Adsorption of benzene on soils under different influential factors: an experimental investigation, importance order and prediction using artificial neural network

2022 ◽  
Vol 306 ◽  
pp. 114467
Author(s):  
Qian Wang ◽  
Jianmin Bian ◽  
Dongmei Ruan ◽  
Chunpeng Zhang
2014 ◽  
Vol 989-994 ◽  
pp. 1814-1820 ◽  
Author(s):  
Ai Jun Shao ◽  
Qing Xin Meng ◽  
Shi Wen Wang ◽  
Ying Liu

Based on predictions of the mine inflow of water and the complexity of influential factors, a method of BP neural network is put forward for mine inrush water prediction in this paper. We chose proper impact factors and establish non-linear artificial neural network prediction model after analyzed the impact factors of mine water inflow in Shandong Heiwang iron, and also made one prediction with normal mine water inflow during the iron mining operation. It turned out that the result can match with the actual prediction data, which make it possible to predict the mine water inflow with the prediction of Artificial Neural Network.


Author(s):  
Zhikai Yao ◽  
Yongping Yu ◽  
Jianyong Yao

Internal leakage is a typical fault in the hydraulic systems, which may be caused by seal damage, and result in deteriorated performance of the system. To study this issue, this article carries out an experimental investigation of artificial neural network–based detection method for internal leakage fault. A period of pressure signal at one chamber of the actuator was taken in response to sinusoidal-like inputs for the closed-loop controlled system as a basic signal unit, and totally, 1000 periodic signal units are obtained from the experiments. The above experimental measurements are repetitively implemented with 11 different active exerted internal leakage levels, that is, totally 11,000 basic signal units are obtained. For signal processing, the pressure signal in the operation condition without active exerted leakage is chosen to generate a baseline with suitable pre-proceed, and the relative values of the other basic signal units (D-value between the baseline and other original signals) act as the global samples of the following artificial neural networks, traditional back propagation neural network, deep neural network, convolution neural network and auto-encoder neural network, separately; 8800 samples by random extraction as train samples to train the above neural networks and the other samples different from the train samples act as test samples to examine the detection accuracy of the proposed method. It is shown that the deep neural network with five layers can obtain a best detection accuracy (92.23%) of the above-mentioned neural networks. In addition, the methods based on wavelet transform and Hilbert–Huang transform are also applied, and a comparison of these methods is provided at last. From the comparison, it is shown that the proposed detection method obtains a good result without a need to model the internal leakage or a complicated signal processing.


Author(s):  
Edi Ismanto ◽  
Noverta Effendi ◽  
Eka Pandu Cynthia

Riau Province is one of the regions known for its plantation products, especially in the oil palm sector, so that Riau Province and regional districts focus on oil palm plants as the main commodity of plantations in Riau. Based on data from the Central Bureau of Statistics (BPS) of Riau Province, the annual production of oil palm plantations, especially smallholder plantations in Riau province has always increased. So is the demand for world CPO. But sometimes the selling price of oil palm fresh fruit bunches (FFB) for smallholder plantations always changes due to many influential factors. With the Artificial Neural Network approach, the Backpropagation algorithm we conduct training and testing of the time series variables that affect the data, namely data on the area of oil palm plantations in Riau Province; Total palm oil production in Riau Province; Palm Oil Productivity in Riau Province; Palm Oil Exports in Riau Province and Average World CPO Prices. Then price predictions will be made in the future. Based on the results of the training and testing, the best Artificial Neural Network (ANN) architecture model was obtained with 9 input layers, 5 hidden layers and 1 output layer. The output of RMSE 0000699 error value and accuracy percentage is 99.97% so that it can make price predictions according to the given target value.


2021 ◽  
Vol 11 (16) ◽  
pp. 7487
Author(s):  
Yo-Hyun Choi ◽  
Sean Seungwon Lee

Reliable estimates of peak particle velocity (PPV) from blasting-induced vibrations at a construction site play a crucial role in minimizing damage to nearby structures and maximizing blasting efficiency. However, reliably estimating PPV can be challenging due to complex connections between PPV and influential factors such as ground conditions. While many efforts have been made to estimate PPV reliably, discrepancies remain between measured and predicted PPVs. Here, we analyzed various methods for assessing PPV with several key relevant factors and 1,191 monitored field blasting records at 50 different open-pit sites across South Korea to minimize the discrepancies. Eight prediction models are used based on artificial neural network, conventional empirical formulas, and multivariable regression analyses. Seven influential factors were selected to develop the prediction models, including three newly included and four already formulated in empirical formulas. The three newly included factors were identified to have a significant influence on PPV, as well as the four existing factors, through a sensitivity analysis. The measured and predicted PPVs were compared to evaluate the performances of prediction models. The assessment of PPVs by an artificial neural network yielded the lowest errors, and site factors, K and m were proposed for preliminary open-pit blasting designs.


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