Furnace Control Research Articles

Improvement of the Foaming Agent Feeding Process to an Electric Arc Furnace by Analyzing the Sound Generated by an Electric Arc and the Coefficient of Variation of Active Power Consumption.

Electric arc furnaces are commonly used in foamed slag technology for the production of steel from steel scrap through an electric process. The effects of using this technology include increased efficiency, reduced consumption of refractory materials, reduced energy consumption, reduced electrode wear, and improved arc stability. The world is constantly looking for solutions to optimize the feeding of the foaming agent to the electric furnace, including determining the moment of starting its feeding. The authors propose using two parameters to determine the optimal moment of introducing the foaming agent: the change in the sound level emitted by the arc furnace and the fluctuations in active power consumption. In order to determine the above parameters, tests were carried out on an industrial alternating UHP arc furnace with a capacity of 70 tons. The sound intensity level was determined at which the feeding of the foaming agent to the furnace's working space should begin. A moving coefficient of power consumption variation was developed and decision variables of the software for online foaming agent feeding were determined. As a result of implementing the developed solutions to the electric furnace control system and conducting comparative tests, savings were obtained in the form of reduced foaming agent consumption.

Impact of furnace and waste layer control on HCl and SO2 in combustion gas from a grate-fired Waste-to-Energy boiler

In Waste-to-Energy (WtE) plants, combustion gas containing HCl and SO2 typically provokes boiler fouling and corrosion, and large consumption of chemicals to meet legal emission standards. HCl and SO2 concentrations in the combustion gas obviously depend on the Cl/S ratio in the incoming waste. However, industrial WtE experiences indicate that conditions established by process control also impact the generation of HCl and SO2 during grate combustion. The latter is studied in the present work through in-depth analysis of industrial WtE process data spanning four years of plant operation. To the authors' knowledge, a similar study was never conducted before. The results suggest that HCl in the combustion gas is strongly influenced by the overall air-to-waste ratio imposed to the (entire) furnace. Findings regarding SO2 rather highlight the significance of conditions established in the waste layer on the grate, i.e., layer thickness and flow rate of primary air. The extent of moisture in the waste seems to enhance these patterns. Whereas state-of-the-art combustion control in WtE plants still only targets the stability of thermal production, the results from this study support a further extension of the control scope with (inorganic-)thermochemical intelligence for boiler corrosion prevention and emissions stabilization.

Fuzzy Logic Controller for Power Control of an Electric Arc Furnace

Electric Arc Furnaces (EAFs) are widely used in the steel manufacturing industry to melt scrap steel by employing a large number of electric arcs. EAFs play an important role in ensuring the efficient production of steel. However, their nonlinear and variable load characteristics have a significant impact on power quality. Because the active power of an electric arc depends on its length, a system for controlling the electrode positions is necessary. This paper presents a control system based on a fuzzy logic controller for the active power control of an electric arc furnace. Individual simulation scenarios were chosen with both reference values and the process taken into consideration. The reference, constant value, step variation, and the sequence of step variation were investigated, as well as step disturbances and the sequence of step disturbances from the viewpoint of the process. Furthermore, the procedure of changing the tap on a transformer was investigated. The proposed solution minimizes the time required for charge elaboration, but the main benefit is that there are no additional costs in the implementation process because the installation remains identical, with the only changes being improvements to soft control management.

A real-time temperature field prediction method for steel rolling heating furnaces based on graph neural networks

