The NH 2 + NO 2 reaction has been studied experimentally and theoretically. On the basis of laser photolysis/LIF experiments, the total rate constant was determined over the temperature range 295−625 K as k 1,exp (T) = 9.5 × 10 −7 (T/K) −2.05 exp(−404 K/T) cm 3 molecule −1 s −1 . This value is in the upper range of data reported for this temperature range. The reactions on the NH 2 + NO 2 potential energy surface were studied using high level ab initio transition state theory (TST) based master equation methods, yielding a rate constant of k 1,theory (T) = 7.5 × 10 −12 (T/K) −0.172 exp(687 K/T) cm 3 molecule −1 s −1 , in good agreement with the experimental value in the overlapping temperature range. The two entrance channel adducts H 2 NNO 2 and H 2 NONO lead to formation of N 2 O + H 2 O (R1a) and H 2 NO + NO (R1b), respectively. The pathways through H 2 NNO 2 and H 2 NONO are essentially unconnected, even though roaming may facilitate a small flux between the adducts. High-and low-pressure limit rate coefficients for the various product channels of NH 2 + NO 2 are determined from the ab initio TST-based master equation calculations for the temperature range 300−2000 K. The theoretical predictions are in good agreement with the measured overall rate constant but tend to overestimate the branching ratio defined as β = k 1a /(k 1a + k 1b ) at lower temperatures. Modest adjustments of the attractive potentials for the reaction yield values of k 1a = 4.3 × 10 −6 (T/K) −2.191 exp(−229 K/T) cm 3 molecule −1 s −1 and k 1b = 1.5 × 10 −12 (T/K) 0.032 exp(761 K/T) cm 3 molecule −1 s −1 , in good agreement with experiment, and we recommend these rate coefficients for use in modeling. ■ INTRODUCTIONThe formation and consumption of nitrogen oxides (NO x ) at high temperatures continue to be an important area of research. Of particular interest is the chemistry of amine radicals. Ammonia is formed in significant quantities in devolatilization of solid fuels 1 and the selectivity in oxidation of amine radicals to form NO or N 2 is important for the yield of NO x . 2 Reactions of amine radicals are also important for the performance of selective noncatalytic reduction (SNCR) of NO using aminebased additives such as ammonia or urea. In the past, both NH 3 oxidation 2−6 and SNCR 2,7−11 have been studied extensively within combustion.Following the detection of NO 2 as an important intermediate in SNCR, 12 the reaction of NH 2 with NO 2 was identified as a key step. 9,11 This reaction has two major product channels:(R1a)The prior analysis of Glarborg et al. 13 provides an overall picture of the kinetics for this reaction. The NH 2 radical can add to either the N atom of NO 2 to form H 2 NNO 2 or one of the O atoms to form H 2 NONO. The H 2 NNO 2 species ultimately produces N 2 O + H 2 O (R1a) after a number of isomerizations. Meanwhile, the H 2 NONO species directly dissociates to H 2 NO + NO (R1b). Both of these sets of products are exothermic relative to reactants, and the transition states on the pathway to forming N...

In this paper, concurrent fault diagnosis problem of modular multilevel converter (MMC) with Kalman filter and optimized support vector machine (SVM) is investigated. The state space model by synthesizing the circulating current and the output current is first established. Recurring to the Kalman filtering theory, the estimation on circulating and output current is realized, the residual is achieved by using the innovation which involved the predicted and measured current. Based on the obtained residual, the residual evaluation function and its threshold are constructed. Then, the fault can be detected according to the proposed fault detection strategy. Once the fault is detected, the fault localization unit is triggered and the residual data is adopted as data set. By employing the optimized SVM with genetic algorithm, the concurrent and intermittent fault localization of MMC can be accomplished. Finally, an 11-level MMC simulation systems with concurrent fault and intermittent fault are set up in MATLAB/Simulink, and the effectiveness of the proposed fault detection and localization method is verified. ARTICLE HISTORY

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Objectives/Scope: In order to maximize the recovery of hydrocarbons from liquids rich shale reservoir systems, the cause and effect relationships between production and the stimulation methods need to be clearly understood. In this study, we integrate a production data regression approach with flow simulation methods to understand the fractured well production behavior and field wide well performance in a liquids rich petroleum system in the Duvernay Basin. Methods, Procedures, Process: Statistical models assume no physical relationship between the model parameters and the response variable, which in this case is produced volumes over a period of time. On the other hand, simulation studies incorporate physical mechanisms of flow to model and predict the production behavior. The simulation models, however, fall short of incorporating all the mechanisms contributing to the production behavior in the complex shale gas reservoir. Thus there is a need for integration of statistical approaches of understanding production behavior along with physics based model and simulation approach. Results, Observations, Conclusions: Multivariate linear regression analysis of the 6 month produced volume and its relationship with parameters such as fracture fluid volumes used, proppant weight placed, and number of stages fractured provides a model with reasonably good correlation. The 6 month produced volumes correlate with large proppant weights, lower fluid placements and greater density of fracture stages. Use of Random Forests machine learning algorithm on the dataset confirms that the total proppant placed, well length completed with fractures have high importance coefficients. In order to examine the well performance using full physical models, fractured well simulations were performed on particular wells using the trilinear model. The trilinear model predictions were compared against other production analyses and the regression model results for consistency. The models showed that in the absence of stress dependent permeability, the production forecast was much higher. Thus, stress dependent permeability appears to be an important factor in the modeling and prediction of production from liquids rich shale reservoirs. Novel/Additive Information: In this study we describe a method to understand the production data from a liquids rich shale reservoir, by integrating multivariate linear regression analysis, machine learning algorithms along with physical model simulations. The results are novel and offer a method to validate either approach to understand cause and effect relationships. This approach may be classified as a new hybrid modeling approach that may potentially be used to optimize stimulation techniques in liquids rich shale reservoirs.