Flame intensity analysis for hot molten metal pouring in the steel industry by applying image segmentation

Pouring large quantities of hot metal (HM) can release substantial amounts of flame. This problem is frequently encountered within the Basis Oxygen Furnace (BOF) steelmaking process where large quantities of HM (frequently exceeding 300 t) are poured into the converter vessels. The HM is contained in specially designed ladles and poured using overhead girder cranes. Excess release of flame may damage surrounding components such as crane ropes and consequently reduce their lifecycle. Therefore, limiting the release of flame during pouring, allows extending the lifetime of the components located in proximity of the ladle. The scope of this paper is to characterise flame generation during different pouring operations at a BOF steelmaking plant and to relate the amount of flame generated to process factors. Due to the complexity of the process under investigation, this paper does not aim to eliminate flame generation, but rather to identify approaches to its mitigation. The proposed approach utilises a standard CCTV camera to record videos of pours. An image segmentation analysis is then performed, where the flame is separated from the background image using pixel information in the CIE L*a*b* colour space. For each frame, flame intensity is then calculated. This process is partially automated for each video making use of MATLAB. A total of 169 videos are analysed and the pours that cause higher flame intensity are identified. In the last steps of the analysis, the process factors with the most significant impact on the flame release are identified and mitigating solutions are proposed

Three-dimensional CFD-DEM simulation of raceway transport phenomena in a blast furnace

Improving energy efficiency in a blast furnace (BF) has a significant effect on energy consumption and pollutant emission in a steel plant. In the BF, the blast injection creates a cavity, the so-called raceway, near the inlet. On the periphery of the raceway, a ring-type zone is formed which is associated with the highest coke combustion rate and temperatures in the raceway. Therefore, predicting the raceway size or in other words, the periphery of the ring-type zone with accuracy is important for estimating the BF’s energy and coke consumption. In the present study, Computational Fluid Dynamics (CFD) is coupled to Discrete Element Method (DEM) to develop a three-dimensional (3D) model featuring a gas–solid reacting flow, to study the transport phenomena inside the raceway. The model is compared to a previously developed two-dimensional (2D) model and it is shown that the assumptions associated with a 2D model, result in an overestimation of the size of the raceway. The 3D model is then used to investigate the coke particles’ combustion and heat generation and distribution in the raceway. It is shown that a higher blast flow rate is associated with a higher reaction rate and a larger raceway. A 10% increase in the inlet velocity (from 200 m/s to 220 m/s) caused the raceway volume to grow by almost 40%. The DEM model considers a radial discretization over the particle, therefore the heat and mass distributions over the particle are analyzed as well

Internet of Things in Sustainable Energy Systems

Our planet has abundant renewable and conventional energy resources but technological capability and capacity gaps coupled with water-energy needs limit the benefits of these resources to citizens. Through IoT technology solutions and state-of-the-art IoT sensing and communications approaches, the sustainable energy-related research and innovation can bring a revolution in this area. Moreover, by the leveraging current infrastructure, including renewable energy technologies, microgrids, and power-to-gas (P2G) hydrogen systems, the Internet of Things in sustainable energy systems can address challenges in energy security to the community, with a minimal trade-off to environment and culture. In this chapter, the IoT in sustainable energy systems approaches, methodologies, scenarios, and tools is presented with a detailed discussion of different sensing and communications techniques. This IoT approach in energy systems is envisioned to enhance the bidirectional interchange of network services in grid by using Internet of Things in grid that will result in enhanced system resilience, reliable data flow, and connectivity optimization. Moreover, the sustainable energy IoT research challenges and innovation opportunities are also discussed to address the complex energy needs of our community and promote a strong energy sector economy