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Electrospinning technology is one of the most important methods to prepare nanofiber materials. Polyvinylidene fluoride （PVDF） is a semi-crystalline thermoplastic. With its unique piezoelectric， dielectric， thermoelectric and biocompatibility， PVDF nanofibers prepared by electrospinning technology are widely used in the electrical， environmental engineering， biomedical， textile and other fields. In this paper， progress in the application of electrospun PVDF nanofibers is summarized based on Chinese patent search results， and its future development trends are predicted.
In order to optimize the operating parameters of a propane recovery process， Box-Behnken design （BBD） and central composite design （CCD） tests in a response surface method were used to optimize the operating parameters of propane recovery rate and unit comprehensive energy consumption as the response values. The optimal test method was selected based on four criteria： variance analysis， Pareto graph analysis， response surface analysis and optimization results. The results show that the quadratic response regression model established using the CCD method is superior to the BBD method. The significance results of the interaction parameters of the CCD response surface analysis are consistent with the analysis of variance. The optimal process parameters obtained using the CCD method are as follows： the de⁃ethanizer tower pressure is 3 250 kPa， the low temperature separator temperature is -55.6 ℃， the liquid phase reflux ratio is 98%， the propane recovery is 98.1%， and the unit comprehensive energy consumption is 114.1 kgce/104 m3. Under the optimal process parameters， the propane recovery rate increased by 4.36%， the product income reached 35 480 yuan/day， and the prediction error was less than 0.2%.
In order to solve the short-term scheduling optimization problem for a single pipeline containing high melting point crude oil， mathematical models have been established for the objective function and constraint conditions in the scheduling process， and an evolutionary algorithm based on hypervolume contribution （HVC） was used to solve the problem. The unique objective value set was obtained by population clustering， and the HVC was then calculated. The selection of reference points was optimized according to the objective value， and an improved non-dominated sorting genetic algorithm based on HVC （NSGA‒HVC） was proposed to solve the multi-objective optimization of the short-term scheduling problem for a single pipeline containing high melting point crude oil. The analysis shows that compared with other algorithms， the NSGA‒HVC algorithm has better solution performance， the hypervolume （HV） indicator is about 10% higher than other algorithms， and the obtained solution set shows better diversity and convergence. The scheduling results obtained using the NSGA‒HVC algorithm have been compared with the existing literature. The results show that the optimization effect of the proposed algorithm for different objectives is improved by 3.1%‒24.1%， and the overall scheduling cost is significantly reduced.
Pure phase ［B，Al］-IM5 zeolites were synthesized by a hydrothermal crystallization method using 1，5-bis （N-methylpyrrolidine）pentane dibromide （MPPBr2） as a template in the range n（SiO2）∶n（Al2O3）=40-80. The ［B，Al］-IM5 zeolites were characterized by X-ray diffractometry （XRD）， scanning electron microscope （SEM）， fluorescence spectrometry （XRF）， inductively coupled plasma emission spectrometry–mass spectrometry （ICP-MS）， static nitrogen adsorption measurements， and chemical adsorption measurements.The results show that the ［B，Al］-IM5 zeolites have a lamellar morphology， the XRD peak intensity is low， the specific surface area is larger than that of the rod-like pilot-scale IM-5 zeolite， and the acid amount and ratio of B acid to L acid are both low. During the crystallization process of ［B，Al］-IM5 zeolites， B atoms enter the molecular sieve framework in a four-coordinate form， and the decomposition of the template agent reduced， which promotes the growth of the crystal planes in specific directions resulting in the observed lamellar morphology.In the gas-phase alkylation reaction of toluene with ethylene， at a temperature of 300 °C and n（SiO2）∶n（Al2O3） of 60， the ethylene conversion with ［B，Al］-IM5 zeolite was 97.0%-98.5%， the toluene conversion （12.0%-10.3%） was close to the theoretical conversion （12.5%），and the selectivity and para-selectivity of p-methylethylbenzene were 41.9%-73.8% and 79.1%–85.8%， respectively. Compared with ZSM-5， the reaction temperature （300 °C） of ［B，Al］-IM5 was lower and the conversion of ethylene （98.5%） was higher.The ［B，Al］-IM5 zeolites can run stably for 105 h， which is 25 h and 78 h longer than that of the modified ZSM-5 zeolite in the literature and the pilot-scale IM-5 zeolite， respectively.
