ABSTRACT
Advancement of the absorbent for CO2 capture is essential in optimizing the performance and reducing the negative environmental effects associated with this technology. Despite ammonia's promise as an absorbent, the volatility limits its practical application and creates potential environmental pollution. Therein, we assess various additives (amino acids, carbonates, and alkanolamines) for ammonia-based solvents using multi-stage circulation absorber from the viewpoints of aerosol emission, ammonia emission, and CO2 capture efficiency. Experimental findings reveal that ammonia volatilization can be inhibited by the protonation of free ammonia by carboxyl groups and the formation of hydrogen bonding between amino/hydroxyl groups and ammonia, with ammonia emission reduced by 21.7 %, aerosol emission reduced by 26.5 %, and CO2 capture efficiency increased to a maximum of 87.8 % under the condition of adding histidine. Moreover, the experiment highlights a positive correlation between total ammonia emission and aerosol concentration/diameter. Additionally, tests combining source abatement with water wash exhibit up to 50.5 % aerosol removal efficiency and up to 76.6 % ammonia removal efficiency. To further mitigate emissions, a comprehensive approach is proposed, achieving an 84.4 % reduction in ammonia emission and a 61.9 % reduction in aerosol emission. Finally, a method for recycling ammonia for desulfurization is suggested.
ABSTRACT
Detecting hydrogen leaks remains a pivotal challenge demanding robust solutions. Among diverse detection techniques, the fiber-optic method distinguishes itself through unique benefits, such as its distributed measurement properties. The adoption of hydrogen-sensitive materials coated on fibers has gained significant traction in research circles, credited to its operational simplicity and exceptional adaptability across varied conditions. This manuscript offers an exhaustive investigation into hydrogen-sensitive materials and their incorporation into fiber-optic hydrogen sensors. The research profoundly analyzes the sensor architectures, performance indicators, and the spectrum of sensing materials. A detailed understanding of these sensors' potentials and constraints emerges through rigorous examination, juxtaposition, and holistic discourse. Furthermore, this analysis judiciously assesses the inherent challenges tied to these systems, simultaneously highlighting potential pathways for future innovation. By spotlighting the hurdles and opportunities, this paper furnishes a view on hydrogen sensing technology, particularly related to optical fiber-based applications.
ABSTRACT
High-frequency signals like vibration and acoustic emission are crucial for condition monitoring, but their high sampling rates challenge data acquisition, especially for online monitoring. Our research developed a novel method for condition identification in undersampled signals using a modified convolutional neural network integrated with a signal enhancement approach. A frequency-domain filtering is applied to suppress similar sidebands and obtain more discriminative features of different conditions, followed by an interpolation-based upsampling in the time domain to restore the signal length and strengthen the low-frequency harmonic information. Enhanced signals are converted into two-dimensional grayscale images for neural network analysis. Tested on bearing datasets and real-world data from regenerative thermal oxidizer lift valve leakage, our method effectively extracts features from low-frequency signals, achieving over 95% fault identification accuracy.
ABSTRACT
Rapid detection of low concentration toluene is highly desirable in environment monitoring, industrial processes, medical diagnosis, etc. In this study, we prepared Pt-loaded SnO2monodispersed nanoparticles through hydrothermal method and assembled a sensor based on micro-electro-mechanical system (MEMS) to detect toluene. Compared with the pure SnO2, the 2.92 wt% Pt-loaded SnO2sensor exhibits a 2.75 times higher gas sensitivity to toluene at about 330 °C. Meanwhile, the 2.92 wt% Pt-loaded SnO2sensor also has a stable and good response to 100 ppb of toluene. Its theoretical detection limit is calculated as low as 12.6 ppb. Also, the sensor has a short response time of â¼10 s to different gas concentrations, as well as the excellent dynamic response-recovery characteristics, selectivity, and stability. The improved performance of Pt-loaded SnO2sensor can be explained by the increase of oxygen vacancies and chemisorbed oxygen species. The electronic and chemical sensitization of Pt to SnO2-based sensor, together with small size and fast gas diffusion of the MEMS design ensured fast response and ultra-low toluene detection. This provides new ideas and decent prospect for developing miniaturized, low-power-consumption, and portable application of gas sensing devices.
ABSTRACT
The control of propagation direction or path of edge states is difficult when the chirality of the excitation source and the boundary structures are determined. Here, we studied a frequency-selective routing for elastic wave based on two types of topological phononic crystals (PnCs) with different symmetries. By constructing multiple types of interfaces between different PnCs structures with distinct valley topological phases, the valley edge states of elastic wave could be realized at different frequencies in the band gap. Meanwhile, based on the simulation of topological transport, it is found that the routing path of elastic waves valley edge states highly depends on the operating frequency and the inputting port of the excitation source. By varying the excitation frequency, the transport path can be switched. The results provide a paradigm for the control of elastic wave propagation paths that could be employed for designing the frequency-dependent ultrasonic division devices.
