Manufacturing Data Research Articles

Partial Discharge Method for State-of-Health Estimation Validated by Real-Time Simulation

Accurate estimation of the state of health (SOH) of batteries for automotive applications, particularly in electric vehicle battery management systems (EV-BMS), remains a critical study area to ensure battery system availability. This paper proposes a comprehensive SOH estimation method that transcends traditional approaches based on estimating the available capacity using the integral of the battery current or estimating the increase in internal resistance. The SOH estimator employs a partial discharge method (PDM) and a linear state-of-charge (SOC) observer based on an equivalent electrical circuit model (ECM), utilizing readily available manufacturer data and designed for real-time applications. The proposed method was tested and validated using three different automotive battery technologies and a real-time simulation on the OPAL-RT platform. The simulations involved voltage and current measurements of pulsed-discharge current profiles under temperature-controlled conditions and an electric vehicle driving profile. The results showed a high accuracy in SOH estimation, with a maximum standard deviation of approximately 0.03497 V for lithium-ion batteries, representing about 7.124% of the mean value of the SOH estimator output. For other technologies, the standard deviations were even lower, all below 0.61% of their respective mean values. These outcomes demonstrate the reliability and accuracy of our method, making it suitable for real-time SOH estimation in EV-BMSs.

Agglomeration and its mechanisms on exporting directly

This paper investigates the effects of agglomeration on manufacturing firms' export mode decision-making. A detailed firm-level dataset combines the manufacturing and product-level transaction trade data from China. We use a dynamic multinomial logit model with random effects to analyze the effects of agglomeration on the transition probabilities of firms' exporting mode. The results indicate that the agglomeration of direct exporters positively affect firm's choice of exporting directly, and further indicate that the agglomeration effect is identified through productivity spillover. Moreover, these effects are not destination-specific, but are industry-specific.

Scientific and Regulatory Lessons Learnt on Building a Chemistry, Manufacturing, and Controls (CMC) Package for COVID-19 Variant Vaccine Updates in the EU—A Regulator’s Perspective

During the COVID-19 pandemic, eight COVID-19 vaccines were authorised in the European Union (EU); as a result of emerging SARS-CoV-2 variants and waning immunity, some of these have been adapted to broaden the immunity against circulating variants. The pace at which variants emerge challenges the technical feasibility to make adapted vaccines available in a suitable timeframe and in sufficient quantities. Despite the current absence of a clear-cut seasonal spread for COVID-19, the EU regulatory approach thus far is a pragmatic approach following a pathway similar to that of seasonal influenza. This approach currently requires chemistry, manufacturing, and controls (CMC—the design, development and consistent manufacture of a specified medicinal product of good quality) and non-clinical data (from product laboratory and animal studies), as well as demonstrating that updated vaccines induce an immune response that can predict clinical efficacy and safety in humans. For CMC data, COVID-19 mRNA vaccine adaptations generally made use of the same formulation, control strategy, manufacturing process, and inclusion of registered manufacturing sites for the drug product; therefore assessment was generally streamlined. The experience gained from the vaccine adaptations, combined with a continuous early regulator-developer scientific discussion, permits increasingly greater predictability for timing and positive regulatory outcomes. Here, we review key aspects of the quality control and manufacture of updating COVID-19 vaccines to protect against new variants. Although most experience has been gained with mRNA vaccines, we note that investment in the streamlining of manufacturing processes for recombinant protein vaccines would facilitate future strain updates/adaptations thereby safeguarding availability of different COVID-19 vaccine types, which is considered of value for public health. We also reflect on the challenges and opportunities in establishing more predictable regulatory mechanisms for future COVID-19 vaccine adaptions and more widely for future vaccines containing rapidly evolving pathogens with the potential to cause health threats.

