Dynamic Process Model Research Articles

Toward a theory of resource flow based on guanxi: Key stakeholders and dynamic process model

Toward a theory of resource flow based on guanxi: Key stakeholders and dynamic process model

Enabling data-driven process dynamic modeling for extractive leaching and chemical precipitation

To address the limitations of static models and gain insight into the processes of extractive leaching and chemical precipitation, a data-driven dynamic modeling strategy is proposed using a Lithium-ion battery recycling case study. The data correlations among pH, temperature, redox potential, conductivity and system state are investigated. Predictive models are then developed to describe the system state online and are employed as surrogate models for time-intensive offline chemical analyses. This enables further process optimization, such as time-saving measures and improved process efficiency through dynamic parameter studies. The proposed strategy serves as a guideline for dynamic modeling and integrates big data methodologies into chemical engineering.

Industrial-scale 12-layered-bed vacuum pressure swing adsorption for fuel cell-grade H2 production from carbon-captured steam methane reforming syngas

Vacuum pressure swing adsorption (VPSA) is primarily used to produce high-purity H2 from CO2-rich syngas in steam methane reforming (SMR) plants. Since carbon capture is essential, the performance of current H2 VPSA processes is affected by feeding carbon-captured SMR syngas (CO2-lean SMR syngas). This study conducted the performance evaluation and optimization for a fuel cell-grade H2 production by an industrial-scale 12-layered-bed VPSA process with a 24-step cycle from CO2-lean SMR syngas. The multi-scale dynamic model for the VPSA process was validated with petrochemical industry data from a current SMR-VPSA plant. A sensitivity analysis using CO2-lean SMR syngas indicated the high loss of H2 during desorption steps, suggesting the need for optimization of the VPSA process. Owing to the complex interconnectivity of 12 beds via 24 steps, the framework of a dynamic model-based deep neural network (DNN) and DNN-based optimization was developed to design and optimize the VPSA process. Even without changing equipment or adsorbent configuration, the VPSA process achieved fuel cell-grade H2 production (>99.9999 % H2 and > 88 % recovery with < 0.2 ppm CO). The results indicate that CO2 capture rates, ranging from 90 to 99 %, can be independently determined under techno-economic and environmental conditions, as this capture range of CO2-lean SMR syngas gives a minor impact on the performance of VPSA process. The proposed three-step framework, which encompasses dynamic modeling, surrogate modeling, and multi-objective optimization, provides a systematic and feasible approach for designing and optimizing the VPSA process in an SMR plant integrated with a carbon capture process, to achieve a low-carbon economy.

Towards a Systemic Concept of the Brain Ishemia Stroke: Monte Carlo Driven in silico Model.

The work proposes a new mathematical model of dynamic processes of a typical spatially heterogeneous biological system, and sets and solves a mathematical problem of modeling the dynamics of the system of neurovascular units of the brain in conditions of ischemic stroke. There is a description of only a small number of mathematical models of stroke in the literature. This model is being studied and a numerical and software implementation of the corresponding mathematical problem is proposed. This work is the first attempt ever aiming to employ a Monte Carlo computational approach for In Silico simulation of the most critical parameters in molecular and cellular pathogenesis of the brain ischemic stroke. In this work, a new mathematical model of the development of ischemic stroke is proposed in the form of a discrete model based on neurovascular units (NVU) as elements. As a result of testing the program with the assignment of empirically selected coefficients, data were obtained on the evolution of the states of the lattice of the cellular automaton of the model for the spread of stroke in a region of the brain tissue. A resulting new theoretical model of the particular pathologically altered biosystem might be taken as a promising tool for further studies in neurology; general pathology and cell biology. For the first time, a mathematical model has been constructed that allows us to represent the spatial dynamics of the development of the affected area in ischemic stroke of the brain, taking into account neurovascular units as single morphofunctional structures.

