Limited Batches Research Articles

Current State and Prospects for Development of Head Lighting for Vehicles. Part 2. Advanced Designs for Vehicle Headlights

As it has been shown in part 1 of this paper, modern head lighting, due to objective design flaws, does not always meet the requirements of existing international standards, which means a decrease in road safety. To eliminate the previously Multibeam type. These types of headlights are currently either undergoing the stage of production tests or are used in extremely limited batches of vehicles in terms of the number of produced units. According with the results of this analysis, recommendations have been formulated for the design of advanced head lighting. The main of these recommendations is the use of single high-power LEDs with total internal reflection reflectors, which will provide a sharp cut-off border with efficient use of the source light flux. To increase the resistance of the headlight to temperature rise, it is proposed to use LEDs or other economical light sources together with phosphor parts. It is shown that adaptability is a useful but optional property of vehicle headlights, since it leads to complicate the design, and this is not always justified. The advantages of the headlight, created taking into account the above recommendations, are described, the main of which are the simplification of its design, reduction in weight and improvement of the thermal parameters of the product.

Spherical powders: Control over the size and morphology of powders for additive manufacturing and enriched stable isotope nuclear targets

Metal powders are a fundamental starting point for fabricating many types of nuclear targets. Elemental powder properties can differ drastically between batches, even when using the same method. Therefore, the variation in morphology and the size of metal powders can cause variable quality and produce inconsistent results with what are otherwise proven target manufacturing techniques. Additive manufacturing has additional requirements for higher quality and more uniform feedstock. The production of spheroidized powders with uniform, reproducible properties and a narrow size distribution represents unexplored opportunities for experiments. These opportunities include experimenting with solid metals that can now flow like liquids, new options for powder handling and dispensing, and new target fabrication methods using additive manufacturing. The Stable Isotope Materials and Chemistry Group at Oak Ridge National Laboratory obtained an AMAZEMET rePowder ultrasonic metal atomization tool for creating limited batches of fully dense, free flowing, spherical powders with a narrow size distribution of extremely rare materials. Early results are presented with materials that were produced. The team explores the anticipated limits of this instrument with extremely rare materials (e.g., enriched stable isotopes) and highlights research into new fabrication techniques that provide additional options benefitting the international nuclear target community.

Professional Development of Education Administrative Service officers in Sri Lanka

This study aimed to investigate the professional development of Sri Lanka Education Administrative Officers. Although several plans and programmes are prepared for optimistic professionalism, still they face difficulties in acquiring effective and well-thought-out professional development opportunities locally and internationally. According to the research problem which was intended to investigate in this study Mixed Method was employed as the main research methodology, and a survey approach and a case study approach were used for studying this research problem. Questionnaires, document surveys, and interviews were employed to gather information from the participants in this study. The participants of this study were the SLEAS officers who represent the open batches and the limited batches of SLEAS for the last thirty years. The researcher selected probability as well as non-probability sampling techniques to select the participants for this study. In order to analyse the qualitative data, thematic analysis was used. It was revealed that currently the newly recruited SLEAS officers are provided in-service training. However, some of the officers have not undertaken pre-service training focusing on the activities of the principal position before their appointment. But such training has been provided to the newly recruited SLEAS officers, not to the principals. According to the responses of most of the participants, currently, there are no such strategies used to identify the training needs of principals. It was revealed that the foreign training opportunities provided to the SLEAS officers and principals are not sufficient, only a few training opportunities are available for officers. There is no transparency mechanism to select officers for foreign training programmes. Some officers are provided opportunities to get foreign training several times. According to the information provided by the participants during the interviewing process and questionnaire survey, the majority of them recommend improving the mechanisms, activities, selection, quality, and other related aspects of training and professional development of principals, and SLEAS officers. As they perceive the existing situation, mechanisms, organization, the quality of the training and professional development of principals and SLEAS officers need to be improved immediately.

A supervised functional Bayesian inference model with transfer-learning for performance enhancement of monitoring target batches with limited data

To increase the monitoring performance of the batch process with serious nonlinearity, uneven-length, and limited-data issues, a supervised transfer-learning based functional Bayesian inference method is developed in this study. The raw uneven-length batch data are firstly transformed into functional data by choosing appropriate orthogonal wavelet basis functions (WBFs). Using the approximation coefficients representing the latent features that are inherent to the raw data, a Gaussian process model and a Bayesian inference method are applied based on the coefficients in each source batch process and the established models are transferred to enhance modeling performance for the target process with limited batches. In the proposed functional method, the unfolding operation is not needed for preprocessing, avoiding distortion of the raw data structure. With the compact support property of WBFs, within-batch detection can be implemented effectively to recognize faults earlier. The advantages of the proposed model are verified using a numerical case and an industrial polytetrafluoroethylene process.

