Model Predictive Control with horizons online adaptation: a steel industry case study

Zanoli, Silvia Maria; Cocchioni, Francesco; Pepe, Crescenzo

doi:10.23919/ecc.2018.8550332

Cited by 11 publications

(15 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reported APC variables structure can be customized based on the single case study, for example introducing additional DVs, e.g., the furnace/air pressure. Furthermore, MVs can also be represented by gas/air flow rates and stoichiometric ratios setpoints: in these cases, some CVs need to be added, for example, the zone temperatures process variables [12,47,48,53].…”

Section: Variable Namementioning

confidence: 99%

“…An MPC horizons adaptation methodology is used (see Section 2.3) [53]. In particular, thanks to the estimated movement parameters, a prediction horizon for each billet to be considered is computed.…”

Section: Furnace Condition Detection and Multi-mode Mpc Definitionmentioning

confidence: 99%

“…For these variables, model mismatch compensation can be obtained through a deadbeat Kalman filter [57,58]. Zone temperature PVs can also be used as intermediate variables between MVs and the billet temperature model [48,53].…”

Section: General Formulation and Control Modes Characterizationmentioning

confidence: 99%

“…, H u (k)) instants, based on the adapted values of H p (k) and H u (k). The authors proposed the following online adaptation law (see [53]):…”

Section: General Formulation and Control Modes Characterizationmentioning

confidence: 99%

“…In Equation ( 12), f ix(x) rounds x to the nearest integer less than or equal to x. The adaptation law reported in Equations ( 12) and ( 13) defines the M i (k) prediction instants related to the MVs future moves in order to ensure several MVs control moves in the initial prediction instants and to ensure a sufficient control horizon [53].…”

Section: General Formulation and Control Modes Characterizationmentioning

confidence: 99%

See 4 more Smart Citations

Multi-Mode Model Predictive Control Approach for Steel Billets Reheating Furnaces

Zanoli

Pepe

Orlietti³

2023

Sensors

Self Cite

View full text Add to dashboard Cite

In this paper, a unified level 2 Advanced Process Control system for steel billets reheating furnaces is proposed. The system is capable of managing all process conditions that can occur in different types of furnaces, e.g., walking beam and pusher type. A multi-mode Model Predictive Control approach is proposed together with a virtual sensor and a control mode selector. The virtual sensor provides billet tracking, together with updated process and billet information; the control mode selector module defines online the best control mode to be applied. The control mode selector uses a tailored activation matrix and, in each control mode, a different subset of controlled variables and specifications are considered. All furnace conditions (production, planned/unplanned shutdowns/downtimes, and restarts) are managed and optimized. The reliability of the proposed approach is proven by the different installations in various European steel industries. Significant energy efficiency and process control results were obtained after the commissioning of the designed system on the real plants, replacing operators’ manual conduction and/or previous level 2 systems control.

show abstract

Section: Variable Namementioning

confidence: 99%

Section: Furnace Condition Detection and Multi-mode Mpc Definitionmentioning

confidence: 99%

Section: General Formulation and Control Modes Characterizationmentioning

confidence: 99%

“…, H u (k)) instants, based on the adapted values of H p (k) and H u (k). The authors proposed the following online adaptation law (see [53]):…”

Section: General Formulation and Control Modes Characterizationmentioning

confidence: 99%

Section: General Formulation and Control Modes Characterizationmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Mode Model Predictive Control Approach for Steel Billets Reheating Furnaces

Zanoli

Pepe

Orlietti³

2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

A combined adaptive entropy‐TOPSIS and model predictive control strategy for mixed loading and delay operations in the reheating furnace

Yang

Luo

Qiao

2023

Optim Control Appl Methods

View full text Add to dashboard Cite

SummaryIn this paper, a combined adaptive entropy‐TOPSIS and model predictive control strategy is proposed to deal with the mixed loading and delay operations in the reheating furnace. Firstly, the mathematical models consistent with the behaviour of the real reheating furnace are built to describe the complicated heat exchange process. Secondly, a dynamical optimization problem for the mixed loading operation and delay operation in the walking beam reheating furnace is obtained. To adjust the weighting factors of the optimization problem in real time, the adaptive entropy‐TOPSIS method is proposed. Then, the rolling horizon approach is applied to solve the proposed optimization problem. Finally, numerical experiments and simulation analysis are undertaken to verify the reliability and accuracy of the proposed strategy. The simulation results demonstrate that the proposed strategy can deal with three typical cases of delays effectively and the control accuracy is successfully improved from 74.79% to 99.17%.

show abstract

High-Level Optimization of a Steel Industry Reheating Furnace

Zanoli

Pepe²,

Astolfi³

et al. 2022

Lecture Notes in Control and Information Sciences - Proceedings

View full text Add to dashboard Cite

Model Predictive Control with horizons online adaptation: a steel industry case study

Cited by 11 publications

References 8 publications

Multi-Mode Model Predictive Control Approach for Steel Billets Reheating Furnaces

Multi-Mode Model Predictive Control Approach for Steel Billets Reheating Furnaces

A combined adaptive entropy‐TOPSIS and model predictive control strategy for mixed loading and delay operations in the reheating furnace

High-Level Optimization of a Steel Industry Reheating Furnace

Contact Info

Product

Resources

About