Premature ventricular contraction burden measured by an insertable cardiac monitor and the incidence of ventricular tachycardia and fibrillation

Abstract Background Insertable cardiac monitors (ICM) have the capability to continuously monitor premature ventricular contraction (PVC) burden over time [1] as well as detect ventricular tachycardia and fibrillation (VT/VF). Objective We investigated the association of PVC burden with incidence of VT/VF in a large real-world cohort of patients implanted with ICMs. Methods Patients implanted with an ICM for various reasons for monitoring and PVC detection turned on were included from the ICM manufacturer's de-identified data warehouse. Patients were included if they had at least 90 days of PVC burden follow-up. Tachycardia episodes that were detected by the ICM were first classified as VT/VF, SVT, or oversensing using an artificial intelligence (AI) model that was pre-trained using over 60,000 manually adjudicated ICM detected tachycardia episodes. If the AI model output probability for VT/VF was greater than 0.2, then those episodes were manually adjudicated for incidence of non-induced spontaneous VT/VF. The PVC burden trends recorded by the device were divided into 4 mutually exclusive patient groups: (1) 0% PVC burden on all days, (2) 1-4% PVC burden on ≥1 day, (3) 5-9% PVC burden on ≥1 day, and (4) >10% PVC on ≥1 day. The incidence rate of spontaneous VT/VF was compared between the PVC burden patient groups using a Generalized Estimating Equations model with negative binomial distribution. Time to first spontaneous VT/VF occurrence after the first day of occurrence of PVC burden for the respective PVC burden groups were estimated using Kaplan-Meier analysis and the groups were compared using the Cox proportional hazards model. Results A total of 5,521 patients were included in the analysis. Patients had an average age of 69±15 years and 49.8% being males. There was a total of 33,393 tachycardia episodes from 2,641 patients that were detected by the ICM. After AI model probability-based adjudications, 691 spontaneous VT/VF episodes were identified from 277 patients. Patients with ≥1 day of PVC burden of 1-4%, 5-9%, and ≥10% were associated with 2.9, 7.3, and 7.8 times increased incidence rate of VT/VF episodes during follow-up period relative to patients with 0% PVC burden on all days of follow-up (Figure A). Kaplan Meier curves for incidence of first spontaneous VT/VF episode for the four groups following occurrence of the first day of qualifying PVC burden for the respective groups are shown in figure B. Patients with a day of PVC burden ≥10% were 3.5 times more likely to develop VT/VF in the future compared to patients with 0% PVC burden on all days. Conclusion Days with high PVC burden, as detected by an ICM with continuous PVC detection capability, were associated with increased risk of VT/VF events in a group of real-world patients implanted with ICMs. PVC burden measured by ICMs may be a risk stratification tool for further investigation to prevent sudden cardiac arrest.

Measurement inaccuracy reported via the Manufacturer and User Facility Device Experience database for an implantable pulmonary artery pressure sensor: recalibration direction and magnitude results

Abstract Introduction Remote monitoring using an implantable pulmonary artery pressure (PAP) sensor (CardioMEMS HF system) in patients with heart failure (HF) has been consistently shown to reduce HF hospitalizations [1]. The success of this approach relies on the accurate measurement of diastolic PAP (dPAP) over time to allow early interventions such as diuretic intensification [2]. However, it remains unclear how the measurement accuracy changes over time after the initial calibration at the time of device implantation. Purpose This study aims to identify measurement inaccuracy events and to quantify the magnitude and direction of required device recalibration for CardioMEMS devices as reported in a large-scale regulatory device database. Methods We used the publicly accessible Manufacturer and User Facility Device Experience (MAUDE) data maintained by the US Food and Drug Administration (FDA). The summary search was performed for the CardioMEMS pulmonary artery pressure sensor for the entire product life cycle from January 2009 to February 2024 [3,4]. The entries related to the measurement accuracy under ‘Device Problems’ categories were obtained. Tested accuracy was reported to have been within ±10mmHg of a reference pressure measurement throughout simulated use for 10 years [5]. Normal range for dPAP is ~8mmHg to 15mmHg [6]. Results A total of 2656 entries were obtained. Following categories were identified for the reported entries: device sensing problem (N=999, 38%); incorrect measurement (N=1349, 52%); incorrect measurement, inappropriate waveform (N=279, 11%), and incorrect, inadequate, or imprecise results or readings (N=29, 1%). After excluding 56 entries for missing information, 2600 entries were included for the analysis. A total of 1676 (64%) entries reported the need for recalibration of the CardioMEMS device. A mean PAP difference (defined as the difference of the mean PAP reference value measured at the time of recalibration minus the CardioMEMS reading obtained during the reference measurement) is shown in Figure 1. Compared to the reference pressure values at the time of recalibration, 488 (29%) of CardioMEMS readings were higher and 1188 (71%) were lower. The absolute recalibration pressure difference was 14.5 ± 9.3 mmHg (mean ± standard deviation, range 0.02-59.3 mmHg). The recalibration by more than 10 mmHg was reported in 1040 (40%) entries. Conclusions Suspected measurement inaccuracy with CardioMEMS device is not uncommon and often necessitate recalibration. Almost one-half (40%) of the recalibrations required more than 10 mmHg correction. These results should inform clinicians to consider recalibration along the course of CardioMEMS monitoring if there is discordance between clinical assessment and the values from the device.Figure 1.Figure 2