Dynamical intervention planning against COVID-19-like epidemics.

COVID-19 has got us to face a new situation where, for the lack of ready-to-use vaccines, it is necessary to support vaccination with complex non-pharmaceutical strategies. In this paper, we provide a novel Mixed Integer Nonlinear Programming formulation for fine-grained optimal intervention planning (i.e., at the level of the single day) against newborn epidemics like COVID-19, where a modified SIR model accounting for heterogeneous population classes, social distancing and several types of vaccines (each with its efficacy and delayed effects), allows us to plan an optimal mixed strategy (both pharmaceutical and non-pharmaceutical) that takes into account both the vaccine availability in limited batches at selected time instants and the need for second doses while keeping hospitalizations and intensive care occupancy below a threshold and requiring that new infections die out at the end of the planning horizon. In order to show the effectiveness of the proposed formulation, we analyze a case study for Italy with realistic parameters.

WASTEFUL TECHNOLOGIES MINI-PRODUCTION

Mini-production is a modern trend that is cost-effective and less costly. Mini production of beer, milk, bakery products can be organized as a separate production or at a restaurant. In modern economic conditions, the role of technologies focused on the use or processing of raw materials of various origins in restaurants is growing. This approach is due to the need to solve problems and improve the economic performance of basic production.
 This trend of doing business is followed by restaurant companies in Ukraine. The concept of creating production and trade complexes of the restaurant industry has gained great popularity among investors who invest in the food industry.
 Restaurant-format mini-breweries are a business niche that is only now beginning to develop actively in Ukraine. The biggest competitive advantage of mini-breweries over large breweries is their ability to brew beer of exclusive taste and recipes in limited batches. Home-brewed beer will add color to the restaurant and will produce a delicious and high-quality product that will evoke positive associations with their brand.
 Processing of local raw materials and production of craft products based on it is a prospect for the development of mini-enterprises in the food industry. Therefore, most manufacturers move away from classic production technologies and create unique products in terms of taste. The most common are the production of craft beer, meat products, cheeses, tinctures, oils on the principles of waste-free technology.

THE POTENTIAL USE OF ANTIBODIES FROM EGG YOLKS OF BIRD EGGS IN THE CONTEXT OF FOOD SECURITY OF THE RUSSIAN FEDERATION

Bering E. proposed the principle of passive immunization at the end of the 19th century. Today, it is still used to treat tetanus, diphtheria, botulism, rabies and poisonous animal bites (snakes, spiders and scorpions). As before, equine antibodies or their fragments are used as an antidote. But the unique properties of antibodies from the yolks of chicken eggs (IgY) make it possible to use them for a wide range of therapeutic and prophylactic purposes. IgY-antibodies are used in several countries (Canada, Germany, Japan, China) on an industrial scale to produce medical and veterinary drugs to protect humans and animals against pathogens, providing highly effective immunological protection. The Romanian Romvac Company SA is a separate company in the series of manufacturers of these drugs. This company produces IgY preparations in limited batches against many antigens and practices the production of personalized antibodies directed at pathogens of a particular patient. This approach is guaranteed to damage the pathogen, however unique it may be. The authors have analyzed many review articles on the use of IgY-technology. These antibodies are nonaddictive, non-toxic, do not interact with rheumatoid factor, complement, or Fc-fragments of immunocompetent cells, and do not cause antibody-dependent reinforcement of infection. Oral administration of specific IgY-antibodies significantly reduces the manifestations of celiac disease and pathological conditions caused by activation of pathogens in the gastrointestinal tract. Passive immunization of young farm animals with IgY-antibodies is economical and practical against many mammals, birds and aquatic animals. The great potential of this new direction can provide a rapid and cost-effective breakthrough in improving the adequate food security of the Russian Federation.

Additive manufacturing technologies of tool electrodes

The experience of using additive technology in relation tocombined methods of tool manufacturing with the impact of electromagnetic and other types of fields on the process., where there are completely or partially no traditional mechanical effects on the tool part, which must have high mechanical strength, wear resistance. It is shown that the field of use of additive technologies is much wider than it was previously accepted., and mainly covers modern flexible-structured production of such branches of mechanical engineering, where there is a constant updating of products and the technological process is based on the production of limited batches of high-tech products, as a rule, according to preliminary orders of the consumer. It is proved that the scientific foundations of the multilayer formation of parts created to date are legitimate for coating, controlled layer-by-layer changes in the performance characteristics of materials, the creation of means of cooling the hot zone of aircraft engines in aerospace engineering, where multilayer templates with specified performance characteristics can be used as a data carrier. A new (according to the authors’ patent) direction of manufacturing unprofiled electrode tools with giving them useful properties of a profile tool is considered. The ways of solving the problem of controlling the operational characteristics of products by creating parts from conjugate layers, the properties of which are formed by optimizing technological modes, are proposed. The ways of effective finishing of combined methods of surface layers of coatings with preservation of useful performance qualities of parts obtained using additive technologies are studied.

