Current Issue : October-December Volume : 2023 Issue Number : 4 Articles : 5 Articles
With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts....
A numerical model of a tower boiler burning low calorific value and high-alkali coal was established; based on the jet rigidity of the flow field in the furnace, the effects of air velocity and air volume flow on the combustion of high-alkali coal and NOx emissions were studied. The optimal parameters obtained by numerical simulation are applied to a typical experimental boiler. The experimental results show that when SOFA operates at about 65 m/s, the rigidity of the jet is obviously increased, the flow field and temperature field in the furnace are uniform, the temperature of the flue gas at the outlet is reduced by 53 K, the CO concentration at the furnace outlet was reduced from 3713 ppm to 57 ppm, the exhaust gas temperature was reduced by 6 K, and the concentration of NOx is reduced to 163 mg/m3. On the other hand, the combustion efficiency is increased by 0.86%, which translates into 2 g/kwh of standard coal, and the problem of easy coking has also been effectively solved. The collaborative optimization of high-alkali coal combustion and NOx emission is realized....
To achieve the digitization of all traffic infrastructure elements and enable three-dimensional digital representation of physical facilities, a multilevel road three-dimensional reverse modeling method is proposed based on road point cloud data obtained by a vehicle laser scanning system. First, based on the distribution characteristics of each target structure in the road scene and the modeling requirements, a levels of detail (LOD) modeling specification is designed, and the required feature data format for each level is defined. Next, the three-dimensional characteristic parameters needed for modeling are extracted from the vehicle point cloud data. Finally, the continuous quadrilateral algorithm is used to reconstruct the road model, the topological structure relationship algorithm is used to reconstruct the intersection model, and the instantiation lofting technology is used to reconstruct the rod-shaped target model, all based on the three-dimensional modeling platform. This approach allows for the rapid reverse reconstruction of three-dimensional road models with different LOD levels. The point cloud data of two sections of urban roads with different slopes and one section of expressways were modeled and compared with the original vehicle-mounted laser point cloud data. The data volume of different level models decreases with decreasing model fineness, and the LOD1 level model has the highest similarity with point cloud data, at approximately 92.17%. The similarity of LOD2 and LOD3 decreases in turn, at 82.91% and 75.25%, respectively. For flat or undulating roads, the overall accuracy of the nearest point distance between different levels of road models and point cloud data is better than 10 cm, significantly higher than that of traditional manual modeling. The results demonstrate that vehicle mobile laser scanning technology provides new modeling data for the rapid realization of threedimensional reverse reconstruction of large-scale traffic infrastructure. Automatic modeling technology can effectively improve modeling efficiency, reduce data redundancy, and ensure model quality....
Complex forms may be easily created with additive manufacturing methods, but managing surface roughness remains a difficulty, even for flat surfaces, because surface quality is dependent on numerous parameters. This research investigates the effect of some printing factors on surface roughness in 3D printing methods. The purpose of this study is to quantify the most influential input printing factors on surface roughness in 3D printing processes. Polyacrylic acid thermoplastic was used to print workpieces, and mathematical models were generated using the regression method to analyze the relationship between process parameters and surface roughness. The exponential model fits the experimental data slightly better than the linear model. Only Ra-90 met all surface roughness classification requirements, while surface roughness measurements in the 0 and 45-degree directions did not meet the requirements and cannot be used to describe the surface roughness. The study highlights the importance of considering input printing parameters when optimizing surface roughness in 3D printing processes, providing valuable insights into the impact of process parameters on surface roughness....
Focusing on the phenomenon of the crack on the autoclave flange, a three-dimensional model of the autoclave was established based on the Ansys Workbench platform. A new finite element analysis flow for optimizing the design of autoclave teeth was established using the Fluent, Static Structure, and Direct Optimization modules. The paper analyzed and discussed the maximum stress and minimum fatigue life at the corresponding position. The variation trend of stress and fatigue life at the corresponding position after fillet optimization was also discussed. The results showed that the equivalent stress of autoclave teeth without fillet optimization reached the maximum under different fillet sizes. The equivalent stress and fatigue life of the autoclave tooth were the same as the rounded corner size obtained by the optimization design. The optimal global solution could be obtained through the optimization design process....
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