UTILIZING GAUSSIAN PROCESS REGRESSION FOR NONLINEAR MAGNETIC SEPARATION PROCESS IDENTIFICATION

This paper presents a novel approach utilizing Gaussian Process Regression (GPR) to identify dynamic models with nonlinear parameters in magnetic separation processes. It aims to address the complex and dynamic nature of these processes by employing advanced modeling methods. The effectiveness of GPR is demonstrated through its application to simulated signals representing real iron ore separation processes, highlighting its potential to enhance existing models and optimize processes. Conducted within the MATLAB, this research lays the groundwork for further advancement and practical implementation. The utilization of GPR in magnetic separation offers innovative modeling of nonlinear dynamic processes, promising improved efficiency and precision in industrial applications.

Collective vision-making practice: A long-run dynamic process model for geothermal market transitioning

Michael Porter’s work on competition highlights how firms can become stagnant or ‘stuck.’ This study extends that concept to markets or fields of institutional activity, examining what occurs when an entire market becomes 'stuck.' Focusing on the geothermal energy market, the research investigates achieving long-term collective action by cultivating a shared vision and mutual understanding among stakeholders with diverse interests. The study employs a longitudinal ethnographic approach, presenting a long-run dynamic process model of collective vision-making practice. This model consists of six first-order dimensions and twelve second-order interactive behavioural practice, illustrating how stakeholders can collaboratively construct and enfold a long-term collective vision. The findings demonstrate the institutional capacity to engage stakeholders and strategically consider whole market possibilities, enabling the creation of a broadly defined collective vision. The research advances a dynamic process model that emphasises sustained institutional interactional practice. The study’s implications are to motivate thought leaders and policymakers, prompting two strategic questions: First, “How can market actors collaborate to unlock value co-creation and shape the future of a ‘stuck’ market?” This involves engaging diverse stakeholder perspectives and exploring future market-making opportunities. Second, “How can all market actors pivot in harmony with a collective vision, fostering long-term shared understandings and commitments in pursuit of market-making efforts?”, This alignment is important for progressing from research and development to the successful implementation of pilot projects and the realisation of sustainable market opportunities.

A method for siting adsorption-based direct air carbon capture and storage plants for maximum CO2 removal

Adsorption-based direct air carbon capture and storage (DACCS) is an emerging approach to mitigate climate change by removing CO2 from the atmosphere. Recent studies show separately that thermodynamic and environmental performance strongly depend on regional ambient conditions and energy supply but neglect regional CO2 storage potentials. To assess DACCS performance holistically, a detailed global analysis is needed that accounts for the interplay of regional ambient conditions, energy supply, and CO2 storage potential. Hence, we present a novel method for the optimal siting of DACCS plants derived from optimising a dynamic process model that uses global hourly weather data and regionalised data on electricity supply and CO2 storage potential. The carbon removal rate (CRR) measures the climate benefit and describes the speed at which a DACCS plant generates net negative emissions. First, we assume that CO2 storage is possible everywhere. For four electricity supply scenarios, we show that the optimal siting of DACCS significantly increases the CRR when comparing the best and worst locations in each scenario: For a DACCS plant with a nameplate capture capacity of 4 kt CO2 y−1, the CRR can be increased by 63% from 2.16 to 3.53 kt CO2 y‑1 when using photovoltaic, and by 39% from 2.95 to 4.1 kt CO2 y‑1 when using wind power. Assuming a carbon-free electricity supply, the CRR varies between 3.17 and 4.17 kt CO2 y‑1 (32%). Second, we significantly narrow down optimal locations for DACCS considering regional CO2 storage potential through CO2 mineralisation. Overall, accounting for the interplay of regional DAC performance, energy supply, and CO2 storage potential can significantly improve DACCS siting.