The impact of green credit on the green transformation of the manufacturing industry

Abstract Manufacturing's green transformation is essential for advancing high-quality development. Green credit, which functions as a market-driven tool, offers support for achieving this transformation. Using manufacturing data from 30 provinces in China from 2005 to 2020, this research establishes a theoretical framework to analyze how green credit and marketization levels influence manufacturing’s green transformation. This research also provides an empirical analysis of how green credit influences this transition. The results are as follows: first, the growth of green credit plays a significant role in driving the green transformation of the manufacturing sector, with this finding remaining valid after a series of robustness and endogeneity tests. Second, the mechanism analysis shows that green credit supports the environmentally friendly transformation of the manufacturing industry through an improvement in R&D intensity. Third, moderation testing reveals that the process of marketization significantly strengthens the positive effect of green credit on the promotion of manufacturing's green transformation Among them are the following, the relationship between the government and the market and the development of the product market inhibit the role of green credit in support of the green transformation of the manufacturing sector. The development of factor markets, the development of non-state-owned economies, and the development of market intermediary organizations and the legal system environment exert a facilitating function, which significantly enhances the positive effect of green credit in the promotion of the green transformation. The conclusions provide fresh insights and empirical evidence for advancing manufacturing's green transformation and hold practical significance for refining green credit policies and advancing market-oriented reforms.

Reducing the replication time for structural estimations: A successful replication of “An Anatomy of International Trade” using GPU computing

AbstractEaton, Kortum, and Kramarz (2011) (EKK) discovered empirical patterns from French manufacturing firms that a baseline firm heterogeneity model could not explain. The authors proposed and estimated a model that closely matches the patterns observed in French data. This paper successfully replicates their findings using author‐provided data, re‐implementing their algorithms in Python and leveraging graphics processing unit computing to significantly boost computational speed. Applying the model to Chinese manufacturing data, despite differences in context, showed that the model explains most observed patterns well.

Pathways for non-manufacturers to drive generic drug repurposing for cancer in the U.S.

Repurposing generic drugs as new treatments for life-threatening diseases such as cancer is an exciting yet largely overlooked opportunity due to a lack of market-driven incentives. Nonprofit organizations and other non-manufacturers have been ramping up efforts to repurpose widely available generic drugs and rapidly expand affordable treatment options for patients. However, these non-manufacturers find it difficult to obtain regulatory approval in the U.S. Without a straightforward path for approval and updating drug labeling, non-manufacturers have relied on off-label use of repurposed drugs. This limits the broad clinical adoption of these drugs and patient access. In this paper, we explore the regulatory landscape for repurposing of small molecule generic drugs within the U.S. We describe case studies of repurposed drugs that have been successfully incorporated into clinical treatment guidelines for cancer without regulatory approval. To encourage greater adoption of generic drugs in clinical practice-that is, to encourage the repurposing of these drugs-we examine existing Food and Drug Administration (FDA) pathways for approval of new uses or indications for generic drugs. We show how non-manufacturers, who are generally more active in generic drug repurposing than manufacturers, could utilize existing regulatory authorities and pathways, and we describe the challenges they face. We propose an extension of the existing 505(b)(2) new drug application (NDA) approval pathway, called a "labeling-only" 505(b)(2) NDA, that would enable non-manufacturers to seek approval of new indications for well-established small molecule drugs when multiple generic products are already available. It would not require new chemistry, manufacturing, and controls (CMC) data or introducing new drug products into the marketplace. This pathway would unlock innovation broadly and enable patients to benefit from the enormous potential of low-cost generic drugs.

