Modular Process Research Articles

Scenario Design for the Evaluation of Human–Machine Interaction in Modular Process Plants

AbstractOperators in modular process plants face new demands due to features like increasing automation and digitalization combined with an architecture allowing for reconfigurations in short time spans. However, operators must be able to master process control tasks at all times. For research purposes on topics like supporting operators in their performance in process control of modular process plants, this article aims at developing scenarios of such plants. Based on three conceptual levels describing the relation of chemical processes and their modularized realization and a neutralization process as use case, four scenarios were developed. Having the common task to increase the throughput, the scenarios differ in terms of complexity and constraints that need to be considered for process control.

A modular flow process intensification towards lipid peptoids nano assembly formation

A modular flow process intensification towards lipid peptoids nano assembly formation

Is Working Memory Necessary for Both Selection and Calculation in Level 1 Visual Perspective Taking? Evidence from Children and Adults.

Previous research has suggested that there are two modular processes of perspective selection and calculation in level 1 perspective taking. Evidence further showed that verbal working memory is associated with both processes in adults. However, research has not tested whether verbal working memory may be associated with working memory in children. Moreover, since perspective taking is associated with spatial working memory, it is necessary to investigate whether spatial working memory links to both processes. By recruiting 9-year-old children and college students in the single-task paradigm and the dual-task paradigm, we conducted two experiments to answer these questions. Results in experiment 1 suggested that verbal working memory correlated with adults' perspective calculation, but with both processes in children's perspective taking. Results in experiment 2 showed that spatial working memory is associated with adults' perspective calculation and children's perspective selection. These findings suggest that different components of working memory play distinct roles in the processing of perspective taking, which is moderated by age.

A fast modularity hardware Trojan detection technique for large scale gate-level netlists

Hardware Trojans (HTs) are a kind of malicious circuit implanted by adversaries and induce malfunction under rare situations. Attackers may insert HTs into untrusted third-party intellectual properties (3PIPs), thus severely threatening the hardware security of ICs. To overcome this issue, state-of-art HT detection techniques are proposed based on feature extraction of gate-level netlists (GLNs). However, these techniques may take a long time to extract HT signals for large scale GLNs. In this paper, we propose a fast modularity HT detection (FMTD) method for large scale GLNs. The GLN modularity algorithm can divide the whole GLN into several small modules with the boundaries of D flip-flops (DFFs) of each module. By analyzing the transition rate of critical signals, preserving suspicious DFFs, and repairing the ring circuit, we can ensure the integrity of HT circuits during the GLN modularity process. Then, the calculation of the testability of each module is conducted in parallel with our self-designed tool. In the self-designed tool, we repair the ring circuit, calculate the testability values, and calibrate the testability values of module boundary signals. Compared with the EDA tools, our self-designed tool has no upper limit of testability values. Then, the testability values are sent to the unsupervised K-means clustering simultaneously to diagnose the HT signals. Facilitated by the modularity of the GLN, the detection time of 105 order signals sample is reduced by up to 90 % when compared to the traditional COTD method, while our MFTD method shows a similar HT detection performance to that of the traditional COTD method. For all 20 kinds of GLN samples in Trust-hub, our FMTD method can obtain a detection accuracy of 100 %, and signal diagnosis precision of more than 93 % with a diagnosis false positive rate lower than 1 %.

Entrepreneurial orientation and project portfolio success: the roles of digitalization and modularization

PurposeThis study explores entrepreneurial orientation (EO) on project portfolio success in new product development projects, with the moderating effects of digitalization capability and modularization process.Design/methodology/approachThe sample data of 204 firms was used to analyze the research hypotheses. This study adopted hierarchical regression to test the theoretical conceptual model incorporating EO, digitalization capability, modularization process, and project portfolio success.FindingsThese results indicate that EO positively affects project portfolio success. More importantly, digitalization capability and modularization process positively moderate the relationship between EO and project portfolio success.Originality/valueProminent studies have focused on different antecedent and consequence factors of project portfolio success; however, the impacts of EO still need to be noticed. This study makes a pioneering effort to make up this gap and investigate the effects of EO on project portfolio success, digitalization capability, and modularization process as moderators, which can enrich the current literature on project portfolio management.

