Search

• Experimental measurements of the adsorption characteristics of MSC30+R32 over a wide temperature range. • Development of an improved adsorption potential model for predicting isotherm data of various adsorbent-adsorbate pairs under supercritical conditions. • Validation of the proposed models using experimental data and comparison with traditional empirical equations. • Evaluation of the performance of an adsorption heat pump utilizing MSC30/R32 as the working fluid under supercritical conditions, with insights into the underlying mechanisms. In the present study, the adsorption isotherms of difluoromethane (R32) on the activated carbon (MSC 30) throughout a temperature range of 25 °C to 150 °C and pressures up to 3000 kPa are measured for possible application in adsorption cooling systems. The Dubinin-Astakhov model was used to fit the experimental data. A thermodynamic model is proposed in this study to investigate the adsorption potential under supercritical conditions. The model is validated using the isotherm data from the experiments and literatures for different working pairs. The validation results exhibited a good agreement between the proposed model and the experimental data, signifying the precision and dependability of the model. The corresponding adsorption isosteric heat model was derived and verified, taking into account both the inclusion and exclusion of the adsorbed volume correction. Furthermore, the developed models were utilized in the equilibrium analysis of an adsorption heat pump to evaluate the performance of the system in terms of the theoretical coefficient of performance (COP) and specific cooling energy (SCE). The analysis covered the driving heat source temperature ranging from 30 °C to 150 °C, with different evaporation temperatures and adsorption temperatures. The results showed that the maximum COP value of 0.47 for the adsorption heat pump system employing the MSC30+R32 pair was achieved at a desorption temperature of 115 °C, at which the SCE was 315.5 kJ·kg−1. The results can potentially improve the accuracy of predicting adsorption behavior and contribute to the development of more efficient and effective adsorption systems. [ABSTRACT FROM AUTHOR]

The thermogravimetric method was employed to measure the adsorption kinetics of difluoromethane (R32) on MSC30 activated carbon powder adsorbent over a wide range of temperatures (25 °C to 150 °C) and pressures (up to 3000 kPa). The accuracy of various adsorption kinetics models, including FD model, LDF model, semi-infinite model, and modified adsorption kinetics models, were assessed to compare their suitability for predicting the kinetics uptake of the MSC30/R32 working pair. Based on the fitting of the Arrhenius equation, the activation energy E a and pre-exponential coefficient D s R p 2 were calculated to be 6262.37 (kJ·kg−1) and 0.00853 (s−1), respectively. The MSC30/R32 pair exhibited the highest diffusion time constant and adsorption rate among the common MSC30/refrigerant pairs. Furthermore, a new mass transfer coefficient model was proposed to enhance the accuracy and broaden the range of applicability of the existing model (Jribi model). Comprehensive validations were carried out using various adsorbent-adsorbate pairs over a wide temperature range. The Jribi model in conjunction with the proposed mass transfer coefficient model achieved high accuracy and precision in predicting the adsorption dynamics of diverse working pairs, both during the initial stage and near the saturation state. This study provides valuable insight into the adsorption kinetics analysis, which is essential in accurately simulating adsorption systems for various applications, including refrigeration and air conditioning. [ABSTRACT FROM AUTHOR]

To improve the accuracy and robustness of urban link travel time estimation with limited resources, this research developed a methodology to estimate the urban link travel time using low frequency GPS probe vehicle data. First, focusing on the case without reporting points for the GPS probe vehicle on the target link in the current estimation time window, a virtual report point creation model based on the K-Nearest Neighbour Rule was proposed. Then an improved back propagation neural network model was used to estimate the link travel time. The proposed method was applied to a case study based on an arterial road in Changchun, China: comparisons with the traditional artificial neural network method and the spatiotemporal moving average method revealed that the proposed method offered a higher estimation accuracy and better robustness. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level route guidance method based on macroscopic fundamental diagram (MFD) for urban road network, which is a combination of central route guidance and distributed route guidance and it considers the intentions of both traffic management and traveler. It is a compromise of system optimum and user optimum. In upper-level route guidance, traffic dynamic evolution model of route guidance sub-region based on MFD is constructed and system optimal dynamic traffic assignment method on traffic guidance sub-region level is proposed. In lower-level route guidance, the traffic guidance paths are generated by solving the optimal path problem of the reactive users. Through analysis of the data gathering from Changchun City, China, it is verified that the proposed method not only meets the real-time requirements of dynamic traffic guidance but also provides benefits for the whole traffic system and individual traveler. [ABSTRACT FROM AUTHOR]

