# Level Control of Blast Furnace Gas Cleaning Tank System with Fuzzy Based Gain Regulation for Model Reference Adaptive Controller

^{*}

## Abstract

**:**

## 1. Introduction

_{2}needed for 1 ton of steel [9]. The fact that iron and steel plants are businesses that operate 24/7 and produce in high quantities allows even small improvements to be made in these facilities to provide big profits on an annual basis. For example, in a facility that produces 10 million tons of pig iron annually, reducing the production cost of hot metal by $1 means that the facility earns $10 million annually from this improvement alone.

## 2. Blast Furnace Gas Cleaning Tank Level Control System

_{2}, N

_{2}, O

_{2}, H

_{2}gases due to the reactions inside. Blast furnace gas is used as a fuel in electricity production due to its pressure and in iron making facilities due to the gases it contains.

## 3. Fuzzy Model Reference Adaptive Controller

#### 3.1. Model Reference Adaptive Controller

#### 3.2. Fuzzy MRAC with Automatic Gain Regulation

## 4. Blast Furnace Gas Cleaning Tank Level Inspection Results and Discussion with Fuzzy MRAC

- Integral of the squared error (ISE) defined as:$$\mathrm{ISE}=\frac{1}{{t}_{s}}{{\displaystyle \int}}_{0}^{{t}_{s}}{\left|r\left(t\right)-{y}_{p}\left(t\right)\right|}^{2}dt$$
- Integral of the control signal change (ICSC) defined for given averaged control signal $\overline{u}\left(t\right)$ as:$$\mathrm{ICSC}=\frac{1}{{t}_{s}}{{\displaystyle \int}}_{0}^{{t}_{s}}{\left|u\left(t\right)-\overline{u}\left(t\right)\right|}^{2}dt$$

