Dynamic Process Model Research Articles

PickT: A Decision-Making Tool for the Optimal Pickling Process Operation.

This research approaches knowledge gaps related to the pickling process dynamic modelling (the lack of predictability and simplicity of existing models) and answers the practical need for a software tool to facilitate the optimum process operation (by delivering estimations of the optimum corrosion inhibitor addition, optimum pickling bath lifetime, corrosion rate dynamic evolution, and material mass loss). A decision-making tool, PickT, has been developed and verified with the help of measurements from two different pickling experiments, both involving steel in hydrochloric acid. The first round of experiments lasted 336 h (each pickling batch duration was 24 h) and Cetilpyridinium bromide (CPB) was the corrosion inhibitor in additions from 8% to 12%. The collected dataset served for the tool development and first verification. The second round of experiments lasted 10 h (each batch duration was 2 h) and involved metformin hydrochloride (MET) in additions between 3.3 g/L and 10 g/L. This dataset served to test the transferability of PickT to other operating conditions in terms of corrosion inhibitor type, additions, batch duration and pickling bath lifetime magnitude. In both cases PickT results are in accordance with experimental findings. The tool advantages consist of the straightforward applicability, the low amount of field data required for reliable forecasts and the accessibility for untrained professionals from the industry.

A novel constant power operation mode of constant volume expansion process for AA-CAES: Regulation strategy, dynamic simulation, and comparison

A novel operation mode of constant volume discharging process for advanced adiabatic compressed air energy storage (AA-CAES) is proposed, which achieves a constant output power by throttling and air supplementation. The dynamic model of the expansion process was developed based on the conservation of mass and energy of the open system. The reliability of the turbine and air storage tank models was verified. The results of the energy and exergy analysis of the expansion process show.

A Process Model of Combining Life Trauma and Life Passion into Cultural Entrepreneurship: A Case Study of Art-Shelter Bunker Theatre

A case study of Art-Shelter Bunker theatre explores the fascinating story of how a traumatic event, a dog bite, inspired the creation of a successful theatre company. Drawing on a narrative inquiry approach, we examine the personal experiences and reflections of the theatre company’s founder, who, after being bitten by a dog, underwent a profound transformation that ultimately led to the creation of a unique and impactful theatre company. Our examination underscores the significance of trauma and resilience within the dynamic model of the creative process, underscoring the crucial role that personal narrative plays in shaping artistic expression. Through this inquiry, we hope to better understand the complex and multifaceted relationship between personal experience, trauma, and creativity.

The attractor structure of functional connectivity in coupled logistic maps

Stylized models of dynamical processes on graphs allow us to explore the relationships between network architecture and dynamics, a topic of relevance in a range of disciplines. One strategy is to translate dynamical observations into pairwise relationships of nodes, often called functional connectivity (FC), and quantitatively compare them with network architecture or structural connectivity (SC). Here, we start from the observation that for coupled logistic maps, SC/FC relationships vary strongly with coupling strength. Using symbolic encoding, the mapping of the dynamics onto a cellular automaton, and the subsequent analysis of the resulting attractors, we show that this behavior is invariant under these transformations and can be understood from the attractors of the cellular automaton alone. Interestingly, noise enhances SC/FC correlations by creating a more uniform sampling of attractors. On a methodological level, we introduce cellular automata as a data analysis tool, rather than a simulation model of dynamics on graphs.

Optimal estimation of injection rate for high-pressure common rail system using the extended Kalman filter

This paper presents an innovative estimation method of the fuel injection rate based on the rail pressure measurement signal for a high-pressure common rail system. A dynamic mathematical model of the injection process is constructed. To meet the requirement of the estimator design, a nonlinear state space model with three state variables is derived. On this basis, an optimal estimation method for the injection rate is proposed based on the extended Kalman filter, and the impact of noise covariance matrices is thoroughly examined. The results demonstrate that the proposed method enables fast convergence of the estimated injection rate. The coefficient R2 values between the estimated and the actual injection rate curves exceed 94%, and the estimated errors of the injection volume are within 5%.

Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study.