СОВРЕМЕННЫЕ ДРЕЙФУЮЩИЕ РОБОТИЗИРОВАННЫЕ УСТРОЙСТВА ДЛЯ КОНТАКТНЫХ ИЗМЕРЕНИЙ ФИЗИЧЕСКИХ ХАРАКТЕРИСТИК АРКТИЧЕСКОГО БАССЕЙНА

A description of all available automatic devices that have constructed to carry out contact measurements of ice and ocean characteristics from drifting sea ice at low air temperatures is given. All the devices under consideration work according to the programmed schedules, transmit the results of measurements to the coastal centers in real time, have GPS navigation. The devices are manufactured individually or in limited batches and are not prototypes or operating models. In addition, all these types of devices have already made successful measurements in the Arctic for a year or more. The description of the devices is given not only according to information from the specialized literature, but also on the basis of the author’s many years of personal experience in working with this observational technique in the Arctic basin.

Cross reality to enhance worker cognition in industrial assembly operations

In this paper, we discuss how Cross Reality (XR) and the Industrial Internet of Things (IIoT) can support assembly tasks in hybrid human-machine manufacturing lines. We describe a Cross Reality system, designed to improve efficiency and ergonomics in industrial environments that require manual assembly operations. Our objective is to reduce the high costs of authoring assembly manuals and to improve the process of skills transfer, in particular, in assembly tasks that include workers with disabilities. The automation of short-lived assembly tasks, i.e., manufacturing of limited batches of customized products, does not yield significant returns considering the automation effort necessary and the production time frame. In the design of our XR system, we discuss how aspects of content creation can be automated for short-lived tasks and how seamless interoperability between devices facilitates skills transfer in human-machine hybrid environments.

Incipient Fault Detection for Multiphase Batch Processes With Limited Batches

Sufficient batches are in general, required for fault detection of batch processes. However, sometimes, it is difficult and may be impractical to conduct multiple cycles and wait until enough batches are available. Without batch-wise normalization step, the nonstationary cannot be efficiently removed by time-wise normalization. The mean values of the nonstationary variables are still time varying and the interval of fault-free data is very wide in each phase. Therefore, the incipient fault, which has a small magnitude including early changing and the slow developing, may be buried by nonstationary trends resulting in low fault detection rate. To address the above issue, a two-layer fault detection method is proposed to detect the incipient fault for multiphase batch processes with limited batches. First, a concurrent variable separation strategy is proposed to distinguish nonstationary variables from stationary variables for multiple batches in each phase. Second, a two-layer fault detection model is constructed to detect the incipient fault. Cointegration analysis-based fault detection model is built to investigate the relationship between nonstationary variables, which can effectively distinguish the incipient fault from the normal trend of the nonstationary variables. Principal component analysis is adopted to describe the correlation of stationary variables. Afterward, a total fault detection model is constructed to monitor the relation between nonstationary variables and stationary variables. To illustrate the feasibility and effectiveness, the proposed algorithm is applied to two multiphase batch processes including fed-batch penicillin fermentation process and semiconductor etch process.

A Two-step Parallel Phase Partition Algorithm for Monitoring Multiphase Batch Processes with Limited Batches

For multiphase batch processes, dividing the whole batch process into different phases by time-varying underlying characteristics could simplify model complexity without much loss of precision. The conventional step-wise sequential phase partition (SSPP) algorithm divides the batch process rely on the previous partition phase, which easily results in error accumulation. Besides, it is hardly applied to batch processes with limited training batches. In this paper, a parallel phase partition algorithm with limited batches is proposed for addressing those issues. By analyzing the contextual information of each time-slice, the batch process can be roughly divided using the time-varying characteristics. Based on the division result, parallel phase partition method is conducted to avoid error accumulation and connect the division result with monitoring system. To better capture the changes of process characteristics, we post-process the division result and update the boundaries between major phases and transition patterns. After phase and transition partition, different statistical models are developed so that the online monitoring can be executed. Experiments of process monitor are designed with an injection modeling process, and experimental results illustrate the process understanding ability and online monitoring performance of our proposed method.

