Superheat

Guojie Zhang, S. Dykas, M. Majkut, K. Smołka, Xiaoshu Cai

Abstract The spontaneous condensation process takes place in many power engineering machines and devices, such as steam turbines, supersonic separators, ejectors etc. In the paper, the effect of the inlet steam superheat degree in steam condensing flow through the IWSEP (International Wet Steam Experimental Project) nozzle was investigated experimentally and numerically. The experiment was conducted for the low inlet pressure values under different inlet superheat conditions. The static pressure on the nozzle walls as well as the liquid phase properties in the three positions along the nozzle centreline were measured. The paper delivers new experimental data desirable for validation of the numerical models. For the numerical simulations, a condensation model using the Benson surface tension model was employed, and its accuracy was checked by comparing with the experimental data of SUT. The results state clearly that the condensation model using the Benson surface tension model can predict the spontaneous condensation process accurately, and the error is less than 5%. With the inlet steam superheat degree increasing, the condensation process moves downstream and the condensation strength weakens, and the nucleation zone range increases about 36mm. The presented results provide a good reference for further research in the field of condensing steam flows and for the improvement of both experimental and numerical methods and models.

Alireza Gholijani, Christiane Schlawitschek, T. Gambaryan-Roisman, P. Stephan

Abstract The present work addresses the influence of the wall superheat, drop impact velocity, and impact diameter on hydrodynamics, heat transport, and evaporation during drop impingement onto a heated solid wall in a pure vapor atmosphere. A generic experimental setup has been designed and built with a temperature-controlled cell that allows investigation of drop impingement in a pure vapor atmosphere. Therein a single drop is generated and falls onto a heated surface due to gravity. The experiments are conducted with refrigerant FC − 72 . The heated surface is formed by a thin metallic layer coated onto an infrared transparent glass, so that the temperature field of the solid-fluid interface can be observed from below with an infrared camera at high spatial and temporal resolution. The heat flux field is derived from the temperature field using a dedicated post-processing procedure. The dynamic evolution of contact line radius is derived using image analysis. The drop shape is observed with a high speed camera, which is synchronized with the infrared camera. Experimental and numerical results for contact line radius and heat flow evolution are compared with each other. This gives an insight to the governing heat transport mechanism during different phases of drop impingement. Experimental and numerical parameter studies reveal that higher wall superheats, higher impact velocities, or larger drop diameters each result in increasing heat flow after the impact. The maximum spreading radius after impingement is increasing with rising impact velocity or impact diameter, and decreasing with rising wall superheat.

Yongxiang Lei, Xiaofang Chen, Mengcan Min, Yongfang Xie

Abstract The superheat degree (SD) in industrial aluminum electrolysis cell is a critical index that can maintain the energy balance, improve the current efficiency and improve production. However, the existing SD identification is mainly relying on artificial experience and the accuracy of SD is far from satisfactory. Further, artificial costs and physical equipment are expensive and time-consuming. In this paper, we propose a deep soft sensor method for SD detection. First, CNN is utilized for flame hole image feature extraction. Second, a semi-supervised extreme learning machine (ELM) that integrates Laplacian regularization is further used for SD classification. The main contributions of the paper are: (1) The proposed CNN-LapsELM utilizes the CNN for flame hole image feature extraction and then ELM for further classification, which fully takes advantage of CNN’s ability for complex feature extraction, ELM’s excellent generalization ability, and high computation efficiency. (2) Both the labeled and unlabeled samples are utilized for the CNN-LapsELM training process. It fully leverages the information contained in unlabeled data. At the same time, Laplacian regularization is utilized for learning the manifold structure of hole image samples, so the performance of the proposed CNN-LapsELM are improved. (3) The proposed CNN-LapsELM algorithm improves the generalization ability and robustness. The comparison result demonstrates that the CNN-LapsELM is superior to the existing SD identification and the accuracy is 87%.

Weichao Yue, Lingfeng Hou, Xiaoxue Wan, Xiaofang Chen, W. Gui

Superheat degree is a core technical parameter and management index of aluminum electrolysis cell. However, the existing methods have limited abilities when applied to superheat degree recognition of aluminum electrolysis cell (SDRAEC). In addition, the important hesitant degree is ignored in the unbalance double hierarchy linguistic term set (DHLTS). To address these issues, an unbalance double hierarchy hesitant linguistic Petri net (UDHHLPN) model and extended TOPSIS is proposed for SDRAEC. In this model, the coupling relationships among variables is made to be explicit knowledge, and the unbalance double hierarchy hesitant linguistic term set (UDHHLTS) is proposed to represent the value of knowledge parameter. The relative entropy is introduced to enhance the performance of extended TOPSIS. Moreover, hybrid averaging UDHHLTS concurrent reasoning algorithm is proposed to improve the reasoning efficiency. Finally, thermal analysis experiments conducted in a real-world aluminum electrolysis plant are used to demonstrate the effectiveness of the proposed method. Compared with other methods, the accuracy of SDRAEC has been increased to 89.00%.

Runze Lin, Yangyang Luo, Xialai Wu, Junghui Chen, Biao Huang, Lei Xie, Hongye Su

The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance. However, in the context of smart manufacturing in the process industry, traditional model-based optimization control methods are unable to adapt to the varying operating conditions of the ORC system or sudden changes in operating modes. Deep reinforcement learning (DRL) has significant advantages in situations with uncertainty as it directly achieves control objectives by interacting with the environment without requiring an explicit model of the controlled plant. Nevertheless, direct application of DRL to physical ORC systems presents unacceptable safety risks, and its generalization performance under model-plant mismatch is insufficient to support ORC control requirements. Therefore, this paper proposes a Sim2Real transfer learning-based DRL control method for ORC superheat control, which aims to provide a new simple, feasible, and user-friendly solution for energy system optimization control. Experimental results show that the proposed method greatly improves the training speed of DRL in ORC control problems and solves the generalization performance issue of the agent under multiple operating conditions through Sim2Real transfer.