An enantioselective and modular platform for C4ʹ-modified nucleoside analogue synthesis enabled by intramolecular trans-acetalizations

C4ʹ-modified nucleoside analogues continue to attract global attention for their use in antiviral drug development and oligonucleotide-based therapeutics. However, current approaches to C4ʹ-modified nucleoside analogues still involve lengthy (9–16 steps), non-modular routes that are unamenable to library synthesis. Towards addressing the challenges associated with their syntheses, we report a modular 5-step process to a diverse collection of C4ʹ-modified nucleoside analogues through a sequence of intramolecular trans-acetalizations of readily assembled polyhydroxylated frameworks. Overall, the 2–3 fold reduction in step-count compares favorably to even recently reported biocatalytic approaches and should ultimately enable new opportunities in drug design around this popular chemotype.

AI-powered modular and general-purpose droplet processing system based on single-sided continuous optoelectrowetting chip

Single-sided continuous optoelectrowetting (SCOEW) is a versatile light-controlled digital microfluidic technology. However, the absence of automated algorithms for generating light patterns to manipulate droplets has constrained SCOEW chips to employing only pre-recorded sequences. This limitation hinders SCOEW platforms in effectively addressing common experimental challenges like droplet pinning. To overcome this issue, we have developed a set of algorithms for the automatic generation of droplet-driving light patterns. By integrating these algorithms with machine vision (using YOLOv3) to detect, locate, and classify droplets through video camera input, and employing a path-planning algorithm (A*) to optimize droplet trajectories, our system achieves intelligent functionalities. These include simultaneous manipulation of multiple droplets, automated merging based on color-coded droplet composition, obstacle avoidance, and resolution of potential droplet manipulation failures (such as droplet pinning). In a practical application, we implemented an intelligent loop-mediated isothermal amplification (LAMP) assay on the platform. This modular approach to droplet processing is easily adaptable to other SCOEW hardware, offering potential integration of customizable functionality. The result is a promising suite of portable tools for a variety of biochemical analysis application.

Solid-State Electrodes Towards a Real-Time Sensor Array for Health Monitoring

The healthcare industry has been transformed by advances in biotechnology, resulting in improved health outcomes. Central to this has been advances in medical diagnostics that can both monitor patients as well as provide actionable data at the point-of-care. However, further improvements are needed to maximize the utility of diagnostics at times where acute care may be required, such as when individuals are unstable after experiencing physical trauma. When medical resources are limited, the ability to rapidly and effectively triage patients that have experienced trauma is critical to maximizing health outcomes. Notably, individuals that have experienced significant trauma can degrade quickly. Therefore, continuous monitoring approaches are needed that can provide critical biometric and biomarker information that will enable updating of the triage in real-time.At present time, patients are assessed continuously using biometric monitoring. However, biometrics are often a lagging indicator to biochemical markers, and thus while easier to measure, less informative. Currently, the ISTAT-8 is a point-of-care unit to perform an automated, rapid biochemical panel that can assess the patient’s ability to maintain homeostasis by monitoring eight biomarkers. However, the ISTAT-8 captures biochemical data only at a point in time instead of continuously; this is a drawback, as the health of patients who have undergone trauma can improve or deteriorate rapidly. Therefore, a continuous biochemical monitoring solution is needed to allow for triage to happen in real-time.Advances in biotechnology have for the first time made real-time biochemical sensing a reality. Unfortunately, commercial continuous biochemical sensors to-date are limited to subcutaneous continuous glucose monitors (CGMs). CGMs monitor glucose concentrations in the interstitial fluid (ISF), which strongly correlates to blood values. While the CGM is now commonly used, the current devices are not easily multiplexed. Therefore, a scalable, easy to insert system is needed to perform multiplexed measurements; microneedle-based platforms have the promise to enable sensing in the ISF leveraging five or more distinct sensors.Here we present a solid-state platform that can enable multiplexed sensing leveraging hollow, wicking microneedles in fluidic contact with solid-state electrodes. Our approach leverages optimized sensor formulations and electrode materials fabricated onto a printed circuit board (PCB). The putative sensor array can be produced in a modular fashion leveraging scalable technologies and processes. Notably, this talk focuses on methods and approaches to fabricate an array of solid-state sensors that are capable of detecting ions and metabolites over extended periods (> 1 week) and can be readily integrated with microneedles for sensing interstitial fluid.

