optimization of high energy intensity systems, case study: electric arc furnace

The paper establishes a framework for finding the optimal inputs into complex systems with higher energy intensity. it has a lower computational load, higher reliability, and better accuracy. the first is carried out through the developed linearization algorithm of a system. there is the ability to match every output to input to reach reliability, and a sophisticated algorithm guarantees accuracy. an electric arc furnace model is chosen to validate the framework because of its nonlinear functions, complexity, and significant energy intensity. the procedure is applied to an eaf model. at the step’s end, liquid mass, liquid temperature, and liquid grade must reach the desired ranges. the step is to be accomplished at the lowest cost in a determined time. the technique linearizes the nonlinear model around an operating point in the first step and reduces the system’s order. a suitable pairing is offered based on minimum interaction and passing some necessary decentralized integral controllable requirements in the second step. the third step is based on discretizing the operating point’s linearized system. this algorithm is iterated around new operating points. a comparison between the nonlinear system and reduced linear systems with the same feeds will be made in any iteration. if the results are compatible with each other, the next optimum feeds are estimated. otherwise, the sample time decreases, and the loop should be repeated for the previous point. the study is performed on a well-known eaf model with 14 state variables and seven input variables. the model’s outcome also is.