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Huang, Y.; Lv, Z.; Liu, Y.; Wang, W. A fuzzy modeling method based on Dirichlet process mixture model for blast furnace gas system. IFAC-PapersOnLine
**2018**, 51, 301–306. [Google Scholar] [CrossRef] - Aslan, Ö.; Altan, A.; Hacıoğlu, R. The Control of Blast Furnace Top Gas Pressure by using Fuzzy PID. In Proceedings of the 5th International Conference on Advances in Mechanical and Robotics Engineering AMRE’2017, Roma, Italy, 27–28 May 2017. [Google Scholar]
- Peng, C.; Chen, L. Model reference adaptive control based on adjustable reference model during mode transition for hybrid electric vehicles. Mechatronics
**2022**, 87, 102894. [Google Scholar] [CrossRef] - Altan, A.; Aslan, Ö.; Hacıoğlu, R. Model Reference Adaptive Control of Load Transporting System on Unmanned Aerial Vehicle. In Proceedings of the 6th International Conference on Control Engineering & Information Technology, Istanbul, Turkey, 25–27 October 2018. [Google Scholar]
- Li, Z.; Hanaoka, T. Plant-level mitigation strategies could enable carbon neutrality by 2060 and reduce non-CO
_{2}emissions in China’s iron and steel sector. One Earth**2022**, 5, 932–943. [Google Scholar] [CrossRef] - Crompton, P. Explaining variation in steel consumption in the OECD. Resour. Policy
**2015**, 45, 239–246. [Google Scholar] [CrossRef] - Bailera, M.; Nakagaki, T.; Kataoka, R. Limits on the integration of power to gas with blast furnace ironmaking. J. Clean. Prod.
**2022**, 374, 134038. [Google Scholar] [CrossRef] - Smil, V. Still the Iron Age: Iron and Steel in the Modern World; Butterworth-Heinemann: Oxford, UK, 2016; ISBN 978-0-12-804233-5. [Google Scholar]
- Sachs, J.; Kroll, C.; Lafortune, G.; Fuller, G.; Woelm, F. Sustainable Development Report 2021; Cambridge University Press: Cambridge, UK, 2021; ISBN 978-1-009-09891-5. [Google Scholar]
- Swennenhuis, F.; de Gooyert, V.; de Coninck, H. Towards a CO
_{2}-neutral steel industry: Justice aspects of CO_{2}capture and storage, biomass-and green hydrogen-based emission reductions. Energy Res. Soc. Sci.**2022**, 88, 102598. [Google Scholar] [CrossRef] - Lopez, G.; Farfan, J.; Breyer, C. Trends in the global steel industry: Evolutionary projections and defossilisation pathways through power-to-steel. J. Clean. Prod.
**2022**, 375, 134182. [Google Scholar] [CrossRef] - Peng, G.; Cheng, Y.; Zhang, Y.; Shao, J.; Wang, H.; Shen, W. Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method. J. Manuf. Syst.
**2022**, 65, 104–114. [Google Scholar] [CrossRef] - Hägglund, T.; Shinde, S.; Theorin, A.; Thomsen, U. An industrial control loop decoupler for process control applications. Control Eng. Pract.
**2022**, 123, 105138. [Google Scholar] [CrossRef] - Dogru, O.; Velswamy, K.; Ibrahim, F.; Wu, Y.; Sundaramoorthy, A.S.; Huang, B.; Xu, S.; Nixon, M.; Bell, N. Reinforcement learning approach to autonomous PID tuning. Comput. Chem. Eng.
**2022**, 161, 107760. [Google Scholar] [CrossRef] - Li, X.F. Fuzzy self-adapting PID control of drum water level in a power plant. IFAC Proc. Vol.
**2007**, 40, 77–83. [Google Scholar] [CrossRef] - Yu, X.; Shen, Y. Transient state modeling of industry-scale ironmaking blast furnaces. Chem. Eng. Sci.
**2022**, 248, 117185. [Google Scholar] [CrossRef] - Zhou, H.; Yang, C.; Sun, Y. Intelligent ironmaking optimization service on a cloud computing platform by digital twin. Engineering
**2021**, 7, 1274–1281. [Google Scholar] [CrossRef] - Zhou, P.; Guo, D.; Chai, T. Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification. Neurocomputing
**2018**, 308, 101–110. [Google Scholar] [CrossRef] - Martínez, M.; Salcedo, J.V.; Muñoz, J. Adaptive design of PID controllers based on an alternative method to root locus. IFAC Proc. Vol.
**2000**, 33, 199–204. [Google Scholar] [CrossRef] - Ziegler, J.G.; Nichols, N.B. Optimum settings for automatic controllers. ASME
**1942**, 64, 759–765. [Google Scholar] [CrossRef] [Green Version] - Joseph, S.B.; Dada, E.G.; Abidemi, A.; Oyewola, D.O.; Khammas, B.M. Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems. Heliyon
**2022**, 8, e09399. [Google Scholar] [CrossRef] - Zhang, Y.; Zhang, J.F. A quantized output feedback MRAC scheme for discrete-time linear systems. Automatica
**2022**, 145, 110575. [Google Scholar] [CrossRef] - Altan, A.; Hacıoğlu, R. Model predictive control of three-axis gimbal system mounted on UAV for real-time target tracking under external disturbances. Mech. Syst. Signal Process.
**2020**, 138, 106548. [Google Scholar] [CrossRef] - Altan, A.; Aslan, Ö.; Hacıoğlu, R. Real-Time Control based on NARX Neural Network of Hexarotor UAV with Load Transporting System for Path Tracking. In Proceedings of the 6th International Conference on Control Engineering & Information Technology, Istanbul, Turkey, 25–27 October 2018. [Google Scholar]
- Sheet, N.A.F. X-15 hypersonic research program. In NASA Armstrong Fact Sheet: X-15 Hypersonic Research Program; 2014. Available online: https://www.nasa.gov/centers/armstrong/news/FactSheets/FS-052-DFRC.html (accessed on 15 October 2022).
- Orr, J.S.; Statler, I.C.; Barshi, I. The X-15 3-65 accident: An aircraft systems and flight control perspective. In Space Safety is No Accident; Springer: Berlin/Heidelberg, Germany, 2015; pp. 249–257. [Google Scholar]
- Mareels, I.M.; Anderson, B.D.; Bitmead, R.R.; Bodson, M.; Sastry, S.S. Revisiting the MIT rule for adaptive control. Adapt. Syst. Control Signal Process.
**1987**, 161–166. [Google Scholar] [CrossRef] - Karthikeyan, R.; Yadav, R.K.; Tripathi, S.; Kumar, G.H. Analyzing Large Dynamic Set-Point Change Tracking of MRAC by Exploiting Fuzzy Logic based Automatic Gain Tuning. In Proceedings of the IEEE Control and System Graduate Research Colloquium, Shah Alam, Malaysia, 16–17 July 2012. [Google Scholar]
- Dinakin, D.; Oluseyi, P. Fuzzy-optimized model reference adaptive control of interacting and noninteracting processes based on MIT and Lyapunov rules. Turk. J. Eng.
**2021**, 5, 141–153. [Google Scholar] - Pal, A.K.; Naskar, I.; Paul, S. A fuzzy-based modified gain adaptive scheme for model reference adaptive control. Inf. Decis. Sci.
**2018**, 701, 315–324. [Google Scholar] - Castro, J.J.; Doyle, F.J., III. A pulp mill benchmark problem for control: Problem description. J. Process Control
**2004**, 14, 17–29. [Google Scholar] [CrossRef] - Yu, X.; Yang, X.; Yu, C.; Zhang, J.; Tian, Y. Direct approach to optimize PID controller parameters of hydropower plants. Renew. Energy
**2021**, 173, 342–350. [Google Scholar] [CrossRef] - Lakmesari, S.H.; Mahmoodabadi, M.J.; Ibrahim, M.Y. Fuzzy logic and gradient descent-based optimal adaptive robust controller with inverted pendulum verification. Chaos Solitons Fractals
**2021**, 151, 111257. [Google Scholar] [CrossRef] - Chitra, M.; Pappa, N.; Abraham, A. Dissolved oxygen control of batch bioreactor using model reference adaptive control scheme. IFAC-PapersOnLine
**2018**, 51, 13–18. [Google Scholar] [CrossRef] - De la Sen, M.; Gil-Aguirrebeitia, C. A stable MRAC design for discrete plants with unmodelled dynamics. Math. Comput. Model.
**1989**, 12, 139–151. [Google Scholar] [CrossRef] - Wang, Z.; Zhang, B.; Li, X.; Zhang, S. Study on application of model reference adaptive control in fast steering mirror system. Optik
**2018**, 172, 995–1002. [Google Scholar] [CrossRef] - Mukherjee, D.; Raja, G.L.; Kundu, P.; Ghosh, A. Design of optimal fractional order Lyapunov based model reference adaptive control scheme for CSTR. IFAC-PapersOnLine
**2022**, 55, 436–441. [Google Scholar] [CrossRef] - Rajesh, R.; Deepa, S.N. Design of direct MRAC augmented with 2 DoF PIDD controller: An application to speed control of a servo plant. J. King Saud Univ.-Eng. Sci.
**2020**, 32, 310–320. [Google Scholar] [CrossRef]

