Introducing a new formula based on an artificial neural network for prediction of droplet size in venturi scrubbers

Abstract

-Droplet size is a fundamental parameter for Venturi scrubber performance. For many years, the correlations proposed by Nukiyama and Tanasawa (1938) and Boll et al. (1974) were used for calculating mean droplet size in Venturi scrubbers with limited operating parameters. This study proposes an alternative approach on the basis of artificial neural networks (ANNs) to determine the mean droplet size in Venturi scrubbers, in a wide range of operating parameters. Experimental data were used to design the ANNs. A neural network was trained based on the liquid to gas ratio (L/G) and throat gas velocity (V gth ), as input parameters, and the Sauter mean diameter (D 32 ) as the desired parameter. The back-propagation learning algorithms were used in the network and the best approach was found. A new formula for the prediction of D 32 using the weights of the network was then generated. This formula predicts mean droplet size in Venturi scrubbers more accurately than the correlations of Boll et al. (1974) and Nukiyama and Tanasawa (1938). The Average Absolute Percent Deviation (AAPD) of our formula and the Boll et al. and Nukiyama and Tanasawa correlations for the full ranges of experimental data are 26. 04%, 40.19% and 32.99%, respectively.

Application of Deep Reinforcement Learning to Predict Shaft Deformation Considering Hull Deformation of Medium-Sized Oil/Chemical Tanker

“…The values in the top 10 cases (Figure 17d) were −1.23 to −1.22 and there was no significant difference in the values of the 10 cases, but the shaft deformation prediction (Figure 17a) did not converge beyond a certain level. Based on these results, various margins of error may occur in the shaft assembly stage depending on the sag tolerance of the engine bed plate, the deviation of the center of the MBs, and the tendency of the operator when the shaft installation is inclined compared to the design [12]. Owing to the above characteristics of the shaft, the fact that the reaction force and bending moment values in the shaft deformation prediction converged to similar values while filtering from all cases to the top 10 cases means that all shaft deformation predictions of the top 10 cases were valid.…”

“…The structure of the deep neural network mimics the human brain and neural network. This technique can perform complex nonlinear analysis that is difficult to achieve with an equation for variables without prior definition [12]. RL changes to the "next state" by selecting an "action" from the "state" based on the content learned through trial and error to obtain a "reward," and an action or action pattern is selected that maximizes the sum of the rewards.…”

Integration of artificial neural networks for multi-source energy management in a smart grid

“…The learning algorithms used in the following are part of MATLAB toolbox of neural networks [18]. One of the indicators, that show the quality of the training operation, is the mean square error [19]- [21]. As shown in the following Figure 4, the mean square error gets the value 7.8488x10 -3 at 73 epochs, which shows that the training operation has worked well, which means that the MLP outputs will converge to the desired outputs perfectly in the three phases of training, testing and model validation [22]- [25].…”