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Direct capture of CO 2 from ambient air is technically feasible today, with commercial units already in operation. A demonstrated technology for achieving direct air capture (DAC) is chemical separation of CO 2 in a steam-assisted temperature-vacuum swing adsorption (S-TVSA) process. However, the potential to develop scalable solutions remains high, requiring a detailed understanding of the impact of both process design and operation on the performance of the DAC unit. Here, we address this knowledge gap by presenting a state-of-the-art process simulation tool for the purification of CO 2 from ambient air by a 5-step S-TVSA process. By considering the benchmark adsorbent APDES-NFC, we conduct multi-objective productivity/energy usage optimization of the DAC unit, subject to the requirement of producing a high purity CO 2 product ( ≥ 95 %). For the base case scenario, we find a maximum productivity of Pr max = 6.20 kg/m 3 /day and a minimum specific equivalent work of W EQ , min = 1.66 MJ/kg. While in reasonable agreement with published data, our results indicate that the description of both competitive adsorption and adsorption kinetics are key factors in introducing uncertainty in process model predictions. We also demonstrate that the application of formal optimization techniques, rather than design heuristics, is central to reliably assess the process performance limits. We identity that system designs employing moderate CO 2 sorption kinetics and contactors with low length-to-radius ratios yield the best performance in terms of system productivity. Finally, we find that moderate-high ambient relative humidities (50–75%) offer significantly favourable performance, and that a wide range of feed temperatures (5–30 ∘ C) can be accommodated via process optimization without a significant impact on performance. [ABSTRACT FROM AUTHOR]

Sustainable waste management strategies are urgently needed due to an increasing global population and increased waste production. In this context, biorefineries have recently emerged as a promising approach to valorize waste streams and supply a broad range of products. This study presents the process model and life cycle assessment (LCA) of a biorefinery concept using a novel biochemical method, a so-called "dark photosynthesis" conversion. This process is coupled to a photo-fermentation using microalgae. Overall, the biorefinery concept can produce hydrogen, lutein, β-carotene, and proteins for animal feed. Apple pomace from apple juice production is used as feedstock for the primary conversion step. A process model was created with the process simulation software Aspen Plus® using experimental and literature data. Results from this model were then used in an LCA. The environmental impacts of the proposed biorefinery concept are relatively high, showing the need for process optimization in several areas. Energy system integration, stream recycling, and higher hydrogen yields are recognized as especially important for improving the environmental performance of this concept. Despite these findings, the model shows the feasibility of implementing the biochemical conversion technologies in a biorefinery concept for effectively utilizing residue streams. [ABSTRACT FROM AUTHOR]

Accurate predictions concerning a forging process can be obtained by numerical simulation, but only with a thorough knowledge of the main process variables. The material flow behavior and the interface effects are already well studied in the literature, but not the machine behavior, although it is required to estimate blow efficiency and deduce the energy actually transmitted to the workpiece. In this paper, an experimental methodology was applied to determine a spring-mass-damping model and its associated parameters for a screw press. The model and its parameters were identified with press strikes performed without billet. For validation, simulations were performed to predict blows on copper billets. The model's predictions were in good agreement with the experimental measurements for ten consecutive blows on a copper billet. The decrease of process efficiency and the evolution from inelastic blows to elastic blows were correctly depicted by the model. [ABSTRACT FROM AUTHOR]

Aligned discontinuous fibre-reinforced composites are becoming more popular because they have the potential to offer stiffness and strength comparable to their continuous counterparts along with better manufacturability. However, the modelling of highly aligned discontinuous fibre composites is still in its infancy. This paper aims to provide a comprehensive review of the available literature to understand how modelling techniques have developed and consider whether all aspects which could affect the performance of aligned discontinuous fibre composites have been addressed. Here, for the first time, a broad view of the advantages, perspectives, and limitations of current approaches to modelling the performance and behaviour of aligned discontinuous fibre composites during alignment, forming, and mechanical loading is provided in one place as a route to design optimisation. [ABSTRACT FROM AUTHOR]

This paperwork explores the transshipment operations at the Port of Constanta, Romania, focusing on the unloading of big bags from barges. Utilizing Business Process Management (BPM) software, the study models the transshipment process to identify optimization opportunities. The investigation reveals challenges such as coordination complexities and potential cargo damage, alongside the benefits of cost-efficiency and flexibility offered by transshipment services. Through literature review and analysis, the study emphasizes the importance of efficient business processes and the role of BPM software in enhancing operational efficiency. By employing Aura Portal Modeller for simulation, the study identifies bottlenecks and proposes optimization strategies for the transshipment process. The proposed measures include real-time cargo assessment, inventory tracking systems, equipment analysis, and storage layout optimization. This research contributes to the understanding of transshipment operations and highlights the effectiveness of BPM software in process visualization and analysis, offering insights for enhancing efficiency and mitigating delays in supply chain management.

