Application of multiphase flow CFD in the gas phase polymerization process

Gas phase polymerization in a fluidized bed reactor is a well-recognized and well-utilized process for the production of polyolefins, with more than 80% of the world’s polypropylene and polyethylene produced using this method. While most polymerization processes involve two phases (gas and solid), three-phase systems (gas, liquid and solid) are often utilized when liquid is recycled to operate the system in condensing mode to increase capacity. The application of multiphase flow computational fluid dynamics (CFD) in this important reactor system has gained popularity in the past few years. The commercial CFD code, Barracuda®, has the potential to be a useful tool for modelling fluidized bed polymerization reactors to make engineering decisions. Barracuda Virtual Reactor® was utilized in this study to provide fundamental insight into the system behaviors in three-phase fluidized bed reactors. The effects of operating conditions on the performance of the reactor were studied. The models incorporated three-dimensional geometry, non-isothermal conditions, and reaction kinetics for polymerization, evaporation and condensation. The distributor plate in the bottom head of the reactor that comprises annular disk was simulated using point source injection, and a unique method was utilized to translate the output from the bottom head to the inlet of the reactor. Barracuda® and Barracuda Virtual Reactor® are registered trademarks of CPFD Software LLC

Gasificaton Transport: A Multiphase CFD Approach & Measurements

The objective of this project was to develop predictive theories for the dispersion and mass transfer coefficients and to measure them in the turbulent fluidization regime, using existing facilities. A second objective was to use our multiphase CFD tools to suggest optimized gasifier designs consistent with aims of Future Gen. We have shown that the kinetic theory based CFD codes correctly compute: (1) Dispersion coefficients; and (2) Mass transfer coefficients. Hence, the kinetic theory based CFD codes can be used for fluidized bed reactor design without any such inputs. We have also suggested a new energy efficient method of gasifying coal and producing electricity using a molten carbonate fuel cell. The principal product of this new scheme is carbon dioxide which can be converted into useful products such as marble, as is done very slowly in nature. We believe this scheme is a lot better than the canceled FutureGen, since the carbon dioxide is safely sequestered

A malignant wheeze!

Asthma is a common disorder presenting with nonspecific features, which may mimic other conditions such as tracheal tumors. Tracheal tumors are often misdiagnosed as asthma. We report a case of a 38-year female who was being worked up for persistent wheeze that was initially attributed to acute asthma, only to be later discovered as tracheal tumor. A high index of suspicion for alternative diagnoses must be kept in mind while evaluating a patient who presents with clinical features suggestive of asthma, but fails to respond to standard therapy. The present case report emphasizes the fact that not all wheezes are asthma

Validation of Automated Data Abstraction for SCCM Discovery VIRUS COVID-19 Registry: Practical EHR Export Pathways (VIRUS-PEEP)

BACKGROUND: The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities. OBJECTIVE: This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients. MATERIALS AND METHODS: This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen\u27s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson\u27s correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00). MEASUREMENTS AND MAIN RESULTS: The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%. CONCLUSION AND RELEVANCE: Our study confirms the feasibility and validity of an automated process to gather data from the EHR

Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry: practical EHR export pathways (VIRUS-PEEP)

BackgroundThe gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.ObjectiveThis study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.Materials and methodsThis observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen’s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson’s correlation coefficient and Bland–Altman plots. The strength of agreement was defined as almost perfect (0.81–1.00), substantial (0.61–0.80), and moderate (0.41–0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00–0.30), low (0.30–0.50), moderate (0.50–0.70), high (0.70–0.90), and extremely high (0.90–1.00).Measurements and main resultsThe cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.Conclusion and relevanceOur study confirms the feasibility and validity of an automated process to gather data from the EHR

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

An approach towards a multi-scale model of the fluidized bed reactor for LLDPE production

Fluidization is a complex process describing the transformation of solid particles into a fluid-like state through suspension in a gas or liquid [1]. This complexity is further increased in reacting systems, such as the production of linear low density polyethylene (LLDPE). Gas phase polymerization in fluidized bed reactors (FBR) is a well-recognized technology for polyolefin production. FBRs in comparison to other reactors, employing slurry or solution polymerization, have added advantages in transporting solids in and out of the reactor and fast reaction occurs due to high mass and transfer rates between the gas and particles [2]. In LLDPE production particle growth is influenced by ethylene, co-monomer and condensing agents. The phenomena occurring in commercial size FBR in reacting conditions cannot fully be explained by experiments on a small, bench scale. Although such experiments provide insight into particles behavior (like flow profiles and softening) and kinetics, but fail to provide information which is fully transferrable to industrial scale. In addition, the commercial size FBRs cannot be fully modelled or experimented on due to their large and multi scale, particles dynamic behavior, and turbulent flow. The main reason for this is that a lot of particles have to be accounted for in order to get results that are representative enough of the reactor dynamics in the bigger scale. In this work, PE softening measurements and lab scale 2D fluidization experiments were conducted on different LLDPE reactor products to determine particle properties (particle size distribution), bubble properties (bubble size distribution and bubble shape) and the process conditions influencing their behavior in FBR. This experimental work is fitted into an FBR simulation module which can be used to deepen understanding of the fluidization fundamentals. The industrial scale FBR for LLDPE production is then modelled with a computational fluid dynamics (CFD) tool. This multi scale model provides information on the fluidization characteristics and thermal behavior focusing on gas and particle distributions within the reactor. Both these approaches (bench scale experimentations and the CFD models) proved useful and showed how challenges like gas distribution in the reactor, bubble types and reactor hot spots, etc. can be traced back to particle behavior caused by softening which are shown on a small scale. In the end this information assists in optimizing our industrial FBR performance

Primary pleural synovial sarcoma: a rare cause of hemorrhagic pleural effusion

Primary pleural synovial sarcoma (PPSS) is a rare malignant pleural tumor comprising < 1% of all primary lung malignancies. Primary pleural mesothelioma (PPM) has many similar features that may cause a diagnostic dilemma due to overlapping clinical and histopathological features. We present the case of a young male with recurrent hemorrhagic pleural effusion without any obvious lung mass who was diagnosed with PPSS. This rare entity must be considered with a high index of suspicion while evaluating pleural tumors