Process Dynamics Research Articles

What is at stake and what does it take? Collaborative governance and policy (in)action in the adoption of a National Forest Programme

AbstractInspired by collaborative governance theory, this study analyzes the process dynamics, outputs, and perceived impacts of a collaborative initiative launched by the Swedish government in 2014. It draws on extensive empirical sources related to the Swedish government's efforts to develop and implement a National Forest Programme (NFP) from 2014 to 2021. These sources include semistructured interviews, observations, public consultation comments, records of meetings and public hearings, reports from dialogues, and enacted policy documents. The results show that the collaboration initially provided a space for joint deliberation and capacity building on complex and contentious issues related to current land use. However, the final programme endorsed by the government in 2018 failed to initiate ambitious proposals on several key issues raised by participating actors, offering little indication of priorities and policy instruments to address fundamental gaps in current policy goals and their implementation. Consequently, the case reveals that it was not sufficient, and perhaps not even desirable, to address existing conflicts and policy problems in a comprehensive collaborative setting run by the Government Offices. The paper concludes with key insights for research on collaboration and suggests ways to move forward with policy designs that integrate multiple and competing policy goals in contested areas.

Improved Exciton Diffusion through Modulating Förster Resonance Energy Transfer for Efficient Organic Solar Cells

Förster resonance energy transfer (FRET) is commonly utilized in organic solar cells (OSCs) to enhance the power conversion efficiency (PCE) by promoting molecular interactions. The PCE is greatly affected by the number of photo‐generated excitons that effectively reach interfaces between the donor and acceptor materials of OSCs. However, the correlation between FRET and exciton diffusion has received limited attention. Therefore, it is crucial to understand and manipulate FRET process for investigating exciton dynamics in OSCs. Herein, BTA3 and its derivatives are chosen as the third components and energy donors to elucidate the intrinsic photophysical mechanism of FRET in OSCs by manipulating their efficiency. The results unambiguously demonstrate that the addition of guest F‐BTA3 exhibits the highest FRET efficiency, leading to a significant enhancement in the PCE of both PM6:Y6 and PM6:PY‐IT host systems. By correlating FRET process and exciton dynamics, this enhancement to remarkable increment in exciton diffusion length is attributed. Experimental results and theoretical simulations indicate high FRET efficiency arises from strong dipole–dipole interaction, and exhibits a positive correlation with exciton diffusion length. This study showcases the effective regulation of exciton diffusion in OSCs through modulation of FRET, offering a novel perspective for optimizing the performance of organic photovoltaic devices.

Submarine Capability Assessment Model Using Analytical Hierarchy Process (AHP) and System Dynamics

Regional dynamics marked by the increase in submarine operations by several countries need to be balanced with increasing Indonesia's capabilities in underwater warfare and contributing to increasing the deterrence of the country's defense system at sea. Therefore, the aim of this research is to consider the competitive dimensions of the development of submarine capabilities on the deterrence effect of a country and assess the extent of the discussion in the literature review. This research is a statement that there is no instrument for assessing the capability and sustainability of submarines in an effort to increase the deterrence effect in the ALKI II area which is linked to current dynamic developments so that this research is expected to contribute to obtaining an instrument for assessing submarine capability and the deterrence effect of submarines. in the ALKI II area.

Spatiotemporal dynamics of cortical somatosensory network in typically developing children.

Sense of touch is essential for our interactions with external objects and fine control of hand actions. Despite extensive research on human somatosensory processing, it is still elusive how involved brain regions interact as a dynamic network in processing tactile information. Few studies probed temporal dynamics of somatosensory information flow and reported inconsistent results. Here, we examined cortical somatosensory processing through magnetic source imaging and cortico-cortical coupling dynamics. We recorded magnetoencephalography signals from typically developing children during unilateral pneumatic stimulation. Neural activities underlying somatosensory evoked fields were mapped with dynamic statistical parametric mapping, assessed with spatiotemporal activation analysis, and modeled by Granger causality. Unilateral pneumatic stimulation evoked prominent and consistent activations in the contralateral primary and secondary somatosensory areas but weaker and less consistent activations in the ipsilateral primary and secondary somatosensory areas. Activations in the contralateral primary motor cortex and supramarginal gyrus were also consistently observed. Spatiotemporal activation and Granger causality analysis revealed initial serial information flow from contralateral primary to supramarginal gyrus, contralateral primary motor cortex, and contralateral secondary and later dynamic and parallel information flows between the consistently activated contralateral cortical areas. Our study reveals the spatiotemporal dynamics of cortical somatosensory processing in the normal developing brain.