How bridge refurbishment projects are performed requires a trade-off between the speed and cost of the project and the amount of traffic disturbances during the project. A possible way to help reach a better balance between these two extremes is the ASTRA Bridge developed in Switzerland. The ASTRA Bridge is a 236-meter long steel ramp system on wheels, which is placed on top of the bridge deck undergoing refurbishment to enable vehicles to continue to pass over the bridge while construction work progresses underneath. This study illustrates new refurbishment processes by using the ASTRA Bridge and presents the first quantitative analysis of the effects of using the ASTRA Bridge on the time, costs and traffic disturbances associated with bridge refurbishment. The bridge investigated is a short-span (50 m long) highway bridge requiring refurbishment of its superstructure. The analysis indicates that the use of the ASTRA Bridge resulted in reductions in duration and costs (14% and 3% for the example), and a substantial reduction in user costs (51% for the example). Although more analysis is required for different types of refurbishment projects, the initial results indicate that the ASTRA Bridge may become an integral part of future highway bridge refurbishment projects. [ABSTRACT FROM AUTHOR]

This paperwork explores the transshipment operations at the Port of Constanta, Romania, focusing on the unloading of big bags from barges. Utilizing Business Process Management (BPM) software, the study models the transshipment process to identify optimization opportunities. The investigation reveals challenges such as coordination complexities and potential cargo damage, alongside the benefits of cost-efficiency and flexibility offered by transshipment services. Through literature review and analysis, the study emphasizes the importance of efficient business processes and the role of BPM software in enhancing operational efficiency. By employing Aura Portal Modeller for simulation, the study identifies bottlenecks and proposes optimization strategies for the transshipment process. The proposed measures include real-time cargo assessment, inventory tracking systems, equipment analysis, and storage layout optimization. This research contributes to the understanding of transshipment operations and highlights the effectiveness of BPM software in process visualization and analysis, offering insights for enhancing efficiency and mitigating delays in supply chain management. [ABSTRACT FROM AUTHOR]

N2O measurements by liquid sensors in aerated tanks are an input to gas-liquid mass-transfer models for the prediction of N2O off-gas emissions. The prediction of N2O emissions from Water Resource Recovery Facilities (WRRFs) was evaluated by three different mass-transfer models using Benchmark Simulation Model 1 (BSM1) as a reference model. Inappropriate selection of mass-transfer model may result in miscalculation of carbon footprints based on soluble N2O online measurements. The film theory considers a constant mass-transfer expression, while more complex models suggest that emissions are affected by the aeration type, efficiency, and tank design characteristics. The differences among model predictions were 10–16% at dissolved oxygen (DO) concentration of 0.6 g/m3, when biological N2O production was the highest, while the flux of N2O was 20.0–24 kg N2O-N/d. At lower DO, the nitrification rate was low, while at DO higher than 2 g/m3, the N2O production was reduced leading to higher rates of complete nitrification and a flux of 5 kg N2O-N/d. The differences increased to 14–26% in deeper tanks, due to the pressure assumed in the tanks. The predicted emissions are also affected by the aeration efficiency when KLaN2O depends on the airflow instead of the KLaO2. Increasing the nitrogen loading rate under DO concentration of 0.50–0.65 g/m3 increased the differences in predictions by 10–20% in both alpha 0.6 and 1.2. A sensitivity analysis indicated that the selection of different mass-transfer models did not affect the selection of biochemical parameters for N2O model calibration. [ABSTRACT FROM AUTHOR]

