# Issues of automated management optimization of fuel consumption for charge mixture sintering to boost productivity and quality of sinter

**Authors:**Ryabchikov M.Y.^{1}, Ryabchikova E.S^{1}, Mukhina E.Y.^{1}, Simusev Y.A^{1}-
**Affiliations:**- Nosov Magnitogorsk State Technical University

**Issue:**Vol 25, No 4 (2017)**Pages:**58-68**Section:**Articles**URL:**https://journals.eco-vector.com/1991-8542/article/view/20311**DOI:**https://doi.org/10.14498/tech.2017.4.%25u- Cite item

## Full Text

## Abstract

We examine issues of sinter quality management by means of changing the level of carbon in charge mixture. We show the main problems: low frequency of control of quality indicators and influence of carbon level in mixture on productivity which has to remain high. To solve the problem of sinter quality management we propose using a double-loop system of automated optimization on the basis of an optimum control system and a loop for cold mechanical strength stabilization. We consider issues of loops coordination and explore influence of their setting parameters on management efficiency. We assessed the effeciency by attainable productivity and cold strength levels. We show the need for a compromise between the mentioned indicators for the efficiency assessment when current disturbing frequency increases. We determined a correspondence between the frequency of control of cold mechanical strength index and the share of conforming sinter.

## Full Text

## Introduction

The production of sinter cake is an important stage of ore processing that provides increasing technical and economic indicators of blast-furnace practice. This is achieved by means of sinter-cake quality optimization [1]. The index of mechanical cold strength, the tumbler index (TI), is the indicator that is generally monitored to establish the quality of sinter cake. One of the main factors affecting the quality of sinter cake is the carbon content in the sinter-feed mixture. The effects of the carbon content on the indicators used to assess the quality of sinter cake have been published in other works [2–4]. The graphs presented in Fig. 1 illustrate the effects of the carbon content on the TI.

**Fig. 1. Effect of the coke carbon content on the sinter-cake strength for the different levels of basicity, showing (a) basicity of CaO/SiO2 1.2; (b) natural basicity; and (c) basicity of 0.6. 1: ore up to 12 mm; 2: a mixture of concentrates and ore; 3: fine concentrate.**

Currently, in the conditions MMK), sinter-cake quality stabilization is achieved by means of sinter cake blending from numerous sinter machines, as well as by centralized processing of the iron-ore mixture supplied to the sinter plants. This production scheme was established when the plant was founded and is connected with the peculiarities of the preparation of the raw material, which is the iron ore [5]. The scheme was arranged at a time when the raw materials being supplied had stable properties. In such conditions, regular monitoring of the TI is required for the diagnostics of gradual, long-term changes in the properties of the baked materials.

However, currently, the properties of the ore supplied to the plant are much more diverse [6]. This results in frequent changes in the nature of the baked materials. Figure 2 presents the results from a previously reported analysis of dynamic changes in the composition of the raw materials, as determined by X-ray fluorescence analysis [6]. These results indicate changes occurring in the composition of the ore within a period of 20–40 h. This may negatively affect the stability of the sinter-cake quality that can be ensured by control of the fuel consumption during baking.

**Fig. 2. An example of changes in the material ore composition in IORM (Q: quartzites; S scarns; W: man-made components).**

However, the arrangement of such control is hampered by the significant effect of the carbon content of the fuel in the sinter-feed mixture on the capacity of the sinter machines. In the modern conditions at MMK, maintaining the high capacity of the sinter machines is the priority objective. Therefore, sinter-cake quality control is a secondary task that limits the achievement of the main objective, which is maximizing capacity.

One of the simplest approaches to such quality control is to maintain the value of the cold mechanical strength index within a set range. In this scheme, a reduction in TI, such that it falls below the allowed level, results in an increase in the outflow from the furnace and a decrease in productivity. An excessive increase in TI will be accompanied by an associated reduction in sinter-cake recovery, which can reduce the effectiveness of the indirect recovery processes in the top part of the blast furnace. This problem requires appropriate and regular monitoring of the mechanical strength of the sinter cake.

