Process Model Research Articles

Prediction of Microstructure Evolution in Ball Mill Liner Forging Process

The liner is affixed to the inner side of the ball mill cylinder to protect the cylinder. Through isothermal compression experiments, Arrhenius constitutive models, peak strain models, critical strain models, dynamic recrystallization dynamic models, and grain size models suitable for the forging process of Mn–Cr–Ni–Mo steel used in ball mill liners were established. By utilizing Deform software, a 3D thermo‐force‐structure coupling model for the hot forging process of ball mill liners was constructed, and the volume fraction of dynamic recrystallization and average grain size during forging was predicted. The response surface model was employed to investigate how process parameters interacted with each other and affected microstructure uniformity in ball mill liners. After optimization, the optimal parameters were determined: initial forging temperature at 1200 °C, forging speed at 30 mm s−1, and friction coefficient at 0.3. Subsequently, a hot forging experiment on ball mill liners was conducted using these optimized parameters; samples were analyzed through backscattered electron diffraction device experiments and microscopic tissue observations. Results demonstrated that microstructural changes observed during actual forging processes aligned with numerical simulation results—thus verifying both the accuracy of the Mn–Cr–Ni–Mo steel material model and numerical simulation method.

Upgrading low quality barite-bearing ore from Azare, Bauchi State, Nigeria: multiparametric modeling and optimization of the beneficiation process

Upgrading low quality barite-bearing ore from Azare, Bauchi State, Nigeria: multiparametric modeling and optimization of the beneficiation process

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.

Mixtures of Dirichlet processes for joint spatial modelling of transcranial magnetic stimulation mapping data

Abstract A patient’s responses to Transcranial Magnetic Stimulation (TMS) pulses on the motor cortex have a complex spatial pattern, making it challenging to understand the response patterns across multiple patients. We developed a mixture of Dirichlet process models to distinguish between patient-specific and shared spatial patterns across multiple patients to provide insight into consistent response patterns essential for developing personalized treatment procedures. The Metropolis–Hastings within Gibbs sampler of the Markov Chain Monte Carlo algorithm was developed for estimation. The model was used to analyse the TMS data of 3 healthy subjects. The study revealed that the primary motor cortex of the hand consistently emerges as a promising region for eliciting optimal responses. This area serves as a key target for brain mapping using TMS to identify cortical hotspots. However, the excitability patterns in this region can vary significantly among patients.

Factors that Affect High School Students Making Them Not Like Mathematics

This research paper explores the factors that make high school students reluctant to study mathematics, which aims to: 1) analyze the general information of the sample; 2) Analyze the external factors that make high school students reluctant to study mathematics; 3) Analyze the internal factors that make high school students reluctant to study mathematics; 4) Prioritize the factors that affect the students' reluctance to study mathematics. The sample used in the research was six high school students in Vientiane Capital and Vientiane Province, who interviewed 1,587 students. The research tools were questionnaires, and the data were analyzed using Analytic Hierarchy Process Model. From the sample group of students, it found that there was female more than males. The external factors that make students not like to study mathematics are due to the difficulty, inadequate mathematics textbooks, usefulness of mathematics in daily life, teachers lacking knowledge, families do not support, society does not pay attention to mathematics, mathematics is not interesting and schools do not pay attention to mathematics. The internal factors that make students not like mathematics are: the method of teaching with new ideas that students are interested and understand easily the concept of mathematics levels at intermediate, the curriculum factor determining the content of the mathematics levels intermediate, the factor in the teaching and learning media making the students creative and rational levels at intermediate. The building factor has not enough books for students to read is at the high level, the family factor is the family encourages doing business is at high level, the social factor is the students often go to the market or the city and the village without a definite purpose is at high level, the environmental factor is students using smart phones is at the highest level, and the economic factor of family is the students having pocket money for schools is at the highest level. The factors that make high school students reluctanting to study mathematics are ordered from high to low as environmental factor, social factor, building factor, curriculum factor, teacher teaching factor, family factor, teaching-learning media factor and economic factors, respectively.