Study on hybrid 3D printing and milling process for customized polyether-ether-ketone components.

Polyether-ether-ketone (PEEK) has been widely applied in various fields due to its excellent mechanical properties and biocompatibility. The efficient and high-quality customized manufacturing of PEEK components are investigated in this study by the hybrid 3D printing and milling process. At first, the alternating hybrid process is selected and verified by comparing two typical hybrid process categories and conducting experiments, respectively. Second, a set of procedures are designed to automate the engineering application of the hybrid process trying to avoid the disadvantages of manual programing. Then, considering the tool length and possible interferences during the hybrid process, a model segmentation algorithm, namely, the exchange principle of avoiding interference (EPAI) is proposed. Based on the introduced EPAI and the programing language Python, the additive and subtractive hybrid manufacturing (ASHM) data processing procedure is proposed and realized by post-processing of the conventional 3D printing codes. Finally, the feasibility experiments have been conducted. The experimental results verify the hybrid manufacturing process in the fabrication of parts with complex internal features. The surface roughness Ra and dimensional error L of the parts have been reduced by 75.5% and 85.2%, respectively, while the shear strength τ has been increased by 14.1%. Compared with conventional milling process, the material consumption is reduced by 48.7%.

Prediction of composite pressure vessels’ burst strength through machine learning

In Hydrogen Fuel Cell Electric Vehicles, hydrogen is stored as compressed gas in tanks made from carbon fiber composite. Tanks are designed to withstand 2.25 times the nominal working pressure. If low variability in tank strength can be demonstrated, this margin of safety could be reduced. This requires accurate prediction of the strength of the vessel and quantification of its uncertainty. In this work, several composite pressure vessels are manufactured using filament winding, and the parameters that control the winding and curing recorded as time signals. Process parameters include fiber tension, winding speed, liner pressure, fiber volume fraction, winding time and curing pressure. The vessels are tested for burst pressure. Several configurations are defined, where each manufacturing parameter is changed. The recorded manufacturing data are fed to machine learning(ML) algorithms and trained to predict the vessel’s burst pressure. These ML algorithms include neural networks, Gaussian process regression GPR, Kernel Principal Component Analysis-Lasso (kPCA-Lasso), and an Ensemble regressor. The KPCA-Lasso and GPR models show good correlation. The Ensemble regressor yields better results by combining the KPCA-Lasso and GPR models. The study demonstrates the potential of ML algorithms in predicting the burst pressure of carbon fiber vessels from the monitored manufacturing data.

Zonal shape reconstruction for Shack-Hartmann sensors and deflectometry

Some metrological means, such as Shack-Hartmann, deflectometry sensors or fringe projection profilometry, measure the shape of an optical surface indirectly from slope measurements. Zonal shape reconstruction, a method to reconstruct shape with a high number of degrees of freedom, is used for all of these applications. It has risen in interest with the use of deflectometers for the acquisition of high resolution slope data for optical manufacturing, especially because shape reconstruction is limiting in terms of shape estimation error.Zonal reconstruction methods all rely on the choice of a data formation model, a basis on which the shape will be decomposed, and an estimator. In this paper, we first study the canonical Fried and Southwell models of the literature and analyze their limitations. We show that modeling the slope measurement by a point-wise derivative as they both do can induce a bias on the shape estimation, and that the bases on which the shape is decomposed are imposed because of this assumption.In the second part of this paper, we propose to build an unbiased model of the data formation, without constraints on the choice of the decomposition basis. We then compare these models to the canonical models of Fried and Southwell.Lastly, we perform a regularized MAP reconstruction, and compare the performance in terms of total shape error of this method to the state of the art for the Southwell and Fried models, first by simulation, then on experimental data. We demonstrate that the suggested method outperforms the canonical models in terms of total shape reconstruction error on a deflectometry measurement of the high-frequency content of a freeform mirror.