Validation of IMU against optical reference and development of open-source pipeline: proof of concept case report in a participant with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant

BackgroundSystems that capture motion under laboratory conditions limit validity in real-world environments. Mobile motion capture solutions such as Inertial Measurement Units (IMUs) can progress our understanding of "real" human movement. IMU data must be validated in each application to interpret with clinical applicability; this is particularly true for diverse populations. Our IMU analysis method builds on the OpenSim IMU Inverse Kinematics toolkit integrating the Versatile Quaternion-based Filter and incorporates realistic constraints to the underlying biomechanical model. We validate our processing method against the reference standard optical motion capture in a case report with participants with transfemoral amputation fitted with a Percutaneous Osseointegrated Implant (POI) and without amputation walking over level ground. We hypothesis that by using this novel pipeline, we can validate IMU motion capture data, to a clinically acceptable degree.ResultsAverage RMSE (across all joints) between the two systems from the participant with a unilateral transfemoral amputation (TFA) on the amputated and the intact sides were 2.35° (IQR = 1.45°) and 3.59° (IQR = 2.00°) respectively. Equivalent results in the non-amputated participant were 2.26° (IQR = 1.08°). Joint level average RMSE between the two systems from the TFA ranged from 1.66° to 3.82° and from 1.21° to 5.46° in the non-amputated participant. In plane average RMSE between the two systems from the TFA ranged from 2.17° (coronal) to 3.91° (sagittal) and from 1.96° (transverse) to 2.32° (sagittal) in the non-amputated participant. Coefficients of Multiple Correlation (CMC) results between the two systems in the TFA ranged from 0.74 to > 0.99 and from 0.72 to > 0.99 in the non-amputated participant and resulted in ‘excellent’ similarity in each data set average, in every plane and at all joint levels. Normalized RMSE between the two systems from the TFA ranged from 3.40% (knee level) to 54.54% (pelvis level) and from 2.18% to 36.01% in the non-amputated participant.ConclusionsWe offer a modular processing pipeline that enables the addition of extra layers, facilitates changes to the underlying biomechanical model, and can accept raw IMU data from any vendor. We successfully validate the pipeline using data, for the first time, from a TFA participant using a POI and have proved our hypothesis.

Integrated usage of artificial intelligence, blockchain and the internet of things in logistics for decarbonization through paradox lens

The objective of this study was to identify, assess, and prioritize resolution strategies to address paradoxes associated with the integrated use of artificial intelligence (AI), blockchain, and the Internet of Things (IoT) in logistics for decarbonization. To achieve these objectives, a four-phase mixed-methods approach was employed, involving the identification, prioritization, and verification of resolution strategies for their significance in managing paradoxes. A panel of 10 experts with experience in adopting AI, blockchain, and IoT in logistics participated in the study. The study identified ten key resolution strategies: education & training, coordination & collaboration, government regulations & support, standardized data format and protocol, modular architecture, interoperability standard, process optimization, security auditing, management participation, and man–machine interactions. Using an integrated multicriteria decision-making approach, the study presented compromised rank of these strategies based on their effectiveness in addressing paradoxes. The most significant resolution strategy identified was modular architecture. This was followed by a second compromise solution set of interoperability standards, process optimization, management participation, coordination & collaboration, and man–machine interactions, and a third compromise solution set including education & training, government regulation & support, standardized data format, and security auditing. The derived solutions were then verified by logistics domain experts. This study provided the first empirical investigation of paradox theory related to the adoption of AI, blockchain, and IoT for decarbonization in logistics. Overall, the study enhanced the understanding of competing demands, tensions, and complexities involved in adopting digital technologies for decarbonization using paradox theory.

