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The development of highly sensitive gas sensors for toxic industrial gases (TIGs) is paramount for environmental monitoring and public safety. Here, the first-principles calculations were employed to systematically investigate the potential of Pt- and Rh-decorated InS (Pt-InS and Rh-InS) monolayers as advanced gas sensing materials for the five TIGs (SO2, NH3, NO, CO, and NO2). The results reveal that Pt and Rh atoms can be stably anchored at the InS monolayer, inducing significant modulation of its electronic properties. The Pt-InS system exhibits strong chemisorption of NH3 and CO, while the other TIGs interact via physisorption. In contrast, the Rh-InS monolayer demonstrates strong chemisorption and distinct electronic responses to all five gases, driven by robust hybridization between the Rh-d and TIG-p orbitals. Based on comprehensive analyses of sensitivity and recovery time, Rh-InS is identified as a theoretically promising candidate for a reusable SO2 sensor at room temperature, boasting a calculated rapid theoretical recovery time of 2.20 s. The Pt-InS system, conversely, shows potential for high-temperature NH3 sensing. Our findings highlight the exceptional and tunable gas sensing capabilities of Pt- and Rh-decorated InS monolayers, offering a theoretical foundation for designing InS-based sensing devices.

It is becoming increasingly important to develop effective combustion technologies for low calorific industrial gases (LCIG) because of the rising energy demand and environmental issues caused by the extensive use of fossil fuels. In this review, the prospect of these opportunity fuels in China is discussed. Then, the recent fundamental and engineering studies of LCIG combustion are summarized. Specifically, the differences between LCIG and traditional fuels in the composition and fundamental combustion characteristics are described. The state-of-the-art combustion strategies for burning LCIG are reviewed, including porous media combustion, flameless combustion, oxy-fuel combustion, and dual-fuel combustion. The technical challenges and further development needs for efficient LCIG combustion are also discussed.

Generally, industrial gas turbines (IGT) face transient behavior during start-up, load change, shutdown and variations in ambient conditions. These transient conditions shift engine thermal equilibrium from one steady state to another steady state. In turn, various aero-thermal and mechanical stresses are developed that are adverse for engine’s reliability, availability, and overall health. The transient behavior needs to be accurately predicted since it is highly related to low cycle fatigue and early failures, especially in the hot regions of the gas turbine. In the present paper, several critical aspects related to transient behavior and its modeling are reviewed and studied from the point of view of identifying potential research gaps within the context of fault detection and diagnostics (FDD) under dynamic conditions. Among the considered topics are, (i) general transient regimes and pertinent model formulation techniques, (ii) control mechanism for part-load operation, (iii) developing a database of variable geometry inlet guide vanes (VIGVs) and variable bleed valves (VBVs) schedules along with selection framework, and (iv) data compilation of shaft’s polar moment of inertia for different types of engine’s configurations. This comprehensive literature document, considering all the aspects of transient behavior and its associated modeling techniques will serve as an anchor point for the future researchers, gas turbine operators and design engineers for effective prognostics, FDD and predictive condition monitoring for variable geometry IGT.

Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.

Variable geometry gas turbines are susceptible to various malfunctions and performance deterioration phenomena, such as variable inlet guide vane (VIGV) drift, compressor fouling, and high inlet air temperatures. The present study investigates the combined effect of these performance deterioration phenomena on the health and overall performance of a three-shaft gas turbine engine (GE LM1600). For this purpose, a steady-state simulation model of the turbine was developed using a commercial software named GasTurb 12. In addition, the effect of an inlet air cooling (IAC) technique on the gas turbine performance was examined. The design point results were validated using literature results and data from the manufacturer’s catalog. The gas turbine exhibited significant deterioration in power output and thermal efficiency by 21.09% and 7.92%, respectively, due to the augmented high inlet air temperature and fouling. However, the integration of the inlet air cooling technique helped in improving the power output, thermal efficiency, and surge margin by 29.67%, 7.38%, 32.84%, respectively. Additionally, the specific fuel consumption (SFC) was reduced by 6.88%. The VIGV down-drift schedule has also resulted in improved power output, thermal efficiency, and the surge margin by 14.53%, 5.55%, and 32.08%, respectively, while the SFC decreased by 5.23%. The current model can assist in troubleshooting the root cause of performance degradation and surging in an engine faced with VIGV drift and fouling simultaneously. Moreover, the combined study also indicated the optimum schedule during VIGV drift and fouling for performance improvement via the IAC technique.