Chatter Stability of Orthogonal Turn-Milling Process in Frequency and Discrete-Time Domains

Abstract As the industry seeks better quality and efficiency, multitasking machine tools are becoming increasingly popular owing to their ability to create complex parts in one setup. Turn-milling, a type of multi-axis machining, combines milling and turning processes to remove material through simultaneous rotations of the cutter and workpiece with the translational feed of the tool. While turn-milling can be advantageous for large parts made of hard-to-cut materials, it also offers challenges in terms of surface form errors and process stability. Because tool eccentricity and workpiece rotation lead to more complexity in process mechanics and dynamics, traditional milling stability models cannot predict the stability of turn-milling processes. This study presents a mathematical model based on process mechanics and dynamics by incorporating the unique characteristics of the orthogonal turn-milling process to avoid self-excited chatter vibrations. A novel approach was employed to model time-varying delays considering the simultaneous rotation of the tool and workpiece. Stability analysis of the system was performed in both the discrete-time and frequency domains. The effects of eccentricity and workpiece speed on stability diagrams were demonstrated and validated through experiments. The results show that the tool eccentricity and workpiece speed alter the engagement geometry and delay in the regeneration mechanism, respectively, leading to significant stability diagram alterations. The proposed approach offers a comprehensive framework for the stability of orthogonal turn-milling and guidance for the selection of process conditions to achieve stable cuts with enhanced productivity.

Swirling Capillary Instability of Rivlin–Ericksen Liquid with Heat Transfer and Axial Electric Field

The mutual influences of the electric field, rotation, and heat transmission find applications in controlled drug delivery systems, precise microfluidic manipulation, and advanced materials’ processing techniques due to their ability to tailor fluid behavior and surface morphology with enhanced precision and efficiency. Capillary instability has widespread relevance in various natural and industrial processes, ranging from the breakup of liquid jets and the formation of droplets in inkjet printing to the dynamics of thin liquid films and the behavior of liquid bridges in microgravity environments. This study examines the swirling impact on the instability arising from the capillary effects at the boundary of Rivlin–Ericksen and viscous liquids, influenced by an axial electric field, heat, and mass transmission. Capillary instability arises when the cohesive forces at the interface between two fluids are disrupted by perturbations, leading to the formation of characteristic patterns such as waves or droplets. The influence of gravity and fluid flow velocity is disregarded in the context of capillary instability analyses. The annular region is formed by two cylinders: one containing a viscous fluid and the other a Rivlin–Ericksen viscoelastic fluid. The Rivlin–Ericksen model is pivotal for comprehending the characteristics of viscoelastic fluids, widely utilized in industrial and biological contexts. It precisely characterizes their rheological complexities, encompassing elasticity and viscosity, critical for forecasting flow dynamics in polymer processing, food production, and drug delivery. Moreover, its applications extend to biomedical engineering, offering insights crucial for medical device design and understanding biological phenomena like blood flow. The inside cylinder remains stationary, and the outside cylinder rotates at a steady pace. A numerically analyzed quadratic growth rate is obtained from perturbed equations using potential flow theory and the Rivlin–Ericksen fluid model. The findings demonstrate enhanced stability due to the heat and mass transfer and increased stability from swirling. Notably, the heat transfer stabilizes the interface, while the density ratio and centrifuge number also impact stability. An axial electric field exhibits a dual effect, with certain permittivity and conductivity ratios causing perturbation growth decay or expansion.

NONLINEAR DYNAMICS METHODS APPLICATIONS TO PRODUCT LIFECYCLE MANAGEMENT

The research being carried out is aimed at applying nonlinear dynamics methods in product life cycle management to improve communication between processes and achieve environmental sustainability. The purpose of the article is to create a nonlinear mathematical model for analysing product lifecycle management. Chaotic nonlinear dynamical processes of product lifecycle are deterministic but have the property that even small uncertainties in initial conditions lead to exponentially growing errors in the management that limit long-term forecasts. The emergence of chaotic behaviour in processes of product lifecycle was explored, focusing on fluctuations. The study employed methods such as systems analysis, correlation analysis, nonlinear dynamics, and the Surrogate model of chaotic Lorenz waterwheel. It was identified that sharp technological innovations as the primary drivers of short-term fluctuations impacting fixed efficiency and effectiveness of product life cycle process development. The resulting nonlinear dynamic model allowed for flexible operation, transitioning between equilibrium, periodic, and chaotic states based on coefficient values for asset growth rates and time constants reflecting product life cycle process dynamics. The research results are valuable for companies seeking to reduce their environmental impact and implement circular economy principles.