Инжиниринговые решения для развития труднодоступных регионов России

The article explores the importance of engineering solutions for the development of hard-to-reach regions of Russia. The relevance of developing small-size mineral deposits in hardtoreach regions and the possibility of achieving this using modular processing plants is considered.

Salmonella Prevalence in Raw Cocoa Beans and a Microbiological Risk Assessment to Evaluate the Impact of Cocoa Liquor Processing on the Reduction of Salmonella

Salmonella in raw cocoa beans (n = 870) from main sourcing areas over nine months was analyzed. It was detected in 71 (ca. 8.2%) samples, with a contamination level of 0.3–46 MPN/g except for one sample (4.1 × 104 CFU/g). Using prevalence and concentration data as input, the impact of thermal treatment in cocoa processing on the risk estimate of acquiring salmonellosis by a random Belgian chocolate consumer was calculated by a quantitative microbiological risk assessment (QMRA) approach. A modular process risk model from raw cocoa beans to cocoa liquor up to a hypothetical final product (70–90% dark chocolate tablet) was set up to understand changes in Salmonella concentrations following the production process. Different thermal treatments during bean or nib steam, nib roasting, or liquor sterilization (achieving a 0–6 log reduction of Salmonella) were simulated. Based on the generic FAO/WHO Salmonella dose–response model and the chocolate consumption data in Belgium, salmonellosis risk per serving and cases per year at population level were estimated. When a 5 log reduction of Salmonella was achieved, the estimated mean risk per serving was 3.35 × 10−8 (95% CI: 3.27 × 10−10–1.59 × 10−7), and estimated salmonellosis cases per year (11.7 million population) was 88 (95% CI: <1–418). The estimated mean risk per serving was 3.35 × 10−9 (95% CI: 3.27 × 10−11–1.59 × 10−8), and the estimated salmonellosis cases per year was 9 (95% CI: <1–42), for a 6 log reduction. The current QMRA model solely considered Salmonella reduction in a single-step thermal treatment in the cocoa process. Inactivation obtained during other process steps (e.g. grinding) might occur but was not considered. As the purpose was to use QMRA as a tool to evaluate the log reduction in the cocoa processing, no postcontamination from the processing environment and ingredients was included. A minimum of 5 log reduction of Salmonella in the single-step thermal treatment of cocoa process was considered to be adequate.

Hyundai's Modular MBSE Approach to ‘Purpose Built Vehicle’ Architecture Development

AbstractThis paper discusses an ongoing effort at Hyundai to develop a model‐based systems engineering (MBSE) methodology for cross‐domain vehicle architecture development that is practical at the enterprise level. Our approach follows a modular modeling process that complements MBSE. In the age of smart mobility, automobile systems are indeed interdependent elements of a system of systems (SoS). Connectedness favors numerous mobility features that emerge due to the interfaces among these constituent systems. Managing emergent product line variations, changing consumer demands coupled with faster market response require manufacturers to modularize their architecture development processes. This can help scale MBSE across vehicle programs. This study proposes a modular system architecture approach for developing Hyundai's purpose‐built vehicle (PBV) concept that maintains a link to the legacy vehicle breakdown structures already in use. Using the Arcadia method, the ‘to‐be’ developed electric vehicle is expressed as a hierarchical functional partitioning of the subsystem modules. A physical architecture is defined as a solution to the functional partitioning based on an existing vehicle breakdown in a combined ‘top‐down/bottom‐up’ workflow thus capturing a realistic system decomposition. In the SoS hierarchy, the PBV is at the top level and is partitioned into multiple levels of nested subsystem architectures owned and developed by designated module teams in a distributed modeling environment. Results of the preliminary architecture modularization effort indicate significant potential for benefits over classical architecture modeling such as iterative knowledge capture, enhanced reusability across projects, products and programs, and distributed vehicle performance development across the extended MBSE enterprise, which includes the tier 1/2 suppliers.

