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A rapid and sensitive method for the determination of inorganic chloride in oil samples
No full textA novel isotope dilution method for the analysis of inorganic chloride in fuel oil matrix is presented. The samples were diluted by isopropyl alcohol:toluene, mixed with 37Cl 12 internal standard and reacted with triethyloxonium tetrafluoroborate at room temperature. This reagent promoted conversion of Cl 12 into stable ethyl chloride (EtCl) which was selectively detected by headspace GC\u2013MS/MS with no matrix effects. A limit of detection of 0.2\u202fmg\u202fkg 121 Cl 12 was obtained in fuel oil. The method was tested on the NIST SRM 1634c (trace elements in residual fuel oil) and validated through a series of robustness tests. Over 18 days, the variation in the signal response was less than 10% and the RSD for quantitative isotope dilution results was below 3%. Overall, the method is fast, simple and allows robust quantitation of inorganic chloride directly in the non-aqueous media.Peer reviewed: YesNRC publication: Ye
Autopilot performance evaluation by using integrated simulators
No full textPeer reviewed: NoNRC publication: Ye
Cuckoo search optimized NN-based fault diagnosis approach for power transformer PHM
No full textAn emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM systems enable a pro-active fault prevention strategy through continuously monitoring the health of complex systems. Power transformer PHM will play a key role in securing and stabling electrical power supply to users, especially in the smart grid. In this paper, we present a novel approach for power transformer fault diagnosis based on cuckoo search optimized neural network, also named it as dissolved gas analysis (DGA) approach. The proposed approach uses the Cuckoo Search (CS) algorithm to select the best parameters of backpropagation (BP) neural network, which can approximate any nonlinear relationships. The paper validates the usefulness and efficiency of the proposed approach by conducting simulation to compare the results to Particle Swarm Optimization (PSO) and Genetic algorithm (GA). The results demonstrated that the proposed approach outperformed other methods such as BP neural network, SVM, GA-BP, and PSO-BP. It significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM.Peer reviewed: YesNRC publication: Ye
Fault diagnosis in chemical processes based on class-incremental FDA and PCA
No full textA class-incremental scheme of fisher discriminant analysis is proposed to improve the performance ofprocess fault diagnosis. Fisher discriminant analysis seeks directions which are efficient for discrimination and has excellent fault detection and diagnostic performance for the sample set with the tag. However, due to the property of the model, it has no detection and diagnostic capabilities forun-seen faults. In order to address this issue, the direction, which is based on a partial -values with the principle component analysis, is proposed in this paper. After a new fault being detected and added into the known fault collection, a class-incremental scheme is used to update the fisher discriminant analysis model to enhance the model's ability for continuous fault identification. The proposed approach is validated by the Tennessee Eastman process for the fault diagnosis. The results demonstrate that the proposed class-incremental fisher discriminant analysis method outperforms other conventional fisher discriminant analysis methods.Peer reviewed: YesNRC publication: Ye
Numerical simulation of ice dynamics on the St. Lawrence River at Montr\ue9al
No full textA numerical model originally developed at the National Research Council to simulate the dynamics of sea ice over large domains has been extended and applied to simulate ice cover dynamics in the St. Lawrence River at Montr\ue9al. The model predicts the evolution of ice cover and provides estimates of ice concentration, ice thickness and internal pressure or stress over space and time subject to forcing by water currents and winds. For this application the model was setup to resolve floating ice dynamics at much higher spatial resolutions and finer time scales than before, and a new boundary condition was developed to support a continuous inflow of ice across the upstream boundary. A new 2D hydrodynamic model of flows in the St. Lawrence River was also developed to provide high-quality spatially-variable predictions of water currents in the region of interest. The new models have been applied to predict the evolution of the ice cover on the river near downtown Montr\ue9al over a 9-12 hour period for several combinations of initial ice condition, river discharge and wind representing typical conditions during spring break-up. Simulations have been carried out to provide estimates of river ice dynamics and downstream ice conditions. This paper provides an overview of the methodologies employed in the study and a summary of the key findings.Peer reviewed: NoNRC publication: Ye
Fractal dimension and directional analysis of elastic and collagen fiber arrangement in unsectioned arterial tissues affected by atherosclerosis and aging