Lignin⁃based degradable materials have received increasing attention in the biomedical field in recent years， but there have been few reports of their application in cell culture microcarriers. In this work， alkylated alkali lignin was used as an initiator for the ring⁃opening polymerization of L-lactide. Lignin⁃grafted⁃poly（L-lactide）（LGPL-H， LGPL-L） samples with different molecular weights were used to prepare microspheres for the first time， and then co-cultured with MG-63 osteoblasts. The ability of the microspheres to induce mineralization was evaluated through biomimetic mineralization， which reflects their bone bioactivity. The cell viabilities of different microspheres decreased in the order LGPL-L>LGPL-H>PLLA. These results show that lignin⁃based polylactic acid microspheres can promote the adhesion and proliferation of human osteoblasts， confirming their potential for application in cell culture microcarriers.
To precisely predict the distributional dynamics of temperature and humidity within the composite structure of a light steel keel， and to address the paucity of research concerning the thermal insulation and moisture exclusion capabilities of the prevailing building envelopes， we have introduced this study introduces a numerical simulation approach. This method was employed to scrutinize and examine the thermal-moisture characteristics embedded within the composite wall. Anchored in the tenets of heat and mass transfer phenomena within porous media， the research confines its focus to the light steel keel composite wall. A three-dimensional transient heat-moisture coupled mathematical model specific to the light steel keel composite wall is adopted. The study evaluates and scrutinizes the thermal and moisture attributes of the composite wall under varying configurations of web opening lengths， structural configurations， and the incorporation of a vapor barrier. Moreover， the simulation elucidates the transient interplay of thermal and moisture dynamics within the composite wall. Findings from the experimental analysis indicate that the presence of light steel keels pierced with web holes markedly alters the temperature and humidity profiles within the composite wall， with variations being dependent on the hole configuration. Notably， distinct performance disparities are discernible among various interlayer materials within the light steel keel composite wall. Expanded polystyrene （EPS） demonstrates superior thermal insulation and moisture adsorption capabilities relative to foam concrete. Furthermore， the introduction of a steam insulation layer enhances the thermal insulation and moisture-resistant properties of foam concrete， thereby mitigating condensation risks. This suggests that the integration of a moisture-proof layer within the wall matrix is instrumental in condensation prevention.
To solve the problems resulting from tack coat tracking in the construction of asphalt pavement and its poor bonding in service， a new type of trackless emulsified asphalt （TEA） with self-migration functionality has been prepared. The road performance of the TEA was evaluated by means of penetration， softening point and tracking resistance tests. The micro-morphology and rheological properties of waterborne polyurethane-modified TEA tack coat were studied. The results showed that the waterborne polyurethane migrated to the surface of the emulsified asphalt tack coat during the curing process. The optimal content of waterborne polyurethane modifier in TEA was 3%. The curing time of the waterborne polyurethane-modified TEA tack coat at 35 ℃ was less than 30 min. The shear strength of the polyurethane-modified TEA tack coat was greater than 1 MPa at 60 ℃， and the tack coat showed no tracking phenomenon at 70 ℃. This has the potential to solve the problems of tack coat tracking in the construction of asphalt pavement and its poor bonding in service. The polyurethane-modified TEA has an outstanding road performance and an environmentally friendly construction process. Therefore， it can be expected to be employed on a large scale in highway construction and maintenance projects， improving the service life of asphalt pavement， affording highway traffic with high quality.
Oil pumps are critical pieces of equipment in the petroleum industry， and their reliability and stability are crucial for oil transportation. With the construction of intelligent and unmanned stations， the demand for oil pump fault diagnosis technology is increasing. Currently， using machine learning for oil pump fault diagnosis has achieved some positive results. However， existing fault diagnosis methods can only diagnose the types of faults included in the model training set. In actual usage， other faults not included in the training set may occur and cannot be correctly identified and diagnosed automatically. To address this issue， this paper proposes a self-learning framework for the fault diagnosis model of oil pumps. By combining signal processing techniques with deep learning methods to extract deep fault features， the separability of industrial field data is improved. By using unsupervised clustering and similarity measurement methods to differentiate known and unknown faults， the framework can identify and record unknown fault patterns that occur and retrain the model using frequently occurring unknown fault data. By adding the self-learning mechanism to the existing fault diagnosis model， the framework can increase its ability to recognize， diagnose， and learn from unknown faults while maintaining the original diagnostic function. To validate the effectiveness of the proposed method， experimental oil pump data collected from an industrial oilfield was used. The results show that the self-learning framework for the fault diagnosis model of oil pumps proposed in this paper can accurately identify unknown faults.
