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Abstract Predicting and optimizing radiative thermal properties have been acknowledged as an efficient way to improve thermal insulation performance of fibrous materials with high porosity. Based on experimental investigation of infrared spectral of ultrafine fibrous insulations with diameters of 520–650 nm, a method of calculating radiative thermal properties was presented by combining Rosseland equation, Mie scattering theory, Beer’s law and Subtractive Kramers–Kronig (SKK) relation. To ensure the calculation correct the uniqueness analysis was performed for Poly(vinylidene fluoride) (PVDF) fibers, which indicated the valid fiber diameter was less than 1.06 μm. The calculated thermal radiative conductivities by using the method agreed well with the measured data. The effect of fiber diameter on the thermal properties of the fibrous insulations was also investigated to minimize the radiative thermal conductivity. The results indicated that the minimized radiative thermal conductivities by regulating fiber diameters could be approximately 25% smaller than those for experimental fiber diameters. The method of predicting and minimizing radiative thermal conductivities of fibrous insulations demonstrated in this paper could be of great advantage to thermal engineering applications aiming to reducing heat loss and saving energy.

Flue gas desulfurization (FGD) and selective catalytic reduction (SCR) technologies have been widely used to control the emissions of sulphur dioxide (SO2) and nitrogen oxides (NOX) from coal-fired power plants (CFPPs). Field measurements of emission characteristics of four conventional CFPPs indicated a significant increase in particulate ionic species, increasing PM2.5 emission with FGD and SCR installations. The mean concentrations of PM2.5 from all CFPPs tested were 3.79 ± 1.37 mg/m3 and 5.02 ± 1.73 mg/m3 at the FGD inlet and outlet, respectively, and the corresponding contributions of ionic species were 19.1 ± 7.7% and 38.2 ± 7.8%, respectively. The FGD was found to enhance the conversion of NH3 slip from the SCR to NH4+ in the PM2.5, together with the conversion of SO2 to SO42-, and increased the primary NH4+ and SO42- aerosol emissions by approximately 18.9 and 4.2 times, respectively. This adverse effect should be considered when updating the emission inventory of CFPPs and should draw the attention of policy-makers for future air pollution control.

Abstract The kinetics of CO 2 absorption in unloaded aqueous ammonia solution were measured using a string of discs contactor with the aqueous ammonia concentrations ranging 0.9–5.4 kmol/m 3 and temperatures ranging 298.3–321.9 K. The reaction rates strongly increase with the concentration and less strongly with temperature. Both the termolecular and zwitterion models were applied in this study as amine solutions. The parameters for both of the models were interpreted. The kinetic rate constants for CO 2 absorption in aqueous ammonia were compared with those for other amines and were found to be around 1/10 that for monoethanolamine. The fitting results for the termolecular mechanism seem more robust than those for the zwitterion mechanism from a statistical perspective.

Reservoir classification and evaluation are critical during the oil and gas exploration and production, and traditional production forecasting often require complex methods such as reservoir modeling, which is time-consuming and will delay the E&P process. With the rapid development of artificial intelligence technology, the production forecasting method based on machine learning has become a new hot spot for research. Since the characteristics of various reservoirs are different, it is difficult for conventional machine learning models to meet the required accuracy due to a large number of unknowns. In this paper, we proposed an improved hierarchical XGBoost (h-XGBoost) modeling method, which can effectively extract the features of different reservoirs and build a hierarchical data model. The study is conducted on tight conglomerates reservoir at the Mahu area in Xinjiang, and the results verify the effectiveness of the method.

Abstract Compared with other traditional energy sources, renewable energy, which results the less pollution and has numerous resources, is a significant factor in addressing the current issues of the serious environmental pollution and the resource depletion. Large-scale renewable energy integrated to the grid could bring change in both morphological structure and operation modes of energy transmission. Therefore, it is necessary to research the evolution mechanism of the future transmission network with a high proportion of the renewable energy. In this paper, an evolution framework of power system with high proportion of renewable energy is proposed. Firstly, a network equivalence and simplification based on power transfer distribution factors (PTDFs) is proposed, which can effectively simplify the decision-making process of evolution of large-scale power system. Then, an annual production simulation (8760 h) which takes into account renewable energy and load fluctuations is used to find out the bottleneck of the power grid. Based on the above methods, evolution strategy of power system with high proportion of renewable energy is studied for finding out optimal expansion strategy. A real power system of Zhejiang province is used as a test system. Test results demonstrate the feasibility of the proposed evolution framework.

To unlock the potential of flexible resources, a multi-time-scale economic scheduling strategy for the virtual power plant (VPP) to participate in the wholesale energy and reserve market considering large quantity of deferrable loads (DLs) aggregation and disaggregation is proposed in this paper. For the VPP multi-time-scale scheduling including day-ahead bidding and real-time operation, the following models are proposed, namely, DLs aggregation model based on clustering approach, economic scheduling model considering DLs aggregation, and DLs disaggregation model satisfying consumers’ requirements, respectively. The proposed strategy can realize the efficient management of massive DLs to reduce the energy management complexity and increase the overall economics with high computation efficiency, which indicate its promising application in the VPP economic scheduling.

The current monitoring of bundle conductors is essential for precisely locating operation failures in transmission lines, leading to a more stable and reliable power grid. This paper proposed a novel method for current reconstruction of bundle conductors, utilizing tunneling magnetoresistive (TMR) sensors. Both the normal and abnormal situations are discussed. The optimal and sub-optimal measurement points for sensors were obtained for concise current measurement with only one uniaxial TMR sensor. The current in each bundle conductor was calculated separately based on the comprehensive algorithm. Validation experiment indicated the error of the measurement and calculation system was no more than 10%, proving the robustness and feasibility of the measurement system and the algorithm. The reconstruction process takes full advantages of TMR sensors and provides a promising solution for transmission line monitoring in power grid.