No full textStructural proteins like collagen and elastin are major constituents of the extracellular matrix (ECM). ECM degradation and remodeling in diseases significantly impact the microorganization of these structural proteins. Therefore, tracking the changes of collagen and elastin fiber morphological features within ECM impacted by disease progression could provide valuable insight into pathological processes such as tissue fibrosis and atherosclerosis. Benefiting from its intrinsic high-resolution imaging power and superior biochemical specificity, nonlinear optical microscopy (NLOM) is capable of providing information critical to the understanding of ECM remodeling. In this study, alterations of structural fibrillar proteins such as collagen and elastin in arteries excised from atherosclerotic rabbits were assessed by the combination of NLOM images and textural analysis methods such as fractal dimension (FD) and directional analysis (DA). FD and DA were tested for their performance in tracking the changes of extracellular elastin and fibrillar collagen remodeling resulting from atherosclerosis progression/aging. Although other methods of image analysis to study the organization of elastin and collagen structures have been reported, the simplified calculations of FD and DA presented in this work prove that they are viable strategies for extracting and analyzing fiber-related morphology from disease-impacted tissues. Furthermore, this study also demonstrates the potential utility of FD and DA in studying ECM remodeling caused by other pathological processes such as respiratory diseases, several skin conditions, or even cancer.Peer reviewed: YesNRC publication: Ye
Experimental methods in chemical engineering: discrete element method-DEM
No full textThe Discrete Element Method (DEM) is a time\u2010driven simulation technique based on a Lagrangian description of particle motion that predicts the flow of granular matter and fine powders in conveying, mixing, drying, and heterogeneous gas\u2010(liquid)\u2010solids reactors. Powders flowing out of bins form bridges, they segregate in suboptimal pharmaceutical V\u2010blenders, and a stream may split into large gulf streams as they enter fluidized bed reactors from standpipes and diplegs. To reduce the uncertainty in scaling up these and other powder process unit operations, researchers apply DEM. It integrates Newton\u2019s second law (acceleration equals the sum of the forces) for each particle and models contact between the particles with springs and dashpots (dampers). It is computationally intensive since it calculates the trajectory of all particles. The availability of open source codes, commercial software, and parallel computer architectures has accelerated its adoption in pharmaceutical, agro\u2010industrial and mineral processes, and geophysics. The accuracy of DEM models depends on how well researchers calibrate the contact model expressions and their parameters: friction coefficients and the coefficient of restitution. Systems exceeding 1\u2009
7\u2009108 particles can require weeks of computational time on large computer clusters. Current research targets non\u2010spherical particle interactions and multiphysics problems including heat transfer, mass transfer, and chemical reactions within the particles. The field has grown to 750 indexed aritcles in WoS in 2017. A bibliographic analysis recognized four research clusters: granular materials, behaviour, particle shape, and deformation; flows, fluidized beds, and computational fluid dynamics; particles, impact, and validation; and granular flow, dynamics, and segregation.Peer reviewed: YesNRC publication: N
Human age prediction based on DNA methylation of non-blood tissues
No full textBackground and Objective: The study of human aging contributes to disease prevention, treatment and life extension. Recently, epigenetics studies have evidenced that there is a close association between DNA methylation and human ages. A quantitatively statistical modeling between DNA methylation and ages could predict the person's age more accurately. Methods: We propose a regression model to predict human age based on gradient boosting regressor (GBR). We collect a total of 1280 publicly available non-blood tissues samples with ages ranged from 0 to 90 years old. We calculate the Pearson correlation between CpG's DNA methylation level and age to select age-related CpGs. Results: Thirteen age-related CpG sites are selected. GBR has the smallest mean absolute deviation to the actual age comparing with other three different models including Bayesian ridge, multiple linear regression, and support vector regression. In the training datasets, the cross-validation results show that the correlation R2 between predicted age and DNA methylation is 0.89, and the mean absolute deviation is 4.66 years. In an independent testing set with 262 samples, the GBR achieves the mean absolute deviation of 6.08 years. Meanwhile we also briefly describe the function of the selected thirteen CpG sites. Conclusions: We build an age predictor to study the association between ages and the DNA methylation of human non-blood tissues. Our new model provides a more accurate estimation of human ages which will be instrumental for understanding the regulation of DNA methylation on human aging and will accurately monitor the individual aging process.Peer reviewed: YesNRC publication: Ye