A novel static mixer for blending hydrogen into natural gas pipelines

A novel static mixer is designed specifically to blend hydrogen into natural gas pipelines, and its effectiveness is validated by numerical simulation method. Firstly, the structural model of the proposed static mixer model for hydrogen-methane blending is introduced, and the evaluation indicators are defined. Secondly, the computational fluid dynamic model for the mixing process is established based on the Large Eddy Simulation(LES) method, and the accuracy of the numerical results is validated against the experimental data of a benchmark gas mixing model. Subsequently, using LES, effects structural parameters (angle and height of trapezoidal baffle, number of mixing elements, and spacing and installation distance of mixing elements) and flow parameters (main flow velocity and hydrogen blending ratio) on the mixing homogeneity and pressure drop of the static mixer are investigated systematically to explore the optimal design and operational conditions. The numerical results showed that the static mixer can significantly improve the mixing efficiency of hydrogen and natural gas with acceptable pressure loss. In the range of flow conditions concerned, a best performance of mixing could be obtained by installing the mixer at a distance of 3D (D is the diameter of the natural gas pipeline) downstream the blending point, setting the spacing between mixing elements as 1D and employing four mixing elements. Finally, the underlying physics of mass transportation are analyzed based on the vortex structures generated by the mixer.

A three-phase process model of posttraumatic stress disorder and growth: Understanding the mechanisms underlying posttraumatic reactions.

This theoretical review proposes a three-phase process model of posttraumatic stress disorder (PTSD) and posttraumatic growth (PTG). By combining trauma-related theories, emotional regulation theories, social support theories, and empirical study findings to elucidate the mechanisms underlying PTSD and PTG, we then propose our model. The proposed model highlights the dynamic characteristics of the PTSD and PTG developmental processes as well as their coexistence and places equal emphasis on the roles of cognitive and emotional activities and social support in three distinct dynamic phases of PTSD and PTG. It emphasizes that PTSD and PTG share similar prerequisites but that these two outcomes later diverge and ultimately manifest via three distinct phases. The specific roles of cognitive regulation, emotional regulation, and social support, and the potential theoretical and practical implications of this model, are discussed. This integrative and dynamic process model has the potential to advance scientific understanding of the mechanisms underlying PTSD and PTG. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Review of Discrete Element Method Applications in Soil–Plant Interactions: Challenges and Opportunities

The discrete-element method (DEM) has become a pivotal tool for investigating soil–plant interactions in agricultural and environmental engineering. This review examines recent advancements in DEM applications, focusing on both the challenges and opportunities that shape future research in this field. This paper first explores the effectiveness of DEM in simulating soil and plant materials, including seeds, roots, and residues, highlighting its role in understanding interactions that affect agricultural practices. Challenges such as long computation times and the complexity of determining accurate contact parameters are discussed, alongside emerging methods like machine learning that offer potential solutions. Notable advancements include the integration of machine learning algorithms for contact parameter estimation, the use of expanded particle models for dynamic processes, and the development of new techniques for detailed post-processing of DEM simulations. The review also identifies key future research directions, including the incorporation of environmental factors such as air and water, and the exploration of residue management for carbon storage and erosion prevention. By addressing these challenges and seizing these opportunities, future research can enhance the accuracy and applicability of DEM models, advancing our understanding of soil–plant interactions and contributing to more sustainable agricultural and environmental practices.

Dynamic Modeling of a Thermochemical System for Solar Fuel Production Based on an Open-Source Framework

This paper describes the dynamic process model for a solar fuels synthesis plant currently being built in Germany. The open-source, equation-based modelling language Modelica is used as the foundation. In this plant, biogas and steam are reformed, producing synthesis gas and subsequently turned into synthetic crude oil. This work contains the necessary model setups for the thermal energy storage, solar-absorbing gas receiver and reforming reactor to consider the thermohydraulic, thermochemical and radiative interactions occurring in the process. The remaining infrastructure is modeled with the Modelica Standard Library. A way to fit these models with experimental data for validation is also outlined.

