Integration of the internet into entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called the internet of things. However .the ubiquitous and wide range iot networks make them prone to cyber attacks. One of the main types of attack is a denial of service, where the attacker floods the network with the large volume of data to prevent nodes from using the services. An intrusion detection mechanism is considered a chief source of protection for information and communication technology. However, conventional intrusion detection methods need to be modified and improved for application to the iot owing to certain limitations, such as resource-constraints devices, the limited memory and battery capacity nodes, and specific protocol stacks.
In the steel industry - Tata steel, India, most of the lime produced in the lime plant is used in the steel-making process at LD shops. The quality of steel produced at LD shops depends on the quality of lime used. Moreover, the lime also helps in the crucial dephosphorization process during steel-making. The calcined lime produced in the lime plant goes to the laboratory for testing its final quality (CaO%), which is very difficult to control. To predict, control and enhance the quality of lime during lime making process, five machine-learning-based models such as multivariate linear regression, support vector machine, decision tree, random forest and extreme gradient boosting have been developed using different algorithms. Python has been used as a tool to integrate the algorithms in the models. Each model has been trained on the past 14 months’ data of process parameters, collected from level 1 sensor devices, to predict the future quality of lime. To boost the model’s prediction performance, hyper-parameter tuning has been performed using grid-search algorithm. A comparative study has been done among all the models to select a final model with the least root mean square error in predicting and control future lime quality. After the comparison, results show that the model incorporating support vector machine algorithm has least value of root mean square error of 1.23 in predicting future lime quality. In addition to this, a self-learning approach has also been incorporated into support vector machine model to enhance its performance further in realtime. The result shows that the performance has been boosted from 85% strike-rate in +/-2 error range to 90% of strike-rate in +/-1 error range in real-time. Further, the above predictive model has been extended to build a control model which gives prescriptions as output to control the future quality of lime. For this purpose, a golden batch of good data has been fetched which has shown the best quality of lime (≥ 94% of CaO%). A good range of process parameters has been extracted in the form of upper control limit and lower control limit to tune the set-points and to give the prescriptions to the user. The integration of these two models (Predictive model and control model) helps in controlling the quality of lime 12 hours before its final production of lime in lime plant. Results show that both models (Predictive model and control model) have 90% of strike-rate within +/-1 of error in real-time. Finally, a human machine interface has been developed to facilitate the user to take action based on control model’s output. Eventually this work is deployed as a lime making process automation to predict and control the lime quality.
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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