This work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamical processes. This means that the attacker may not know the physical nature of the process; an LSTM network is sufficient to mislead the process operator. Our experimental studies were conducted in an industrial control network containing a magnetic levitation process. The model training, evaluation, and structure selection are described. The chosen LSTM network very well mimicked the considered process. Finally, based on the obtained results, we formulated possible protection methods against the considered types of cyber-attack.

Model-Based Decision Support System for Electric Arc Furnace (EAF) Online Monitoring and Control

In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of Sidenor in Basauri, Spain. The system consists of a dynamic process model based on energy and mass balances, including thermodynamic calculations for the most important metallurgical reactions, with particular focus on the modelling of the dephosphorisation reaction, as this is a critical parameter for production of high-quality steel grades along the EAF process route. A statistical scrap characterization tool is used to estimate the scrap properties, which are critical for reliable process performance and accurate online process control. The underlying process models and control functions were validated on the basis of historical production and measurement data of a large number of heats produced at the Sidenor plant. The online implementation of the model facilitates the accurate monitoring of the process behaviour and can be applied for exact process end-point control regarding melt temperature as well as oxygen, carbon and phosphorus content. Embedded within a model predictive control concept, the model can provide useful advice to the operator to adjust the relevant set-points for energy and resource-efficient process control.

Sustainability Assessment of a Solar Energy-Assisted Flue Gas Amine-Based CO2 Capture Process Using Fully Dynamic Process Models

Sustainability Assessment of a Solar Energy-Assisted Flue Gas Amine-Based CO₂ Capture Process Using Fully Dynamic Process Models

Using Stop Motion Animations to Activate and Analyze High School Students’ Intuitive Resources about Reaction Mechanisms

Mechanisms are part of the high school curriculum in many countries. Although research shows that university students struggle with mechanistic reasoning, very little is known about learning mechanisms in high school. Understanding the cognitive resources that high school students intuitively activate is critical to fostering meaningful learning. A readily available tool for modeling dynamic processes are stop-motion animations (SMAs). In this study, 55 high school students were asked to create SMAs of their intuitive ideas of nucleophilic substitution reactions. In a second step, the students evaluated two pre-made SMAs modeling the textbook-based stepwise (SN1) and concerted (SN2) mechanisms. Within the student-generated SMAs, SN1- and SN2-like mechanisms were equally distributed; after viewing the textbook-style animations, most students rated an SN2 mechanism as more likely. However, no group modeled both types of mechanisms or reasoned that both mechanisms were possible. Students used diverse chemical concepts as well as plausibility in their reasoning. However, simultaneous movement of atoms, conformational changes, and the idea of competing and boundary mechanisms do not seem intuitive. In conclusion, SMAs were found to be an appropriate tool for activating, analyzing, and discussing students’ resources “on the fly”, and these ideas can serve as a starting point for promoting productive mechanistic reasoning.

A Hybrid Strategy Enhanced Sub-Sampled Recursive Second Order Algorithm for Chemical Process Intelligent Identification

The accurate dynamic model of the chemical process is an important condition for the successful implementation of advanced control in the plant. In this paper, an efficient second order algorithm for long short-term memory (LSTM) network training is proposed for chemical process intelligent identification. A novel Hessian inverse recursion method is adopted to achieve fast convergence and avoid the high-cost operation of the classic second order optimization method. Besides, more information is back propagated since the proposed method retains the real curvature information of the neural network. Considering the large amount of chemical process data, a sub-sampled recursive second order-stochastic gradient descent (SRSO-SGD) algorithm which uses sub-sampling method and hybrid strategy is proposed. The identification experiment on a delayed coker fractionator shows that the proposed sub-sampled neural network second order training algorithm has better performance than other learning algorithms in terms of model identification accuracy and convergence speed. By adopting a hybrid strategy that performing Hessian inverse estimation every 3 training epochs, the expensive Hessian inverse calculation cost in the identification process is further reduced while low training and testing errors are maintained.

