Managing Drilling Wastes: Detoxification of Two Formaldehyde-Releasing Biocides

Summary An increased awareness of the environmental impact and operational costs associated with freshwater usage and wastewater disposal in energy production has shifted industry interest toward replacing freshwater sources with lower-quality or recycled water in oilfield applications, and has highlighted the importance of addressing toxicity as part of a successful waste-management plan. Poor-quality and recycled waters often contain high concentrations of bacterial assemblages, which can cause operational challenges such as corrosion, slime formation, and souring. Microbial-control agents, such as biocides, are subsequently necessary to manage bacteriological problems. However, these chemicals are highly reactive and can react indiscriminately with biological targets, making their toxicity both a performance metric and an ecological, human-health, and disposal concern. The luminescent-bacteria toxicity test presented in this work, for instance, is a key regulatory parameter in the pumpoff and landspray disposal of drilling fluids in Alberta, Canada. Considering the necessary toxicity of biocides, controlled detoxification following use is a pertinent factor in responsible hazard management. Formaldehyde-releasing agents are the most widely used category of microbial-control additives that slowly and continuously release small amounts of formaldehyde, a toxic environmental pollutant and known human carcinogen. This research evaluated the acute (short-term) aquatic toxicity of two liquid formaldehyde-releasing biocides, identified necessary parameters for their detoxification, measured the resulting change in their toxicity over time, and used regulatory requirements for toxicity testing set by the Alberta Energy Regulator (AER) for drilling-waste management to evaluate the practical relevance of this detoxification to waste-management practices. The additives investigated were a tetrakis(hydroxymethyl)phosphonium sulfate (THPS)-based product, and a 1,3-dimethylol-5,5-dimethylhydantoin (DMDMH)-based product. Laboratory results suggest that the THPS-based additive was more toxic than the DMDMH-based additive on a percent volume basis, and pH was an important factor in THPS toxicity. Aeration alone decreased the toxicity of DMDMH over the course of the experiment, while a combination of aeration and pH increase were necessary to decrease the toxicity of THPS over the same time period. This work presents a proof of concept for a relatively simple and cost-effective detoxification of the evaluated additives, highlights the key parameters for this process, and uses toxicity-threshold levels referenced by the AER drilling-waste-management directive to evaluate their application in waste-assessment practices.</jats:p

Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis

Background/Objectives: Transcatheter aortic valve replacement (TAVR) has been introduced as an optimal treatment for patients with severe aortic stenosis, offering a minimally invasive alternative to surgical aortic valve replacement. Predicting these outcomes following TAVR is crucial. Artificial intelligence (AI) has emerged as a promising tool for improving post-TAVR outcome prediction. In this systematic review and meta-analysis, we aim to summarize the current evidence on utilizing AI in predicting post-TAVR outcomes. Methods: A comprehensive search was conducted to evaluate the studies focused on TAVR that applied AI methods for risk stratification. We assessed various ML algorithms, including random forests, neural networks, extreme gradient boosting, and support vector machines. Model performance metrics&mdash;recall, area under the curve (AUC), and accuracy&mdash;were collected with 95% confidence intervals (CIs). A random-effects meta-analysis was conducted to pool effect estimates. Results: We included 43 studies evaluating 366,269 patients (mean age 80 &plusmn; 8.25; 52.9% men) following TAVR. Meta-analyses for AI model performances demonstrated the following results: all-cause mortality (AUC = 0.78 (0.74&ndash;0.82), accuracy = 0.81 (0.69&ndash;0.89), and recall = 0.90 (0.70&ndash;0.97); permanent pacemaker implantation or new left bundle branch block (AUC = 0.75 (0.68&ndash;0.82), accuracy = 0.73 (0.59&ndash;0.84), and recall = 0.87 (0.50&ndash;0.98)); valve-related dysfunction (AUC = 0.73 (0.62&ndash;0.84), accuracy = 0.79 (0.57&ndash;0.91), and recall = 0.54 (0.26&ndash;0.80)); and major adverse cardiovascular events (AUC = 0.79 (0.67&ndash;0.92)). Subgroup analyses based on the model development approaches indicated that models incorporating baseline clinical data, imaging, and biomarker information enhanced predictive performance. Conclusions: AI-based risk prediction for TAVR complications has demonstrated promising performance. However, it is necessary to evaluate the efficiency of the aforementioned models in external validation datasets