Using froude and weber numbers to represent the changes in the flow pattern from stratified to stratified-wavy or plug for wire-on-tube condenser

According to manufacturing data, a theoretical study of condensation inside a heat exchanger with wires on tubes.examined flow patterns with variable refrigerant mass flow rates. Wire-on-tube condensers are adopted with tubes, which have multiple diameters (3.25, 4.83 and 6.29 mm). In addition to the use of two refrigerants (R-134a, R-600a) working at condensing temperatures of (54.4, 45, and 35 °C). The Equal Equation Solver software (EES) was used to obtain the results for the mass velocity range from 8 to 167 kg s−1m−2. Using the non-dimensionless numbers, the model determines the refrigerant flow pattern. Weber number (We) and Froude number (Fr) were used for mass velocity in a range below 100 kg s−1 m−2, which is recommended flow inside the wire-on-tube condenser. The initial transition of the two-phase flow pattern from stratified to stratified-wavy occurred at beginning of condensation for the quality range (0.78-0.42) was Fr (2.7-2.0) and We (6.1-4.0) for R-134a, while the quality range (0.85-0.39) was Fr (5.0-2.4) and We (7.0-4) for R-600a. Second, as the quality range approached, the stratified-wavy flow was replaced by a plug at quality (0.23-0.06) were Fr (1.5-0.4) and We (3.8-2.1) for R-134a and for R-600a the quality range (0.22-0.05) were Fr (1.7-0.55) and We (3.7-2.2).

Parametric multi-level modeling of Lean Six Sigma

Lean Six Sigma is a methodology that combines Lean manufacturing and Six Sigma into a single framework for process improvement. This combination merges their tools and techniques to overcome their deficiencies while achieving bottom-line improvement. However, existing literature emphasizes general LSS implementation with very little work on modeling the interaction between Leanness and Six Sigma. This work developed a multi-level model containing leanness and Six Sigma levels to generalize LSS implementation. Three hierarchical levels—Six Sigma (level 3), leanness (level 2), and a fattening level (level 1—Sigma quality level) are defined for two models (parametrized and non-parametrized). Aggregate measures (process efficiency, asset intensity, production time, etc.) are considered at levels 2 and 3 to achieve the desired optimization goal at that level. For testing, three process measures [asset intensity (AI), planned stoppages (PS), wastes and rework (WR)] from the manufacturing data of a global brand spanning 18 months were analyzed. The maximum log-likelihood method was used to estimate the model parameters and they converged to 85.2%, 11.2%, and 8.87% for AI, PS, and WR respectively, with a strong correlation between AI and PS. A metaheuristic algorithm was extended to solve both models. The obtained process yield (0.111111 and 0.4132471), defects per million opportunities (DPMO) (12,426 and 18,046), and sigma level (3.6 and 3.74) for the parametrized and non-parametrized models respectively indicated optimal model performance and a need for the strategic use of LSS tools in improving the production output. Also, further reduction in wastes, stoppages, and downtime was observed upon tuning the parametric variables. This model provides a mathematical approach to analyzing LSS implementation and provides a basis for future adaptation to any organization.

A heuristic method for multi-objective hybrid flow shop scheduling problem with parent-child relationships and space constraints

ABSTRACT In modern ship manufacturing, a ship is divided into hundreds of blocks in the design stage, and the productivity of the block manufacturing process is vital to ship delivery. This paper investigates the production scheduling problem of ship blocks with the parent-child relationship; this relationship is characterised by the fact that sibling jobs at the same BOM layer must be processed on adjacent machines at their assembly stages. We formulate this problem as a multi-objective hybrid flow shop scheduling problem: the first objective is to minimise the total deviation from the target time of the jobs and the second one is to minimise the total processing time. To solve this problem, we propose a heuristic method based on the multi-objective genetic algorithm, in which an adjacent machine priority method is first proposed to generate feasible solutions that satisfy the space constraints. Then, we improve the individual quality in crossover and mutation of the algorithm via tabu search. We verify the performance of our algorithm by test instances generated from real manufacturing data of a shipbuilding company. Results show that the proposed method outperforms the existing algorithms when identifying the Pareto optimal solutions, especially for large-sized problems.

Mapping Per- and Polyfluoroalkyl Substance Footprint from Cosmetics and Carpets across the Continental United States.

