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A dynamic, first-principles process model for a steelmaking electric arc furnace has been developed. The model is an integrated part of an application designed for optimization during operation of the furnace. Special care has been taken to ensure that the non-linear model is robust and accurate enough for real-time optimization. The model is formulated in terms of state variables and ordinary differential equations and is adapted to process data using recursive parameter estimation. Compared to other models available in the literature, a focus of this model is to integrate auxiliary process data in order to best predict energy efficiency and heat transfer limitations in the furnace. Model predictions are in reasonable agreement with steel temperature and weight measurements. Simulations indicate that industrial deployment of Model Predictive Control applications derived from this process model can result in electrical energy consumption savings of 1–2%.

A model-based system for real-time monitoring and operational support has been developed for the Composition Adjustment by Sealed argon-bubbling with Oxygen Blowing (CAS-OB) process. The model of the system is based on a previously developed dynamic model using first principles, i.e., mass and energy balances, reaction kinetics, and thermodynamics. Adaptive estimation of state variables has been implemented using a Kalman filter to ensure that the model is able to correct for deviations between measured and calculated temperatures in real-time operation. The estimation technique reduces the standard deviation of the predicted end temperature from 19.5 °C to 5.5 °C in a data series with more than 1000 heats. The system also includes an endpoint optimisation, which calculates the amount of scrap or oxygen to be added to achieve the target temperature at the end of the heat. The model has been implemented in the Cybernetica® CENIT™ framework. The overall model can be regarded as a hybrid digital twin, where a first principles model is adapted in real time using process measurements. The system also includes user interfaces for operators where process predictions can be followed, and suggested optimised inputs are presented. The system has been deployed at two refining stations at SSAB Europe OY in Raahe, Finland. The optimized suggestions for oxygen and scrap are presented to the operators in the graphical user interface. A projected temperature profile is calculated into the near future using the planned operational procedure as well as the projected temperature profile using optimised inputs. Both profiles are displayed in the user interface. Based on these trajectories, the operator can decide on whether to follow the nominal trajectory, or the recommendation from the optimisation This will help the operators make better decisions, which in turn reduces the number of rejected heats in the CAS-OB process.

Many dynamic models encounter numerical integration problems because of a large span in the dynamic modes. In this paper we develop a numerical integration scheme for systems that include a gas phase, and solid and liquid phases, such as a gas-solid reactor. The method is based on neglecting fast dynamic modes and exploiting the structure of the algebraic equations. The integration method is suitable for a large class of industrially relevant systems. The methodology has proven remarkably efficient. It has in practice performed excellent and been a key factor for the success of the industrial simulator for electrochemical furnaces for ferro-alloy production.

The high-carbon ferromanganese furnace is a semi-continuous unit process, with a number of complex, and partly unknown mechanisms taking place inside it. The furnace operation has been characterized by fluctuations in vital process variables. Many of the operational problems are caused by the lack of information about the internal conditions. This paper presents an object-oriented model of the furnace and its future application in monitoring the furnace conditions. The object-orientation ensures that the model becomes easy to modify as new process knowledge is collected.

Many dynamic models encounter numerical integration problems because of a large span in the dynamic modes. In this paper we develop a numerical integration scheme for systems that include a gas phase, and solid and liquid phases, such as a gas-solid reactor. The method is based on neglecting fast dynamic modes and exploiting the structure of the algebraic equations. The integration method is suitable for a large class of industrially relevant systems. The methodology has proven remarkably efficient. It has in practice performed excellent and been a key factor for the success of the industrial simulator for electrochemical furnaces for ferro-alloy production.

This chapter contains sections titled: Introduction The Hall‐Heroult process Motivation Experimental conditions Towards a novel control philosophy Results and discussion Conclusions Acknowledgements