Investigating the impact of the dynamics of entrepreneurial intentions on ventures’ formalization

Purpose To overcome economic hardship and survival, informal entrepreneurs must continually activate specific resilience capabilities and reassess their entrepreneurial intentions (EI). Accordingly, they may develop transformational characteristics to design a model supporting intersectorial business growth. This study aims to examine the EI ecosystem of change resulting from the above endeavors and proposes an entrepreneurial intention transformation model (EITM). Design/methodology/approach Using the EI questionnaire developed by Liñán and Chen, data was collected from street vendors in the main markets of the Cameroonian capital (N = 307) to capture the determinants of behavior reflecting the propensity to activate entrepreneurial events within informal ventures. Findings It was uncovered that perceived collective support, resilience, orientation capability and inferred policies influence the dynamics of EI in the informal sector. Additionally, while revisiting current entrepreneurial lifecycle models, the study distinguished between the entrepreneurial events of “renegare” and formalization. Furthermore, the construction of an informal entrepreneurial lifecycle model confirms the need to acknowledge the formalization and “deformalization” processes as integrative parts of an intersectorial entrepreneurial lifecycle model. Originality/value This study extends existing research by proposing an intersectorial entrepreneurial lifecycle to recognize the dynamics of EI on ventures’ formalization. The EITM encompasses the EI dynamic process model and the proposed entrepreneurial lifecycle.

Searching for Possible Design Solutions to Extend the Service Life of the HPC of a Powerful NPP Turbine after Damage to the Rotor Blades

The aim of this research was to analyze and evaluate possible design solutions for the flow path of the high-pressure cylinder (HPC) of the K-1000-60/3000 turbine and the possibility of their short-term implementation in a nuclear power plant after an accidental destruction of the working blades of the last stage of the HPC in conditions of the shortage of generating capacities and a tense operation state of the power system. To achieve the set goals, an appropriate methodology was developed; a mathematical model of thermo-and gas dynamic processes in the flow path of a steam turbine was improved. To make appropriate decisions on changing the design of the turbine, the necessary computational studies of the HPC of the K-1000-60/3000 turbine of the designed structure, the structure without rotor blades of the 5th stage and the structure with four first stages (without the fifth stage) were carried out for both turbine steam flows. The results of the study of thermo- and gas dynamic parameters of the flow showed the presence of differences in the operating conditions of all turbine stages, and these are the most significant for the 4th and 5th stages. A significant redistribution of thermal differences in the stages, and a decrease in the internal power of the HPC of the turbine by 35.5 MW and 6.6 MW respectively, as well as a decrease in their internal efficiency by 11.8 % and 2.1 %, respectively, were noted. Taking into account all the aspects related to the operation of the power unit, the most successful solution for the rapid resumption of operation of a powerful turbine plant used by a nuclear power plant after an accidental damage to the working blades is a HPC design without the working blades of the fifth stage. It is planned to carry out additional studies of the HPC design by replacing the nozzle apparatus of the fifth stage with an appropriate simulator.

Analysis of dynamic methods for calculating NPP building structures

Introduction. The present article concerns the seismic performance of various building structures, including NPP objects. The provided materials generalize the results of the author’s longstanding work connected with the dynamics of building structures.Aim. To analyze the seismic performance of various building structures, including NPP objects, and represent the author’s material on the dynamics of building structures.Materials and methods. The presented materials include a definition of the dynamics concept, computational modeling of dynamic processes, main correlations of dynamic analysis methods, and a description of impacts (accelerograms). In addition, the concepts of resonance and vibration diagnostics in the dynamic analysis, assessment of the building strength and stability, as well as the description of applied software systems are provided. Some of the presented materials are characterized by the scientific novelty. The following methods were used in the study. The method of nonclassical modal superposition for determining the response of a structure under dynamic impacts together with floor response spectra were presented by the author at the ABAQUS (SIMULIA) International Conference in Barcelona, 2011. The essence of the method consists in the reliable account for a “soil” damper, modeling the energy outflow into the soil during the vibrations of a building, which ensures reliable results. A fundamental solution for accounting the ductility of the foundation slab under seismic impacts was applied. This method ensures the seismic response of the structure to be reliably determined, as well as the floor response spectra to be obtained in the conditions of the aircraft crashing for buildings, whose impact on the equipment is transmitted through the foundation slab. Additionally, the method of accounting the dynamic characteristics of building fragments (floor/ wall), obtained using the vibration diagnostic results, was applied to verify computational models, as well as to analyze the strength and floor response spectra. The appendix includes the realistic test problem of the seismic analysis, developed by the author.Results. The provided materials generalize the results of the author’s longstanding work in the field of NPP building structures. In addition to the scientific novelty, the materials are widely applied in the development of NPP designs both in the Russian Federation and abroad.Conclusion. The materials, presented in the article, can be used to prepare the “Introduction to Dynamics” lecture course, including, for newcomer countries that are at the beginning of the nuclear facility (NF) construction.