Incorporating continuous carbon fibre-reinforced polymer (CCFRP) parts within additive manufacturing processes presents a significant advancement in the fabrication of robust lightweight parts, particularly relevant to aerospace, engineering, and various industrial sectors. Nonetheless, prevailing additive manufacturing methodologies for CCFRP parts exhibit notable limitations. Techniques reliant on resin and extrusion entail extensive and costly post-processing procedures to eliminate support structures, constraining design versatility and complicating small-scale production endeavours. In contrast, laser sintering (LS) emerges as a promising avenue for industrial application. It facilitates the efficient and cost-effective manufacturing of resilient parts without needing support structures. However, the current state of research and technological capabilities has yet to yield an LS machine that integrates the benefits of continuous fibre reinforcement with the inherent advantages of the LS process. This paper describes the systematic development process according to VDI 2221 of a new type of LS machine with automated continuous fibre integration while keeping the advantages of the LS process. The resulting physical prototype of the machine is also presented. Furthermore, this study presents an approach to integrate the cost and Product Carbon Footprint of the process in the product design. For this purpose, a machine state model was developed, and the costs and Product Carbon footprint of a part were analysed based on the model. The promising potential for future lightweight products is demonstrated through the production of CCFRP parts. [ABSTRACT FROM AUTHOR]

The extensive utilization of fossil fuel energy has caused severe degradation to our environment, therefore the search for new clean efficient energy is the need of the hour. Photocatalytic conversion of CO2 to solar fuels, and artificial photosynthesis, offer a promising solution for the energy crisis and global warming. Improving efficiency in the photo-reduction of CO2 to fuels involves developing highly efficient catalysts and optimizing photoreactor configuration. Photocatalysis is a process in which light radiations having energy equal to or greater than the band gap energy (Ebg) of a semiconductor strikes on its surface and generates electron (e−) hole(h+) pairs. The photogenerated electrons and holes participate in various oxidation and reduction processes to produce final products. This field focuses on harnessing solar energy to drive the conversion of carbon dioxide into hydrocarbon fuels, showcasing significant potential for sustainable energy solutions. The global methanol market was valued at $30.9 billion in 2023 and is projected to reach $38 billion by 2028, growing at 4.2 % CAGR during the forecast period. For determining the feasibility of reactions on a larger scale, simulations must be performed at different conditions for obtaining higher conversion and cost-effective management of the process at the industrial level. So, a simulation of methanol photoreactors using different software was done to examine the kinetics of methanol reactors by employing ASPEN, DWSIM, and MATLAB software for simulating experimental data.

The dynamic and complex nature of development processes calls for improvements in risk management methods. To deal with the risks, the authors advocate for a paradigm shift. The focus is moving from individual risk to risk interactions. Standards suggest integrating methods to address the limitations imposed by existing approaches. As a result, new strategies are needed to express and assess risk connections. The paper proposes a global framework and its simulator for: (a) representing individual risk events and including them in a product development, (b) expressing risk interactions, (c) integrating risks hierarchically and (d) evaluating risks. The solution is based on distinction between individual risk events and global process and its risk behaviour. Risk behaviour is characterised on task level. Process behaviour enables the integration of task risk into the development process. Risk management is viewed as a part of the development process, with final risk assessed bottom-up from individual risk to process risk. Consequently, the main contribution is not only in taking risk interactions into account but also in contextualising risk assessment in relation to development processes. Simulation is conducted for hair dryer design. However, the solution is global and can be used for other manufacturing and development processes. [ABSTRACT FROM AUTHOR]

ABSTRACTThe present paper introduces a novel temporal acceleration strategy for computationally efficient prediction of residual stresses and deformations in wire arc additive manufacturing (WAAM) components. It employs a semi-analytical approach, dividing temperature into the analytical and the complementary fields. The analytical field is obtained by a closed-form solution, and the complementary field is employed to solve the boundary conditions. A temporal acceleration factor for the heating period is applied. Meanwhile, the diffusion time in the analytical field is manipulated to guarantee accurate temperature prediction. Validation via WAAM experiments, including both a thin-wall structure and a practical engineering component with characteristic dimensions on the order of metres, indicates that the predicted stresses is 50 MPa lower than the experimental values. Moreover, the discrepancy of between the predicted deformations and the experimental measurements is less than 10%, demonstrating reasonable accuracy can be achieved with attractive computational efficiency.