DBFact applied to minimum variance performance assessment for nonminimum phase multivariate systems from closed‐loop data

AbstractThis paper introduces an approach for determining a minimum variance control (MVC) benchmark for nonminimum phase (NMP) multi‐input multi‐output (MIMO) systems using closed‐loop operational data. The MVC benchmark is derived from the MVC law of DBFact factorization introduced by Lima, Trierweiler, and Farenzena. Unlike other factorization methods, DBFact offers advantages such as non‐iterative computation and ensuring internal stability of the MVC law. This approach considers the inherent directionality of NMP MIMO systems, enhancing the reliability of the control performance index. However, the original method relies on prior knowledge of the process model. To overcome this limitation, this paper proposes a method for calculating the MVC benchmark when prior knowledge is absent. It introduces a MIMO system identification strategy employing minimally invasive signal tests. The methodology is evaluated across various control conditions using a quadruple‐tank plant with additional time delays. The study emphasizes the importance of directionality in assessing MIMO system performance, particularly in evaluating individual loop performances. Results demonstrate the identification procedure's effectiveness in accurately calculating the proposed MVC benchmark, even with a mere 1% increase in output variance considered.

Novelty Seeking Differences in Temporal Dynamics for Novelty and Appropriateness Processing of Creative Information: An ERP Investigation

ABSTRACTNovelty seeking has been found to affect creative performance, but its impact on the temporal dynamics of creative information processing remains unclear. Creative information is identified by two key indicators—novelty and appropriateness. To explore the effect of novelty seeking on the temporal processing of novelty and appropriateness, a revised alternative uses task (AUT) was conducted with 29 high novelty‐seeking (HNS) and 31 low novelty‐seeking (LNS) individuals. Event‐related potentials (ERPs) and spontaneous blink rate (EBR) were used to measure temporal dynamics and infer physiological mechanisms. (1) For novelty processing, HNS individuals performed quicker information processing (shorter N1/P2 latency) and had a greater capacity to recognize semantic distance (higher P600 peak amplitude). (2) For appropriate processing, HNS individuals also had a faster information processing rate (shorter N1/P2 latency). (3) HNS individuals had higher baseline EBR and showed enhancements in cognitive speed based on real‐time EBR. In contrast, the LNS group did not show the same improvement even with increased attention allocation. These findings expand the application of the Novelty Seeking Model (NGM) in creative information processing. Additionally, the results of EBR suggest that dopamine might be the critical physiological mechanism through which novelty‐seeking influences creative information processing.

Growing community-based composting programs in China: Implementation and policy lessons from eight cases

Localised processing of waste into resources is a prime target towards circular economy, yet community-led composting programs have reported significant implementation challenges. There are no implementation process models or guidelines for programs spanning community, waste, governance, legislation domains: each community learns anew. Here we report a multi-case study of community-led composting program implementations in eight urban sites in China. In-depth key-informant interview information was analysed as cycles of activity towards increasingly-complex needs/targets (like simple recycling; complex composting). Cross-community comparisons suggest transferable implementation lessons: delegation of certain roles is more effective than simply involving stakeholders; enthusiasts can significantly mobilize stakeholders but cannot replace them; paperwork burden could negate funding availability benefits; access to specific expertise (technical, legal, operational) at crucial moments was key; stakeholder learning was most effective via personal meetings and introductions. City policies incorporating these implementation lessons could facilitate scaling-up in China. Future studies can investigate generalizability.

Straightforward identification of flow curve and yield locus parameters from three-point bending experiments

Material testing and modeling is one of the cornerstones of virtual analysis of sheet metal forming processes. However, it is also becoming more and more relevant for incoming goods inspection, especially in view of the increasing amount of recycled material or frequent changes of suppliers, e.g. to provide workers, processes and/or process models with relevant information about a new batch of material. Efficient material testing and straight-forward test evaluation is essential for this. The flow curve and yield locus are central to describe the forming behavior of sheet metal materials. However, the parameters of the associated models are currently determined in various tests on different systems and with special sample geometries. The present work presents a methodology that allows the determination of a set of flow curve and yield locus parameters from three three-point bending tests only. The evaluation routine does not require finite element simulation and processes only the force-displacement information of the bending tests, which also places low demands on the measurement technology. The results were compared with a conventionally determined parameter set using a validation test, and the results are of reasonable quality, especially considering the minimal effort involved.

An Integrated Approach: A Hybrid Machine Learning Model for the Classification of Unscheduled Stoppages in a Mining Crushing Line Employing Principal Component Analysis and Artificial Neural Networks.

This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations.