Characteristics of a safety-overrunning ball-type clutch in overload mode

Problem. The article investigates the operation of a ball safety-overrunning clutch in the overload mode. It is shown that its operation occurs in two stages – the initial one, when the balls retain engagement with the grooves, and the final one, when the balls are disengaged from the grooves of the outer half-clutch. Methodology. By analyzing the geometry of the clutch, expressions for calculating the geometric and kinematic relationships between the parts of the clutch at both stages of its operation were obtained. Results. The obtained results also make it possible to perform technological calculations necessary for the manufacture of clutch parts with helical grooves – the length of the mill stroke and, accordingly, the technological time.Originality. Due to the analysis of the force interaction of the ball elements at the final stage of the clutch operation, the dependencies for determining the elastic moment of the clutch at this stage of operation were obtained. The expressions for calculating the geometric parameters necessary to calculate its elastic moment were also obtained, which allows its estimation at any design parameters of the clutch. Practical value. The results obtained, together with other studies by the authors, are the source of information for modeling dynamic processes in drives with ball overrunning safety clutches, in particular, they make it possible to determine the load in such drives taking into account transient processes. Promising areas for further research are the analysis of the force interaction of the clutch parts at the beginning of a new cycle of its operation – the entry of the balls into engagement with the subsequent grooves of the outer half-clutch, as well as experimental studies of drives with the developed clutchs.

Neural extended Kalman filters for learning and predicting dynamics of structural systems

Accurate structural response prediction forms a main driver for structural health monitoring and control applications. This often requires the proposed model to adequately capture the underlying dynamics of complex structural systems. In this work, we utilize a learnable extended Kalman filter (EKF), named the neural extended Kalman filter (neural EKF) throughout this article, for learning the latent evolution dynamics of complex physical systems. The neural EKF is a generalized version of the conventional EKF, where the modeling of process dynamics and sensory observations can be parameterized by neural networks, therefore learned by end-to-end training. The method is implemented under the variational inference framework with the EKF conducting inference from sensing measurements. Typically, conventional variational inference models are parameterized by neural networks independent of the latent dynamics models. This characteristic makes the inference and reconstruction accuracy weakly based on the dynamics models and renders the associated training inadequate. In this work, we show that the structure imposed by the Neural EKF is beneficial to the learning process. We demonstrate the efficacy of the framework on both simulated and real-world structural monitoring datasets, with the results indicating significant predictive capabilities of the proposed scheme.

Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85.

Fibrobacter succinogenes is a cellulolytic bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the automatic reconstruction of metabolic model workspace. The reconstruction was based on genome annotation, five template-based orthology methods, gap filling, and manual curation. The metabolic network of F. succinogenes S85 comprises 1,565 reactions with 77% linked to 1,317 genes, 1,586 unique metabolites, and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for the computation of elementary flux modes. A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the root mean squared error of 19%. The resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step toward the integration of omics microbial information into predictive models of rumen metabolism. IMPORTANCE F. succinogenes S85 is a cellulose-degrading and succinate-producing bacterium. Such functions are central for the rumen ecosystem and are of special interest for several industrial applications. This work illustrates how information of the genome of F. succinogenes can be translated to develop predictive dynamic models of rumen fermentation processes. We expect this approach can be applied to other rumen microbes for producing a model of rumen microbiome that can be used for studying microbial manipulation strategies aimed at enhancing feed utilization and mitigating enteric emissions.