Latent variable based concurrent multi‐trends analysis method for monitoring batch processes with irregular and limited batches

AbstractIn practice, two problems, limited batches and irregular batch trajectories, may simultaneously exist in batch processes, making process modelling and monitoring a great challenge. However, previous work did not solve the two problems simultaneously. Motivated by such cognition, for those processes with limited modelling batches and irregular batch durations, this work proposes a LV (Latent Variable)‐based concurrent multi‐trends analysis method, which combines the advantages of multi‐set variable correlation analysis and trend analysis. First, cross set correlation analysis is implemented to reduce the variable dimensionality and extract the latent variables. Then, temporal evolution trends of latent variables are described by multiple polynomials, formulating multi‐trends models. For online monitoring, the sequential nature also provides an easy but effective way to check the operation status of a new sample. Additionally, a trends based updating strategy is proposed to accommodate normal batch‐wise variations to develop a more reliable monitoring system. The application to a typical batch process with uneven‐length and limited batches illustrates the online monitoring performance of the proposed method.

Factory Tests of Temper-Rolling Fluid Volgol-350 on the 2030 Mills at the Novolipetskii Metallurgical Combine

are presented from import substitution of Qwerl 275-2 temper-rolling fluid (Quacker Chemical B.V.) by domestic Volgol-350 temper-rolling fluid (VPO Volgokhimneft). A range of methods for evaluating the quality of temper-rolling fluids were developed in the course of testing. Lab studies and industrial testing of limited batches of Volgol-350 fluid made it possible to determine the optimum requirements for protective capacity, attainment of a clean surface on steel strip, and elimination of excessive lubricating properties. Volgol-350 temper-rolling fluid has been used in the production of cold-rolled products at the NLMK since 2006.

Phase analysis and statistical modeling with limited batches for multimode and multiphase process monitoring

For multimode batch processes, the conventional modeling methods in general require that sufficient batches should be available for every mode, which, however, cannot be guaranteed in practice. It may be impractical to conduct enough trial runs and wait until sufficient batches are available before development of monitoring models for each mode. Starting from limited batches, how to derive reliable process information and develop monitoring models has been an important question for successful online multimode batch process monitoring. To address this problem, this article proposes a phase analysis and statistical modeling strategy with limited batches. One mode which has obtained sufficient batches is chosen as the reference mode while the other modes which can only get limited batches work as alternative modes. Starting from limited batches, the proposed algorithm addresses two issues, concurrent phase partition and analysis of between-mode relative changes. First, for each alternative mode, generalized time-slices are constructed by combining several consecutive time-slices within a short time region to explore local process correlations. The time-varying characteristics are then concurrently analyzed across modes so that multiple sequential phases are identified simultaneously for all modes. Then phase-representative data units are arranged by variable-unfolding the conventional time-slices for the reference mode and the generalized time-slices for each alternative mode respectively. Between-mode statistical analysis is performed within each phase where the relative changes from the reference mode to each alternative mode are analyzed. From the between-mode perspective, different types of relative variations in each alternative mode are separated and modeled for online monitoring. Starting from limited batches, online batch process monitoring can be conducted, providing reliable fault detection performance. The proposed algorithm is illustrated with a typical multiphase batch process with multiple modes.

A Step-wise sequential phase partition algorithm with limited batches for statistical modeling and online monitoring of multiphase batch processes

For batch processes, sufficient batches are in general required for statistical modeling and process monitoring. However, sometimes, it is difficult and may be impractical to conduct multiple cycles and wait until enough batches are available. Thus, how to derive reliable process information based on limited batches has been an important question. Starting from limited modeling batches, this article proposes a phase partition and process monitoring strategy for multiphase batch processes. First, a step-wise sequential phase partition algorithm is developed with limited batches where a generalized time-slice is constructed by combining several consecutive time-slices within a short time region to analyze changes of process characteristics. Multiple phases are thus identified in sequence along time direction which are described by different phase models. The feasibility and performance of the proposed method for online process monitoring are illustrated with experimental data from a typical multiphase batch process.

