Process Model Research Articles

Process Modelling and Simulation of Reactive Distillation for the Synthesis of High Purity Mono Ethylene Glycol

Process Modelling and Simulation of Reactive Distillation for the Synthesis of High Purity Mono Ethylene Glycol

BPM application in clinical process improvement: a women 'hospital case study

PurposeThe childbirth process is a complex and vital event that requires careful analysis and improvement. This experience can shape a woman's perspective on motherhood and even affect her mental health. Healthcare providers must prioritize improving the birth experience for women. In this interdisciplinary research, a combination of business process modeling (BPM) and medicine have been used with the aim of realizing an improved delivery experience and increased maternal satisfaction.Design/methodology/approachThe data collection of this study was done by observing 518 childbirth processes and interviewing the chief of labor, chief residents, and midwives in the obstetrics and gynecology department of a hospital in Tehran from October 2022 to February 2023.FindingsThe research has been done in four main stages. The first phase is to model the primary process and sub-processes of normal vaginal delivery (NVD). The second phase is validation using expert confirmation and process mining (PM). The third phase is the analysis of the causes of maternal dissatisfaction in labor. The fourth phase of the heuristics redesigning and improving the process, in which for the first time three new categories have been presented including hospital-based, patient-based, and medical technique-based results show BPM intervention effect can be far-reaching in improving patient care and optimizing operational efficiency.Originality/valueThis study is one of only a few to adopt a process-oriented perspective to show how BPM can be used in clinical processes and has specifically examined an essential clinical process, i.e. childbirth.Highlights Developing business process management (BMP) applications in a medical special process related to childbirth as interdisciplinary research.A combination of qualitative and quantitative techniques contains engineering software and management approaches for a Case study, Implementation of BPM lifecycle in the women's hospital in Iran, Tehran, for a clinical process, which is called, normal vaginal delivery (NVD) process for fetal expulsion normally.Modeling NVD clinical process and sub-process for the first time by BPMN2.0 notations in visual paradigm (VP) software and Validation of the made model with process mining (PM), by Disco process mining software. This was done through event log collection from HIS at the hospital.Improving the childbirth process by redesigning heuristics and Introducing two new categories special for clinical process improvement for the first time.Clinical process improvement heuristics obtained in this research are not consistent with the previous seven categories presented in previous studies such as Marlon Dumas' book. Therefore, we have introduced two new heuristics to redesign clinical processes compatible with medical centers, including hospital-based, patient-based, and medical technique-based.Providing a framework for clinical process modeling and improvement containing steps and tools.

Ultrasonic welding of Cork Wood/PLA composites: effect of welding factors on lap shear strength performance

Ultrasonic welding of Cork Wood/PLA composites: effect of welding factors on lap shear strength performance

Reliability-Based Design Optimization of Additive Manufacturing for Lithium Battery Silicon Anode

Abstract With the blooming of the electric vehicle market and the advancement in the lithium-ion battery industry, silicon anode has shown great potential for the next-generation battery. Using the state-of-the-art additive manufacturing technique (three-dimensional holographic lithography), researchers have demonstrated that silicon anode can be fabricated as a three-dimensional bicontinuous porous microstructure. However, the volume fluctuation of the silicon anode caused by lithiation during the discharging process causes continuous capacity decay and poor cycling life. Besides, uncertainties are inherent in the manufacturing and usage processes, making it crucial to systematically consider them in the silicon anode design to improve its performance and reliability. To fill the gap between current silicon anode research and future industrial need, this study established a digital twin to investigate the optimal design for silicon anode under the uncertainties of additive manufacturing and battery usage. This study started with developing multiphysics finite element models of the silicon anode lithiation process to investigate the volume fluctuation of silicon. Then, surrogate models were built based on the results from the finite element models to reduce computational cost. The reliability-based design optimization was employed to find the best design point for the silicon anode, in which an outer optimization loop maximized the objective function and an inner loop dedicated to reliability analysis. Finally, the Pareto optimal front of the silicon anode designs was obtained and validated, which shows over 10% improvements in the silicon anode's total capacity and rate capability.

