Coal characterization for ECBM recovery: Gas sorption under dry and humid conditions, and its effect on displacement dynamics

AbstractEnhanced Coal Bed Methane (ECBM) recovery is a technique under investigation as a possible approach to the geological storage of CO2 in a carbon dioxide capture and storage (CCS) system. This technology allows enhancing the recovery of coal bed methane by injecting CO2 in the coal seam at supercritical conditions. Through an in situ sorption/desorption process the displaced methane is produced and the adsorbed CO2 is permanently stored. In the case of coal, the uptake of CO2, CH4 and N2 is a combination of adsorption on its surface and penetration (absorption) into its solid matrix, the latter resulting in coal’s swelling. These two processes act simultaneously, making the coal a challenging material to be studied, in particular with respect to the understanding of the fundamental aspects of gas adsorption. High pressure sorption data of CO2, CH4 and N2 on a coal sample from Australia are presented; the interpretation of the experimental data takes into account the dual nature of the sorption process and a Langmuir-like model is applied to the sorption data, by fitting the isotherm parameters to the experimental values. The results confirm that this equation is a valuable option to describe gas sorption on coal. Moreover, a one-dimensional mathematical model previously derived is used to perform numerical simulations on the performance of ECBM recovery in coal beds. Important insights are obtained regarding the gas flow dynamics during displacement and the effects of gas sorption on the ECBM operation

Neurophysiological and BOLD signal uncoupling of giant somatosensory evoked potentials in progressive myoclonic epilepsy: a case-series study

In progressive myoclonic epilepsy (PME), a rare epileptic syndrome caused by a variety of genetic disorders, the combination of peripheral stimulation and functional magnetic resonance imaging (fMRI) can shed light on the mechanisms underlying cortical dysfunction. The aim of the study is to investigate sensorimotor network modifications in PME by assessing the relationship between neurophysiological findings and blood oxygen level dependent (BOLD) activation. Somatosensory-evoked potential (SSEP) obtained briefly before fMRI and BOLD activation during median-nerve electrical stimulation were recorded in four subjects with typical PME phenotype and compared with normative data. Giant scalp SSEPs with enlarger N20-P25 complex compared to normal data (mean amplitude of 26.2\u2009\ub1\u20098.2\u2009\u3bcV after right stimulation and 27.9\u2009\ub1\u20093.7\u2009\u3bcV after left stimulation) were detected. Statistical group analysis showed a reduced BOLD activation in response to median nerve stimulation in PMEs compared to controls over the sensorimotor (SM) areas and an increased response over subcortical regions (p\u2009\u20092.3, corrected). PMEs show dissociation between neurophysiological and BOLD findings of SSEPs (giant SSEP with reduced BOLD activation over SM). A direct pathway connecting a highly restricted area of the somatosensory cortex with the thalamus can be hypothesized to support the higher excitability of these areas

Combined Modality Treatment Including Methotrexate-Based Chemotherapy For Primary CENTRAL Nervous System Lymphoma: A Single Institution Experience

Chemotherapy including high-dose methotrexate (HD-MTX), with or without radiotherapy, is standard treatment for primary central nervous system lymphoma (PCNSL). It remains controversial whether addition of other drugs will add to therapeutic efficacy. We report here on 41 patients with PCNSL treated using a combined treatment modality, including HD-MTX (3.5 g/m2 for 2 cycles) prior to whole brain radiotherapy (WBRT). In 22 patients, the chemotherapy was intensified by adding high-dose cytosine arabinoside (HD-AraC) (2g/m2 for 4 doses for 2 cycles). Complete remission at the end of the combined treatment was obtained in 23 of 34 assessable patients (67%), and the predicted 5-year overall and disease-free survival rates were 24% and 46%, respectively, without differences between treatment groups. The addition of HD-AraC was complicated by severe infections in 17/22 (77%) patients, resulting in 3 toxic deaths. Our study indicates that addition of HD-AraC may not improve clinical outcome in PCNSL, while it increases toxicity. More targeted and less toxic therapies are warranted

