Submerged arc welding (SAW), renowned for its high deposition rate and superior weld quality, is the go-to method for joining thick metals in heavy structures. However, industry beams and columns welded with SAW can e...
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Submerged arc welding (SAW), renowned for its high deposition rate and superior weld quality, is the go-to method for joining thick metals in heavy structures. However, industry beams and columns welded with SAW can exhibit detrimental defects like undercut, porosity, and burn-through, significantly impacting weld properties. This study addresses this challenge by presenting a multi-objective optimization approach for SAW parameters on AISI 1020 mild steel. Aiming to optimize tensile strength, hardness, and bead width, the study employs Taguchi's design of experiments and couples the multi-objective jaya algorithm with an artificial neural network (ANN). This synergistic combination yielded optimal process parameters: 417 A welding current, 20.7 mm electrode stick-out, 33.7 V voltage, and 505.8 mm/min transverse speed. These settings translated into exceptional weld characteristics, with ultimate tensile strength reaching 427 MPa, hardness of 73.9 HRB, and bead width of 14.29 mm. Confirmation tests further validated these findings, demonstrating minimal error and solidifying the effectiveness of the optimization approach. This research paves the way for enhanced weld quality and process control in heavy structural applications.
The traditional operation of the cascade hydropower stations system (CHPS) mainly focus on the maximization of power generation benefits, but ignores the interference of CHPS operation to the river ecosystem, therefor...
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The traditional operation of the cascade hydropower stations system (CHPS) mainly focus on the maximization of power generation benefits, but ignores the interference of CHPS operation to the river ecosystem, therefore, carrying out the multi-objective optimal operation (MOOP) of CHPS considering ecological demands is crucial. In this paper, a MOOP model considering the ecological objective is established. To effectively solve the MOOP problems, a novel multi-objective jaya algorithm (MOCOM-jaya) is proposed, where the quality of the initial population is enhanced based on the chaotic sequence, the later disturbance term and Gaussian mutation are incorporated to improve the local search ability, the elite opposition-based learning is adopted to broaden the optimization space. The proposed algorithm is applied to the study of MOOP of CHPS in the Wujiang river, and the results show that compared with MOPSO and NSGA-II, MOCOM-jaya can gain the solution set with better convergence and distribution for the MOOP. The competition relationship between the power generation objective (PGO) and the ecological objective (ECO) is revealed based on the partial replacement ratio method. The results show that the competitiveness of PGO and ECO experienced a trade lead with the increase of power generation. The mean competitiveness ratios of PGO to ECO ( (CPRP-E)over-bar) in three typical years (dry, normal, wet) are 3.22, 3.17 and 3.15, indicating that the PGO is dominant in the competition with the ECO as a whole.
Serious air pollution poses destructive effects on environmental safety and human health, which is a public threat worldwide. Therefore, it is important to develop a reliable air pollution forecasting system to monito...
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Serious air pollution poses destructive effects on environmental safety and human health, which is a public threat worldwide. Therefore, it is important to develop a reliable air pollution forecasting system to monitor the air quality in advance. Most of the existing researches lack data feature mining and uncertainty analyses of the predictions, leading to insufficient results. This study proposes a novel forecasting system that comprises a hybrid data preprocessing-analysis module, a combined deterministic prediction module, and a probabilistic forecasting module to overcome the above-mentioned drawbacks. Specifically, the air pollution data are decomposed and reshaped to eliminate negative disturbances to achieve high-quality data input for forecasting. Then, four individual models and a multi-objective weight determination strategy are combined to achieve the point forecasting results. After obtaining fitting errors, their distributions are analyzed to achieve forecasting intervals under different confidence levels. Finally, twelve datasets from three cities in China were employed for experiments and the obtained results have shown that the proposed model achieves more accurate and stable predictions than other benchmark models, providing reliable and comprehensive information for monitoring air quality.
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