The productivity of the sintering process depends on many factors. When the sinter-feed mixture composition is constant, productivity is significantly affected by the gas permeability of the sinter-feed mixture layer, which is connected with its moisture content, as well as by the carbon content of the sinter-feed mixture. These dependences are of an extreme nature.

When the sinter-feed humidity is not sufficient, the low-agglomerated fine fractions prevent airflow to the burning area, reducing the gas permeability of the layer. In the case of excessive humidity, the faded charge pieces also reduce gas permeability. Furthermore, the evaporation of excessive moisture requires greater heat input from fuel burning.

Figure 3(a) presents the results of a previous study and shows an example of the dependence of the output of good sinter cake on the carbon content [7]. When there is limited carbon content in the sinter cake, the resulting agglomerated sinter cake has insufficient strength, and an increased rate of return is observed, which is detrimental to the capacity of the sinter machine. A plot of the dependence of the output of class +5 mm on the carbon content in the charge is presented in Fig. 3(b). With the increased carbon content, the resistance of the melt region is increased, which reduces the vertical rate of baking and therefore reduces the sinter machine capacity.

**Fig. 3. (а) Dependence of capacity on the proportion of on-specification sinter cake, and (b) the output of class +5 mm on the carbon content [7]. Пр = St; П = P.**

Considering the dependences presented in Fig. 3, it can be concluded that that the peaks in the capacity and strength of the sinter cake are obtained at almost the same value of carbon content. However, the output of class +5 mm does not actually represent a strength characteristic. The strength is measured by the TI in the ISO standard. Thus, maintaining maximum capacity does not necessarily ensure maximum cold mechanical strength.

This feature is taken into account in the equation for fuel consumption control *C* proposed in a previous study [8]:

$\Delta C=1.25\cdot \Delta c\cdot M,\Delta c=\left({a}_{0}-{b}_{0}{r}_{0}\right)-\left(a-br\right),$ (1)

where *r* is the ratio of the amount of air to the total quantity of sinter cake, *r*_{0} is the set share of fines or the required drum index, *M* is the total quantity of sinter cake, and *a*_{0}, *b*_{0}, *a*, and *b* are statistically defined set parameters for maintaining the required sinter-cake strength. The disadvantage of using Eq. (1) is that controlling the stability of the raw material to statistically determine the parameters requires repeated testing, which is very difficult for the sintering process.

The following formula has been proposed [9, 10] to detect the interaction between the capacity and the various factors of the process for the determination of their rational ratios for an agglomerated charge of a set composition:

$W=\frac{ph\rho}{d}{\left(\frac{h\rho}{p{\tau}^{2}{d}^{2}}\right)}^{k},W=\frac{h\rho}{\tau}{\left(\frac{\upsilon}{q}\right)}^{k},$ (2)

where

*W* is the specific capacity,

*h* is the height of the charge layer,

*ρ* is the charge bulk weight,

*τ* is the duration of the baking process,

*υ* is the vertical rate of baking,

*p* is the overall charge gas-dynamic impedance,

*d* is the equivalent diameter of the pelletized charge,

*k* is determined by the experimental data from the chemical composition of the raw material.

By using (2), process optimization and capacity forecasts are achieved by the selection of the vacuum value generated by the exhauster, the charge-layer height, and the speed of the sinter belt, which determines the baking duration. A similar dependence has been proposed in another work [11]:

$W={\left(\frac{p}{d}hp\right)}^{1/2}{\left(\frac{p}{d}\frac{1}{hp}\right)}^{k/2}{\tau}^{k}.$ (3)

The use of these methods for capacity control in conditions of raw material variability is limited by possible variations in the statistically defined parameter *k*, as well as by the absence of a record of the associated changes in sinter-cake quality [9–11]. One potential approach to the task of capacity maximization is the development of an automatic fuel-consumption optimization control system based on the incorporation of an external control system (ECS) into the sinter-cake quality-control circuit.

## Automatic sinter machine capacity and quality optimization system

A schematic of the automatic optimization system is presented in Fig. 4. The system includes two control circuits. The first circuit is a step-mode ECS, which stores the maximum value of the regulated parameter [12]. The task of the second circuit is to frequently monitor and maintain the index of cold mechanical strength TI within a set range.

**Fig. 4. Schematic of the automatic optimization system. Коррекция — Correction; Модель — Model; изм — Change**

The pulse generator GI1 generates commands for reading the current capacity *Z _{n}* in the time interval Δ

*t*

_{1}. Storage unit ZY1 is designed for storing the maximum capacity

*Z*

_{max}. The signum relay (SR) forms a signal according to the following condition:

$\sigma =\left\{\begin{array}{l}1{\text{whendZ}}_{\text{n}}\Delta {Z}_{\u043d},\\ 0{\text{whendZ}}_{\text{n}}\le \Delta {Z}_{\u043d},\end{array}\right.$ (4)

where Δ*Z*_{H} is the value of the dead-band area, and *n* is the operating cycle number.

The reverse trigger (RT) changes the direction of the correction of the set carbon content in mixture U1 according to the following condition:

${\text{U1}}_{\text{n}}=\left\{\begin{array}{l}-{\text{U1}}_{\text{n-1}}\text{when\sigma}=\text{1,}\\ {\text{U1}}_{\text{n-1}}\text{when\sigma}=\text{0.}\end{array}\right.$ (5)

The pulse generator GI2 is used for modeling the set frequency of mechanical strength index (TI) monitoring. The results of modeling the measured values of the TI index (TI_{ch}) are used in the computing unit (CU) for verification of the mechanical strength for compliance with the requirements. The permitted range 60% < TI < 70% is assumed for the assessment of the system’s serviceability. When TI_{ch} falls below the threshold value, the ECS operation is locked (U = U2), and the command U2 = *k* is generated to increase the carbon content in the mixture. When TI_{ch} > TI_{max}, U2 = −k to reduce the carbon content in the mixture. To coordinate the operation of the quality stabilization circuit with the ECS [13], the value stored in storage unit ZY1 is reset, and the sign of U1 is changed.

An object delay τ_{3} of 1 h is assumed during the operation examination of the proposed system. This interval consists of the baking, transportation, and material-cooling operations. In actual production conditions, this interval could be 2 h or more. With regard to the absence of inertia features of the object that are comparable with this delay, the value of the dead-band area Δ*Z*_{H} is assumed to be zero. System setting was performed by varying the ECS operation cycle time Δ*t*_{1} and the carbon-content change step ΔC in mixture U, which is affected by parameter *k*.

Figure 5 presents the accepted statistical properties of the dependence of the capacity on the carbon content in the charge, as well as the search process in the absence of limitations on the TI index. The stability of the search process is ensured at Δ*t*_{1} > τ_{3}.

**Fig. 5. Accepted statistical features of the process and the search process without any limitations on the TI value (Δ t_{1} = 80 min, ΔC = 0.1%).**

## Examination of the operation of the control system under disturbance

To examine the operability of the system in conditions close to the actual conditions, a disturbance in TI index values was generated by means of a regular transition from one dependence TI_{f} = *f*(*C*_{f}) to another with different periods *T _{t}*. These dependences are presented in Fig. 6(a)

*.*

Figure 6(b) presents the changes in TI_{f} that could be obtained by maintaining a stable carbon content in the mixture of 3.5% or 5.1%. It is clear that the values of TI_{f} regularly go beyond the allowed range, which requires carbon content correction.

**Fig. 6. (a) Assumed dependences TI _{f} = f(C_{f}) and (b) change in TI_{f} connected with the modeled disturbance effect T_{t} = 41.9 h at a constant carbon content of 3.5% (1) and 5.1% (2), as well as the assumed allowed limits of TI (3, 4).**

Figure 7 presents an example of the search process at *T _{t}* = 41.9 h; ΔC = 0.1%;

*k*= 3; Δt

_{1}= 80 min; and Δt

_{2}= 160 min. It can be observed that the quality-stabilization circuit is activated regularly when the value of TI

_{ch}moves beyond the permitted range.