The active role of co-evolving haloes in stellar bar formation

Abstract We use idealised N-body simulations of equilibrium discs in live and static haloes to study how dark matter co-evolution impacts the assembly of stellar particles into a bar and the halo response. Initial conditions correspond to a marginally unstable disc according to commonly used disc stability criteria, and are evolved for the equivalent of about 150 disc dynamical times (10Gyr). An extensive convergence study ensures accurate modelling of the bar formation process. Live haloes lead to the formation of a strong bar, but the same disc remains unbarred when evolved in a static halo. Neither seeded disc instabilities, nor longer (60Gyr) simulations result in the formation of a bar when the halo is static. When the live halo is replaced with a static analogue at later times the previously robust bar slowly dissipates, suggesting: (1) the co-evolution of the disc and halo is critical for the assembly and long-term survival of bars in marginally unstable discs; and (2) global disc stability criteria must be modified for discs in the presence of live haloes. In our live halo runs, a ‘dark bar’ grows synchronously with the stellar bar. Processes that inhibit the transfer of angular momentum between the halo and disc may stabilise a galaxy against bar formation, and can lead to the dissolution of the bar itself. This raises further questions about the puzzling stability of observed discs that are marginally unstable, but unbarred.

Solving crystallization/precipitation population balance models in CADET, Part II: Size-based Smoluchowski coagulation and fragmentation equations in batch and continuous modes

A particle size-based Smoluchowski coagulation and fragmentation equation was solved in the free and open source process modeling package CADET. The WFV and MCNP schemes were selected to discretize the internal particle size coordinate. Weights in these schemes were modified to preserve and conserve the zeroth and third moments for size-based equations. Modified propositions and proofs for the scheme are provided. Analytical Jacobians were derived and implemented to reduce the solver’s runtime. A two-dimensional Smoluchowski coagulation and fragmentation equation with axial position as external coordinate was formulated and discretized to support simulations of continuous particulate processes in dispersive plug flow reactors. Five 1D and four 2D test cases were used to validate the implementation and benchmark the solver’s performance. The runtime, L1 error norm, L1 error rate, particle size distribution moments up to sixth order and several scalar metrics were analyzed in detail.

Non-smooth Bayesian optimization in tuning scientific applications

Tuning algorithmic parameters to optimize the performance of large, complicated computational codes is an important problem involving finding the optima and identifying regimes defined by non-smooth boundaries in black-box functions. Within the Bayesian optimization framework, the Gaussian process surrogate model produces smooth mean functions, but functions in the tuning problem are often non-smooth, which is exacerbated by the fact that we usually have limited sequential samples from the black-box function. Motivated by these issues encountered in tuning, we propose a novel Gaussian process model called a clustered Gaussian process (cGP), where the components are dynamically updated by clustering. In our studies, the performance of cGP can be better than stationary GPs in nearly 90% of the experiments and better than non-stationary GPs in nearly 70% of the repeated experiments while requiring less computational cost. cGP provides a novel approach for dynamic GP, computes more efficiently than recursive partitioning, and discovers non-smoothness regimes. We provide extensive experiments including high-performance computing (HPC) and industrial simulation functions to show the effectiveness of our methods.

Comprehensive modeling of far-infrared drying process for rough rice in a combined FIR dryer

This study introduces a novel single-parameter model for the far-infrared (FIR) drying process of rough rice in a combined FIR dryer, aiming to streamline the prediction process and enhance its efficiency. The experiments involved three levels of FIR intensity (0, 500, and 1000 W m−2) and three levels of inlet air temperature (30, 40, and 50 °C), with a comprehensive examination of various mathematical thin-layer models to predict the drying process. The study developed a comprehensive single-parameter model by investigating the interrelationship of the models’ coefficients under distinct drying conditions, demonstrating its capability to faithfully depict the drying characteristics of rough rice within the combined FIR dryer with an R2 exceeding 0.99. The findings also provide valuable insights into the impact of drying conditions on the drying time, percentage of cracked kernels (PCK), and amylose content of rough rice, revealing increasing trends in PCK and amylose content with higher drying temperature and FIR intensity, offering practical and efficient tools for optimizing the drying process and enhancing the quality of dried rice products. Overall, this research significantly advances rice drying technology and offers practical solutions for improving the efficiency and quality of rice drying processes.