A machine learning-based approach for improving plasmid DNA production in Escherichia coli fed-batch fermentations.

Artificial Intelligence (AI) technology is spearheading a new industrial revolution, which provides ample opportunities for the transformational development of traditional fermentation processes. During plasmid fermentation, traditional subjective process control leads to highly unstable plasmid yields. In this study, a multi-parameter correlation analysis was first performed to discover a dynamic metabolic balance among the oxygen uptake rate, temperature, and plasmid yield, whilst revealing the heating rate and timing as the most important optimization factor for balanced cell growth and plasmid production. Then, based on the acquired on-line parameters as well as outputs of kinetic models constructed for describing process dynamics of biomass concentration, plasmid yield, and substrate concentration, a machine learning (ML) model with Random Forest (RF) as the best machine learning algorithm was established to predict the optimal heating strategy. Finally, the highest plasmid yield and specific productivity of 1167.74mg L-1 and 8.87mg L-1/OD600 were achieved with the optimal heating strategy predicted by the RF model in the 50 L bioreactor, respectively, which was 71% and 21% higher than those obtained in the control cultures where a traditional one-step temperature upshift strategy was applied. In addition, this study transformed empirical fermentation process optimization into a more efficient and rational self-optimization method. The methodology employed in this study is equally applicable to predict the regulation of process dynamics for other products, thereby facilitating the potential for furthering the intelligent automation of fermentation processes.

Exploring non-steady-state charge transport dynamics in information processing: insights from reservoir computing

Exploring nonlinear chemical dynamic systems for information processing has emerged as a frontier in chemical and computational research, seeking to replicate the brain’s neuromorphic and dynamic functionalities. In this study, we have extensively explored the information processing capabilities of a nonlinear chemical dynamic system through theoretical simulation by integrating a non-steady-state proton-coupled charge transport system into reservoir computing (RC) architecture. Our system demonstrated remarkable success in tasks such as waveform recognition, voice identification and chaos system prediction. More importantly, through a quantitative study, we revealed that the alignment between the signal processing frequency of the RC and the characteristic time of the dynamics of the nonlinear system plays a crucial role in this physical reservoir’s performance, directly influencing the efficiency in the task execution, the reservoir states and the memory capacity. The processing frequency range was further modulated by the characteristic time of the dynamic system, resulting in an implementation akin to a ‘chemically-tuned band-pass filter’ for selective frequency processing. Our study thus elucidates the fundamental requirements and dynamic underpinnings of the non-steady-state charge transport dynamic system for RC, laying a foundational groundwork for the application of dynamical molecular scale devices for in-materia neuromorphic computing.

Penggunaan Video Comment dalam Meningkatkan Minat Belajar Peserta Didik Mapel PAI di SDN 4 Mrayan Ngrayun Ponorogo Tahun Pelajaran 2023/2024

This Classroom Action Research (CAR) aims to explore the effectiveness of using Video Comment as a learning medium in enhancing students' interest in Islamic Education subject at SDN 4 Mrayan Ngrayun Ponorogo for the academic year 2023/2024. Recognizing the need for innovation in the learning process dynamics, this study identifies that students' learning interest can be a crucial indicator in achieving an effective and efficient learning process. Implemented in three cycles, this research collected data through observation, interviews, and document analysis to assess changes in students' learning interest since the implementation of Video Comment. The findings indicate a significant increase in students' learning interest from the pre-cycle with a learning interest of 36%, rising to 61% in cycle I, 69% in cycle II, and reaching an average of 78% in cycle III. This increase signifies that the integration of Video Comment in the learning process contributes positively to enhancing students' learning interest. Further analysis reveals that Video Comment successfully provides engaging and interactive content that facilitates students' involvement in the learning material. Moreover, the study identifies supporting and inhibiting factors in the use of Video Comment, where 94% of students showed an increase in learning interest as a direct result of this innovative learning process. The conclusion of this study underscores the importance of using innovative learning media, such as Video Comment, in boosting students' learning interest. The practical implications of this research offer insights for educators to integrate relevant learning technologies with modern students' needs, as a strategy to improve the quality of the learning process in today's educational environment.