The Resin Transfer Moulding process receives great attention from both academia and industry, owing to its superior manufacturing rate and product quality. Particularly, the progression of its mould filling stage is crucial to ensure a complete reinforcement saturation. Contemporary process simulation methods focus primarily on physics-based approaches to model the complex resin permeation phenomenon, which are computationally expensive to solve. Thus, the application of machine learning and data-driven modelling approaches is of great interest to minimise the cost of process simulation. In this study, a comprehensive dataset consisting of mould filling patterns of the Resin Transfer Moulding process at different injection locations for a composite dashboard panel case study is presented. The problem description and significance of the dataset are outlined. The distribution of this comprehensive dataset aims to lower the barriers to entry for researching machine learning approaches in composite moulding applications, while concurrently providing a standardised baseline for evaluating newly developed algorithms and models in future research works. [ABSTRACT FROM AUTHOR]

The Paris COP21 held on December 2015 represented a step forward global GHG emission reduction: this led to intensify research efforts in renewables, including biofuels and bioliquids. However, addressing sustainable biofuels and bioliquid routes and value chains which can limit or reverse the ILUC (indirect land-use change effect) is of paramount importance. Given this background condition, the present study targets the analysis and modelling a new integrated biomass conversion pathway to produce renewable advanced fuels, enabling the issue of indirect land-use change (ILUC) of biofuels to be tackled. The bioenergy chain under investigation integrates the decentralized production of biogas through anaerobic digestion and its upgrading to biomethane, followed by a centralized conversion to liquid transport fuels, involving methane reforming into syngas, Fischer–Tropsch (FT) synthesis, and methanol synthesis. The methodology adopted in this work stem from extensive literature review of suitable bio/thermo-chemical conversion technologies and their process modelling using a commercial flow-diagram simulation software is carried out. The major significance of the study is to understand the different modelling approaches, to allow the estimation of process yields and mass/energy balances: in such a way, this work aims at providing guidance to process modellers targeting qualitative and quantitative assessments of biomass to biofuels process routes. Beyond FT products, additional process pathways have been also explored, such as MeOH synthesis from captured CO2 and direct methane to methanol synthesis (DMTM). The analysis demonstrated that it is possible to model such innovative integrated processes through the selected simulation tool. However, research is still needed as regards the DMTM process, where studies about modelling this route through the same tool have not been yet identified in the literature. [ABSTRACT FROM AUTHOR]

This review paper aimed to draw the red line passing through almost 25 years of research on waterjet cutting carried out at WJ_Lab, the waterjet laboratory of the Department of Mechanical Engineering of Politecnico di Milano. The purpose was not to just historically analyse the obtained scientific results by themselves but to make them even more useful by introducing the concept of the waterjet digital twin passing through the accuracy improvements due to microAWJ. This strategy effectively creates synergy among the topics and gives the opportunity to researchers in this field to both have an example of how research in industrial manufacturing processes can be guided by scientific and industrial needs, at least from the author's point of view, and to appreciate how it can be made useful for further improvements by introducing a powerful concept as the digital twin. [ABSTRACT FROM AUTHOR]

We work towards a vocabulary to represent processes and temporal logic specifications as graph-structured data. Different fields use incompatible terminologies for describing essentially the same process-related concepts. In addition, processes can be represented from different perspectives and levels of abstraction: both state-centric and event-centric perspectives offer distinct insights into the underlying processes. In this work, we strive to unify the representation of processes and related concepts by leveraging the power of knowledge graphs. We survey approaches to representing processes and reasoning with process descriptions from different fields and provide a selection of scenarios to help inform the scope of a unified representation of processes. We focus on processes that can be executed and observed via web interfaces. We propose to provide a representation designed to combine state-centric and event-centric perspectives while incorporating temporal querying and reasoning capabilities on temporal logic specifications. A standardised vocabulary and representation for processes and temporal specifications would contribute towards bridging the gap between the terminologies from different fields and fostering the broader application of methods involving temporal logics, such as formal verification and program synthesis.

Sustainable waste management strategies are urgently needed due to an increasing global population and increased waste production. In this context, biorefineries have recently emerged as a promising approach to valorize waste streams and supply a broad range of products. This study presents the process model and life cycle assessment (LCA) of a biorefinery concept using a novel biochemical method, a so-called “dark photosynthesis” conversion. This process is coupled to a photo-fermentation using microalgae. Overall, the biorefinery concept can produce hydrogen, lutein, β-carotene, and proteins for animal feed. Apple pomace from apple juice production is used as feedstock for the primary conversion step. A process model was created with the process simulation software Aspen Plus® using experimental and literature data. Results from this model were then used in an LCA. The environmental impacts of the proposed biorefinery concept are relatively high, showing the need for process optimization in several areas. Energy system integration, stream recycling, and higher hydrogen yields are recognized as especially important for improving the environmental performance of this concept. Despite these findings, the model shows the feasibility of implementing the biochemical conversion technologies in a biorefinery concept for effectively utilizing residue streams.