The paper adopts the improved ViBe algorithm, and initializes the background sample according to the original algorithm. Using the idea of image binarization, the current frame image is subjected to the foreground detection of the traditional ViBe, and then the second judgment is made according to the number of times the target is judged, and the original is changed. The fixed background factor in the algorithm background update strategy increases the background update probability during shadow elimination. Experiments show that the method can eliminate shadows in time and improve the accuracy of motion vehicle detection in different scenarios. At the same time, the paper designs a multi-lane traffic flow monitoring system based on improved ViBe algorithm combined with IoT ZigBee technology. The flow monitoring accuracy of the algorithm is verified. [ABSTRACT FROM AUTHOR]

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust. [ABSTRACT FROM AUTHOR]

Short-term traffic flow prediction is one of the most important issues in the field of adaptive traffic control system and dynamic traffic guidance system. In order to improve the accuracy of short-term traffic flow prediction, a short-term traffic flow local prediction method based on combined kernel function relevance vector machine (CKF-RVM) model is put forward. The C-C method is used to calculate delay time and embedding dimension. The number of neighboring points is determined by use of Hannan-Quinn criteria, and the CKF-RVM model is built based on genetic algorithm. Finally, case validation is carried out using inductive loop data measured from the north–south viaduct in Shanghai. The experimental results demonstrate that the CKF-RVM model is 31.1% and 52.7% higher than GKF-RVM model and GKF-SVM model in the aspect of MAPE. Moreover, it is also superior to the other two models in the aspect of EC. [ABSTRACT FROM AUTHOR]

This paper aims at the traffic flow agglomeration effect characteristics and rapid evacuation requirement in sudden disaster; operation time of intraregional boundaries traffic signal coordination was presented firstly. Then intraregional boundaries intersection group dynamic subdivision and consolidation method based on relative similarity degree and similarity coefficient of adjacent intersections was put forward. As to make the traffic control strategy adapt to traffic condition of different intraregional boundaries intersection groups, this paper proposes an intraregional boundaries traffic signal coordination and optimization technology based on organic computing theory. Finally, this paper uses Delphi 7.0, MapX, and Oracle developing a software package, combined with Paramics V6 Simulator to validate the methods of this paper. The result shows that it can obviously improve disaster affected regional traffic signal control efficiency which reduces average traffic delay by 30–35%, decreases vehicle queue by more than 20% and reduces evacuation time more than 13.06%. [ABSTRACT FROM AUTHOR]

[Display omitted] • TS-1 membrane immobilization using oriented aggregation of nanocrystals. • A new immobilized micron-sized spherical TS-1 membrane catalyst was designed. • The compatibility of carriers with different shapes and materials was demonstrated. • The catalyst recycling was efficient and the catalytic activity remained stable. Molecular sieve membranes are widely used in separation and catalysis due to their good chemical and thermal stability, resistance to contamination, long life, and easy modification. Although immobilized molecular sieve membranes combine catalytic reactivity with recycling separation properties, preparing high-performance TS-1 molecular sieve membrane catalysts is still challenging. In this study, the oriented aggregation of nanocrystals was used in constructing molecular sieve membrane catalysts. By interfering with the crystallization of TS-1, pre-crystal modification with cetyltrimethoxysilane was employed to change the crystal growth behavior. Thus, uniform and dense molecular sieve membranes were prepared through the modulated crystallization of TS-1. This method reduced the requirements of the carrier surface properties for the loading of the membrane layer to enable the construction of membrane layers on carriers of different materials and shapes. Using this method, the effective H 2 O 2 utilization and the epichlorohydrin yield of the micron-sized spherical film catalyst prepared with the inexpensive templating agent TPABr in the chloropropene epoxidation reaction reached 95.89% and 90.22%, respectively. [ABSTRACT FROM AUTHOR]