A Multi-Objective Optimization of the Anchor-Last Deployment of the Marine Submersible Buoy System Based on the Particle Swarm Optimization Algorithm

Marine submersible buoy systems hold significant value as critical equipment in marine science research. This study examines a marine submersible buoy system that includes an anchor block, mooring line, battery compartment, power supply cable, and submersible buoy. The anchor-last deployment method is a conventional strategy for deploying marine submersible systems. Initially, the other components are positioned on the sea surface, followed by the deployment of the anchor block from the ship’s deck. The anchor block will pull the battery compartment and submersible buoy into the water and eventually sink to the seabed. In this deployment process, ocean currents have a relatively large impact on the anchor block’s landing position. Increasing the weight of the anchor block will make the anchor block land on the seabed sooner, which can minimize the impact of ocean currents. However, an overabundance of weight can generate a significant strain on both the cables, potentially resulting in cable breakage. In order to find the parameters that can make the anchor block reach the seabed as soon as possible and ensure that the tension force of the cables does not exceed the maximum, a dynamic model of the deployment process is established based on computational fluid dynamics (CFD) and solved using the Runge–Kutta method of the fourth order. Particle swarm optimization is employed to optimize the key parameters. The penalty function is used to constrain the particle space. The findings indicate that the utilization of particle swarm optimization is efficacious for optimizing the parameters of submersible buoy systems for marine applications. Optimized parameters allow the anchor block to reach the seafloor quickly and the tension on the cables to not exceed the given value.

Dynamic modelling and simulation of eutectic freeze crystallization process for recovery of ammonium sulphate from aqueous solutions

The advancement of industrial sectors generates large amount of wastewater containing ammonium sulphate (AS) ions, yet the current AS recovery technologies are energy-extensive and produce excessive sludge. In this study, a dynamic model for a batch crystallizer was developed to investigate the eutectic freeze crystallization process of AS in aqueous solutions using the method of moment (MoM). The variables affecting the crystal size distribution of AS and ice were described in terms of zeroth moment μ0, first moment μ1, second moment μ2 and third moment μ3. The μ0 of ice crystals increased significantly until 2 s and slowed down thereafter while μ0 of AS crystal increased steadily and reached a constant value of 1/m3s after 1 s. The μ1, μ2, and μ3 of the AS and ice crystals depicted a similar increasing trend. Through process optimization using a 3-D plot on the initial concentration of the system and initial temperature of cooling fluid which had been identified as the significant parameters via iterative plot using MATLAB software, the AS and ice reached maximum crystallization at 255 K which was close to the eutectic point of AS-water system at 254 K. The dynamic model which comprised of population, mass and energy balance equations established in this study demonstrated effectiveness in the prediction of crystallization of AS in aqueous solutions.

Deterministic random walk model in NetLogo and the identification of asymmetric saturation time in random graph

Interactive programming environments are powerful tools for promoting innovative network thinking, teaching science of complexity, and exploring emergent phenomena. This paper reports on our recent development of the deterministic random walk model in NetLogo, a leading platform for computational thinking, eco-system thinking, and multi-agent cross-platform programming environment. The deterministic random walk is foundational to modeling dynamical processes on complex networks. Inspired by the temporal visualizations offered in NetLogo, we investigated the relationship between network topology and diffusion saturation time for the deterministic random walk model. Our analysis uncovers that in Erdős–Rényi graphs, the saturation time exhibits an asymmetric pattern with a considerable probability of occurrence. This behavior occurs when the hubs, defined as nodes with relatively higher number of connections, emerge in Erdős–Rényi graphs. Yet, our analysis yields that the hubs in Barabási–Albert model stabilize the the convergence time of the deterministic random walk model. These findings strongly suggest that depending on the dynamical process running on complex networks, complementing characteristics other than the degree need to be taken into account for considering a node as a hub. We have made our development open-source, available to the public at no cost at https://github.com/bravandi/NetLogo-Dynamical-Processes.

Dynamics modeling and flight characteristics of folded multi-body aircraft

This paper studies the dynamic modeling method of fold-able multi-body aircraft based on the dynamics theory of multi-body system. Considering the multi-body aircraft as a complete multi-rigid body system, the dynamic model for the free unfolding process of the multi-body aircraft after horizontal launch is established. On the basis of the multi-body dynamics model, the coil spring moment model and the damping moment model for the flexible connection between the flight units are added. Taking a certain type of fold-able three-section wing aircraft as an example, the numerical simulation calculation is carried out. The influence of the flexible connection stiffness coefficient between the flight unit on the dynamic characteristics of the fold-able multi-body aircraft in the free deployment process and the relationship between the stiffness of the flexible connection and the maximum initial folding angle are studied. Finally, the stability envelope between the stiffness coefficient of the flexible connection and the maximum initial folding angle is determined.