Parallelizing process model integration for model predictive control through oracle design and analysis for a Grover’s algorithm-inspired optimization strategy

In model predictive control (MPC), a process dynamic model is utilized to make predictions of the value of the objective function and constraints throughout a prediction horizon. In one method of solving this problem, the time required to find the optimal values of the decision variables depends on the time required to perform the arithmetic operations involved in computing the model predictions. Methods for attempting to reduce the computation time of an MPC could then include developing approximate (reduced-order or data-driven) models for a system that take less time to solve, or to parallelize the computations using, for example, multiple cores or CPU’s. However, an observation in all of these cases is that the values of the process states across the prediction horizon are not the values returned by the optimization problem; the manipulated input trajectory is the desired decision variable. An optimization strategy that cannot explicitly return the process states but can generate some representation of them that then leads to computation of the desired process input would thus be suitable for MPC. Quantum computers achieve their parallelism through creating values that cannot all be returned. They can then operate on this set of values to return a number that is meaningful with respect to that set of values that could not all be returned. Motivated by this, we wish to investigate an idea for utilizing quantum parallelism in developing a representation of an objective function that depends on the solution of a process dynamic model, but then only returning the control actions that minimize the objective function value dependent on those solutions. To do this, several steps are necessary. The first is to locate a quantum algorithm which has the desired characteristics for achieving the goals. In this work, we perform these steps using an amplitude amplification strategy based on Grover’s algorithm on a quantum computer. The second is to analyze the algorithm with respect to its ability to translate across problems in the MPC domain, with respect to both its ability to handle nonlinear systems and to handle a variety of different structures of the set of all possible objective function values given the allowable values of the decision variables. We thus evaluate the benefits and limitations of the algorithm from this perspective. We do not wish to imply that this algorithm is more computationally-tractable for use with MPC than classical optimization techniques traditionally applied in an MPC context. Rather, we wish to understand the manner in which such an algorithm would be designed and when it is appropriate for MPC problems (in the sense of returning the correct answers to the optimization problem), as a step toward better understanding the interactions of the quantum properties of quantum algorithms with control goals. We also see this as forming an important first step in algorithm design/analysis, which can then translate to future works in comparing this technique computationally with classical “competitor” algorithms to guide further work in searching for relevant quantum algorithms for control applications.

Stability analysis of rolling mill system for flexible rolling process based on maximum Lyapunov exponent

Flexible rolling technology is the current development trend of strip production industry. However, due to the simultaneous change of mechanical, process and strip specification parameters in the flexible rolling process, the motion state of the system is difficult to analyze and stability control is hard to achieve. In this paper, the active motion characteristics of rolls in flexible rolling technology are considered, and the dynamic rolling process model is established to reflect the influence mechanism of process and specification parameters on the dynamic rolling force. The dynamic model of a 4-high rolling mill was developed and the structure-process-strip coupling strategy was applied to couple the models. The Runge-Kutta method was applied to solve the dynamic equation to obtain the maximum Lyapunov exponential spectrum for a single parameter variation. It is noteworthy that the two-parameter dynamics method was adopted to solve the dynamics on the two-parameter plane considering the nature of simultaneous variation of the system parameters, which solves the limitations of the traditional analytical method and is suitable for the application of the flexible rolling system. The results suggest that the parameters influence the motion state in the form of coupling, the influence pattern of each parameter on the stability is clarified, the evolution of the stable domain under the effect of parameter coupling is revealed, and the parameter matching strategy is determined. The results will provide a solution for the system parameter setting of flexible rolling technology and a theoretical reference for enhancing the stability of the rolling mill.

Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex.

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.