Adaptive Approach for Reservoir Modeling Reduces Project Time, Improves Collaboration

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 23935, “Breaking the Chain: Novel Use of Adaptive Model Approach for Reservoir Modeling Reduces Time Duration of Project and Improves Collaboration,” by Anthony R. Thompson, SPE, and Cesar E. Zerpa Bolivar, SPE, Saudi Aramco. The paper has not been peer reviewed. Copyright 2024 International Petroleum Technology Conference. _ The complete paper details how the reservoir modeling workflow can be accelerated, and uncertainty reduced, even for challenging greenfield prospects by constructing multiple small fit-for-purpose integrated adaptive models instead of one all-encompassing model. This workflow reduces the number of items on the critical path while simultaneously enabling static and dynamic data to be integrated before the static model is finalized. In addition, this approach enables new ideas to be evaluated more rapidly without affecting the critical path of the project. Adaptive Models Adaptive models are constructed to evaluate a single parameter, although this does not preclude an adaptive model being used to answer more than one question simultaneously. Critical to the successful use of adaptive models is the fact that they can be constructed quickly. This enables the various inputs received to be assessed and quality-controlled within the static and dynamic models the moment the input or interpretation becomes available. The construction of adaptive models normally is best achieved through the construction of a workflow in the static modeling package. Initially, the workflow may only consist of the construction of a grid in which to start to place wells. As data become available and are quality-controlled into the model, in most cases, this can be included into the workflow, enabling more constraints to be applied to the subsequent modeling framework. When the adaptive model is being simulated dynamically, it is critical that the model runs quickly. The use of a sector model can be considered for greenfields because the history-matching parameters before production starts are limited to the vicinity of the well. Full-field models are only required when an event occurs across the whole of the reservoir. Adaptive Modular Modeling Process In contrast to the classical approach to reservoir modeling, which uses a chain-type approach, the use of adaptive models brings components together in the sequence of work completion. This means that the various components of the model can be competed when the resource or team member is available. Thus, until very near the end, the project is unlikely to be waiting on the input of one person.

Beyond fossil: the synthetic fuel surge for a green-energy resurgence

Abstract This review offers a comprehensive overview of synthetic fuels as promising alternatives to conventional fossil fuels. The carbon-neutral potential of synthetic fuels when produced using renewable energy and captured CO2, offering significant opportunities to mitigate CO2 emissions, is discussed. Moreover, the efficiency of synthetic fuels is presented, as they do not require dedicated agricultural land or substantial water resources, addressing concerns related to the land-use change and water scarcity associated with traditional biofuels. The economic viability of synthetic fuels is explored, highlighting the advancements in technology and decreased renewable-energy costs, coupled with their independence from food crops, mitigating concerns about potential impacts on food prices. Major investments by industry leaders such as Porsche, Highly Innovative Fuels Global, and ExxonMobil, totalling $1 billion, aimed at achieving an annual production of 550 million litres by 2026, are covered in detail. This study is further extended by emphasizing the scalability of synthetic fuel production through modular processes, enabling tailored facilities to meet regional demands and contribute to a decentralized and resilient energy infrastructure. Additionally, the ‘drop-in’ nature of synthetic fuels that are seamlessly compatible with existing fuel storage, pipelines, and pumps, facilitating a smooth transition without requiring extensive infrastructure changes, is highlighted. Challenges such as the current high cost of synthetic fuel production are acknowledged, necessitating supportive government policies and incentives for widespread adoption. Overall, synthetic fuels have emerged as promising contenders in the pursuit of sustainable and adaptable energy solutions, with tangible benefits for the environment, economy, and existing energy infrastructure.