Building Integrated Photovoltaics (BIPV): Analysis of the Technological Transfer Process and Innovation Dynamics in the Swiss Building Sector

Solar has confirmed its dominance among all power generation technologies, and along with the demand for zero-emission buildings, Photovoltaics (PV) is contributing to transforming the building skin. More than 200 products for Building Integrated Photovoltaics (BIPV) are commercialized nowadays in the EU market. However, only 1–3% of all PV installations are BIPV due to the weak penetration in the construction sector. At the state of the art, the sector lacks a specific analysis from a construction technology perspective, describing the dynamics and the traits that BIPV innovation articulates on construction and architectural processes. The authors, elaborating a new model from the building technology sector to explore the relationship between PV technology and architectural innovation, aim to identify the main principles, forms, and approaches that describe the structural organization of the “integrability” concept of PV in buildings. This study applies the method to a database of 233 real buildings located in Switzerland, a unique country leading with a 10% BIPV rate on PV installations documented between the years 1997 and 2023. The novel findings of the research suggest the definition of the levels of innovation and the reference traits of the innovative process of BIPV in the Swiss construction sector, which can also be used in other practical applications and contexts. The results of the paper are expected to impact both the scientific academy and the key players from the construction sector, encouraging the adoption of an integrated research and design approach to revolutionize the energy role of building skins with PV.

MiRNA- and Cell Line-Specific Constraints on Precursor miRNA Processing of Stably Transfected Pancreatic Cancer and Other Mammalian Cells.

MicroRNAs (miRNAs) regulate approximately one-third of all human genes. The dysregulation of miRNAs has been implicated in the development of numerous human diseases, including cancers. In our investigation focusing on altering specific miRNA expression in human pancreatic cancer cells, we encountered an interesting finding. While two expression vector designs effectively enhanced miR-708 levels, they were unable to elevate mature forms of miR-29b, -1290, -2467, and -6831 in pancreatic cancer cell lines. This finding was also observed in a panel of other non-pancreatic cancer cell lines, suggesting that miRNA processing efficiency was cell line specific. Using a step-by-step approach in each step of miRNA processing, we ruled out alternative strand selection by the RISC complex and transcriptional interference at the primary miRNA (pri-miRNA) level. DROSHA processing and pri-miRNA export from the nucleus also appeared to be occurring normally. We observed precursor (pre-miRNA) accumulation only in cell lines where mature miRNA expression was not achieved, suggesting that the block was occurring at the pre-miRNA stage. To further confirm this, synthetic pre-miRNA mimics that bypass DICER processing were processed into mature miRNAs in all cases. This study has demonstrated the distinct behaviours of different miRNAs with the same vector in the same cell line, the same miRNA between the two vector designs, and with the same miRNA across different cell lines. We identified a stable vector pre-miRNA processing block. Our findings on the structural and sequence differences between successful and non-successful vector designs could help to inform future chimeric miRNA design strategies and act as a guide to other researchers on the intricate processing dynamics that can impact vector efficiency. Our research confirms the potential of miRNA mimics to surmount some of these complexities.

Data-driven auto-tuning strategy for RTO-MPC based on Bayesian optimization

Real-time optimization (RTO) and model predictive control (MPC) are extensively employed in industrial processes to enhance economic objectives. However, the tuning of the system remains a challenge, in particular, for cases where an explicit model relating the RTO objective and the process dynamics is unknown. To address this issue, an online data-driven auto-tuning strategy leveraging two Bayesian optimization (BO) techniques is proposed. This strategy is useful for situations where the RTO objective and the process dynamics are detectable but their exact functional forms are unclear. The proposed strategy views the RTO objective as a black-box objective and interprets the steady-state conditions as black-box equality constraints within the RTO layer. In this context, the upper-trust-bound based constrained BO (UTB-CBO) method is adopted to optimize the setpoints and enhance solution feasibility. Additionally, the proposed approach can take into account measurable disturbance inputs explicitly and account for their consequential influence on objective optimization by considering disturbance variations as contextual information. Simultaneously, another contextual BO scheme is implemented to automatically tune the MPC controller parameters for improving tracking performance upon accepting the setpoints optimized by the RTO. Simulation results based on a continuous stirred-tank reactor system are given to illustrate the effectiveness of the proposed approach.

Exploring local, experimenting with transnational: Producing Turkish television series

This study explores the factors influencing the rising global appeal of Turkish TV series, focusing on the production process and economic dynamics with the global and local media distribution of these dramas. It assesses how market-driven media production conditions and creative professionals’ roles influence the creation of Turkish series by interviewing nine industry insiders. The study found that market-oriented media practices and executives’ understanding of Turkish audience preferences significantly direct the creative output, which is predominantly aimed at local viewers.