Aligned discontinuous fibre-reinforced composites are becoming more popular because they have the potential to offer stiffness and strength comparable to their continuous counterparts along with better manufacturability. However, the modelling of highly aligned discontinuous fibre composites is still in its infancy. This paper aims to provide a comprehensive review of the available literature to understand how modelling techniques have developed and consider whether all aspects which could affect the performance of aligned discontinuous fibre composites have been addressed. Here, for the first time, a broad view of the advantages, perspectives, and limitations of current approaches to modelling the performance and behaviour of aligned discontinuous fibre composites during alignment, forming, and mechanical loading is provided in one place as a route to design optimisation.

In the cold rolling process of steel strip products, strip breakage is an undesired production failure which can lead to yield loss, reduced work speed and equipment damage. To perform a root cause analysis, conventional physics-based approaches which focus on mechanical and metallurgical principles have been applied in a retrospective manner. With the advancement of data acquisition technologies, numerous process monitoring data is collected by various sensors deployed along this process; however, conventional approaches cannot take advantage of these data. In this paper, a machine learning-based approach is proposed to characterise and model strip breakage in a predictive manner. First, to match the temporal characteristic of strip breakage which occurs instantaneously, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from three facets – physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using recurrent neural networks (RNNs), which are specialised in discovering underlying patterns embedded in time-series data. An experimental study using real-world data collected from a cold-rolled electrical steel strip manufacturer revealed the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this work, a c lassless over sampling t echnique, Covert , was developed to improve historical datasets from industrial processing plants to aid process modelling. Using kernel density estimation and nearest neighbour algorithms, sparse regions are identified and resampled, developing a more balanced dataset. When applied to a real dataset from a geothermal power plant, Covert outperforms current best practice (Smote) in uniformly populating the input feature space and generating credible data in the output variable. When used to develop a data-driven model Covert improved model accuracy by 20% when predicting outside the original data's feature space. Smote , however, reduced model accuracy by 6% in the same feature space. Developing reliable models of industrial processes continues to be a significant hurdle in developing a digital twin. Using Covert , existing imbalanced historical data can be used to extend the range of applicability of any process model. [Display omitted] • Development of a new classless data resampling algorithm. • Enriching historical process datasets for process modelling. • Improving extrapolation of process models by up to 20% using data augmentation. [ABSTRACT FROM AUTHOR]

Slaughterhouses are significant industrial water users, ranking second highest in the livestock processing sector, and a potential pollution source for water bodies due to effluent discharge. This paper uses the wastewater composition of an actual slaughterhouse and models three alternative scenarios for the wastewater management; no treatment and discharge, primary treatment and discharge, and tertiary treatment followed by partial water reuse. All three scenarios are assessed in terms of their environmental performance following a Life Cycle Assessment, by using the Environmental Footprint 3.0 method. The results revealed that the best scenario for the slaughterhouse wastewater treatment is the third scenario with an overall footprint of 0.255 milliEcopoints (mPt), compared to 2.45 mPt and 1.31 mPt of the first and the second scenario, respectively. [Display omitted] • Three alternative scenarios for slaughterhouse wastewater treatment are assessed. • Primary treatment includes coagulation, flocculation and dissolved air flotation. • Tertiary treatment also includes membrane bioreactor and advanced oxidation process. • Tertiary treatment reduces the overall environmental impact by 47–90%. [ABSTRACT FROM AUTHOR]

