Application of foam assisted water-alternating-gas flooding and quantification of resistivity and water saturation by experiment and simulation to determine foam propagation in sandstone.

Foam flooding by Foam Assisted Water-Alternating-Gas (FAWAG) is an important enhanced oil recovery method that has proven successful in experimental and pilot studies. The present study is carried out to monitor the movement of the foam front once injected into the porous medium. This study aims to investigate applications of resistivity waves to monitor foam propagation in a sandstone formation. In the present lab-scale experiments and simulations, resistivity measurements were carried out to monitor the progression of foam in a sand pack, and the relationships between foam injection time and resistivity, as well as brine saturation, were studied. The brine saturation from foam simulation using CMG STAR is exported to COMSOL and calculated true formation resistivity. A diagram was produced summarizing the progression of foam through the sand pack in the function of time, which enabled us to establish how foam progressed through a porous medium. A surfactant and brine mixture was injected into the sand pack, followed by nitrogen gas to generate the foam in situ. As foam progressed through the sand pack, resistance measurements were taken in three zones of the sand pack. The resistance was then converted into resistivity and finally into brine saturation. As foam travels through the sand pack, it is predicted to displace the brine initially in place. This gradually increases each zone's resistivity (decreases the brine saturation) by displacing the brine. Also, an increase in the surfactant concentration results in higher resistivity. Finally, a comparison of three different surfactant concentrations was evaluated in terms of resistivity results, water saturation, and foam propagation monitoring to obtain the optimum surfactant concentration involved in foam flooding.

Artificial Intelligence Enhanced Reliability Assessment Methodology With Small Samples.

Due to the high price of the product and the limitation of laboratory conditions, reliability tests often get a small number of failed samples. If the data are not handled properly, the reliability evaluation results will incur grave errors. In order to solve this problem, this work proposes an artificial intelligence (AI) enhanced reliability assessment methodology by combining Bayesian neural networks (BNNs) and differential evolution (DE) algorithms. First, a single hidden layer BNN model is constructed by fusing small samples and prior information to obtain the 95% confidence interval (CI) of the posterior distribution. Then, the DE algorithm is used to iteratively generate optimal virtual samples based on the 95% CI and small samples trends. A reliability assessment model is reconstructed based on double hidden layers BNN model by combining virtual samples and test samples in the last stage. In order to verify the effectiveness of the proposed method, an accelerated life test (ALT) of the subsurface electronic control unit (S-ECU) was carried out. The verification test results show that the proposed method can accurately evaluate the reliability life of a product. And compared with the two existing methods, the results show that this method can effectively improve the accuracy of the reliability assessment of a test product.

Risk assessment of marine oil spills using dynamic Bayesian network analyses.

Oil spills are serious threats to the marine ecosystem. Especially when an oil spill is faced with extreme weather, the consequences might be more severe. Until now, no such researches focus on the risk of these extreme scenarios. This paper proposes a novel dynamic assessment method to quantify the risk of oil spills in extreme winds based on dynamic Bayesian networks (DBNs). The physical models of advection, spreading, evaporation, and dispersion are transformed into DBNs, and the vulnerability model is established according to coastline types and socio-economic resources. By integrating all the sub-models, the overall DBN to quantify the dynamic risk of oil spills occurring in extreme winds is obtained. The proposed method is demonstrated by the Laizhou Bay. The developed model is validated by a three-axiom-based approach. Temporal and spatial dynamics of risk caused by oil spills in potential locations could be calculated. Based on the developed DBN, the risk of the Laizhou Bay coast caused by oil spills in annual extreme wind speeds corresponding to different mean recurrence intervals is studied. In addition, the effects of the occurrence time of annual extreme winds are also researched.

A complex-valued slow independent component analysis based incipient fault detection and diagnosis method with applications to wastewater treatment processes.