Создание и развитие высоконадежных информационно-управляющих систем с элементами искусственного интеллекта для перспективных технологических комплексов

Methods and means of improving complex technological equipment are shown through the operational assessment of the quality of implemented technologies realtime. This is proved by the fact that implementing technological operations on universal equipment each time requires modeling dynamic processes and taking into account a large number of uncertainty factors that affect the geometry generation quality. It is not possible to be a priori aware of these factors. There is a need to create new information technologies with the possibilities of universal application for immediate understanding of various dynamic processes in diagnostic, identification and control systems. Standard computer systems for statistical analysis and optimization of dynamic processes with the possibilities of universal application for various implementations of modern technologies have been introduced. The possibility of using integrative criteria and methods of artificial intelligence for diagnostic systems, identification and control of advanced technological complexes is shown. The implementation of information systems for the management of complex objects of various technological purposes is presented. The proposed modeling methods and approaches have been tested at various machine-building enterprises when processing parts on turning, milling and grinding machines, both universal and CNC. The research results made it possible to implement new principles of automated control and optimal adjustment of technological processes in real time and create an automated system for evaluating their quality, which allows increasing the efficiency and reliability of management decisions by conducting optimization directly on operating equipment. Based on the methods and approaches described above, new results have been obtained in the implementation of plasma technologies for the modification of geometrically complex surfaces of mechanical engineering products aimed at increasing wear resistance, hardness and other technical characteristics of the working surfaces of precision engineering products. A fairly complete approbation of methods, approaches, procedures and decision-making criteria for various technologies allows them to be recommended for universal applicability.

Autoregressive double latent variables probabilistic model for higher-order dynamic process monitoring

The application of multivariate statistical analysis in process monitoring has emerged as a significant research topic, with a focus on consideration of data correlations. The present study investigates an anomaly detection method based on autoregressive double latent variables probabilistic (ADLVP) model for industrial dynamic processes. Specifically, the ADLVP model incorporates two distinct types of latent variables (LVs) to capture the internal relationships within the data from both quality-correlated and uncorrelated perspectives. Moreover, the model employs autoregressive modeling to characterize the double latent variables with time-dependence, enabling them to unveil more intricate higher-order autocorrelations between industrial measurements. The model parameters and the double latent variables can be iteratively determined using the expectation maximization (EM) algorithm, upon which the statistics for process monitoring are devised. Finally, the proposed method is validated in two industrial studies, and experimental results demonstrate that the ADLVP model outperforms its counterparts in dynamic processes monitoring.

Machine learning-based predictive control of an electrically-heated steam methane reforming process

Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient energy carrier that significantly reduces greenhouse gas emissions. However, the primary method of industrial hydrogen production, steam methane reforming (SMR), relies on the combustion of hydrocarbons as the heating source for the reforming reactions, resulting in significant carbon emissions. To address this issue, an experimental setup of an electrically-heated steam methane reformer (e-SMR) has been constructed at UCLA, and a lumped first-principle dynamic process model was built based on parameters estimated from the experimental data in a previous study. Subsequently, the first-principle dynamic process model was implemented into the computational model predictive control (MPC) scheme, successfully driving the hydrogen production rate to the desired setpoint. While these works are important and pave the way for developing MPC for large-scale e-SMR processes, the first-principle process model may not accurately reflect the actual process behavior, particularly as the process behavior changes with time. Therefore, the development and establishment of an adaptive data-driven approach for implementing model predictive control in the e-SMR process is necessary. To address this need, the present work investigates the construction of recurrent neural network (RNN) models for an e-SMR process in-depth, utilizing data from an experimentally-validated first-principle model. Specifically, a long short-term memory (LSTM) layer was utilized in the RNN model to effectively capture the complex correlations present in long-term sequential data. Subsequently, this LSTM-based RNN process model was employed to design an MPC, and its performance was evaluated through comparison with proportional–integral (PI) control. To address potential disturbances and variability in a typical e-SMR process, three distinct approaches were developed: MPC with an integrator, MPC with real-time online retraining (transfer learning), and offset-free MPC. These approaches effectively eliminated the offset caused by disturbances. Overall, this study underscores the effectiveness of utilizing RNN models to capture process dynamics in an experimental e-SMR process. It also outlines strategies for employing RNN-based control and multiple approaches to address disturbances in general processes with partially infrequent and delayed measurement feedback. This approach is particularly valuable in scenarios where developing first-principle models for a new process may be challenging.