Evaluating spatial resolution and imperfect detection effects on the predictive performance of inhomogeneous spatial point process models trained with simulated presence-only data

Evaluating spatial resolution and imperfect detection effects on the predictive performance of inhomogeneous spatial point process models trained with simulated presence-only data

Empowering Unskilled Production Systems Consultants through On-the-Job Training Support: A Digital Triplet Approach

This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement process knowledge of skilled production systems consultants, facilitating imitation by unskilled consultants. We adopted the Digital Triplet (D3) concept, an extension of the authors’ digital twin framework to intelligent activities, aligning with our study objectives. Recognizing the difficulty and inadequacy of knowledge transfer in production systems consulting OJT, we propose an OJT support method integrating a decision-making modeling approach for skilled consultants’ processes based on the Generalized Production Systems Consulting Process Model (GCPM) from prior literature into traditional OJT methods involving self-learning and direct instruction. This method enables the construction of a domain-specific GCPM, formalizing the improvement process flow implemented by skilled consultants and linking it to production improvement expertise and tools. In a case study focused on energy-saving improvement, we constructed and tested a domain-specific GCPM’s efficacy in facilitating the transfer of difficult-to-transfer knowledge. The results indicate that domain-specific GCPM facilitates such knowledge transfer, including specialized improvement, knowledge utilization, rationale, and adaptation to specific cases.

Foreign market expansion of ecosystems: a process model

ABSTRACT Ecosystems of actors that need to interact to create value for end users are becoming an integral part of firms’ strategic realities as they reach new markets. However, the phenomenon of ecosystem internationalisation has not been explored in comparison to its practical prevalence. We conceptualise this phenomenon as agent-led structuration on new markets. We build on the structural ecosystem literature and literature on internationalisation challenges to create our recursive process model of ecosystem creation on foreign markets. Creating ecosystems on foreign markets necessitates new ecosystem structures that are adjusted to value propositions by orchestrators through interaction with foreign market actors. The model explains how ecosystem orchestrators drive the international expansion of ecosystems through blueprinting and aligning mechanisms and how these mechanisms help manage liabilities of foreignness. The model accounts for the agency of involved actors, and with the help of the bottleneck concept bridges international business and ecosystem literatures.

Kinetic Modeling and Techno-Economic Analysis of a Methanol-to-Gasoline Production Repurposed Refinery Equipment.

Methanol-to-gasoline (MtG) is an alternative process for the production of liquid fuel that involves the conversion of methanol into light and heavy hydrocarbons. The attractiveness of this option relies on multiple feedstocks that can be used to produce methanol, which can impact the greenhouse gases emitted into the atmosphere in comparison to petroleum-based feedstocks. However, one of the challenges of this alternative gasoline production is its high investment cost. This study presents a solution to increase affordability by using repurposed equipment from petrochemical industries. The study employs three modeling techniques, i.e., kinetic modeling, process modeling, and techno-economic analysis. First, a lumped MtG kinetic mechanism using an hierarchical zeolite socony mobil-5 (HZSM-5) catalyst was developed and validated against the literature data to enable high-accuracy process modeling and optimization. Then, the kinetics were implemented in Aspen Plus V12 for process simulations of the fixed-bed MtG, optimization of the operating conditions, and heat integration to increase energy efficiency. Lastly, a techno-economic analysis (TEA) gives the levelized cost of the e-gasoline along with the sensitivity analysis to demonstrate the variables that mainly affect the cost. The TEA showed a price for the e-gasoline with a gray methanol feedstock of 1.82 USD/L, which is comparable with the current average retail gasoline price (1.29 USD/L). Additionally, increasing the capacity by 161 times can reduce the levelized price of e-gasoline to 0.761 USD/L.

A Wiener-process-inspired semi-stochastic filtering approach for prognostics

Semi-stochastic filtering method has been termed as one of the typical methods for predicting remaining useful life (RUL). Despite of its simple and direct framework, selecting or determining the conditional distribution of the condition monitoring (CM) measurements given the RUL is a challenging task remaining to be solved. To address this issue, this paper presents a novel Wiener-process-inspired semi-stochastic filtering approach for prognostics. First, the relationship between the degradation monitoring data modeled by Wiener process and the RUL of the system is explored. Inspired this relation, the conditional distribution of the CM measurements give the RUL is directly established and used as the observation equation in semi-stochastic filtering based prognosis method, and the parameters in Wiener process and semi-stochastic filtering model are estimated by the maximum likelihood estimation method. Then, the RUL of the concerned in-service system can be predicted with a probabilistic distribution based on the constructed state equation and the estimated model parameters using the CM data to date. To do so, the proposed prognosis method has an ability to integrate the historical data and the real-time CM data of the in-service system. Finally, the developed method is validated by the wear data of milling cutters and Lithium-ion button cells.