Multivariate statistical process monitoring are the essential approaches to achieve better prognostics and health management (PHM) of process industries. However, incipient faults and complex behaviors (such as nonlinearity and dynamics) always render the traditional multivariate statistical process monitoring approaches inadequate. Thus, a complex-valued slow independent component analysis (CSICA) is proposed, which is able to extract optimized features from a complex-valued matrix containing both of raw data and their changing rates by resorting to a complex-valued independent component analysis operation and a batch of phase shifts. These features, named slow independent components (SICs), not only guarantee the statistical independence but also capture slowly-changing patterns, thus refining both dynamic and non-Gaussian information mostly related with incipient faults. The proposed algorithm together with novel statistics, Is2, If2 and SPE, as well as their control limits can sequentially detect incipient faults effectively. Then, together with the novel differential mapping reconstructed contribution plot (DM-RCP) and Granger causality analysis, the proposed method can accurately locate rooting causes of incipient faults. Finally, the proposed framework of process monitoring is validated through two data sets from a simulation platform and an oxidation-ditch-based wastewater treatment plant, respectively. The results demonstrate that the proposed method can achieve more accurate and efficient performances than conventional methods.

circ_0044516 functions in the progression of gastric cancer by modulating MicroRNA-149-5p/HuR axis.

Circular RNAs (circRNAs) have emerged as a multifunctional class of RNAs, while there is limited knowledge on their functions in the development of cancers. Herein, we performed the current study to probe into the regulatory mechanism of circ_0044516 in malignant behaviors of gastric cancer (GC) cells with the involvement of microRNA (miR)-149-5p/human antigen R (HuR) axis. Firstly, the expression levels of circ_0044516 in GC cell lines and normal gastric mucosal epithelial cells were determined by qRT-PCR, and GC cell lines with the highest expression of circ_0044516 were screened for further cell experiments. Subsequently, the biological functions of silenced circ_0044516 in GC were identified by CCK-8, colony formation, and transwell assays. Xenograft mouse models were established for in vivo verification. Furthermore, luciferase reporter, RIP, RNA pull-down assay and rescue experiments were performed to explore the sponge regulatory mechanism of circ_0044516. circ_0044516 was suggested to be highly expressed in GC cell lines, and circ_0044516 could promote GC cell proliferation, migration and invasion, as well as in vivo tumor growth. In addition, silenced circ-0044516 reversed the promotive roles in cell viability caused by overexpressed HuR. Furthermore, circ_0044516 mainly localized in the cytoplasm, which may act as a miR-149-5p sponge to modulate HuR expression, thereby playing an essential role in GC development. This study suggests that circ_0044516 may promote HuR expression through sponging miR-149-5p, thereby playing a part in GC progression.

Reliability evaluation of auxiliary feedwater system by mapping GO-FLOW models into Bayesian networks.

Bayesian network (BN) is a widely used formalism for representing uncertainty in probabilistic systems and it has become a popular tool in reliability engineering. The GO-FLOW method is a success-oriented system analysis technique and capable of evaluating system reliability and risk. To overcome the limitations of GO-FLOW method and add new method for BN model development, this paper presents a novel approach on constructing a BN from GO-FLOW model. GO-FLOW model involves with several discrete time points and some signals change at different time points. But it is a static system at one time point, which can be described with BN. Therefore, the developed BN with the proposed method in this paper is equivalent to GO-FLOW model at one time point. The equivalent BNs of the fourteen basic operators in the GO-FLOW methodology are developed. Then, the existing GO-FLOW models can be mapped into equivalent BNs on basis of the developed BNs of operators. A case of auxiliary feedwater system of a pressurized water reactor is used to illustrate the method. The results demonstrate that the GO-FLOW chart can be successfully mapped into equivalent BNs.

Real-time reliability evaluation methodology based on dynamic Bayesian networks: A case study of a subsea pipe ram BOP system.

