Smoking flares represent visual evidence of atmospheric emissions from industrial plants, so smoke suppression has become an important issue of public concern. Current environmental regulations limit the amount of time a flare can smoke. Steam-assisted and air-assisted flares are designed to provide smokeless combustion by adding steam or air into the combustion zone, which promote turbulence for mixing and educt more air into the flame. In addition, the injection of steam also presents beneficial chemistry interactions with carbon particles. On the other hand, experimental studies have shown that an excess of steam or air can dramatically reduce the flare efficiency, leading to large emissions of highly reactive volatile organic compounds. This investigation is intended to improve understanding of the thermochemistry effects of steam and air addition on the combustion efficiency of industrial flares. For this purpose, we have utilized Fluent 6.2 to predict the combustion efficiency for different steam/fuel and air/fuel ratios on a set of turbulent non-premixed flames. On the basis of this analysis, mitigation strategies to reduce hydrocarbon emissions for flares can be developed in the future.

BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status of patients who are critically ill. With the advent of increased computational power and the ability to store and rapidly process big data sets in recent years, these physiologic data show promise in identifying specific outcomes and/or events during patients' ICU hospitalization. METHODS: We introduced a methodology designed to automatically extract information from continuous-in-time vital sign data collected from bedside monitors to predict if a patient will experience a prolonged stay (length of stay) on mechanical ventilation, defined as >4 d, in a pediatric ICU. RESULTS: Continuous-in-time vital signs information and clinical history data were retrospectively collected for 284 ICU subjects from their first 24 h on mechanical ventilation from a medical-surgical pediatric ICU at Boston Children's Hospital. Multiple machine learning models were trained on multiple subsets of these subjects to predict the likelihood that each of these subjects would experience a long stay. We evaluated the predictive power of our models strictly on unseen hold-out validation sets of subjects. Our methodology achieved model performance of >83% (area under the curve) by using only vital sign information as input, and performances of 90% (area under the curve) by combining vital sign information with subjects' static clinical data readily available in electronic health records. We implemented this approach on 300 independently trained experiments with different choices of training and hold-out validation sets to ensure the consistency and robustness of our results in our study sample. The predictive power of our approach outperformed recent efforts that used deep learning to predict a similar task. CONCLUSIONS: Our proposed workflow may prove useful in the design of scalable approaches for real-time predictive systems in ICU environments, exploiting real-time vital sign information from bedside monitors. (ClinicalTrials.gov registration NCT02184208.

No abstract

No abstract