A dynamic alarm management strategy for chemical process transitions

BACKGROUND Capnography is commonly used for nurse-administered procedural sedation. Deciphering which capnography waveform abnormalities deserve intervention (and therefore alarms to signal the event to clinicians) from those that do not is an essential step towards successfully implementating this technology into practice. It is possible that capnography alarm management may be improved by using machine learning to create a ‘smart alarm’ that can alert clinicians for apneic events that are predicted to be prolonged. OBJECTIVE To determine the accuracy of machine learning models for predicting, at the 15-second time point, if apnea will be prolonged (defined as apnea that persisted for 30 seconds or more). METHODS A secondary analysis of an observational study was conducted. We selected several candidate models to evaluate, including a random forest model, generalized linear model (logistic regression), lasso regression, ridge regression and the XGBoost model. Out of sample accuracy of the models was calculated using 10-fold cross-validation. The net benefit decision analytic measure was used to assist with deciding whether using the models in practice would lead to better outcomes on average than the default capnography alarm management strategies currently in place. The default strategies are: 1) the aggressive approach, which involves triggering an alarm after brief periods of apnea (typically 15 seconds); and 2) the conservative approach, which involves triggering an alarm for only prolonged periods of apnea (typically 30 seconds). RESULTS A total of 384 apneas longer than 15 seconds were observed in 61 of the 102 patients who participated in the observational study. Nearly half of the apneas (n=180) were prolonged. The random forest had the best discrimination (AUROC 0.66) and calibration. Net benefit associated with the random forest model exceeded the aggressive alarm management strategy but was lower than the conservative alarm management strategy. CONCLUSIONS Decision curve analysis indicated that using a random forest model would lead to a better outcome for capnography alarm management compared to an aggressive strategy where alarms are triggered after 15 seconds of apnea. The model would not be superior to the conservative strategy, where alarms are only triggered after 30 seconds. CLINICALTRIAL n/a

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OPTIMIZATION AND VALIDATION OF A CHEMICAL PROCESS FOR URANIUM, MERCURY AND CESIUM LEACHING FROM CEMENTED RADIOACTIVE WASTES

AECL Nuclear Review ◽

10.12943/cnr.2015.00050 ◽

2015 ◽

Vol 4 (2) ◽

pp. 131-139

Author(s):

Nicolas Reynier ◽

Rolando Lastra ◽

Cheryl Laviolette ◽

Nabil Bouzoubaâ ◽

Mark Chapman

Keyword(s):

Sulfuric Acid ◽

Chemical Process ◽

Management Strategy ◽

Fission Products ◽

Concentrated Sulfuric Acid ◽

Direct Leaching ◽

The Cost ◽

Optimized Conditions ◽

Term Management

Canadian Nuclear Laboratories (CNL) is developing a treatment and long-term management strategy for a legacy cemented radioactive waste that contains uranium, mercury, and fission products. Extracting the uranium would be advantageous for decreasing the waste classification and reducing the cost of long-term management. The chemical leachability of 3 key elements (U, Hg, and Cs) from a surrogate cemented waste (SCW) was studied with several lixiviants. The results showed that the most promising approach to leach and recover U, Hg, and Cs is the direct leaching of the SCW with H2SO4 in strong saline media. Operating parameters such as particle size, temperature, pulp density, leaching time, acid and salt concentrations, number of leaching/washing steps, etc. were optimized to improve key elements solubilization. Sulfuric leaching in saline media of a SCW (U5) containing 1182 ppm of U, 1598 ppm of Hg, and 7.9 ppm of Cs in the optimized conditions allows key elements solubilisation of 98.5 ± 0.4%, 96.6 ± 0.1%, and 93.8 ± 1.1% of U, Hg, and Cs, respectively. This solubilization process was then applied in triplicate to 7 other SCWs prepared with different cements, liquid ratios, and at different aging times and temperatures. Concentrated sulfuric acid is added to the slurry until the pH is about 2, which causes the complete degradation of cement and the formation of CaSO4. Sulfuric acid is particularly useful because it produces a leachate that is amenable to conventional ion exchange technology for the separation and recovery of uranium.

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