A novel real-time reliability evaluation methodology is proposed by combining root cause diagnosis phase based on Bayesian networks (BNs) and reliability evaluation phase based on dynamic BNs (DBNs). The root cause diagnosis phase exactly locates the root cause of a complex mechatronic system failure in real time to increase diagnostic coverage and is performed through backward analysis of BNs. The reliability evaluation phase calculates the real-time reliability of the entire system by forward inference of DBNs. The application of the proposed methodology is demonstrated using a case of a subsea pipe ram blowout preventer system. The value and the variation trend of real-time system reliability when the faults of components occur are studied; the importance degree sequence of components at different times is also determined using mutual information and belief variance.

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

Application of Petri nets to performance evaluation of subsea blowout preventer system.

This paper presents an application of deterministic and stochastic Petri nets (DSPN) to evaluate the performance of subsea blowout preventer (BOP) system. The overall subsea BOP system is comprised of five mechanical subsystems and five electrical subsystems, which can be viewed as a series-parallel system. In regard to common cause failures, TimeNET 4.0 toolkit is utilized to develop and analyze the DSPN models. Availability and reliability of the subsea BOP system and its subsystems are obtained. Besides, the effects of failure rate and repair time of each component on system performance are researched.

Influence of electrical resistivity and machining parameters on electrical discharge machining performance of engineering ceramics.

Engineering ceramics have been widely used in modern industry for their excellent physical and mechanical properties, and they are difficult to machine owing to their high hardness and brittleness. Electrical discharge machining (EDM) is the appropriate process for machining engineering ceramics provided they are electrically conducting. However, the electrical resistivity of the popular engineering ceramics is higher, and there has been no research on the relationship between the EDM parameters and the electrical resistivity of the engineering ceramics. This paper investigates the effects of the electrical resistivity and EDM parameters such as tool polarity, pulse interval, and electrode material, on the ZnO/Al2O3 ceramic's EDM performance, in terms of the material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR). The results show that the electrical resistivity and the EDM parameters have the great influence on the EDM performance. The ZnO/Al2O3 ceramic with the electrical resistivity up to 3410 Ω·cm can be effectively machined by EDM with the copper electrode, the negative tool polarity, and the shorter pulse interval. Under most machining conditions, the MRR increases, and the SR decreases with the decrease of electrical resistivity. Moreover, the tool polarity, and pulse interval affect the EWR, respectively, and the electrical resistivity and electrode material have a combined effect on the EWR. Furthermore, the EDM performance of ZnO/Al2O3 ceramic with the electrical resistivity higher than 687 Ω·cm is obviously different from that with the electrical resistivity lower than 687 Ω·cm, when the electrode material changes. The microstructure character analysis of the machined ZnO/Al2O3 ceramic surface shows that the ZnO/Al2O3 ceramic is removed by melting, evaporation and thermal spalling, and the material from the working fluid and the graphite electrode can transfer to the workpiece surface during electrical discharge machining ZnO/Al2O3 ceramic.

Reliability analysis of the electrical control system of subsea blowout preventers using Markov models.

Reliability analysis of the electrical control system of a subsea blowout preventer (BOP) stack is carried out based on Markov method. For the subsea BOP electrical control system used in the current work, the 3-2-1-0 and 3-2-0 input voting schemes are available. The effects of the voting schemes on system performance are evaluated based on Markov models. In addition, the effects of failure rates of the modules and repair time on system reliability indices are also investigated.

Investigation of the charging characteristics of micrometer sized droplets based on parallel plate capacitor model.

The charging characteristics of micrometer sized aqueous droplets have attracted more and more attentions due to the development of the microfluidics technology since the electrophoretic motion of a charged droplet can be used as the droplet actuation method. This work proposed a novel method of investigating the charging characteristics of micrometer sized aqueous droplets based on parallel plate capacitor model. With this method, the effects of the electric field strength, electrolyte concentration, and ion species on the charging characteristics of the aqueous droplets was investigated. Experimental results showed that the charging characteristics of micrometer sized droplets can be investigated by this method.