In the billet reheating process during steel rolling, the real-time and accurate prediction of the temperature field is a prerequisite for the dynamic regulation of the heating process, which is crucial for ensuring the quality of billet reheating and reducing the energy consumption of the reheating furnace. The most commonly used finite element thermal field simulation and analysis methods are unable to meet the demand for real-time prediction under dynamic working conditions. Furthermore, traditional machine learning methods struggle to handle irregular data that includes spatial location information, resulting in inaccurate temperature field predictions, which in turn affect the furnace control efficiency and the quality of the product. In light of these limitations, this study proposes a Graph Neural Network (GNN)-based temperature field prediction method for steel rolling reheating furnaces. This method considers the interactions between the nodes within the reheating furnace and constructs a temperature field topology graph to effectively capture these interactions, including heat conduction and convection. Additionally, in the feature fusion and state updating mechanism of the model, we introduce process parameters, such as the air-fuel ratio, as additional inputs to enhance the ability of the GNN to handle complex variables. This enables real-time accurate simulation of the internal temperature distribution of the reheating furnace. The experimental results demonstrate that the model prediction error is controlled within 2.9 % and the response time is maintained within 20 ms, thereby validating the reliability and efficacy of the method in practical applications.

Systematic Research and Optimization Design of Intelligent Electric Furnace Control System

Systematic Research and Optimization Design of Intelligent Electric Furnace Control System

Design Approach for an Advanced Multi-Channel Pyrometer for Bulk Oven Processes

Industrial processes such as smelting and sintering require stable and precise temperature control of furnaces. To achieve this, accurate temperature measurements are required. Pyrometry allows for contact- less measurement of bulk materials and is particularly suitable for high temperature applications. One of the main influences on the accuracy of pyrometric measurements is the knowledge of the emissivity in the spectral measurement range. To reduce this dependence, two-color pyrometers or multi-color pyrometers can be used. With this in mind, the IRS is further developing their existing pyrometer technology by designing an advanced multi-channel pyrometer for bulk oven processes in a joint venture with Stange Elektronik GmbH and New Generation Kilns Grün GmbH. The design approach is explained here and the considered methods of achieving emissivity independent temperature measurements are examined.

Numerical and experimental studies on the heat transfer characteristics and process optimization of the billet soaking furnace

The precision of furnace temperature control in the soaking furnace is critical to the quality of billet heating. This paper develops a three-dimensional(3D) mathematical model of the heating process of steel billets in the soaking furnace, which can be monitored online in real time. The accuracy of the model was verified by black-box experiments. A comparative analysis of the model’s simulated and experimental data revealed a hit rate exceeding 70 % (with a relative error margin within ± 5 %). Leveraging this mathematical model, we propose a direct optimization strategy that effectively saves 24 % of heating time, accelerates the process of achieving temperature uniformity within the billet, and boosts overall heating effectiveness. This approach offers potential for broader application in optimizing the heating processes of various materials.

Ensemble learning soft sensor method of endpoint carbon content and temperature of BOF based on GCN embedding supervised ensemble clustering

Abstract The endpoint control of Basic Oxygen Furnace (BOF) steelmaking depends on the prediction of the endpoint carbon content and temperature. However, predicting these variables is challenging because of the numerous working conditions in the industrial field and the volatility of the sensor data collected during BOF steelmaking. The accuracy of prediction models in ensemble learning depends significantly on the initial distribution of data. However, the complex nature of BOF steelmaking data makes it challenging to generate diverse subsets, which ultimately affects the accuracy of predictions. This paper presents a new approach called Graph Convolutional Network Node Embedding Supervised Ensemble Clustering (GESupEC) for soft sensor modelling in ensemble learning to tackle these issues. GESupEC utilizes a similarity graph derived from a co-association matrix and employs graph convolutional networks to extract structural information among nodes. By optimising the clustering loss within the network, GESupEC learns compact node representations that are useful for the clustering task. Furthermore, it generates a reconstruction matrix based on the similarity of node embeddings. This matrix helps with the extraction of a suitable subset of data for BOF steelmaking through matrix decomposition. After that, the gradient boosting decision tree regression sub-model is established based on the data subset. An ensemble strategy called Gray Relational Analysis Weighted Average is proposed, which assigns weights based on the grey relation similarity between test samples and different data subsets. This weighted average strategy aims to enhance the accuracy of carbon content and temperature predictions. When tested with actual BOF steelmaking generation process data, the prediction accuracy of carbon content reached 88.6% within the error range of ±0.02%, and the prediction accuracy of temperature reached 92.6% within the error range of ±10 °C.

