Nano-titanium can be used in the field of anticorrosive coatings due to its excellent corrosion resistance. In this paper, phenolic resin was modified by nano-titanium using a physicochemical method. The nano-titanium-modified phenolic resin was used as a matrix to prepare the anticorrosive coating. The microstructures of the coatings were analyzed by Scanning Electron Microscope (SEM), X-ray spectroscopy (EDS) and Fourier transform infrared spectroscopy (FTIR). Raman and UV spectrum adhesion of the coating was tested by a scratching method. The corrosion behavior was studied by electrochemical workstation and salt spray test. The results showed that the corrosion resistance of pure phenolic resin coating was significantly improved by the nano-titanium-modified phenolic resin. The coating containing 4% titanium nanoparticles exhibited the best corrosion resistance, with the highest impedance and the smallest corrosion current. The coating remained intact after 480 h of salt spray, showing the best salt spray resistance performance.
This article proposes a modeling method for the SPSO-WNN neural network in absorbers used in absorption refrigeration systems. The method establishes the input-output structure of the absorber model by analyzing the absorber’s working principles. A WNN neural network with a single implied layer is selected as the internal structure of the model, and the number of nodes in the implied layer is determined using the root mean square error and a trial-and-error method of decision coefficients. To address the limitations of the WNN neural network and improve the model’s prediction accuracy, the article uses Morlet wavelet basis functions and a simple particle swarm algorithm to improve the determination of the traditional neural network’s activation function in the hidden layer and the parameters of the neural network’s weights, scaling factor, and translation factor. The results of experimental simulations show that the absorber SPSO-WNN model established in this paper has less error than the traditional WNN neural network model, with the root mean square error of the two outputs being 1.73% and 2.36%, respectively. The model established by this method is simple and effective and can be used as an equation constraint in the optimal operation control strategy of absorption refrigeration systems.
The utilization of polymer additives for drag reduction stands as a crucial method in achieving reduced resistance in pipelines.As a common pipe fitting in the pipeline system,the study of the sudden expansion pipe flow holds significant importance for flow drag reduction and energy conservation.The numerical simulation method is used to examine the turbulent flow of the polymer PAM(polyacrylamide)solution in a sudden expansion pipe through the Cross model,and the flow field,drag reduction patterns and the variations in the recirculation zone are also analyzed.Results indicate that,the pressure recovery maximum of PAM solution is greater than that of water under the same Reynolds number.The drag reduction rate in the upstream straight pipe exhibits an initial increase followed by a decrease with the rise of Reynolds number.The local resistance coefficient of sudden expansion in PAM solution increases with the increase of Reynolds number,and shows a drag reduction effect,but the drag reduction rate is noticeably lower than that of the straight pipe.Furthermore,under the same Reynolds number,the length of the recirculation zone in PAM solution experiences an increased length compared to pure water.This alteration and the observed drag reduction effect are related to the change of turbulent kinetic energy.
The wastewater treatment capacity of algal-bacterial symbiotic systems can be further improved by optimizing the process parameters during advanced wastewater treatment using the response surface method.This study employed the response surface method(RSM)to optimize the aeration volume and CO2 volume fraction in algal-bacterial symbiotic system,analyzing its impact on the removal rates of nutrients to achieve optimal removal efficiency.The results indicate that the removal rates of Total Nitrogen(TN)and Ammonium Nitrogen(NH3-N)are significantly influenced by the aeration volume,whereas the removal rates of Total Phosphorus(TP)and Chemical Oxygen Demand(COD)are more susceptible to changes in CO2 volume fraction.The optimal aeration conditions predicted by the response surface method were an aeration volume of 0.639 L/min and a CO2 volume fraction of 3.849%.Under these conditions,the removal rates of TN,TP,NH3-N and COD reached their were maximum values within 2 days,achieving 70.040%,72.925%,100%and 83.612%respectively.
Rapid and accurate automatic landslides identification is of great significance to geological hazard survey,management and risk assessment.In this paper,Google Earth images are utilized to construct a sample set of historical landslides,and the Mask R-CNN object detection algorithm is employed to detect massive landslides automatically.The proposed method is demonstrated by two case studies in Danba County,Sichuan Province and Yongjing County,Gansu Province.Considering the factors which affect the occurrence of landslides,taking Danba County as an example,this paper uses the multi-layer perceptron mode to obtain the occurrence probability map of landslides.The accuracy of landslides detection is further validated by comparing the distribution of landslides detected by Mask R-CNN against the landslides occurrence probability map.The results show that the precision of landslide detection is 96%,recall is 85%,F1 score is 0.90,and 79%of the points are distributed in the area where the probability of landslides occurrence is higher than 80%;the precision of loess landslides detection is 98%,recall is 65%,and F1 score is 0.78.This method can effectively improve the automation of geological hazard information acquisition and the accuracy of landslides detection.