**Figure 9.**MIT rule fuzzy MRAC, MRAC (adaptation rate 0.01, 0.04 and 0.1) parameter adaptation ${\theta}_{1}$, ${\theta}_{2}$, and $\gamma $.

**Figure 10.**Lyapunov rule fuzzy MRAC, MRAC (adaptation rate 0.01, 0.04 and 0.1) parameter adaptation ${\theta}_{1}$, ${\theta}_{2}$, and $\gamma $.

**Figure 12.**Tank level control for Lyapunov rule fuzzy MRAC and MRAC with different adaptation rates.

$\left|\mathit{e}\right|$$\mathbf{and}\mathit{d}\mathit{e}/\mathit{d}\mathit{t}$ | NB | S | PB |
---|---|---|---|

VS | VS | VS | VS |

S | S | VS | S |

B | B | S | B |

VB | VB | B | VB |

EB | EB | VB | EB |

Controller | Adaptation Gain | Overshoot (%) | Settling Time (s) | ISE | ICSC |
---|---|---|---|---|---|

PID | - | 27% | 325 | 0.003067 | 0.005607 |

MIT MRAC | 0.01 | 0 | >500 | 0.000423 | 0.004198 |

0.04 | 2% | 175 | 0.000407 | 0.004313 | |

0.10 | 37% | >500 | 0.000571 | 0.004586 | |

MIT fuzzy MRAC | [0.02, 0.09] | 7% | 150 | 0.000397 | 0.004343 |

Lyapunov MRAC | 0.01 | 0 | >500 | 0.000382 | 0.004205 |

0.04 | 0 | 190 | 0.000388 | 0.004339 | |

0.10 | 29% | 450 | 0.000555 | 0.004548 | |

Lyapunov fuzzy MRAC | [0.02, 0.09] | 8% | 140 | 0.000379 | 0.004367 |

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**MDPI and ACS Style**

Aslan, Ö.; Altan, A.; Hacıoğlu, R.
Level Control of Blast Furnace Gas Cleaning Tank System with Fuzzy Based Gain Regulation for Model Reference Adaptive Controller. *Processes* **2022**, *10*, 2503.
https://doi.org/10.3390/pr10122503

**AMA Style**

Aslan Ö, Altan A, Hacıoğlu R.
Level Control of Blast Furnace Gas Cleaning Tank System with Fuzzy Based Gain Regulation for Model Reference Adaptive Controller. *Processes*. 2022; 10(12):2503.
https://doi.org/10.3390/pr10122503

**Chicago/Turabian Style**

Aslan, Özgür, Aytaç Altan, and Rıfat Hacıoğlu.
2022. "Level Control of Blast Furnace Gas Cleaning Tank System with Fuzzy Based Gain Regulation for Model Reference Adaptive Controller" *Processes* 10, no. 12: 2503.
https://doi.org/10.3390/pr10122503