Connecting process models to response times through Bayesian hierarchical regression analysis.

Process models specify a series of mental operations necessary to complete a task. We demonstrate how to use process models to analyze response-time data and obtain parameter estimates that have a clear psychological interpretation. A prerequisite for our analysis is a process model that generates a count of elementary information processing steps (EIP steps) for each trial of an experiment. We can estimate the duration of an EIP step by assuming that every EIP step is of random duration, modeled as draws from a gamma distribution. A natural effect of summing several random EIP steps is that the expected spread of the overall response time increases with a higher EIP step count. With modern probabilistic programming tools, it becomes relatively easy to fit Bayesian hierarchical models to data and thus estimate the duration of a step for each individual participant. We present two examples in this paper: The first example is children's performance on simple addition tasks, where the response time is often well predicted by the smaller of the two addends. The second example is response times in a Sudoku task. Here, the process model contains some random decisions and the EIP step count thus becomes latent. We show how our EIP regression model can be extended to such a case. We believe this approach can be used to bridge the gap between classical cognitive modeling and statistical inference and will be easily applicable to many use cases.

Energy consumption optimization model and system for the pellet drying process of straight grate

The drying process is a crucial stage in the thermal treatment of iron ore pellets in a straight grate, as it has a direct impact on pellet quality and energy consumption. Consequently, this paper develops a simulation model for the pellet bed drying process, which is validated through industrial data and enables the visualization of temperature and moisture distribution within the pellet bed. Based on the simulation model, an optimization model for the drying process is developed to minimize the specific energy consumption per tonne of green pellets. The constraints of the drying process are the moisture content requirements and burst temperature. A genetic algorithm is employed to optimize the parameters such as trolley speed, pellet bed thickness, air temperature, and air pressure. The results show that, with an absolute error criterion of 10 °C, the temperature calculation accuracy of the simulation model exceeds 86%, effectively describing the drying process in a straight grate sintering machine. Under yield limitations, the optimal cumulative energy consumption per tonne of green pellet is reduced by 1.50% during the drying process. Finally, a simulation and optimization system for the drying process in the straight grate is implemented in C++. This system not only generates essential data for designing the drying section in a straight grate but also lays a theoretical foundation for system optimization and optimal control.

Computationally Efficient Algorithm for Modeling Grain Growth Using Hillert's Mean-Field Approach.

To investigate the interconnected effects of manufacturing processes on microstructure evolution during hot-rolling, a through process model is required. A novel numerical implementation of the mean-field approach was introduced to efficiently describe the grain growth of larger systems and extended durations. In this approach, each grain is embedded within an average medium and interacts with the average medium, thus avoiding the complexities of individual grain interactions. The proposed upsampling approach dynamically adjusts the simulation grain ensemble, ensuring efficiency and accuracy regardless of the initial number of grains present. This adaptation prevents undersampling artifacts during grain growth. The accuracy of the model is verified against analytical solutions and experimental data, demonstrating high agreement. Moreover, the effects of different initial conditions are successfully investigated, demonstrating the model's versatility. Due to its simplicity and efficiency, the model can be seamlessly integrated into other microstructure evolution models.

The relationship between young college students' recognition of national COVID-19 crisis governance capabilities and the improvement of national identity: the mediating role of online participation in public health critical events.

