Nowadays, the world is facing the threat of energy exhaustion. In order to realize the maximum utilization of energy, this paper designs an organic Rankine cycle system for low-temperature waste heat generation. Firstly, the equipment and components in the organic Rankine cycle system are modeled. Secondly, combining specific analysis, designed the low temperature waste heat power generation organic Rankine's circulatory system, and analyses the relevant evaluation index, performance or independent variables is studied the effects of main parameters on the system performance, and finally, in finite time thermodynamic analysis method, analyzed the unit of each independent variable parameters on the heat transfer area of the influence of output power. It provides a powerful basis for the design of the system and the improvement and optimization of its performance. The simulation results show that the system designed in this paper can effectively recover waste heat from waste gas and realize the function of power generation, which has a certain practical value.
Model-based methods utilize atmospheric scattering model to effectively dehaze images but introduce unwanted artifacts. By contrast, recent model-free methods directly restore dehazed images by an end-to-end network and avoid artificial errors. However, their dehazing ability is limited. To address this problem, we combine the advantages of supervised and unsupervised learning and propose a semisupervised knowledge distillation network for single image dehazing named SSKDN. Specially, we respectively build a supervised learning branch and an unsupervised learning branch by four attention-guided feature extraction blocks. In the supervised learning branch, the network is optimized by synthetic images. In the unsupervised learning branch, we dehaze real-world images by dark channel prior and refine dehazing network (RefineDNet) (another dehazing method) and use these dehazed images as fake ground truths to optimize network using prior information and knowledge distillation. Experimental results on synthetic and real-world images demonstrate that the proposed SSKDN performs better than state-of-the-art methods and owns powerful generalization ability.
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