Exergy Analysis of Coal-Based Series Polygeneration Systems for Methanol and Electricity Co-Production.

This paper quantifies the exergy losses of coal-based series polygeneration systems and evaluates the potential efficiency improvements that can be realized by applying advanced technologies for gasification, methanol synthesis, and combined cycle power generation. Exergy analysis identified exergy losses and their associated causes from chemical and physical processes. A new indicator was defined to evaluate the potential gain from minimizing exergy losses caused by physical processes-the degree of perfection of the system's thermodynamic performance. The influences of a variety of advanced technical solutions on exergy improvement were analyzed and compared. It was found that the overall exergy loss of a series polygeneration system can be reduced significantly, from 57.4% to 48.9%, by applying all the advanced technologies selected. For gasification, four advanced technologies were evaluated, and the largest reduction in exergy loss (about 2.5 percentage points) was contributed by hot gas cleaning, followed by ion transport membrane technology (1.5 percentage points), slurry pre-heating (0.91 percentage points), and syngas heat recovery (0.6 percentage points). For methanol synthesis, partial shift technology reduced the overall exergy loss by about 1.4 percentage points. For power generation, using a G-class gas turbine decreased the overall exergy loss by about 1.6 percentage points.

Combination of cylindrical confinement and spark discharge for signal improvement using laser induced breakdown spectroscopy.

Spark discharge has been proved to be an effective way to enhance the LIBS signal while moderate cylindrical confinement is able to increase the signal repeatability with limited signal enhancement effects. In the present work, these two methods were combined together not only to improve the pulse-to-pulse signal repeatability but also to simultaneously and significantly enhance the signal as well as SNR. Plasma images showed that the confinement stabilized the morphology of the plasma, especially for the discharge assisted process, which explained the improvement of the signal repeatability.

Signal quality improvement using cylindrical confinement for laser induced breakdown spectroscopy.

In our previous work, we found that there was great potential to improve the pulse-to-pulse signal repeatability using a moderate cylindrical cavity confinement. However, the improvement was achieved only with certain experimental parameters; while under other conditions, there was no improvement or even worse repeatability. In the present work, the experimental configuration was redesigned and unexpected uncertainty from the variation of the laser and cavity alignment and the laser ablated aerosols were avoided. With these two improvements, we demonstrated that the cavity can always increase the signal repeatability. In addition, image taken by ICCD verified that the confinement improved the stability of the plasma morphology as expected.

Quantitative carbon measurement in anthracite using laser-induced breakdown spectroscopy with binder.

Quantitative carbon measurement in anthracites remains difficult with laser-induced breakdown spectroscopy (LIBS) due to its relatively high measurement uncertainty. To improve the measurement repeatability, binders to bind the anthracite powder together were utilized for LIBS measurement. Results showed that the optimized binder Na(2)SiO(3)·9H(2)O, with Si from the binder as the internal calibration element, can yield the overall best measurement precision and accuracy. Using 15 anthracites for calibration and 7 anthracites for validation and with optimized percentage of Na(2)SiO(3)·9H(2)O as binder, the average value of the measurement's relative standard deviation (RSD), R(2), and root mean square error of prediction (RMSEP) were 12.1%, 0.76, and 6.25%, respectively, proving the applicability of binder for carbon measurement in anthracites.

A PLS model based on dominant factor for coal analysis using laser-induced breakdown spectroscopy.

Thirty-three bituminous coal samples were utilized to test the application of laser-induced breakdown spectroscopy technique for coal elemental concentration measurement in the air. The heterogeneity of the samples and the pyrolysis or combustion of coal during the laser-sample interaction processes were analyzed to be the main reason for large fluctuation of detected spectra and low calibration quality. Compared with the generally applied normalization with the whole spectral area, normalization with segmental spectral area was found to largely improve the measurement precision and accuracy. The concentrations of major element C in coal were determined by a novel partial least squares (PLS) model based on dominant factor. Dominant C concentration information was taken from the carbon characteristic line intensity since it contains the most-related information, even if not accurately. This dominant factor model was further improved by inducting non-linear relation by partially modeling the inter-element interference effect. The residuals were further corrected by PLS with the full spectrum information. With the physical-principle-based dominant factor to calculate the main quantitative information and to partially explicitly include the non-linear relation, the proposed PLS model avoids the overuse of unrelated noise to some extent and becomes more robust over a wider C concentration range. Results show that RMSEP in the proposed PLS model decreased to 4.47% from 5.52% for the conventional PLS with full spectrum input, while R(2) remained as high as 0.999, and RMSEC&P was reduced from 3.60% to 2.92%, showing the overall improvement of the proposed PLS model.

A partial least squares and wavelet-transform hybrid model to analyze carbon content in coal using laser-induced breakdown spectroscopy.