Per- and polyfluoroalkyl substances (PFAS) released from common consumer products, such as cosmetics and carpets, are nonpoint sources of environmental contamination. However, detailed information on PFAS mass and emission rates from these products is limited. Here, we propose a methodology to develop PFAS footprint from the manufacturing and supply chain data of cosmetics and carpets. Our analysis combines geospatial and statistical assessments to understand how the production and consumption of these products contribute to existing PFAS contamination hotspots in the Continental United States (CONUS). Statewide mass estimations revealed that North Carolina and New York contribute to the major PFAS mass released from cosmetics, while Georgia and California contribute to the major PFAS mass released from carpets. The average per capita PFAS footprint from carpets and cosmetics is about 103 mg/year. Upon disposal, over 60% of the mass eventually ends up in landfills. The accumulation of PFAS stocks in landfills, primarily from carpets and to some extent from cosmetics, highlights the critical need to cease the production and use of PFAS in consumer products. Coastal counties are particularly vulnerable due to higher population and therefore higher consumption of these PFAS-tainted consumer products. Additionally, counties with densely populated areas and with preexisting contamination sources would face increased vulnerability to PFAS contamination released from various consumer products.

Virtual warehousing through digitalized inventory and on-demand manufacturing: A case study

Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially for conventional (non-digital) manufacturing. In this paper, we explore the key elements required for successful implementation of a virtual warehouse for legacy parts based on a combination of part digitalization, on-demand manufacturing, and part validation. Our proposed framework for adoption of virtual warehouse includes development of a digital inventory which includes supply chain and manufacturability data, identification, and selection of suitable parts for on-demand manufacturing, selection of on-demand manufacturing technology, fit-for-purpose validation of the parts. Our framework is exemplified through a case study, and we conclude that the building of an effective virtual warehouse requires several enablers, including availability of digital data about technical and supply chain characteristics of parts, but also a suitable part identification tool. This part identification tool needs to be flexible to include comparison with reference parts already produced by different on-demand manufacturing technologies.

Lessons on the use of real-world data in medical device research: findings from the National Evaluation System for Health Technology Test-Cases.

Aim: Although the US FDA encourages manufacturers of medical devices to submit real-world evidence (RWE) to support regulatory decisions, the ability of real-world data (RWD) to generate evidence suitable for decision making remains unclear. The 2017 Medical Device User Fee Amendments (MDUFA IV), authorized the National Evaluation System for health Technology Coordinating Center (NESTcc) to conduct pilot projects, or 'Test-Cases', to assess whether current RWD captures the information needed to answer research questions proposed by industry stakeholders. We synthesized key lessons about the challenges conducting research with RWD and the strategies used by research teams to enhance their ability to generate evidence from RWD based on 18 Test-Cases conducted between 2020 and 2022. Materials & methods: We reviewed study protocols and reports from each Test-Case team and conducted 49 semi-structured interviews with representatives of participating organizations. Interview transcripts were coded and thematically analyzed. Results: Challenges that stakeholders encountered in working with RWD included the lack of unique device identifiers, capturing key data elements and their appropriate meaning in structured data, limited reliability of diagnosis and procedure codes in structured data, extracting information from unstructured electronic health record (EHR) data, limited capture of long-term study end points, missing data and data sharing. Successful strategies included using manufacturer and supply chain data, leveraging clinical registries and registry reporting processes to collect and aggregate data, querying standardized EHR data, implementing natural language processing algorithms and using multidisciplinary research teams. Conclusion: The Test-Cases identified numerous challenges working with RWD but also opportunities to address these challenges and improve researchers' ability to use RWD to generate evidence on medical devices.

Fast and accurate modelling of twin-screw compressors: A generalised low-order approach

Twin-screw compressors (TSC) are commonly used in heat pump processes due to their robustness and flexibility. They exhibit two core properties, i.e. the swept volume and the built-in volume ratio (BVR), which heavily influence their capacity limits and off-design efficiency. This work presents a new low-order model (i.e. a polynomial model), which can accurately predict a TSC’s behaviour. The model uses the external pressure ratio and volumetric compressor inlet flow rate to calculate isentropic efficiency and compressor speed. The input parameters are normalised with a reference flow rate (calculated from the swept volume) and the BVR, respectively. This results in a generalised model of low numerical cost, which can be used for explorative studies independent of the specific machine size and BVR. A gain in computational speed by a factor of 375 is achieved compared to a semi-empiric reference model. The model displays very good predictive accuracy when used to predict the performance of machines with similar BVRs, but different sizes. A mean deviation from the manufacturer data of 4.29%, 0.88°C and 1.38% for the shaft power, the outlet temperature and the compressor speed can be observed, respectively. When there is a difference in size and BVR, the prediction accuracy is still reasonable but significantly declines for small and very large pressure ratios. Nevertheless, the proposed new approach extends the state-of-the-art by introducing a low-order model, which combines the advantages of low computational cost, high accuracy, physically correct predictions over a wide operational range and scalability to different machine capacities and BVRs. The validation for different fluids indicates a good general prediction accuracy relatively independent of the used fluid.

