Drag coefficient modeling of heterogeneous connected platooning vehicles via BP neural network and PSO algorithm

The Tobacco Leaf Redrying Process Parameter Optimization Based on IPSO Hybrid Adaptive Penalty Function

In the tobacco redrying process, process parameter settings are greatly influenced by ambient temperature and humidity, and the moisture content of the tobacco leaf. In the face of complex and variable tobacco leaf characteristics, it is difficult to accurately adapt the process parameters to fluctuations in the incoming material characteristics by manual experience alone. Therefore, an improved optimization method combining an improved particle swarm optimization algorithm (IPSO) and an adaptive penalty function is proposed, which can adaptively recommend the best combination of process parameters according to the dynamic incoming characteristics of the tobacco leaf, to reduce the deviation in the outlet moisture and temperature of the roaster under different processing standards of the tobacco leaf. Firstly, the Radial Basis Function (RBF) Neural Network is used to fit the relationship between process parameters and roaster exit moisture content and temperature. Then, taking the standard tobacco leaf redrying export quality as the optimization goal, the optimization algorithm is used to search for the optimal solution. From the high-dimensional nature of the process operating conditions, the difficulty of this study lies in searching for the optimal solution under complex nonlinear constraints of multiple processes. To improve the convergence speed and accuracy of the searching algorithm, the position update method of the particle swarm optimization algorithm is improved, and the adaptive penalty function is combined to search for the optimal global solution to the optimization problem. Redrying experiments are conducted using the method proposed in this paper. Compared with the manual regulation of outlet moisture and temperature, the fluctuation range values are reduced by 7.5% and 11.8%, respectively, which has good application prospects and promotion value.

Numerical Analysis of Aeroacoustic Phenomena Generated by Heterogeneous Column of Vehicle

The last decade has seen an exponential interest in conventional and unconventional energy issues. This trend has also extended to road transport issues and is driven by expectations to minimize fuel and/or energy consumption and negative environmental impact. In the global literature, much attention is focused on the work of autonomous transport, both passenger and trucks, and on the phenomena of platooning. The paper presents original aerodynamic and aeroacoustic tests of heterogeneous vehicle columns. In the work, models of a car, a van and a truck were built, followed by heterogeneous columns with different distances between the vehicles. Computational fluid dynamics (CFD) methods and two turbulence models, k−ω shear stress transport (SST) and large eddy simulation (LES), were used in this study. The study enabled the determination of drag coefficients and lift force. Application of the Ffowcs Williams–Hawkings (FW-H) analogy allowed for the determination of the distributions of sound pressure levels generated by moving vehicles and columns of vehicles. In order to verify the developed models, acoustic field measurements were made for the following passages: passenger car, van, and truck. Acoustic pressure level and A-weighted sound level (SPL) were measured in Krakow and in its vicinity. Research has shown that grouping vehicles into optimal columns and maintaining distances between vehicles using modern control systems can result in significant energy savings and reduce harmful emissions to the environment.

Risk Management of Island Petrochemical Park: Accident Early Warning Model Based on Artificial Neural Network

Island-type petrochemical parks have gradually become the ‘trend’ in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was “safer”. The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks.