circuited rotor using methods of electromagnetic field theory and finite element methods. Obtained results. The necessity of improving mathematical models for induction motors with short-circuited rotors to establish new or refine connections between diagnostic features and diagnosed defects has been demonstrated. Refined mathematical expressions for calculating the specific conductivity of non-uniform air gaps in induction motors with static eccentricity are provided. Modeling was performed using the FEMM environment for a statically eccentric two-pole induction motor with a short-circuited rotor. It has been proven that the harmonic order values obtained using the numerical-field method are consistent with those obtained analytically. Findings. Based on the field approach and using the finite elements method, an analysis of the distribution of magnetic field in a two-pole induction motor with a short-circuited rotor was conducted. Harmonic analysis of the magnetic field in the air gap was performed to identify the fundamental harmonic and higher and lower-order harmonics when eccentricity occurs. The influence of static rotor eccentricity on the electromagnetic processes of the induction motor was analyzed. Practical value. The results of the study can be utilized for functional diagnosis of the rotor winding of induction motors based on the radial component of the magnetic field. This will contribute to enhancing the reliability of induction motors and enable the prevention of failure in induction motors with short-circuited rotors.