Improving the young college students' national identity is crucial for ensuring social stability and fostering development during public health critical events such as COVID-19. Young college students' recognition of national COVID-19 crisis governance capabilities can influence their national identity, and online participation in public health criticalevents may serve as a crucial role in shaping this intricate relationship. To investigate this possibility, the present study established an intermediary model to examine the impact of online participation in public health critical events on young college students' recognition of national COVID-19 crisis governance capabilities and improvement of national identity. This cross-sectional survey study employed a convenience sampling method to investigate a total of 3041 young college students in China. The correlations between study variables were analyzed using Spearman's rank correlation. The mediation model was established using PROCESS Model 4 with 5000 bootstrap samples in SPSS. The bias-corrected bootstrap method provided statistical efficacy and identification interval estimation. Young college students' recognition of national COVID-19 crisis governance capabilities (r=0.729, P<0.001) and online participation in public health critical events (r=0.609, P<0.001) were positively correlated with improvement of their national identity. The relationship between these two factors was partially mediated by online participation in public health critical events (Indirect effect estimate=0.196, P<0.001). Online participation in public health critical events played a mediating role in the association between college students' recognition of national COVID-19 crisis governance capabilities and the improvement of national identity. Our findings provide a novel intervention strategy for improving college students' national identity, which is to encourage their online participation in public health critical events.

ParkLIV – engaging non-users in green space management

ABSTRACT Everyone has the right to feel welcome, safe and be able to access public green spaces without fear, anxiety or stress. However, the term “accessibility” is primarily used in relation to physical accessibility, while social and cultural dimensions of accessibility to urban public green spaces remains underexplored. Methods and tools for universal design and management that also meets the needs of marginalised groups, are needed. This requires involvement of both users and potential users through participatory methods, building on individual needs and perspectives. In Sweden, traditionally marginalised groups and non-users are rarely engaged in the development of public urban green spaces. Local governments may have the will, but lack resources and appropriate methods in order to do so. Research has recently described a further engagement of users as an unleashed potential in relation to planning and management of urban green spaces. Based on the study of three test beds and the use of public green space governance and management theory, we developed a process model for user participation in green space maintenance with focus on involving marginalised groups and non-users. The model balances what is theoretical optimal with what is practically feasible within the daily work of a municipal organisation. We present an empirically tested process model that can form the basis for future maintenance of urban green spaces, with the use of limited resources.

Simulation of a Heat Transfer Mechanism in a Shell and Tube Coil Heat Exchanger

Statement of the problem. The mechanism of heat exchange between counterflow circuits in a shell-and-tube coil heat exchanger, inside which heating and heated coolants flow, is discussed. It is required to build computer models of the movement of coolants in different operating conditions, to simulate the mechanism of heat exchange in the countercurrent mode of movement of coolants in a shell-and-tube coil heat exchanger. This problem was solved in the Solidworks Flow Simulation software package. Results. A mathematical model of the heat exchange process in a shell-and-tube coil heat exchanger was obtained with visualization of the mechanisms of coolant movement inside the heating and heated circuits. Conclusions. Based on the simulation results, a comparison was made of various computer models of the apparatus obtained. The optimal design has been determined.

Study on Permeability Evolution Law of Rock Mass under Mining Stress

In order to study the stress–strain–permeability coefficient relationship of overlying strata in a fractured zone after coal mining, taking the Changcun coal mine in the Changzhi basin as an example, the permeability evolution law of coarse sandstone, fine sandstone, siltstone and mudstone during a stress–strain process was analyzed through a triaxial compression permeability test. The generalized model of the rock mass permeability evolution process under mining stress was summarized, and then a coupling model of the stress–water pressure–permeability coefficient of fractured rock was established based on the continuum model of rock mass. The results showed that the maximum permeability coefficient of different coal overburden types was quite different, and the peak strength of the rock mass preceded the maximum permeability coefficient during the rock mass failure process; the permeability coefficient first decreased and then increased, reaching its maximum value after the peak stress, which occurred during the strain-softening stage; the generalized model of rock mass permeability included the compaction stage, elasticity stage, stable fracture stage, unstable fracture stage, macroscopic failure stage and residual strength stage.