With the growing need to reduce carbon dioxide (CO 2) emissions, there have been substantial efforts to identify new solvents that can improve the overall performance of chemical-absorption CO 2 capture processes. Given the large number of potential solvents, computer-aided molecular and process design (CAMPD) approaches can play a critical role in accelerating the search for optimal solvents by enabling the systematic exploration of solvent candidates and process conditions. One of the challenges in developing such a framework is the requirement for a process model that can be used to capture the interactions between process performance and solvent structure without significantly increasing its numerical complexity. In the current work, a model for the absorption–desorption of CO 2 is developed using the predictive SAFT- γ -Mie group-contribution approach, allowing the performance of numerous solvents to be assessed without the need for extensive experimental data. In order to avoid convergence difficulties when solving this highly nonlinear model, a tailored initialization strategy is established, using an adapted inside-out algorithm to prime a nonlinear equation solver for each column, providing a good initial guess for the whole flowsheet. Tests on three solvents confirm the robustness of the approach. Building on this enhanced numerical stability, the model is validated by comparison against pilot-plant data, showing good accuracy. A detailed parametric study of the effect of the key process variables is undertaken; the important role of the CO 2 capture rate, of the lean solvent temperature and loading, and of the desorber pressure is highlighted. The results of the parametric study are used to formulate an optimization problem which is successfully solved for four solvents. A large reduction in total annualized cost and energy requirements is achieved by tuning the operating conditions to each solvent considered. The predictive capability, robustness, and reliability of the proposed model and associated initialization strategy open the way for the evaluation of a large number of novel solvents. • A predictive model of CO 2 chemisorption processes applicable to novel solvents. • High degree of numerical stability thanks to a robust initialization strategy. • Key operating parameters identified by techno-economic parametric study. • Optimization of overall process performance for selected amine-based solvents. [ABSTRACT FROM AUTHOR]

Most data-aware process modelling approaches have been developed from a process perspective and lack a full-fledged data modelling approach. In addition, the evaluation of data-centric process approaches reveals that, even though their value is acknowledged, their usability is a point of concern. This paper presents a data-aware process modelling approach combining full-fledged domain modelling based on UML class diagrams and state charts with BPMN and DMN. The approach is illustrated by means of an elaborated example with multiple business processes on top of a joint domain model. A proof-of-concept has been implemented using the MERODE code generator, linking the resulting prototype application to a Camunda BPM engine, making use of RESTful web services. The proposed approach is evaluated against 20 requirements for data-aware processes and demonstrates that the majority of these are already satisfied by the out-of-the-box combination of the Camunda BPM engine with the prototype generated from a MERODE domain model. [ABSTRACT FROM AUTHOR]

To improve good project outcomes, increasing attention has been paid for many years to building a successful partnership between client and provider in delivering outsourcing services. However, studies on the consideration of service provider selection in aeronautics considering its specific constraints are lacking. More generally, among the various selection criteria for suitable providers, risk mitigation criteria are among the most important driving forces, but they are also less studied. This paper proposes a new methodology with a framework using qualitative process modelling and logical representation methods to provide suitable elements for managing the quality of service providers in the aeronautics context. The approach uses the analysed information and the capitalised knowledge of practical experience to deploy the procedures helpful in carrying out all necessary actions for continuous improvement. The application context concerns the provider control in the quality department of a leading provider of significant aeronautical manufacturers. The applied methodology enables a summary of the services undertaken by the research department of risk management, integrating both the characteristics of the solicited engineering companies and the target service. As a result, we established performance indicators for a concrete vision of the benefits an engineering company provides while respecting the constraints imposed by aeronautical standards. This study provides a methodology that can be of direct interest to several actors in the aeronautical field (e.g. quality departments, research departments, purchasing departments, project managers, quality managers, and buyers of engineering services) for building or improving their processes for risk mitigation in the selection of services providers. More generally, similar entities can rely on the proposed approach in other fields characterised by complex supply chains (e.g. automotive manufacturing, healthcare manufacturing, chemical manufacturing, and technology manufacturing). [ABSTRACT FROM AUTHOR]

The Functional Resonance Analysis Method (FRAM) is a novel research methodology that uses qualitative data collection methods to map and model complex healthcare processes by identifying and depicting the cumulative activities required to produce an outcome. The FRAM aims to identify the variability that can emerge in a process when healthcare activities are performed under dynamic conditions. With this knowledge, health care system design, safety, and quality improvement recommendations can be developed with a greater understanding of everyday process functionality. Researchers interested in using the FRAM require both an understanding of the methodology itself, as well as an understanding of how to effectively plan and conduct qualitative research. The purpose of this paper is to provide practical guidance to researchers on planning and operationalizing qualitative data collection and analysis methods to inform the building of a FRAM model. A combination of literature and practical experience will be used to examine and suggest appropriate ways for researchers to carry out this work. [ABSTRACT FROM AUTHOR]