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An accurate NOx concentration prediction model plays an important role in low NOx emission control in power stations. Predicting NOx in advance is of great significance in satisfying stringent environmental policies. This study aims to accurately predict the NOx emission concentration at the outlet of boilers on different operating conditions to support the DeNOx procedure. Through mutual information analysis, suitable features are selected to build models. Long short-term memory (LSTM) models are utilized to predict NOx concentration at the boiler's outlet from selected input features and exhibit power in fitting multivariable coupling, nonlinear, and large time-delay systems. Moreover, a composite LSTM model composed of models on different operating conditions, like steady-state and transient-state condition, is prosed. Results of one whole day of typical operating data show that the accuracy of the NOx concentration and fluctuation trend prediction based on this composite model is superior to that using a single LSTM model and other non-time-sequence models. The root mean square error (RMSE) and R2 of the composite LSTM model are 3.53 mg/m3 and 0.89, respectively, which are better than those of a single LSTM (i.e., 5.50 mg/m3 and 0.78, respectively).
Subject(s)
Coal , Organizations , Data Collection , Environmental PolicyABSTRACT
Particle detection is a key procedure in particle field characterization with digital holography. Due to various background noises, spurious small particles might be generated and real small particles might be lost during particle detection. Therefore, accurate small particle detection remains a challenge in the research of energy and combustion. A deep learning method based on modified fully convolutional networks is proposed to detect small opaque particles (e.g., coal particles) on extended focus images. The model is tested by several experiments and proved to have good small particle detection accuracy.
ABSTRACT
Dynamic stringy objects such as liquid rims and ligaments are frequently observed in important applications such as the multiphase breakup of fuel droplets. We develop a new method based on digital in-line holography to automatically measure complicated stringy objects. A static spring mounted on a rotator is measured to validate the effectiveness and accuracy of the method. The sections are extracted along the skeleton of the spring in a depth-of-field extended image and then sized and located as individual particles using a hybrid method. The surface points of sections are stitched together to visualize the entire spring. Local thickness errors smaller than 5.3%, and z errors smaller than 230 µm are achieved. This method is applied to characterize the spatial-temporal features of the liquid rim formed in the bag-type regime of the aerodynamic breakup of a falling drop. The evolution of the rim/ligament structures is continuously captured in seven frames, lasting in 1.58 ms. This Letter extends the application of digital holography as an effective 3D diagnostic tool.
ABSTRACT
The effects of the individual scattering process on the formations of both the particle hologram and its corresponding reconstructed three-dimensional particle image are investigated using the Debye series. A particle hologram model using the Debye series decomposes the object wave into different scattering modes and thus permits evaluating the effects of the individual scattering process [diffraction, reflection, transmission, refractions with (p-1) internal reflections] on the particle holography quantitatively. In the Gabor inline holography of a transparent droplet, the transmission light causes small discrepancies between the hologram fringes of an opaque particle (diffraction) and a transparent particle near the zero point of the Bessel-like modulation function, eventually giving rise to the glory spot in the center of the reconstructed dark particle image. For off-axis holography, this paper reveals the effects of reflection, particularly total reflection by bubbles, transmission, and refractions with (p-1) internal reflections of the scattered light on the formation and the reconstructed glory spot images of typical forward and backward off-axis holography.
ABSTRACT
A digital holographic particle tracking velocimetry system is applied to quantitatively study the drop atomization induced by capillary waves, and the breakup caused by increased sound pressure levels. A wavelet-based algorithm is used for particle detection and autofocusing with a wide size range of 20 µm-2 mm. To eliminate the influence of large particles on small particles, a two-step detection method is adopted. Large drops are first characterized and simulated by a diffraction-based model. Then the contributions of the drops are subtracted from the original hologram followed by the detection of small droplets. Finally, the velocity and size distribution of the secondary droplets are obtained from the experimental holograms. The results demonstrate the validity of the digital in-line holographic technique for the atomization and breakup study of acoustically levitated drops.
ABSTRACT
The 3D measurement of the particles in a gas-solid pipe flow is of great interest, but remains challenging due to curved pipe walls in various engineering applications. Because of the astigmatism induced by the pipe, concentric ellipse fringes in the hologram of spherical particles are observed in the experiments. With a theoretical analysis of the particle holography by an ABCD matrix, the in-focus particle image can be reconstructed by the modified convolution method and fractional Fourier transform. Thereafter, the particle size, 3D position, and velocity are simultaneously measured by digital holographic particle tracking velocimetry (DHPTV). The successful application of DHPTV to the particle size and 3D velocity measurement in a glass pipe's flow can facilitate its 3D diagnostics.