Application of Bayesian networks in quantitative risk assessment of subsea blowout preventer operations.

This article proposes a methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry. The method involves translating a flow chart of operations into the Bayesian network directly. The proposed methodology consists of five steps. First, the flow chart is translated into a Bayesian network. Second, the influencing factors of the network nodes are classified. Third, the Bayesian network for each factor is established. Fourth, the entire Bayesian network model is established. Lastly, the Bayesian network model is analyzed. Subsequently, five categories of influencing factors, namely, human, hardware, software, mechanical, and hydraulic, are modeled and then added to the main Bayesian network. The methodology is demonstrated through the evaluation of a case study that shows the probability of failure on demand in closing subsea ram blowout preventer operations. The results show that mechanical and hydraulic factors have the most important effects on operation safety. Software and hardware factors have almost no influence, whereas human factors are in between. The results of the sensitivity analysis agree with the findings of the quantitative analysis. The three-axiom-based analysis partially validates the correctness and rationality of the proposed Bayesian network model.

Nesfatin-1 in newborns: relationship with endocrine and metabolic and anthropometric measures.

OBJECTIVE: To explore the relationship between nesfatin-1 and growth and development in newborns. METHODS: Blood samples for nesfatin-1, ghrelin, insulinlike growth factor-1 (IGF-1), insulin and glucose were obtained from preterm (n = 53) and term infants (n = 60), including appropriate for gestational age (AGA) (n = 32) and small for gestational age (SGA) infants (n = 28). The relationship between nesfatin-1 and other metabolic hormones or anthropometric parameters was evaluated. RESULTS: The concentrations of nesfatin-1, ghrelin and insulin and the homeostasis model assessment-insulin resistance index (HOMA-IR) were higher in SGA than AGA infants (p = 0.0358, 0.0163, 0.0001 and 0.0051, respectively), but IGF-1 levels and homeostasis model assessment-insulin sensitivity index (HOMA-ISI) were lower (p = 0.033 and 0.0001, respectively). Nesfatin-1 levels in SGA infants were higher on postnatal day 0 (PNDO) than in AGA infants (p = 0.0358) and lower on PND7 (p = 0.0002) and PND28 (p = 0.0488). A negative correlation showed between nesfatin-1 and oral calorie intake (r = -0.446; p = 0.017) and HOMA-ISI (r = -0.398; p = 0.036), and a positive correlation between nesfatin-1 and HOMA-IR (r = 0.43; p = 0.023) in SGA infants. CONCLUSION: Nesfatin-1 is involved in the physiological regulation of intrauterine and postnatal growth and development in SGA infants.

[Relationship of plasma ghrelin, IGF-1 and insulin with the growth and development of 2 -7 year-old children with small for gestational age at birth].

OBJECTIVE: To explore the relationship of Ghrelin, insulin-like growth factor-1 (IGF-1) and insulin with the growth and development of 2 -7 year-old children with small for gestational age (SGA) at birth. METHODS: The levels of ghrelin, IGF-1, IGFBP-3, insulin and glucose were measured in the children with preterm SGA and term SGA and compared with the children with preterm appropriate for gestational age (AGA) and term AGA. The correlation of ghrelin with IGF-1, IGFBP-3 and insulin was analyzed. RESULTS: Plasma ghrelin in preterm SGA was higher than that in term SGA (P < 0.05), and there was no significant difference between preterm SGA and preterm AGA (P > 0.05). Plasma ghrelin in preterm AGA and term SGA was higher than that in term AGA (P < 0.05, P < 0.01 respectively). Serum IGF-1 and IGFBP-3 in preterm SGA were lower than those in term SGA (P < 0.05 for all) and serum IGF-1 and IGFBP-3 in preterm AGA were much lower than those in term AGA (P < 0.0001 for all). The level of serum insulin was the highest in term SGA. The trend of insulin resistance index (IRI) was similar to insulin. There were negative correlations of ghrelin with other indexes (weight SDS, IGF-1, IGFBP-3, insulin and IRI) in preterm SGA and term SGA (in preterm SGA r = -0.683, P < 0.002; r = -0.749, P < 0.001; r = -0.828, P < 0.001; r = -0.694, P < 0.005; r = -0.822, P < 0.001; in term SGA r = -0.792, P < 0.001; r = -0.707, P < 0.002; r = -0.615, P < 0.01; r = -0.648, P < 0.005; r = -0.679, P < 0.005). CONCLUSION: Ghrelin is involved in the regulation of growth and development of preterm and SGA children, regardless of the magnitude of their catch up growth. As a re-regulatory factor to insulin, ghrelin regulates the energy metabolism in a form of negative feedback.