An Interpretable Light Attention–Convolution–Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability, we develop a light attention–convolution–gate recurrent unit (LACG) architecture with three sub-modules—a basic module, a brand-new light attention module, and a residue module—that are specially designed to learn the general dynamic behavior, transient disturbances, and other input factors of chemical processes, respectively. Combined with a hyperparameter optimization framework, Optuna, the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process. The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models, including the feedforward neural network, convolution neural network, long short-term memory (LSTM), and attention-LSTM. Moreover, compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1, the LACG parameters are demonstrated to be interpretable, and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM. This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling, paving a route to intelligent manufacturing.

Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries

Focusing on the requirements for efficient and accurate control in large-scale process industries with integrated “distributed-decentralized” characteristics, a novel hierarchical coordinated predictive control strategy for process industries is proposed with a multiagent system as the computational paradigm. This approach comprehensively considers the overall state of the system, the interactions of control actions among agents, the constraints of processes and energy consumption to solve the problems of poor flexibility of agent decision-making in the narrow consensus strategy and strong interaction of parts of the system. The proposed hierarchical control strategy requires each agent to perform three tasks at each time step. First, each agent iteratively obtains a consistent basis for closed-loop prediction in a distributed way. Then, each agent independently proposes a control scheme and determines its own priority by playing games based on the economic performance of the scheme. Next, each agent calculates its own optimal dynamic predictive control sequence in order of priority based on the system’s dynamic process model. Finally, by considering the temperature-control process of heating an alumina ceramic block in a high-power microwave reactor with six microwave sources, the effectiveness of the proposed hierarchical coordinated predictive control strategy is verified under different communication topologies by comparing it with the centralized model predictive control strategy.

INCREASING THE RELIABILITY OF THE SYSTEM OF AUTOMATIC VOLTAGE REGULATION OF LOW-VOLTAGE CIRCUITS OF ELECTRICAL DIRECT CURRENT MOVEMENT COMPONENTS

Purpose. The purpose of this work is the analysis of possible directions for improving the performance indicators of electric rolling stock of direct current railways. The work is devoted to the improvement of systems of automatic voltage regulation of low-voltage circuits where the power source is a direct current generator. The number of electric locomotives and electric trains of railways and their units, which have exhausted their service life, but will be in service for a long time, preserves the relevance of research into possible directions of modernization and the specifics of system operation. Methodology. The work uses the analysis and generalization of previously performed research. The methodological basis of the research is the mathematical apparatus of the theory of automatic control and the theory of reliability. The object of research is the process of functioning of low-voltage control circuits of electric rolling stock of direct current railways with direct current generators as an energy source. The subject of the study is a voltage regulator for own needs based on a modern element base. Findings. A variant of the system of automatic voltage regulation of low-voltage circuits with generator power sources for electric rolling stock of direct current is offered. The basis of the system is the voltage regulation node, which in the existing structure is made on the basis of a carbon vibration regulator on a strip. As an alternative, a system based on operational amplifiers and the use of proportional or proportional-integral laws of the regulator in the structure is proposed. Modeling of dynamic processes indicates the structural stability of the system and qualitative transition processes. A comparison of the no-failure indicators of the nodes indicates a possible increase in terms between services and cost savings. The comparison is made for systems that are in operation for a long time and those that are offered. Originality. It is proposed to implement the node of the system of automatic voltage regulation on a modern elemental basis, which allows to improve the reliability of the system and implement a small, economical, modernization of rolling stock of railways with an exhausted service life. Practical value. The obtained results can be used by enterprises that carry out major repairs of electric trains and direct current electric locomotives with extended service life. The implementation of the proposed units makes it possible to replace existing contact-based relay systems with fast contact wear, frequent maintenance and debugging. As a result, new units can be transferred to service during current repairs due to the introduction of non-contact systems and increase the probability of trouble-free operation and use the personnel who are always present in locomotive depots. This reduces the cost of maintenance due to the introduction of a modern element base and exclusion from the technological process of maintenance of existing units.