Data-Driven Modal Decomposition Methods as Feature Detection Techniques for Flow Fields in Hydraulic Machinery: A Mini Review

Cavitation is a quasi-periodic process, and its non-stationarity leads to increasingly complex flow field structures. On the other hand, characterizing the flow field with greater precision has become increasingly feasible. However, accurately and effectively extracting the most representative vibration modes and spatial structures from these vast amounts of data has become a significant challenge. Researchers have proposed data-driven modal decomposition techniques to extract flow field information, which have been widely applied in various fields such as signal processing and fluid dynamics. This paper addresses the application of modal decomposition methods, such as dynamic mode decomposition (DMD), Proper Orthogonal Decomposition (POD), and Spectral Proper Orthogonal Decomposition (SPOD), in cavitation feature detection in hydraulic machinery. It reviews the mathematical principles of these three algorithms and a series of improvements made by researchers since their inception. It also provides examples of the applications of these three algorithms in different hydraulic machinery. Based on this, the future development trends and possible directions for the improvement of modal decomposition methods are discussed.

The temporal dynamics of task processing and choice in a novel multitasking paradigm.

This study investigated the temporal dynamics of task performance and voluntary task choice within a multitasking paradigm in which the task-related processing outcomes themselves determined the to-be-performed task. In the novel forced-no-go trials, the stimulus for one task required an overt response, but the stimulus for the other task was associated with a no-go response. Task performance results showed that participants often processed the no-go task's stimulus before switching to the go-task. Dual-task interference effects and switch costs indicated various forms of multitasking interference, with their underlying causes appearing to overlap, as engagement in parallel processing seemed to be limited by switch-related reconfiguration processes. Intermixing free-choice trials, where both stimuli were associated with overt responses, revealed costs associated with switching between processing modes, providing new evidence that the distinctions between free and forced task goals stem from differences in their internal representations rather than alterations in processing due to different presentations in the environment. Task choice results align with this perspective, demonstrating a preference for repeating a free- over a forced-choice task. Furthermore, these free-choice results illuminate the interplay of cognitive (task-repetition bias) and environmental constraints (first-task bias) in shaping task choices: It appears that task-specific information increases goal activations for both task goals concurrently, with participants favoring central processing of the second- over the first-presented task to optimize their behavior when shorter central processing is required (task repetition). Overall, this study offers new insights into the dynamics of task processing and choice in environments requiring the balance of multiple tasks.

EFNN-Nul0- a trustworthy knowledge extraction about stress identification through evolving fuzzy neural networks

This paper presents a novel hybrid architecture, denoted as EFNN-Nul0 (evolving neuro-fuzzy system based on null-unineurons), meticulously crafted for stress identification within the realm of pattern classification. The model seamlessly integrates neural networks, fuzzy systems, and n-uninorms to cultivate knowledge within an adaptive, real-time learning context. Neuro-fuzzy rules, encapsulating interdependencies among input features through IF-THEN type relationships, are formulated utilizing null-unineurons derived from n-uninorms. This construction enables the articulation of both AND- and OR-connections, thereby augmenting the interpretability of the generated rules. The evolution of neurons is facilitated by an extended adaptation of the autonomous data partition method (ADPA). To dynamically interpret the evolution of rules, the paper introduces (i) a mechanism that tracks changes in rules over data stream samples, providing insights into the process dynamics as a structural active learning component, and (ii) a concept for incrementally updating feature weights. These weights encapsulate the varying impact levels of features on stress identification, enabling the reduction of rule length by effectively masking out less significant features. The outcomes of the rules are represented by certainty vectors, recursively updated through an indicator-based recursive weighted least squares (I-RWLS) approach. Neuron activation levels play a pivotal role in determining the weights, ensuring stable local learning. The effectiveness of the proposed model is substantiated through a comprehensive comparison with existing hybrid and evolving approaches in the literature, focusing on binary and multi-class pattern classification. Aspects that identify special characteristics of neuro-fuzzy models related to interpretability will also be demonstrated in this work. The results demonstrate the model's superior performance, characterized by consistently higher accuracy trend lines over time. Furthermore, the model's interpretability is underscored by the coherent neuro-fuzzy rules, contributing to its remarkable accuracy (the EFNN-Nul0 achieved approximately 99% accuracy in stress identification), establishing its efficacy in addressing stress identification within classification problems.