**Fig. 7. Example of the search process in the proposed system, showing (1) carbon content; (2) productivity; and (3) modeled values of TI _{f}.**

When studying the effect of the system parameters on the control results, *T _{t}*

_{,}ΔC,

*k*, Δt

_{1}, and Δt

_{2}were varied (an excess of three levels of variation). The scores of the results include the average capacity value for 100 h of modeling and the proportion of on-specification products. When calculating the proportion of on-specification products, the upper limit in the TI index value was increased by 2.5%, and the lower limit was similarly reduced.

Figure 8 presents the array of obtained modes at different values of *T _{t}* and the achievable ratios between the sinter-cake quality and capacity. It is clear that a reduction in

*T*results in the necessity for a compromise between capacity and sinter-cake quality.

_{t}

**Fig. 8. Achievable ratio between the sinter-cake quality and productivity at (а) T_{t} = 209.5 h and (b) T_{t} = 23.3 h.**

Figure 9 presents the effects of the parameters ΔC and *k* on capacity. According to the results of the computational experiments presented in Fig. 10, the boundaries showing the maximum achievable capacity do not depend on parameter *T _{t}* showing the frequency features of quality disturbances. A value of ΔC exists at which the maximum possible capacity is achieved. Increasing the value of

*k*reduces the capacity due to the negative effect of the quality-correction circuit on the step-mode ECS operation.

**Fig. 9. Effect of parameters ΔС and k on productivity.**

It was found that parameter Δ*t*_{1} does not have a significant effect on the proportion of on-specification sinter cake. Figure 10 presents the effects of the parameters ΔC, *k*, and Δ*t*_{2} on the proportion of on-specification sinter cake in the form of an achievable maximum quality limit.

With a high frequency of quality disturbances (*T _{t}* = 23.3 h), it was found that an increase in ΔC resulted in a reduction in quality due to a more active ECS counteraction of corrections made by the quality-control circuit. Conversely, an increase in

*k*contributed to an increase in the proportion of on-specification sinter cake. This situation means that expediency in the coordination of the circuits’ operation frequency features is required [13]. However, a practical solution to these problems will require more complete information about the disturbances affecting the object. This will allow the extremes of the drift in statistical properties of the controlled object to be considered.

Figure 10(b) indicates that a reduction in the proportion of on-specification sinter cake occurs when Δt_{2} is increased. It is clear that selection of the value of Δt_{2} should be coordinated with the frequency features of active disturbances.

**Fig. 10. Effect of parameters Δ С, k, and Δt_{2} on the proportion of on-specification sinter cake. 1–3: argument ΔС; 4–6: argument ΔС·k; 1, 4: T_{t} = 209.5 h; 2, 5: T_{t} = 69.8 h; 3, 6: T_{t} = 23.3 h.**

## Conclusion

A practical implementation of the proposed method of control optimization of the productivity of the sinter process will require the development of an automated information collection system. A comprehensive analysis of the disturbances affecting the process is required, as they will result in extreme drifts in the statistical features of the process capacity.

A further increase in the effectiveness of the proposed control system is possible by implementing models for sinter-cake quality forecasting [2–4], as well as by establishing the relationship between the proportion of returned material and its cold mechanical strength. This second task could be achieved by mathematical modeling of the sinter-cake destruction process. The structure of and algorithm for this adaptation to the model are examined in another work [14].

## About the authors

### Mihail Yu Ryabchikov

Nosov Magnitogorsk State Technical University
**Author for correspondence.**

Email: journal@eco-vector.com

(Ph.D. (Techn.)), Associate Professor.

38, prospekt Lenina, Magnitogorsk, Chelyabinsk Region, 455000, Russian Federation### Elena S Ryabchikova

Nosov Magnitogorsk State Technical University
Email: journal@eco-vector.com

(Ph.D. (Techn.)), Associate Professor.

