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Simulation and Optimal Control of Electric Arc Furnace

Fathi, Amir Hossein | 2016

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 49076 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Saboohi, Yadollah
  7. Abstract:
  8. The study presents a comprehensive electric arc furnace model for simulation and control application to predict chemical processes, heat and mass transfer. The model consists of arc module, chemical – slag module and heat transfer sector to observe fundamental phenomena affecting on energy consumption. Two modules act independently of each other and the two module calculations apply in the heat transfer sector. Arc module predicts the amount of energy dissipated from the arcs using arc currents and arc lengths; chemical and slag module calculates chemical energy, changes of elements/compounds, slag height and slag quality; while the heat-transfer sector uses calculations of the other two modules in order to establish a reference energy system (RES) for each zone in the EAF with respect to the variations in arc length, slag height, and bath height during a Tap to Tap Time (TTT). The model is validated with different batches of a 105 ton EAF with an 85 MVA. Compering simulation results with measured data in the case of forming foamy slag show the model can predict liquid steel temperature with ±40°K precision, slag components with 8.5% precision (except Al2O3) and steel elements with %0.1 absolute error. Since calculation time is satisfactory, the model can be employed to monitor EAF. The optimization framework entails six steps: 1) dividing a batch process into four phases, 2) dividing an EAF as a Multi Input Multi Output (MIMO) system into five MIMO systems, 3) Utilizing multi-objective performance index: loss minimization, useful power maximization and operating cost minimization, 4) classifying losses into two categories controllable losses and uncontrollable losses, and neglecting uncontrollable losses from the objective funcion, 5) Substituting endpoint constraints with path constraints to separate four batch phases and 6) dividing the main optimization problem into some optimization problems. In order to evaluate the developed optimization framework, a heat of a modern EAF is compared with the suggested heat by the framework. The results reveal the framework brings reducing useful energy cost to 11.29% and as well as tap to tap time to 7.9%. In fact, the framework using slag height and arc length to increase chemical energy share (as energy carrier that is cheaper than electric energy) in compare to electric energy
  9. Keywords:
  10. Electric Arc Furnaces ; Modeling ; Reference Energy System ; Optimal Control ; Matter-Energy ; Flow Optimization

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