Investigating the Characterization and Grindability Behaviour of Farin-lamba Cassiterite toward Effective Tin Oxide Production

Cassiterite has been an essential source of tin; its exploration and exploitation have become a priority worldwide. The effective beneficiation of cassiterite depends mainly on its grindability and effective liberation. The Modified Bond's grindability test is a method used to determine the work index, which is crucial in estimating the energy needed to grind an ore. This is crucial during mineral processing, as a slight deviation would affect the company's operating expenditure (OPEX). This study investigates the work index for Farin-Lamba cassiterite with reference to silica sand sourced from Igbokoda. The test ore (cassiterite) was analyzed using Energy Dispersed X-ray fluorescence spectrometer (ED-XRS), Petrographic Analysis, and Scanning Electron Microscope equipped with an Energy Dispersive Spectrum (SEM-EDS) to understand their chemical and mineralogical characteristics in relation to their grindability. The test ore and the reference material (silica sand) underwent comminution using 500g of each to a 100% passing 500 μm array of sieves arranged in (√2) series from 500μm to 63μm onto an automatic sieve shaker. The chemical and mineralogical analysis revealed the presence of gangue such as SiO2 and Al2O3, which increases the energy needed during communition; further, the presence of rough and large grain size in the ore also increases the energy needed for communition. The results were subjected to Gaudin Schuman's equation to determine the ore's work index, which was 14.664 KWh/ton for the test ore, which is standard for cassiterite, which ranges from 10-15 kWh/ton. Further, the energy needed for comminution was calculated to be 33.7272 Kwh, providing valuable insights into the energy efficiency of the grinding process. The evaluation of the grindability of Farin-Lamba cassiterite in relation to the reference ore not only contributes toward understanding the ore processing dynamics but also provides information needed for the optimization of energy consumed during the process of tin oxide production.

Preparation and characterization of cellulose fluorescent material: Experiment and simulation

The extensive use of fossil based materials has caused serious pollution problems, the full utilization of biomass resources to prepare high value-added new materials is of great significance for the environmental protection and sustainable social development. For this purpose, this study explored the preparation process and molecular dynamics simulation of cellulose fluorescent materials. Firstly, bacterial cellulose was dissolved in a solution of NaOH and urea at low temperature, followed by a solution blending and hot pressing with hyperbranched polyamide. It was found that the addition of hyperbranched polyamide could effectively filled in the internal pores of cellulose hydrogel, thereby enhancing the fluorescence effects and tensile properties, especially the elongation at break of cellulose materials. The optimal amount of hyperbranched polyamide added was 5 wt%. Molecular dynamics simulation showed that the hydrogen bonds and interaction with cellulose increased as the concentration of hyperbranched polyamide increased.

PREDICTION OF ELECTROCHEMICAL PROCESSES OF LOCAL DISSOLUTION OF STAINLESS STEELS. PART 2. ANALYTICAL AND SIMULATION MODELING OF PROCESS DYNAMICS

To analyze the process of pitting corrosion, a state graph was used, which displays the possible states of local dissolution of chromium-nickel steels, taking into account the additional state "unstable passivation of macropitting". The developed analytical model for calculating the process before the formation of stable pitting is based on homogeneous Markov chains. The calculation of conditional probabilities of system changes from one possible state to another determines the time in the VisualStudio 2010 environment on the platform of NetFramework using the C# programming language. A comparison of the calculation of states with “unstable passivation of macropitting” (UPM) and without it allowed us to establish for stainless steels (12X18N10T, 10X17N13M2T, 08X22N6T) that the formation time of stable pitting taking into account UPM is less than the formation time without it, but more for 08X22N6T steel, which is due to the increased chromium content in its composition. A simulation model (SM) has been created, which is based on the Monte Carlo method, in which a random number generator is used to implement random processes of local dissolution due to SM, a sequence of states in which the system is located before its transition to a stable state is obtained. Also, due to this model, it became possible to consider the change in the potential of the system in the process of local dissolution of the studied steels. The use of two modeling approaches taking into account the state of the UPM allowed us to establish that IT is better consistent with experimental results both on a quantitative and qualitative level. An algorithm for selecting the values of the mode parameters is proposed, which became the basis for predicting the formation of macropitting processes. For citation: Vinogradova S.S. Prediction of electrochemical processes of local dissolution of stainless steels Part 2. Analytical and simulation modeling of process dynamics. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2024. V. 67. N 6. P. 119-126. DOI: 10.6060/ivkkt.20246706.6972.