Experimental Analysis and Optimization of Clearance between Punch and Die in Sheet Metal Blanking Process for Soft Steel

Blanking process is commonly used process in production of sheet metal industries to fabricate components. The Global market of sheet metal production is facing highly competition. Due to unavailability of skilled operators and shortage of affordable CAD/CAM systems, it is difficult to predict optimum clearance between punch and die to obtain high quality in small scale industries. Blanking is metal fabricating process and it is characterized by complete material separation. The simulation of the shearing process is complicated as the shear band formed is narrow and fracture criterion is inappropriate. The effect of potential parameters like thickness of sheet, punch-die clearance, tool wear, and punch geometry are studied to investigate their interactions on blanking process. It is important to select process leading parameters which can lead the process such that two similar products from two separate materials can be manufactured on same mold with high quality. This study will help to investigate the effect of thickness of sheet, clearance between punch and die and type of material on blanking process and will help to predict optimal set of parameters to obtain reasonable quality. A mathematical model of blanking process is developed and design of experiments method is used to predict optimum punch-die clearance for soft steel which will result into high quality of component.

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 Study on Measuring the Impact of Business Process Management on an Organization’s Business Performance Superiority

The study's overarching goal is to quantify the effect of BPM on the improved business performance of participating organisations. The research used the corporate process management life cycle methodology to identify the concept of superiority. Along with describing the operational and competitive dimensions of business performance superiority, the opinions and responses of the 89 managers who made up the sample were utilised to describe (process identification and design, process modelling and documentation, process monitoring and controlling, and process optimisation). To validate the study's hypothesised model and draw attention to BPM's role in explaining why some organisations outperform others, researchers performed multiple regression analysis. Organisational managers must provide moral and financial support for business process orientation within the business entrepreneurship window in order for their organisations to maintain a competitive advantage in an uncertain, risky, and potentially volatile environment.

The Evolution of Entrepreneurial Opportunity: The Case of a Finnish Start-up in the Telecom Industry

Abstract This study represents a longitudinal in-depth investigation of entrepreneurial opportunity development in a Finnish start-up company. A qualitative interpretive case study method is applied here that enables us to provide in-depth findings to promote opportunity-related research in the fields of entrepreneurship and International Entrepreneurship. We create an empirical process model that unlocks the longitudinal development of entrepreneurial opportunity and internal and external features contributing to it. The findings indicate that the development process represents a transformation from the assessment of demand into the creation of actual solutions. In addition, the process model reveals how the objectives and motivations of individuals, as well as external features in the industry and events, drive the development of entrepreneurial opportunity.

A Data-Driven Approach for Forecasting Current Direction With a Hybrid Model of Empirical Mode Decomposition and Warped Gaussian Process

Abstract Ocean current forecasting is essential for tidal renewable energy generation and operation. However, forecasting the current direction at multiple points along the water depth are still lacking of comprehensive studies. In this study, a data-driven approach was developed to attain short-term prediction in the current direction with reasonable uncertainty quantification. The developed approach employed empirical mode decomposition (EMD) and the warped Gaussian process (WGP) in the forecasting process. The ocean current data, which were measured by a seabed-mounted acoustic Doppler current profiler in the Haitian Strait, were used to illustrate the developed approach. The measured current direction data were preprocessed with the average shifting method to obtain the principal and random components for the improvement of the forecasting accuracy. The random components were decomposed into intrinsic mode functions (IMFs) and residuals. The principal components, IMFs, and residuals of the current direction were then forecasted by the WGP approach. The forecasting performance of the developed approach was investigated through comparisons with those of single standard GP, single WGP, and EMD + GP models. The effects of the kernel function and training input on the forecasting efficiency and precision were investigated. The extrapolation performances of the proposed model for a 1-step prediction and multistep-ahead prediction were also examined.

Chemical engineering perspective of pyrolysis reactor and condenser system for biomass valorisation

The manuscript investigates the design, operating, and economic parameters of components in a biomass pyrolysis system. It covers two key components, the pyrolysis reactor and condenser, with plans to discuss other components in future publications. The study relies on experimental data from a fixed-bed reactor, bio-oil retention experiments using various vials, and computational modeling of the pyrolysis process using a lumped model. The experimental data were thoroughly analysed, considering different types of pyrolysis reactors and heating methods. Dimensionless numbers were employed to predict the impact of changing feed characteristics during pyrolysis. The second part of the study focuses on the operation of conventional condensers and how Bio-oil deposition on their surfaces affects efficiency and cost. Experimental estimation of Bio-oil film thickness on stainless steel and glass surfaces is presented, along with its effects. Finally, a hybrid condenser, combining stainless steel with a glass lining, is proposed to enhance efficiency and reduce costs based on operational requirements.