The fluorescence of CDs was dramatically quenched in GSH-AuNPs solutions based on FRET, and then the fluorescent intensity was effectively recovered with the addition of BPO. The detection of BPO was achieved using GSH-AuNPs / CDs as nanoprobe accompanying with GSH-AuNPs from dispersion state to aggregation state. [Display omitted] • A sensitive FRET probe based on GSH-AuNPs / CDs is fabricated for BPO detection. • Sensitive and selective detection of BPO is achieved by regulating the FRET effect. • It offers a facile alternative for monitoring BPO additive amount in food. Herein, a sensitive fluorescence nanoplatform for benzoyl peroxide (BPO) detection is constructed from carbon dots (CDs) and glutathione capped gold nanoparticles (GSH-AuNPs). The fluorescence of CDs is first quenched due to the fluorescence resonance energy transfer (FRET) effect in the presence of GSH-AuNPs, and then effectively recovered when adding BPO. The detection mechanism lies in the aggregation of AuNPs in a high salt background due to oxidation of GSH caused by BPO, thus the amount of BPO is reflected by the variations of the recovered signals. The linear range and detection limit in this detection system is found to be 0.05–200 μM (R2 = 0.994) and 0.1 µg g−1 (3σ/K), respectively. Several possible interferents with high concentration show little influence on BPO detection. The proposed assay exhibits good performance for BPO determination in wheat flour and noodles, demonstrating its applicability for facile monitoring BPO additive amount in real foods. [ABSTRACT FROM AUTHOR]

In this work, a La3+ assisted glutathione-capped gold nanoclusters and carbon dots (GSH-Au NCs/CDs) nanoplatform for sensitive detection of fenthion (FEN) is fabricated. The fluorescence response of GSH-Au NCs significantly increases due to aggregation-induced emission enhancement (AIEE) effect induced by La3+, which is further enhanced with adding FEN due to the coordination between La3+ and FEN. Taking the fluorescence intensity of CDs as the signal background, the ratiometric fluorescence of GSH-Au NCs and CDs has a good linear relationship with the FEN concentration from 0.01 to 1.10 μg mL−1, and detecting FEN exhibits a good sensitivity at a low detection limit of 6.74 ng g−1. The La3+ assisted GSH-Au NCs/CDs nanoplatform demonstrates desirable selectivity and applicability for monitoring trace level of FEN in fruit and vegetable samples. The non-enzymatic strategy by taking advantage of successive AIEE of GSH-Au NCs has a great potential for facile screening organophosphate pesticides in agro-products. [Display omitted] • A non-enzymatic ratiometric fluorescence probe for FEN is facilely fabricated. • Sensitive detection of FEN is realized by La3+ induced successive AIEE of Au NCs. • The La3+-CDs/Au NCs system is applicable for FEN residue analysis in agro-products. [ABSTRACT FROM AUTHOR]

Exploring the maximum grain yields (GYs) and GY gaps in maize (Zea mays L.) can be beneficial for farmer to identify the GY-limiting factors and take adaptive management practices for a higher GY. The objective of this work was to identify the optimum maize plant density range and the ways to narrow maize GY gaps based on the variation of the GYs, dry matter (DM) accumulation and remobilization with changes in plant density. Field experiments were performed at the 71 Group and Qitai Farm in Xinjiang, China. Two modern cultivars, ZhengDan958 and ZhongDan909, were planted at 12 densities, ranging from 1.5 to 18 plants m−2. With increased plant density, single plant DM decreased exponentially, whereas population-level DM at the pre- (DMBS) and post- (DMAS) silking stages increased, and the amount of DM remobilization (ARDM) increased exponentially. Further analysis showed that plants were divided four density ranges: range I (<6.97 plants m−2), in which no DM remobilization occurred, DMBS and DMAS correlated significantly with GY; range II (6.97–9.54 plants m−2), in which the correlations of DMBS, DMAS, and ARDM with GY were significant; range III (9.54–10.67 plants m−2), in which GY and DMAS were not affected by density, DMBS increased significantly, and only the correlation of DMAS with GY was significant; and range IV (>10.67 plants m−2), in which the correlations of DMBS and ARDM with GY decreased significantly, while that of DMAS increased significantly. Therefore, ranges I and II were considered to be DM-dependent ranges, and a higher GY could be obtained by increasing the population-level DMAS, DMAS, and ARDM. Range III was considered the GY-stable range, increasing population-level DMBS, as well as preventing the loss of harvest index were the best way to enhance maize production. Range IV was interpreted as the GY-loss range, and a higher GY could be obtained by preventing the loss of HI and population-level DMAS. [ABSTRACT FROM AUTHOR]