We have investigated the flow and heat transfer characteristics of superheated steam with low Reynolds number （Re in=1 881-10 348） during the reflooding process in a narrow channel after a large break loss of coolant accident （LBLOCA）. The computational fluid dynamics （CFD） method is used to investigate the influence of heating surface heat flux， pressure conditions and inlet velocity on the flow and heat transfer characteristics of low Reynolds number superheated steam based on the selected turbulent model. The Gnielinski and Dittus-Boelter empirical correlations are compared，and a modified Dittus-Boelter empirical correlation is proposed. The results show that the simulation using the SST k-ω turbulence model in the Reynolds average Navier-Stokes （RANS） approach is the closest to the results obtained by large eddy simulation （LES）， and the calculation requirement is lower. The convective heat transfer capacity of superheated steam increases with the increase of pressure and inlet velocity， but decreases with higher heating surface heat flux. Compared with the numerical simulation results， the Nusselt number predicted by the Dittus-Boelter and Gnielinski empirical correlation is lower， and the modified correlation reduces the error to within 15%. This work provides a valuable reference for the development of flow and heat transfer analysis of superheated steam with low Reynolds number during reflooding processes.
Compared with passive suspension， PID control cannot adjust the parameters in real time， hence the optimization effect of reducing vehicle acceleration and improving vehicle ride comfort is limited. In this paper， the mathematical model and road excitation model of 1/4 vehicle semi-active suspension are established by Simulink， and a fuzzy control strategy is introduced under the original PID control system. With the body vertical acceleration and its rate of change as the input of fuzzy PID control， real-time dynamic adjustment of the PID controller is realized within the original parameter range， and the performance index of the suspension is simulated and analyzed. The results show that the optimization effect of the semi-active suspension under PID control is 17.1%， while the optimization effect of the semi-active suspension under fuzzy PID control is more ideal than that of the passive suspension by 35.9%， and the dynamic deflection of the suspension is taken into account.
The concentration sequence of harmful gases in the air has high complexity， nonlinearity and volatility， which results in the accurate prediction of gas concentration being very challenging.In view of this， a Transformer prediction model （VMD-Transformer-ECM） based on variational mode decomposition （VMD） and an error compensation model （ECM） is proposed.Firstly， the gas concentration time series is decomposed into intrinsic mode functions （IMF） with different frequencies by VMD to reduce the complexity and non⁃stationarity of the prediction model input.The Transformer model is then used to predict the modal components obtained by decomposition， and the prediction results are reconstructed to obtain the preliminary prediction value.Finally， the error sequence is predicted by ECM， and the error prediction value is used to compensate the preliminary prediction value， so as to further improve the prediction accuracy of the model.The proposed model was validated using different datasets.The results show that compared with other models， the VMD-Transformer-ECM model has the smallest mean absolute percentage error （MAPE） and root mean square error （RMSE） and the largest determination coefficient R 2 for the prediction of CO2 and other harmful gas concentrations. When the prediction step is 3 h， the MAPE of the model for CO2 concentration prediction is 4.38%， RMSE is 35.44×10-6， and R 2 is 0.94. Our proposed model has higher prediction accuracy and better prediction performance than other models currently employed.
In order to solve the problem of low quality prediction accuracy caused by the complexity and variability of batch processes， a batch process quality prediction method based on an improved attention mechanism （PCAM）-temporal convolution network （TCN）-bidirectional long short-term memory network （BiLSTM） is proposed.Firstly， the kernel entropy component analysis （KECA） method is used to reduce the dimension of the batch process data to improve the efficiency of subsequent prediction process of input data.Then， the TCN network is used to extract the feature information of the data， and the improved position attention mechanism and channel attention mechanism are used to adaptively learn the two-layer BiLSTM network parameters of the encoder–decoder structure. According to the strength of the correlation between input and output， different weights are assigned to input features， so as to retain all the necessary input information and reduce the interference of secondary information.Finally， a cross-validation algorithm is used to optimize the hyperparameters of the proposed model， and the PCAM-TCN-BiLSTM prediction model is established.The prediction performance of the proposed model was experimentally verified on the penicillin fermentation simulation platform.The results show that compared with other models， the difference between the predicted value and the real value of the proposed model is small， and the evaluation indexes have the best performance of all the models.The root mean square errors （RMSE） of substrate concentration， penicillin concentration and cell concentration prediction were 0.009 2，0.034 6 and 0.023 2 g/L， respectively， the mean absolute errors （MAE） were 0.008 5，0.012 0 and 0.007 9 g/L， respectively， and the determination coefficients （R 2） were 0.998 7，0.992 3 and 0.991 5， respectively. The results show that the prediction accuracy of the proposed model is high and the prediction performance is good.
In this paper， we study the existence of solutions to a class of fractional Schrödinger equations with critical exponents. For some weak conditions on the perturbation term， it is proved by the mountain pass theorem that the equation has at least one non-trivial solution.