Deep learning for volatility forecasting in asset management

Predicting volatility is a critical activity for taking risk- adjusted decisions in asset trading and allocation. In order to provide effective decision-making support, in this paper we investigate the profitability of a deep Long Short-Term Memory (LSTM) Neural Network for forecasting daily stock market volatility using a panel of 28 assets representative of the Dow Jones Industrial Average index combined with the market factor proxied by the SPY and, separately, a panel of 92 assets belonging to the NASDAQ 100 index. The Dow Jones plus SPY data are from January 2002 to August 2008, while the NASDAQ 100 is from December 2012 to November 2017. If, on the one hand, we expect that this evolutionary behavior can be effectively captured adaptively through the use of Artificial Intelligence (AI) flexible methods, on the other, in this setting, standard parametric approaches could fail to provide optimal predictions. We compared the volatility forecasts generated by the LSTM approach to those obtained through use of widely recognized benchmarks models in this field, in particular, univariate parametric models such as the Realized Generalized Autoregressive Conditionally Heteroskedastic (R-GARCH) and the Glosten–Jagannathan–Runkle Multiplicative Error Models (GJR-MEM). The results demonstrate the superiority of the LSTM over the widely popular R-GARCH and GJR-MEM univariate parametric methods, when forecasting in condition of high volatility, while still producing comparable predictions for more tranquil periods.publishedVersionPeer reviewe

Prognostic role of BNP in children undergoing surgery for congenital heart disease: analysis of prediction models incorporating standard risk factors.

BACKGROUND: The routine use of brain natriuretic peptide (BNP) in pediatric cardiac surgery remains controversial. Our aim was to test whether BNP adds information to predict risk in pediatric cardiac surgery. METHODS: In all, 587 children undergoing cardiac surgery (median age 6.3 months; 1.2-35.9 months) were prospectively enrolled at a single institution. BNP was measured pre-operatively, on every post-operative day in the intensive care unit, and before discharge. The primary outcome was major complications and length ventilator stay \u3e15 days. A first risk prediction model was fitted using Cox proportional hazards model with age, body surface area and Aristotle score as continuous predictors. A second model was built adding cardiopulmonary bypass time and arterial lactate at the end of operation to the first model. Then, peak post-operative log-BNP was added to both models. Analysis to test discrimination, calibration, and reclassification were performed. RESULTS: BNP increased after surgery (p CONCLUSIONS: Our data indicates that BNP may improve the risk prediction in pediatric cardiac surgery, supporting its routine use in this setting

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Coal characterization for ECBM recovery: Gas sorption under dry and humid conditions, and its effect on displacement dynamics

Enhanced Coal Bed Methane (ECBM) recovery is a technique under investigation as a possible approach to the geological storage of CO2 in a carbon dioxide capture and storage (CCS) system. This technology allows enhancing the recovery of coal bed methane by injecting CO2 in the coal seam at supercritical conditions. Through an in situ sorption/desorption process the displaced methane is produced and the adsorbed CO2 is permanently stored. In the case of coal, the uptake of CO2, CH4 and N2 is a combination of adsorption on its surface and penetration (absorption) into its solid matrix, the latter resulting in coal’s swelling. These two processes act simultaneously, making the coal a challenging material to be studied, in particular with respect to the understanding of the fundamental aspects of gas adsorption. High pressure sorption data of CO2, CH4 and N2 on a coal sample from Australia are presented; the interpretation of the experimental data takes into account the dual nature of the sorption process and a Langmuir-like model is applied to the sorption data, by fitting the isotherm parameters to the experimental values. The results confirm that this equation is a valuable option to describe gas sorption on coal. Moreover, a one-dimensional mathematical model previously derived is used to perform numerical simulations on the performance of ECBM recovery in coal beds. Important insights are obtained regarding the gas flow dynamics during displacement and the effects of gas sorption on the ECBM operation.ISSN:1876-610

CO2 storage through ECBM recovery: An experimental and modeling study

The permeability of the coal seam is the main petrophysical property controlling the performance of the ECBM operation, since it affects both the CO2 injection and CH4 recovery. In the present paper, coal swelling of intact coal samples is studied both under unconstrained and constrained conditions. Unconstrained swelling experiments are performed in a view cell under a static high pressure gas atmosphere, whereas gas injection experiments are carried out in a flow cell, where the sample is subjected to a given hydrostatic confinement. Both experiments are performed by using different gases, namely He, CO2, CH4 and N2, and under typical coal seam conditions, i.e. at high pressure and at 45 ∘C. The results of the unconstrained coal sample showed that swelling increases monotonically with pressure up to a few percents for adsorbing gases, with CO2 swelling coal more than CH4 that swells more than N2, whereas for helium, a non-adsorbing gas, volume changes are negligible. The results of the flow experiments were successfully described using a mathematical model consisting of mass balances accounting for gas flow and adsorption, and mechanical constitutive equations for the description of porosity and permeability changes during injection. Results showed increase in permeability with decreasing effective pressure on the sample. Moreover, when CO2 is used a permeability reduction was observed compared to Helium, which can be explained by taking into account the effects of swelling on the flow dynamics.ISSN:1876-610