A Comprehensive Decision-Making Approach for Strategic Product Module Planning in Mass Customization

This paper explores the integrated optimization of complex coupled industrial manufacturing systems and production strategies based on user customization needs. Two optimization metrics are considered: one is whether the production process of engineering manufacturing is simplified, and the other is whether it is based on the customization requirements of the customer. These two metrics are interrelated, and cases may even be conflicting. Considering the interdependence between engineering manufacturing and user requirements, this paper develops an integrated customized modular engineering manufacturing process to minimize production and maintenance costs and improve efficiency while meeting user customization requirements. This paper takes expert evaluation as an important decision indicator and optimizes the production process strategy on this basis. Finally, a case study is given to illustrate the applicability of the proposed process model.

Transfer matrix modeling for asymmetrically-nonuniform curved beams by beam-discrete strategies

Nonuniform and curved beams can be found in extensive engineering scenarios, ranging from big-scale structures of buildings, bridges to small mechanical systems of aerofoils, blades, micromachines, robotics and sensors. However, variable curvature and transverse asymmetry often lead to a complicated dynamic modeling. In this paper, two beam-discrete strategies are comparatively presented for the free vibration analysis of transversely asymmetric and curved beams and even their combinations. The proposed methodology involves discretizing a general nonuniform and curved beams into a sequence of constant beam segments with non-coaxial nodes or inclination angles, which enables a modular transfer matrix modeling process and hence offers a new way to divide the complex modeling into easier steps. A unified transfer matrix is derived for constant beam elements with non-coaxial nodes and inclination angles accounting for the shear deformation, rotary inertia and axial load in any combinations. The proposed beam-discrete strategies with a unified transfer matrix provide a new perspective in contrast to previous investigations. The advantages of easy programming and small degrees of freedom in the traditional transfer matrix method is extended to transversely asymmetric nonuniform and curved beams. Comparative validation with several engineering beams confirms these advantages.

Design of the 1650-1700nm U band doped fiber laser

Increasing the power of thulium-doped fiber lasers is an important direction for the future development of fiber lasers. Thulium ions exhibit excellent optical properties in the U-band, making them valuable for research. This paper has established a modular design process for fiber lasers, investigating the energy level system of Thulium ions, electron transition processes, power propagation equations, pump power threshold, laser output power, and Bragg grating reflectance for specific wavelength photons. The reflectance of the front and rear cavity mirrors and Bragg grating was also redesigned. Finally, by establishing a physical model and employing numerical simulations, the relationship between laser onset power and pump light input power was determined: when the pump light input power reaches 10.1W, a population inversion occurs, and the fiber laser begins to generate laser output power. The laser output power linearly increases with the increase in pump light input power. When the pump light input power reaches 100W, the laser output power reaches its maximum value of 14.17W. This provides valuable data support for the laboratory preparation of 1650-1700nm thulium-doped fiber lasers.

Modular Approach for Online Vertical Obstacle Detection

Poles, towers, and other vertical structures are a significant hazard to low-altitude flight operations. Three-dimensional sensors have the ability to perceive these objects; however, detection is hindered by the overwhelming preponderance of returns from other surfaces. This paper first evaluates existing approaches to finding vertical structures. Then the paper proposes a fast, modular process that efficiently removes extraneous points and automatically distills remaining data to find significant vertical structures with application to dense and sparse datasets. We apply our approach to the analysis of 200 million LiDAR points from a variety of real-world scenes. With our algorithm, the average density of identified pole returns for communication towers increases from 0.1% to over 60%. Finding other vertical structures is more difficult, but prevalence generally increases for these objects also.

Lessons learnt from daily oral PrEP delivery to inform national planning for PrEP ring introduction for women in low-income and middle-income countries: a qualitative inquiry of international stakeholders

IntroductionSome African countries plan to introduce and scale-up new long-acting pre-exposure prophylaxis methods (LA-PrEP), like the monthly dapivirine vaginal ring (PrEP ring) and injectable cabotegravir. National costed implementation plans, roadmaps...