Two-Layer Linear MPC Approach Aimed at Walking Beam Billets Reheating Furnace Optimization

Abstract

In this paper, the problem of the control and optimization of a walking beam billets reheating furnace located in an Italian steel plant is analyzed. An ad hoc Advanced Process Control framework has been developed, based on a two-layer linear Model Predictive Control architecture. This control block optimizes the steady and transient states of the considered process. Two main problems have been addressed. First, in order to manage all process conditions, a tailored module defines the process variables set to be included in the control problem. In particular, a unified approach for the selection on the control inputs to be used for control objectives related to the process outputs is guaranteed. The impact of the proposed method on the controller formulation is also detailed. Second, an innovative mathematical approach for stoichiometric ratios constraints handling has been proposed, together with their introduction in the controller optimization problems. The designed control system has been installed on a real plant, replacing operators’ mental model in the conduction of local PID controllers. After two years from the first startup, a strong energy efficiency improvement has been observed.

MPC-based energy efficiency improvement in a pusher type billets reheating furnace

“…Furthermore, DC&DS block exploits other information, i.e. local control loops conditions (Figure 8, Plant Signals & Parameters), status information related to the selected process variables (Figure 8, right side, u-d-y-b Status), and control requirements related to MVs manipulation (Figure 8, right side, Decoupling Matrix) [19,20]. The status information related to the selected process variables defines which process variables have to be included in the MPC problem at each control instant.…”

“…According to some additional specifications that have been defined for the considered case study, only zone 6 fuel flow rate should be moved for the cited zCV. With regard to this aspect, observe the second row of Table 5 (related to zone 6 temperature): the "0" value indicates the MVs to be inhibited for its control [20]. Based on the received information, DC&DS block performs different operations and checks, e.g.…”

“…bad detection and data conditioning. From this phase, the possibly modified process variables values (Figure 8, left side, u(k-1), d(k), y(k), b(k)), the final status values for process variables (Figure 8, left [20].…”

“…In Figure 8 targets (Figure 8, u-y Target) and zCVs constraints (Figure 8, y Constraints); these terms are provided to DO module [19,20].…”

Fuel Costs Minimization on a Steel Billet Reheating Furnace Using Genetic Algorithms

“…Some works present improvements in proportional integral derivative (PID) controllers responsible for controlling internal temperatures of the furnace in each zone, using air and fuel flow rates as measurements [12][13][14]. Under the black-box identification paradigm, first-order and secondorder structures with a pure time delay for a given operational point are generally selected to represent models as transfer functions.…”