38, prospekt Lenina, Magnitogorsk, Chelyabinsk Region, 455000, Russian Federation### Elena Yu Mukhina

Nosov Magnitogorsk State Technical University
Email: journal@eco-vector.com

Senior Lecture.

38, prospekt Lenina, Magnitogorsk, Chelyabinsk Region, 455000, Russian Federation### Yury A Simusev

Nosov Magnitogorsk State Technical University
Email: journal@eco-vector.com

Graduate Dtudent.

38, prospekt Lenina, Magnitogorsk, Chelyabinsk Region, 455000, Russian Federation## References

- Товаровский И.Г. Нормативная оценка влияния параметров доменной плавки на расход кокса и производительность // Сталь. - 2014. - № 5. - С. 4-11.
- Рябчиков М.Ю., Гребенникова В.В. Моделирование комплексного влияния производственных факторов на механическую прочность металлургического агломерата // Металлург. - 2013. - № 4. - С. 40-47.
- Рябчиков М.Ю., Гребенникова В.В., Рябчикова Е.С. Контроль качества металлургического агломерата с использованием модели восстановимости // Сталь. - 2014. - № 2. - С. 4-8.
- Рябчиков М.Ю., Гребенникова В.В., Рябчикова Е.С. Моделирование прочности металлургического агломерата после восстановления с целью организации непрерывного контроля его качества // Теория и технология металлургического производства. - 2013. - № 1 (13). - С. 10-12.
- Гладских В.И., Лекин В.П., Хасанов Н.И. и др. Современное состояние подготовки шихтовых материалов к агломерации в ОАО «ММК» // Вестник Магнитогорского государственного технического университета им. Г.И. Носова. - 2007. - № 3. - С. 29-30.
- Рябчиков М.Ю. Проблемы управления качеством металлургического агломерата на основе результатов оперативных рентгенофлуоресцентных анализов // Качество и жизнь. - 2016. - № 2 (10). - С. 13-20.
- Парсункин Б.Н., Андреев С.М., Рябчикова Е.С., Гребенникова В.В. Автоматизация технологических процессов и производств в металлургии. Ч. 1. Подготовка рудных материалов. Агломерация и производство окатышей. - Магнитогорск: Изд-во Магнитогорск. гос. техн. ун-та им. Г.И. Носова, 2012. - 199 с.
- Corina Maria Dinis, Gabriel Nicolae Popa, Angela Iagar. Mathematical Modeling and Simulation in Mathlab / Simulink of Processes from Iron Ore Sintering Plants / Wseas transactions on systems, Issue 1, Volume 8, January 2009, P. 34-43.
- Панычев А.А., Никонова А.П. Оптимизация технологических параметров на основе математических моделей при агломерации михайловских и лебединских концентратов // Металлург. - 2008. - № 10. - C. 46-51.
- Касаткин А.Г. Основные процессы и аппараты химической технологии. Изд. 6-е. - М.: ГНТИ химической литературы, 1955.
- Ганин Д.Р., Панычев А.А. Новая модель агломерационного процесса // Металлург. - 2013. - № 5. - С. 44-47.
- Парсункин Б.Н., Бушманова М.В. Расчет переходных процессов в системах экстремального регулирования с запоминанием экстремума // Магнитогорск: МГТУ им. Г.И. Носова, 2003. - 164 с.
- Рябчиков М.Ю., Рябчикова Е.С. Системы экстремального регулирования на основе комбинации поисковых оптимизационных алгоритмов // Мехатроника, автоматизация, управление. - 2015. - Т. 16. - № 5. - С. 300-306.
- Рябчиков М.Ю., Гребенникова В.В., Рябчикова Е.С., Богданов Н.В. Модель разрушения металлургического агломерата // Известия высших учебных заведений. Черная металлургия. - 2016. - Т. 59. - № 3. - С. 159-166.