Aspen Simulation Study of Dual-Fluidized Bed Biomass Gasification

This article establishes a thermodynamic model of a dual-fluidized bed biomass gasification process based on the Aspen Plus software platform and studies the operational control characteristics of the dual-fluidized bed. Firstly, the reliability of the model is verified by comparing it with the existing experimental data, and then the influence of different process parameters on the operation and gasification characteristics of the dual-fluidized bed system is investigated. The main parameters studied in the operational process include the fuel feed rate, steam/biomass ratio (S/B), air equivalent ratio (ER), and circulating bed material amount, etc. Their influence on the gasification product composition, reactor temperature, gas heat value (QV), gas production rate (GV), carbon conversion rate (ηc), and gasification efficiency (η) is investigated. The study finds that fuel feed rate and circulating bed material amount are positively correlated with QV, ηc, and η; ER is positively correlated with GV and ηc but negatively correlated with QV and η; S/B is positively correlated with GV, ηc, and η but negatively correlated with QV. The addition of CaO is beneficial for increasing QV. In actual operation, a lower reaction temperature in the gasification bed can be achieved by reducing the circulating bed material amount, and a larger temperature difference between the combustion furnace and the gasification furnace helps to further improve the quality of the gas. At the same time, GV, ηc, and η need to be considered to find the most optimized operating conditions for maximizing the benefits. The model simulation results agree well with the experimental data, providing a reference for the operation and design of dual-fluidized beds and chemical looping technology based on dual-fluidized beds.

Condition-based reallocation and maintenance for a 1-out-of-2 pairs balanced system

In a system consisting of multiple functionally exchangeable units, differences in units’ degradation levels can significantly affect the system’s performance. Dynamic reallocation of these units can improve performance and prolong the lifetime of the system. This study is the first to quantify the benefit of incorporating reallocation into a condition-based maintenance framework for a 1-out-of-2 pairs balanced system—a system with two pairs of units that functions if there is at least one functioning pair. The balance condition requires that the two units in the same pair should be active or inactive at the same time. Unit degradation is modeled as a Gamma process and is inspected periodically. A Markov decision process model is developed to determine the optimal integrated reallocation and maintenance policy that minimizes the long-run average cost per unit time. A numerical study illustrates that the proposed integrated reallocation and maintenance policy significantly outperforms other limited policies, including reallocate-only and maintain-only policies.

Artificial intelligence and human integration: a conceptual exploration of its influence on work processes and workplace learning

ABSTRACT This paper examines the influence of Artificial Intelligence (AI) on job and organizational processes, drawing on sociotechnical systems (STS) theory. It discusses the emergence of AI and its implications for HRD on individual, group, and organizational learning. AI has changed the trajectory of work processes and outcomes, paving the way for the future of work. An AI-human integration process model is proposed to reinforce its influence on job design in response to AI applications in the workplace. Learning is experienced between humans and between humans and AI. AI’s cognitive intelligence is capable of learning from each other through a process of deep learning. Humans, too, can learn from AI to modify their response towards working with AI-enabled applications. The model illustrates the relationships between individuals, work processes, and sociotechnical systems through multilevel interactions. Of relevance to HRD are two critical processes, namely learning and innovating, that facilitate the continuation of AI-human integration, enabling employees to embrace AI as an integral part of their work life. The paper offers several implications for HRD research and practice, recognizing opportunities for workplace learning and the potential constraints and risks arising from AI adoption that are applicable globally and internationally.

Machine Learning Based Decision-Making: A Sensemaking Perspective

The integration of machine learning (ML), functioning as the core of various artificial intelligence (AI)-enabled systems in organizations, comes with the assertion that ML models offer automated decisions or assist domain experts in refining their decision-making. The current research presents substantial evidence of ML’s positive impact on business and organizational performance. Nonetheless, there is a limited understanding of how decision-makers participate in the process of generating ML-driven insights and enhancing their comprehension of business environments through ML outcomes. To enhance this engagement and understanding, this study examines the interactive process between decision-makers and ML experts as they strive to comprehend an environment and gather business insights for decision-making. It builds upon Weick’s sensemaking model by integrating ML’s pivotal role. By conducting interviews with 31 ML experts and ML end-users, we explore the dimensions of sensemaking in the context of ML utilization for decision-making. Consequently, this study proposes a process model which advances the organizational ML research by operationalizing Weick’s work into a structured ML-driven sensemaking model. This model charts a pragmatic pathway, outlining the interaction sequence between decision-makers and ML tools as they navigate through recognizing and utilizing ML, exploring opportunities, assessing ML model outcomes, and translating ML models into action, thereby advancing both the theoretical framework and its practical deployment in organizational contexts.