ABSTRACT
It is very important to choose a suitable method and catalyst to treat coking wastewater. In this study, Fe-Ce-Al/MMT catalysts with different Fe/Ce molar ratios were prepared, characterized by XRD, SEM, and N2 adsorption/desorption, and treated with coking wastewater. The results showed that the optimal Fe-Ce-Al/MMT catalyst with a molar ratio of Fe/Ce of 7/3 has larger interlayer spacing, specific surface area, and pore volume. Based on the composition analysis of real coking wastewater and the study of phenol simulated wastewater, the response surface test of the best catalyst for real coking wastewater was carried out, and the results are as follows: initial pH 3.46, H2O2 dosage 19.02 mL/L, Fe2+ dosage 5475.39 mL/L, reaction temperature 60 °C, and reaction time 248.14 min. Under these conditions, the COD removal rate was 86.23%.
ABSTRACT
Current computer vision tasks based on deep learning require a huge amount of data with annotations for model training or testing, especially in some dense estimation tasks, such as optical flow segmentation and depth estimation. In practice, manual labeling for dense estimation tasks is very difficult or even impossible, and the scenes of the dataset are often restricted to a small range, which dramatically limits the development of the community. To overcome this deficiency, we propose a synthetic dataset generation method to obtain the expandable dataset without burdensome manual workforce. By this method, we construct a dataset called MineNavi containing video footages from first-perspective-view of the aircraft matched with accurate ground truth for depth estimation in aircraft navigation application. We also provide quantitative experiments to prove that pre-training via our MineNavi dataset can improve the performance of depth estimation model and speed up the convergence of the model on real scene data. Since the synthetic dataset has a similar effect to the real-world dataset in the training process of deep model, we finally conduct the experiments on MineNavi with unsupervised monocular depth estimation (UMDE) deep learning models to demonstrate the impact of various factors in our dataset such as lighting conditions and motion mode, aiming to explore what makes this kind of models training better.