Part-load analysis and preliminary annual simulation of a constant inventory supercritical CO2 power plant for waste heat recovery in cement industry

The present work investigates the part-load performance of a MW-scale sCO2 power plant designed as waste heat recovery unit for an existing cement plant located in Czech Republic, in the framework of the H2020 funded project CO2OLHEAT. The study first presents the selected power plant configuration and then focuses on the evaluation of its part-load operation due to variation of flue gas mass flow rate and temperature. The range of flue gas conditions at the outlet of the upstream process is retrieved from a preliminary statistical analysis of historical trends obtained through the cement plant monitoring. The numerical model developed for this study aims at providing realistic results thanks to the adoption of turbomachinery performance maps provided by the turbomachinery manufacturer of the project. Moreover, heat exchangers have been modelled through a discretized approach which has been validated against manufacturer data, while piping inventory and pressure losses have been assessed through a preliminary sizing that considers the actual distances to be covered in the cement plant. Performance decay is estimated for the whole range of flue gas conditions, reporting the most significant power cycle parameters, and identifying the main causes of efficiency loss. The part-load analysis is carried out considering a constant CO2 inventory, in order to reduce the system complexity and capital cost and simplify plant operation. Results show that the operation entails minor variation of the compressors operative points in the whole range of operating conditions of the cement plant, avoiding the risk of anti-surge bypass activation. Moreover, the plant is able to work close to the nominal thermodynamic cycle efficiency (20.5 %–23.0 %) for most of the year and benefits from part-load operation in terms of overall performance. In the last part of the work, a preliminary techno-economic analysis of the plant is also presented to highlight the potential advantages of sCO2 technology for waste heat recovery applications. The results of the part-load performance of the plant are combined with the flue gases data obtained from the preliminary statistical analysis and the cement plant historical monitoring. An annual electricity production equal to 13′909.7 MWh is obtained, corresponding to 6560 equivalent hours and a system capacity factor of 74.9 %. The investment cost of each CO2OLHEAT plant component is estimated by means of cost correlations obtained from literature and the non-discounted payback time is computed as a function of the electricity selling price. The results show that, even considering electricity prices before 2022, the payback time of the CO2OLHEAT plant is estimated to be lower than 8 years, justifying the industrial interest in the proposed technology.

Influences of different parameters on selected properties of gears for robot-like systems.

For a drive unit for axes of robots and robot-like systems (RLS) usually a motor-gearbox arrangement is chosen due to its high-power density. The combination of a high-ratio gearbox and a high-speed electric motor ensures a very compact and efficient design of the drive train. The transmission properties primarily determine the properties of the axes and the whole robot system. Robots and RLS use various types of high-ratio precision gearboxes based on different operating principles. Due to the different operating principles, it is difficult to describe comparable properties across all different types. In addition, there are many influences on the properties which significantly determine their shapes and values. These influencing parameters are insufficiently documented and are often poorly accessible for profound comparability and further consideration. In this paper, an overview of the properties of robot gearboxes is given. Based on these properties, different robot gearboxes can be systematically evaluated and compared to one another. The properties are influenced by various design, operating or manufacturing factors such as the gearbox size, the operating torque and speed or the manufacturing process. In a further step, these influences on the most relevant properties, efficiency and stiffness, are determined and systematically evaluated. This evaluation is based on the specification data of various robot gearbox manufacturers. The properties efficiency and stiffness show a dependency on the gearbox size, the operating torque, speed as well as the ambient temperature and on the transmission ratio. The shown procedure can also be adapted to other properties.