1,720,956 research outputs found
Optimal Indicator-Variable Approach for Trajectory Synchronization in Uneven-Length Multiphase Batch Processes
Full text linkPartial least-squares regression models assessing the end-point product quality in batch processes require that all of the measured variable trajectories across the historical batches have the same length. Most of the conventional and advanced methodologies for batch synchronization need some prior knowledge about the process to carry out one or more of the following activities: partitioning of the batches into phases, selection of an appropriate indicator variable that is then used to synchronize the batches, or selection of a reference batch to which all other batches are matched. We present an optimal indicator-variable approach for phase partitioning and trajectory synchronization in uneven-length multiphase batch processes. The main advantages are that partitioning into phases and selection of the most appropriate indicator variable within each phase are performed automatically rather than manually and are carried out simultaneously rather than disjointly based on a surrogate optimization framework that maximizes the performance of the product quality assessment model under development. Therefore, differently from conventional and advanced synchronization methodologies currently available, the proposed method is completely process-agnostic, which enhances applicability to complex batch processes. Also, in terms of computational times, it scales favorably with the calibration data set size. An industrial fed-batch process for the manufacturing of a specialty chemical and a simulated fed-batch process for the manufacturing of penicillin are used as test beds and demonstrate that the new indicator-variable approach has a superior performance than models built using other synchronization strategies
Backstepping methodology to troubleshoot plant-wide batch processes in data-rich industrial environments
Full text linkTroubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity
Data Analytics Can Help Reduce Energy Consumption in the Industrial Manufacturing of Specialty Chemicals
No full textBatch processes are operated following recipes that consist of a sequence of steps of given time lengths carried out in different pieces of equipment. Large variability in the length of a processing step can cause that step to become a bottleneck for the entire process, thus leading to an increase of the energy consumption per unit of product manufactured. Debottlenecking the process can therefore lead to reduction of the energy requirements. We consider the case of a batch reaction that is a key step in the industrial manufacturing of a polymer additive. The available data historians revealed that, over a period of 12 months of operation, the length of the reaction step ranged between 0.9 and 2.8 h, with an average value of 1.3 h. This acted as a limit to the performance of the overall manufacturing system, but no cause was initially identified to explain this behavior. Advanced analytics on the process data historians by means of multivariate statistical techniques revealed that over 40% of the batches had been affected by intervention of a safety interlock in the reactor, whose occurrence strongly correlated to an increase of the batch length. Reconfiguration of the interlock system resulted in a reduction of both average batch length and batch length variability. Namely, over the 6-month assessment that followed this study, a 29% reduction in the average batch length for the reactor under investigation was observed, which resulted in an 8% reduction of the overall process cycle duration, thus entailing significant energy savings. Furthermore, an 11% reduction on nitrogen consumption was achieved
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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