One of the major challenges facing fault diagnosis tools is their exposure to noise. The presence of noise may cause false alarms or the inability to detect a progressive fault in the early stages of its occurrence. Continuing previous efforts to address such a problem, in this paper, a noise‐robust diagnosis system for an industrial gas turbine is presented. The proposed structure employs a set of deep residual compensation extreme learning machines (DRCELMs). In this model, an optimal number of compensating blocks are trained to recover some of the lost useful information in the face of noise. Training and testing data required to develop the fault diagnosis model are generated by a performance model of the studied gas turbine. The t ‐distributed stochastic neighbor embedding algorithm is employed for visualizing the gas path faults. Furthermore, the performance of the DRCELM is evaluated by comparing it with six other diagnosis models. The results indicate higher robustness of the DRCELM compared to other fault diagnosis systems. The proposed model presents a classification accuracy of >97% in noisy data and an accuracy of >98% in noise‐free data and combined data, while the average of fault positive rate and fault negative rate in noisy data is less than 2.5%.

In this study, a novel gas burner combining air swirl and an inverse diffusion flame (IDF) is designed for industrial applications. Numerical simulations using the Reynolds-averaged Navier–Stokes (RANS) method and simplified reaction mechanisms are conducted to predict the turbulent flow and combustion performance of the burner. Detailed flow structures, flame structures and effects of burner configurations are examined. The simulation results indicate that the swirl action of the burner creates a central recirculation zone and two external recirculation zones at the burner head, which stabilize combustion. The tangential velocity is minimal at the center of the burner and decreases with increasing distance from the outlet. As the distance from the exit increases, the maximum tangential velocity gradually decreases, and the peak value shifts towards the wall. This decrease in tangential velocity with axial distance signifies the gradual dissipation of the swirl effect, which disappears near the chamber outlet. The comparisons reveal that altering the number of burner fuel nozzles is more effective in reducing NO emissions than changing the inclination angle of the fuel nozzles, in the given conditions. Favorable combustion conditions are achieved when there are 16 fuel nozzles and the nozzle inclination angle is 60°, resulting in a 28.5% reduction in NO emissions at the outlet, compared to the reference condition.

Predictive maintenance is one of the key aspects of Industry 4.0. The article presents the results of experimental tests of nitrogen purification filters in the installation of a low-pressure, metal processing device. The aim of the research was to develop a predictive algorithm for making decisions regarding the replacement of used filters, based on flow analysis and measurement of the pressure difference in front of and behind the tested filter. For the purposes of the research, a special test stand was constructed, which made it possible to determine the operating characteristics of three selected filters. Based on the tests carried out, the limit characteristics of the parameters measured were determined, identifying the need to replace filters in the gas installation.

Computational fluid dynamics was employed to predict the early stages of the aerodynamic breakup of a cylindrical water column, due to the impact of a traveling plane shock wave. The unsteady Reynolds-averaged Navier–Stokes approach was used to simulate the mean turbulent flow in a virtual shock tube device. The compressible flow governing equations were solved by means of a finite volume-based numerical method, where the volume of fluid technique was employed to track the air–water interface on the fixed numerical mesh. The present computational modeling approach for industrial gas dynamics applications was verified by making a comparison with reference experimental and numerical results for the same flow configuration. The engineering analysis of the shock–column interaction was performed in the shear-stripping regime, where an acceptably accurate prediction of the interface deformation was achieved. Both column flattening and sheet shearing at the column equator were correctly reproduced, along with the water body drift.

Improving the performance of industrial gas turbines has always been at the focus of attention of researchers and manufacturers. Nowadays, the operating environment of gas turbines has been transformed significantly respect to the very fast growth of renewable electricity generation where gas turbines should provide a safe, reliable, fast, and flexible transient operation to support their renewable partners. So, having a reliable tools to predict the transient behavior of the gas turbine is becoming more and more important. Regarding the response time and flexibility, improving the turbine performance during the start-up phase is an important issue that should be taken into account by the turbine manufacturers. To analyze the turbine performance during the start-up phase and to implement novel ideas so as to improve its performance, modeling, and simulation of an industrial gas turbine during cold start-up phase is investigated this article using an integrated modular approach. During this phase, a complex mechatronic system comprised of an asynchronous AC motor (electric starter), static frequency converter drive, and gas turbine exists. The start-up phase happens in this manner: first, the clutch transfers the torque generated by the electric starter to the gas turbine so that the turbine reaches a specific speed (cranking stage). Next, the turbine spends some time at this speed (purging stage), after which the turbine speed decreases, sparking stage begins, and the turbine enters the warm start-up phase. It is, however, possible that the start-up process fails at an intermediate stage. Such unsuccessful start-ups can be caused by turbine vibrations, the increase in the gradients of exhaust gases, or issues with fuel spray nozzles. If, for any reason, the turbine cannot reach the self-sustained speed and the speed falls below a certain threshold, the clutch engages once again with the turbine shaft and the start-up process is repeated. Consequently, when modeling the start-up phase, we face discontinuities in performance and a system with variable structure owing to the existence of clutch. Modeling the start-up phase, which happens to exist in many different fields including electric and mechanical application, brings about problems in numerical solutions (such as algebraic loop). Accordingly, this study attempts to benefit from the bond graph approach (as a powerful physical modeling approach) to model such a mechatronic system. The results confirm the effectiveness of the proposed approach in detailed performance prediction of the gas turbine in start-up phase.