Business process model and notation (BPMN) is a popular notation used for process modelling mainly due to its high expressiveness. However, BPMN has shortcomings when dealing with specific domains (namely Hazard Analysis and Critical Control Points systems), struggling to model activity duration, quality control points, activity effects and monitoring nature. To tackle these limitations, the business process model and notation extended expressiveness (BPMN-E2) was proposed. In this paper, a multiperspective conformance checking algorithm is developed focusing on detecting non-conformity between an event log and a process model, regarding the information provided by the new elements within BPMN-E2. The proposed algorithm follows a two-step approach that starts by converting the model into a directly follows model (annotated with conformance rules), which is then used in a second phase to perform conformance checking effectively. This modular approach allows to apply the proposed algorithm to other process model notations than BPMN-E2. An event log clustering technique was also developed to downsize large-event logs without compromising data relevance. In this way, both the multiperspective algorithm and the log-downsize clustering technique here proposed are a key contribution to improve conformance checking in process modelling, as evinced in the proof-of-concept provided. [ABSTRACT FROM AUTHOR]

A computationally efficient heat transfer simulation of flashlamp-assisted automated tape placement (ATP) is put forward in this work. The simulation combines distinct 1D finite element models representing the tow, the deposited material, and the resulting stack with appropriate transfer of temperature information to ensure field continuity. Direct comparison against a validated 2D model of ATP shows good agreement in the irradiation region, underneath and beyond the roller vicinity with errors up to 14°C. The combined solution of 1D models requires only 1-2% of the computational effort needed for an equivalent 2D analysis without compromising results resolution, whilst it is better suited for providing the full material temperature history throughout consecutive processing cycles. The accuracy and fast computation render this method appealing for studies which require an iterative execution of the model in a practical timeframe such as in optimisation schemes, inverse solutions, training of surrogate models and stochastic simulation. [ABSTRACT FROM AUTHOR]

This study presents a novel experimental set-up for laboratory-scale simulation of cogging and open-die forging processes during ingot-to-billet conversion of advanced engineering alloys. The experimental set-up is designed to be cost-effective, employing a remotely operated manipulator assembly constructed from readily available “off-the-shelf” components used in conjunction with a conventional uni-axial load-frame - equipment that is available in most materials testing laboratories. Small test-bars of C101 copper alloy were subjected to multi-stroke cogging operations with intermittent rotation at ambient and elevated temperatures (20–600 °C). Prior to forging, the as-received material underwent heat treatments to coarsen the starting grain structure, and to help demonstrate the capability of the apparatus to achieve grain refinement via recrystallisation (dynamic and static) and recovery processes within the deformed material. The resulting microstructural evolution and mechanical property changes of the forged material have been investigated using light microscopy (LM), Vickers hardness (HV) testing, and electron backscatter diffraction (EBSD). The deformed C101 alloy exhibited measurable grain refinement after forging at elevated temperatures, thus demonstrating the effectiveness of the designed miniaturised open-die forging set-up.

Incorporating continuous carbon fibre-reinforced polymer (CCFRP) parts within additive manufacturing processes presents a significant advancement in the fabrication of robust lightweight parts, particularly relevant to aerospace, engineering, and various industrial sectors. Nonetheless, prevailing additive manufacturing methodologies for CCFRP parts exhibit notable limitations. Techniques reliant on resin and extrusion entail extensive and costly post-processing procedures to eliminate support structures, constraining design versatility and complicating small-scale production endeavours. In contrast, laser sintering (LS) emerges as a promising avenue for industrial application. It facilitates the efficient and cost-effective manufacturing of resilient parts without needing support structures. However, the current state of research and technological capabilities has yet to yield an LS machine that integrates the benefits of continuous fibre reinforcement with the inherent advantages of the LS process. This paper describes the systematic development process according to VDI 2221 of a new type of LS machine with automated continuous fibre integration while keeping the advantages of the LS process. The resulting physical prototype of the machine is also presented. Furthermore, this study presents an approach to integrate the cost and Product Carbon Footprint of the process in the product design. For this purpose, a machine state model was developed, and the costs and Product Carbon footprint of a part were analysed based on the model. The promising potential for future lightweight products is demonstrated through the production of CCFRP parts.