Development of an automatic subsea blowout preventer stack control system using PLC based SCADA.

An extremely reliable remote control system for subsea blowout preventer stack is developed based on the off-the-shelf triple modular redundancy system. To meet a high reliability requirement, various redundancy techniques such as controller redundancy, bus redundancy and network redundancy are used to design the system hardware architecture. The control logic, human-machine interface graphical design and redundant databases are developed by using the off-the-shelf software. A series of experiments were performed in laboratory to test the subsea blowout preventer stack control system. The results showed that the tested subsea blowout preventer functions could be executed successfully. For the faults of programmable logic controllers, discrete input groups and analog input groups, the control system could give correct alarms in the human-machine interface.

Nuclear factor-kappa B expression in alveolar macrophages of mechanically ventilated neonates with respiratory distress syndrome.

BACKGROUND: Inflammatory reaction and injury in mature lungs are associated with activation of nuclear factor-kappaB (NF-kappaB) to trigger proinflammatory cytokine release. In preterm infants with immature lungs, this mechanism is not yet fully understood, therefore we investigated this mechanism in mechanically ventilated neonates with respiratory distress syndrome (RDS). METHODS: Serial samples of the airway aspirates (AA) were obtained during mechanical ventilation from 21 preterm infants with RDS, of which 12 were survivors (birth weight 1.48 +/- 0.32 kg and gestational age 31 +/- 1.5 weeks) and 9 nonsurvivors (1.34 +/- 0.31 kg and 30 +/- 2 weeks). Seven neonates matched for age and birth weight without respiratory disorders served as controls. Alveolar macrophages (AM) of AA were isolated by differential adherence, some were cultured with lipopolysaccharide (LPS) for 1 h. Then, nuclear extracts of AM were analyzed by electrophoretic mobility shift assay for NF-kappaB expression. The NF-kappaB inhibitor (IkappaB-alpha protein) in cytoplasmic extracts was detected by Western blot, and concentrations of IL-1beta and IL-8 in AA by enzyme-linked immunosorbent assay (ELISA). RESULTS: On day 2 NF-kappaB expression in AM was significantly increased in the survivors and nonsurvivors at 33.3 +/- 9 and 54.8 +/- 10.2 relative density units (RDU) compared to control infants (11.1 +/- 6.7; p < 0.01). Expression of IkappaB-alpha was significantly higher in controls than that in the survivors and nonsurvivors on days 2 and 4. Moreover, in the nonsurvivors of RDS, expression of NF-kappaB was decreased following LPS stimulation in vitro on day 4. IL-1beta and IL-8 levels in the AA supernatant were higher in the survivors than in controls on days 2 and 4, but lower than those of the nonsurvivors on day 2. There were close correlations between the expression of NF-kappaB and levels of IL-1beta (r = 0.78, p < 0.01), and IL-8 (r = 0.81, p < 0.01) in AA. CONCLUSION: There were alterations in NF-kappaB activity in the AM of mechanically ventilated preterm neonates with RDS, mediated by decreased synthesis and increased degradation of IkappaB.