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Laser-induced breakdown spectroscopy (LIBS) has the potential to serve as a valuable tool in the field of metal failure estimation. In this work, 12Cr1MoV steel, a material with different grain size grades, was selected as the experimental sample. Spectral and acoustic data were recorded during the laser ablation process. Initially, it was revealed that the acoustic energy did not exhibit a significant downward trend with the continuous laser shots, but the acoustic energy fluctuations became more intense. In order to enhance the capacity to assess the grain size grade of heat-resistant steel, we advanced a novel proposition to integrate acoustic data with spectral data. Two data fusion strategies were proposed for the integration of spectral and acoustic data: first, dimensionality reduction followed by combination, and second, combination followed by dimensionality reduction. Subsequently, two classification models, linear discriminant analysis (LDA) and support vector machines (SVM), were constructed utilising three data types: spectral data, acoustic spectral data, and the aforementioned combined data set. The performance of the model trained on the combined data obtained based on the first strategy is superior to models trained on a single data type (spectral data or acoustic spectral data), achieving a classification accuracy of 92.29%. The second strategy yielded unsatisfactory results due to the significant difference in dimensions between spectral data and acoustic spectral data. To address this, a modification was proposed by carrying out spectral feature screening on spectra data using RFE before data fusion and studying the impact of the number of remaining variables after RFE processing on model performance. The results showed that the model achieved the highest classification accuracy of 98.85%. The measurement illustrates the effectiveness of integrating spectral and acoustic spectral data for enhanced metal assessment. [ABSTRACT FROM AUTHOR]

Accurate prediction of nitrogen oxide (NOx) emission is crucial for effectively controlling pollution in municipal solid waste incineration processes. However, it is challenging to construct a NOx emission prediction model with high prediction accuracy and easy engineering application. To address this, this paper proposes a robust and easily applicable NOx emission trend prediction model oriented to engineering applications, utilizing the partial least squares (PLS) method with the time series reconstruction and exponential weighting (TS‐EW‐PLS). The model is verified using operational data from an actual waste incineration process, and comparative analysis with the PLS model showed that the TS‐EW‐PLS model achieved a remarkable improvement of 27–38 % in prediction performance. [ABSTRACT FROM AUTHOR]

Char gasification is a feasible technology to recycle CO2 and utilize biomass energy. However, the impact of different species of biomass on the gasification is still unclear. For the assessment of the feedstock effect on CO2 gasification, the present work prepared over 40 different wood char samples on a high temperature (around 1523 K) flame reactor, which simulate the rapid heating rates experienced in industrial reactors. Then, the curves of conversion (X) and conversion rate (dX/dt) versus temperature, and the conversion rate versus fractional conversion were derived from the linear non-isothermal thermogravimetric analysis. Three models (volumetric model, grain model, and random pore model) are examined for their goodness of fit between the model and experimental data, and their estimated activation energy. Finally, links between different biomass property parameters and different reactivity indexes were investigated. The results obtained show that different feedstocks result in different gasification reactivities of the resultant chars, with T50 and Tmax varying in a wide range of about 170 K and 200 K. But when conversion is plotted against dimensionless temperature, regardless of the feedstock origin, all the experimental plots can be described by only one curve for X = 0.2–0.95. We attributed the slightly superior prediction of the RPM to the increased amount of adjustable kinetic parameters, but lower E values were obtained by using RPM. In addition to the potassium content, the cellulose and lignin contents of the feedstock were found to have relatively high correlation with the char reactivity indexes. [ABSTRACT FROM AUTHOR]

The rational control of intrinsic defects in materials can significantly enhance their scientific and technological potentials, but it remains a long‐standing challenge in nanocrystal‐in‐glass composites (NGCs). Herein, a defect engineering strategy mediated by the mixed alkali effect is proposed and experimentally demonstrated for NGCs. Interestingly, the hybridization of large alkali ions can effectively increase the barrier of Li+ migration and reduce the Li‐related defects in LiNbO3 NGC. As a result, a novel LiNbO3 NGC with greatly reduced Li‐related defects, high crystallinity of over 60%, and excellent optical transmission are successfully fabricated. This unique NGC configuration facilitates efficient transverse second harmonic generation (TSHG) in a broad wavelength region. Based on the above effects, a standard TSHG device is fabricated and implemented to monitor ultrashort optical pulses with duration in the order of ≈10−13 s over a broad wavelength region, down to 780 nm. The proposed strategy not only provides a new idea for defect engineering in materials science but also has great significance for boosting the practical applications of NGCs in ultrashort optical pulse monitoring. [ABSTRACT FROM AUTHOR]

The emission of carbon dioxide (CO2) by thermal power plants is the main factor leading to the greenhouse effect. The measurement of CO2 is of great significance to carbon emission data statistics. In this paper, a measurement system based on tunable diode laser absorption spectroscopy (TDLAS) was built to measure the spectrum of CO2 near 1579 nm. To simplify the experimental system, a direct absorption signal fitting method is proposed to replace etalons for time‐frequency conversion. The method was applied to the measurement of four different concentrations of CO2 in the range of 7%–16%, and the measurement results were compared with the multipeak method, another etalon‐free method. The results show that the mean relative error of the measurement results is 4.23%, which is 1.76% lower than the measurement results of the multipeak method. The experimental research provides a feasible idea to simplify the CO2 measurement system based on TDLAS. [ABSTRACT FROM AUTHOR]

Accurately predicting trends in NOx emission is essential for effectively controlling pollution in municipal solid waste incineration (MSWI) power plants. However, the MSWI process exhibits notable dynamic nonlinearity, time series characteristics, and fluctuations that are distinct from those present in fossil fuel combustion processes. Therefore, the model must possess excellent capabilities in handling time series and nonlinear features while achieving adaptive updates to account for complex working conditions. To address these issues, we have developed a robust prediction model for NOx emission trends using the bi‐directional long short‐term memory (Bi‐LSTM) deep learning algorithm. This model encompasses maximum information coefficient and expert experience for input variables selection, parameter optimization using the linear inertial weight particle swarm algorithm (LDWPSO), and an adaptive update strategy based on probabilistic statistics. The prediction performance of this model was compared to that of the traditional and widely used backpropagation neural network (BPNN), extreme learning machine (ELM), and LSTM. Furthermore, we verified the adaptive update effect of the proposed model using additional data. The results demonstrate that the proposed model exhibits robust prediction and adaptive capabilities. This study's originality is presenting a satisfactory trend prediction for NOx emission from the MSWI process using an adaptive LDWPSO‐(Bi‐LSTM) model. It will be essential for the optimization and control of NOx emissions from the MSWI process. [ABSTRACT FROM AUTHOR]