A partial least squares (PLS) and wavelet transform hybrid model are proposed to analyze the carbon content of coal by using laser-induced breakdown spectroscopy (LIBS). The hybrid model is composed of two steps of wavelet analysis procedures, which include environmental denoising and background noise reduction, to pretreat the LIBS spectrum. The processed wavelet coefficients, which contain the discrete line information of the spectra, were taken as inputs for the PLS model for calibration and prediction of carbon element. A higher signal-to-noise ratio of carbon line was obtained after environmental denoising, and the best decomposition level was determined after background noise reduction. The hybrid model resulted in a significant improvement over the conventional PLS method under different ambient environments, which include air, argon, and helium. The average relative error of carbon decreased from 2.74 to 1.67% under an ambient helium environment, which indicated a significantly improved accuracy in the measurement of carbon in coal. The best results obtained under an ambient helium environment could be partly attributed to the smallest interference by noise after wavelet denoising. A similar improvement was observed in ambient air and argon environments, thereby proving the applicability of the hybrid model under different experimental conditions.

Application of a spectrum standardization method for carbon analysis in coal using laser-induced breakdown spectroscopy (LIBS).

Measurement of coal carbon content using laser-induced breakdown spectroscopy (LIBS) is limited by its low precision and accuracy. A modified spectrum standardization method was proposed to achieve both reproducible and accurate results for the quantitative analysis of carbon content in coal using LIBS. The proposed method used the molecular emissions of diatomic carbon (C2) and cyanide (CN) to compensate for the diminution of atomic carbon emissions in high volatile content coal samples caused by matrix effect. The compensated carbon line intensities were further converted into an assumed standard state with standard plasma temperature, electron number density, and total number density of carbon, under which the carbon line intensity is proportional to its concentration in the coal samples. To obtain better compensation for fluctuations of total carbon number density, the segmental spectral area was used and an iterative algorithm was applied that is different from our previous spectrum standardization calculations. The modified spectrum standardization model was applied to the measurement of carbon content in 24 bituminous coal samples. The results demonstrate that the proposed method has superior performance over the generally applied normalization methods. The average relative standard deviation was 3.21%, the coefficient of determination was 0.90, the root mean square error of prediction was 2.24%, and the average maximum relative error for the modified model was 12.18%, showing an overall improvement over the corresponding values for the normalization with segmental spectrum area, 6.00%, 0.75, 3.77%, and 15.40%, respectively.

A nonlinearized multivariate dominant factor-based partial least squares (PLS) model for coal analysis by using laser-induced breakdown spectroscopy.

A nonlinearized multivariate dominant factor-based partial least-squares (PLS) model was applied to coal elemental concentration measurement. For C concentration determination in bituminous coal, the intensities of multiple characteristic lines of the main elements in coal were applied to construct a comprehensive dominant factor that would provide main concentration results. A secondary PLS thereafter applied would further correct the model results by using the entire spectral information. In the dominant factor extraction, nonlinear transformation of line intensities (based on physical mechanisms) was embedded in the linear PLS to describe nonlinear self-absorption and inter-element interference more effectively and accurately. According to the empirical expression of self-absorption and Taylor expansion, nonlinear transformations of atomic and ionic line intensities of C were utilized to model self-absorption. Then, the line intensities of other elements, O and N, were taken into account for inter-element interference, considering the possible recombination of C with O and N particles. The specialty of coal analysis by using laser-induced breakdown spectroscopy (LIBS) was also discussed and considered in the multivariate dominant factor construction. The proposed model achieved a much better prediction performance than conventional PLS. Compared with our previous, already improved dominant factor-based PLS model, the present PLS model obtained the same calibration quality while decreasing the root mean square error of prediction (RMSEP) from 4.47 to 3.77%. Furthermore, with the leave-one-out cross-validation and L-curve methods, which avoid the overfitting issue in determining the number of principal components instead of minimum RMSEP criteria, the present PLS model also showed better performance for different splits of calibration and prediction samples, proving the robustness of the present PLS model.

[Life cycle assessment of the infrastructure for hydrogen sources of fuel cell vehicles].

In order to promote the application of life cycle assessment and provide references for China to make the project of infrastructure for hydrogen sources of fuel cell vehicles in the near future, 10 feasible plans of infrastructure for hydrogen sources of fuel cell vehicles were designed according to the current technologies of producing, storing and transporting hydrogen. Then life cycle assessment was used as a tool to evaluate the environmental performances of the 10 plans. The standard indexes of classified environmental impacts of every plan were gotten and sensitivity analysis for several parameters were carried out. The results showed that the best plan was that hydrogen will be produced by natural gas steam reforming in central factory, then transported to refuelling stations through pipelines, and filled to fuel cell vehicles using hydrogen gas at last.