Journal article Open Access

Real-Time Lime-Storage Tracking Model in SteelMaking Plant

Vipul Kumar Tiwari; Kumar Gaurav; Umesh Kumar Singh; Jose Martin Korath; Manish Kumar Singh

Sponsor(s)
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)

In Tata Steel Ltd.- India, the calcined lime produced in the Merz-kiln is stored in the respective bins for its further use in steel making at LD shops. The quality of lime controls the quality of steel, refractory life and productivity. It also helps in removing the impurities during the steel-making process. Longer and inefficient storage of calcined lime results into degradation of the lime quality due to air slaking and fines generation. To optimize the storage time, a model has been developed which tracks the live charging, storage and discharging of lime at each respective bin. The model further gives recommendations in the form of preferences for charging and discharging of the bins. Python has been used as a tool for the model development. By the integration of level 1 and level 2 automation, it has become easier to achieve this aim by using data from sensor devices. Level 1 sensors have been installed in each respective bin to get the information about the level of materials inside the bin. Further this crucial data is stored in level 2 automation system to use it in the model. Model’s result shows the live tracking of calcined-lime stored in the bins. It generates a logical layer of material inside the bin and provides the age (storage time in hours) of each layer. Based on the age of layers, model gives the preferences for charging and discharging of the bins. Eventually It provides a decision-making platform to the plant user based on preferences for better lime-storage management. The system developed also contains a HMI (Human-machine interface) where user can visualize the live tracking and preferences for each bin given by the model. The system also captures the action taken by the user based on model’s preferences. Ultimately, it optimizes the storage time and controls the lime quality inside the bin. Eventually, it also controls the degradation of lime quality due to long storage. The model has been validated quantitatively with the real-time data of processing plant captured by the level 1 sensors. The result shows that model is able to track the level of material inside the bin, age of each layer and its storage duration. The result also shows the name of preferred bins to be charged/discharged to optimize the storage duration. As per requirements, the calcined lime stored in the bins is drawn to use it in the steel-making process.

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