Reconstruction based fault diagnosis using concurrent phase partition and analysis of relative changes for multiphase batch processes with limited fault batches

For batch processes, if sufficient fault batches are available, fault characteristics can be well understood and extracted, providing important information for fault diagnosis. However, sometimes, it is difficult and may be impractical to get sufficient batches for every fault case. Thus, how to derive reliable fault information based on limited batches has been an important question for fault diagnosis which, however, has not been well addressed yet. Starting from limited fault batches, this article proposes a fault diagnosis strategy based on reconstruction technique for multiphase batch processes. Two important modeling procedures are implemented by making full use of limited fault batches, concurrent phase partition and analysis of relative changes. First, for each fault case, a generalized time-slice is constructed by combining several consecutive time-slices within a short time region to explore local process correlations. The time-varying characteristics of normal and fault statuses are then jointly analyzed so that multiple sequential phases are identified simultaneously for all fault cases and normal case. Then, in each phase, monitoring models are developed from normal case with sufficient batches and each fault case is related with normal case for relative analysis to explore the relative changes (i.e., the fault effects). Comprehensive subspace decomposition is implemented where alarm-responsible fault deviations are extracted and used to develop fault reconstruction models which can more efficiently recover fault-free part and identify fault cause. Starting from limited batches, the proposed algorithm can efficiently distinguish different fault cases and offer reliable fault diagnosis performance. It is illustrated with a typical multiphase batch process, including one normal case and three fault cases with limited batches.

ISO/IEC 17025 Sysmex R-500 Hematology Reticulocyte Analyzer Validation

The Sysmex R-500 (R-500) Hematology Analyzer is a bench-top system appropriate for the analysis of limited batches of blood samples. The R-500 provides percentage proportional (RET%), absolute reticulocyte (RET#), and absolute red blood cell (RBC#) counts. The system was validated at the Doping Control Laboratory of Athens, according to the International Committee for Standardization in Hematology, International Standards Organization (ISO/IEC) 17025, and World Antidoping Agency (WADA) specifications. The instrument calibration was performed according to the manufacturer and validation parameters comprised linearity, precision, uncertainty (intermediate and long-term precision), comparability, effect of drift, carryover, stability, and accuracy. The linearity and the comparability studies for RET#, RET%, and RBC# were expressed in regression factors (R2) and coefficients of correlation [r(x, y)], respectively. For the precision studies, the coefficients of variation for RET#, RET%, and RBC# were 9.49%, 9.83%, and <1.5%, respectively. For the intermediate precision studies, the coefficients of variation for RET#, RET%, and RBC# were 3.1%, 3.6%, and 0.6%, respectively. Carryover was found to be negligible. Sample stability was demonstrated at both room temperature and at 4 degrees C over a 24-hour period. Comparability studies for the R-500 were performed using a Sysmex SE-9500. The total evaluation led to the conclusion that the R-500 is an accurate and precise analyzer and because of to its relatively limited size, it can be considered a portable instrument, capable to be used in sports competition and training sites, where doping control and health tests are conducted. The analytical methodology of RET% measurement by the R-500 has been incorporated into the Doping Control Laboratory of Athens' Scope of Accreditation according to the ISO/IEC 17025 and WADA specifications.

A technical review of the feasibility of producing certified reference materials for the measurement of gaseous pollutants in ambient air

Abstract Within the frame of the EU sponsored project ‘Cermatair’ (Certified Reference Materials for the Measurement of Gaseous Pollutants in Ambient Air, contract G6RD-CT-2001-00517) the feasibility of preparing and certifying reference materials for the measurement of sulphur dioxide, nitrogen dioxide, carbon monoxide and benzene in ambient air was studied. The project focused on measurements at concentration levels corresponding to limit values given in EU Ambient Air Quality Directives and covered reference materials for the reference methods specified in these Directives and for alternative methods based on diffusive sampling. State-of-the-art technologies for the production and certification of the reference materials were identified through literature surveys. Limited batches of reference materials were prepared and, wherever appropriate, tested for homogeneity. The reference materials were subsequently tested in small-scale external verifications, performed by 2–4 laboratories other than the preparation laboratory, aimed at identifying possible discrepancies between concentration values from the preparation processes and experimental values. The results of these verifications revealed possible mechanisms of certification (based on preparation or measurements). The remaining materials were subjected to a one-year stability study.

A Reliable Neural Network Model Based Optimal Control Strategy for a Batch Polymerization Reactor

One of the most important issues of empirical model based batch process optimal control is that the calculated optimal control profile can degrade very significantly when applied to the actual process because of model−plant mismatches. “Optimal on the model” can be quite different from “optimal on the process”. To address this issue, this paper presents a reliable optimal control method where the optimization objective function includes an additional term to penalize wide model prediction confidence bounds at the end point of a batch. Bootstrap aggregated neural networks are used to model a batch polymerization reactor from limited batches of process operational data. The model can predict the number-average molecular weight, weight-average molecular weight, and monomer conversion at several points during a batch from the batch recipe and reactor temperature profile. A further advantage of bootstrap aggregated neural network models is that model prediction confidence bounds can be obtained. By penalization of wide model prediction confidence bounds at the end point of a batch, the calculated optimal control profile is much more reliable in the sense that, when it is applied to the actual process, the degradation in the control performance is limited.