Research and Development of a Big Data Application Platform for Intelligent Blast Furnace Intensive Management and Control.

The blast furnaces of Anshan Iron and Steel have completed large-scale modernization, and a large amount of information technology has been popularized and applied to the process of blast furnaces. This paper takes the Anshan Iron and Steel blast furnace group as the research background. Based on big data and industrial Internet technology, combining the smelting process mechanism of blast furnace production and using artificial intelligence, cloud analysis, and other technologies, the data management platform was used to effectively integrate the data of each process of the blast furnace and design the data asset catalogue. The big data application platform for the intensive control of the blast furnace was established. The data were in multidimensional in-depth mining, and the intelligent application model of the blast furnace was established. The visual intelligent monitoring of the safe production and operation of the blast furnace was realized, and the production operation of the blast furnace was guided. The overall information and intelligent level of production operation and management of the blast furnace have been improved.

N2O emission factors in bubbling fluidized bed incineration of municipal sewage sludge: The China case

The N2O emissions resulting from sludge incineration are estimated using the default values published by the Intergovernmental Panel on Climate Change (IPCC), which may differ significantly from the actual emissions. In this investigation, N2O emissions from four sludge incineration lines in two plants were monitored for varying durations. The variation in N2O emission factors (EFs) between incineration lines of the same plant was much smaller than the difference between different plants. Data on N2O EFs obtained from brief monitoring may contain variabilities of up to 30%. N2O EFs were more sensitive to temperature changes at low temperatures, necessitating extended monitoring periods to improve the reliability of N2O monitoring outcomes in cases of low furnace temperatures. Excessive use of the SNCR system to reduce NOx emissions resulted in concentrations of N2O and NH3 in the exhaust gases exceeding NOx levels. In the case of furnace temperature control and advanced reburning technology, it is advisable to utilize actual monitoring data or the smaller default values provided by the IPCC in China. Otherwise, the estimated N2O emissions may exceed the actual emissions.

Two-stage day-ahead and intra-day scheduling considering electric arc furnace control and wind power modal decomposition

As the uncertainty in energy supply increases, engaging various flexible resources in power systems has emerged as an effective strategy to address wind curtailment issues. Existing research insufficiently explores how EAFs can participate in reducing wind curtailment or optimizing flexible power system resources to decrease CO2 emissions from TTPs and enhance wind energy absorption across various timescales. This study introduces a dual timescale, dual-tier scheduling methodology incorporating EAF regulation and wind power modal decomposition. The day-ahead model integrates EAF demand response to decrease wind curtailment, a comprehensive wind power allocation, and a TTP carbon minimization model. The intra-day model employs wind power modal decomposition for optimizing BESSs within WFs and schedules TTPs to minimize CO2 emissions. Implemented through iterative genetic algorithms and CPLEX solver techniques, simulation results from a real-case scenario indicate that incorporating EAF loads reduces wind curtailment by 40.49 % and cuts CO2 emissions by 2.5 % in the day-ahead phase. Furthermore, by applying modal decomposition, TTPs and BESSs absorb fluctuating wind power components, ensuring maximal wind utilization and substantial CO2 reduction at TTPs. This approach offers vast potential to enhance power system flexibility, advance energy-intensive industries' transition, and foster low-carbon initiatives at TTPs.

Monitoring and adjustments of combustion parameters in reheating furnaces using a digital tool

Improving the energy efficiency of a reheating furnace is not a simple task because it involves a large number of parameters that, most of the time, are connected in a complex way. To be feasible, the evaluation of such a vast number of variables must be supported by digital tools that are embedded with artificial intelligence. The SMS group has developed a digital tool that effectively supports operators and engineers in enhancing energy efficiency within the reheating furnaces of hot rolling mills. This paper presents a quick study case of the use of Viridis Performance, an application of Viridis Energy & Sustainability Suite, in the analysis and correction of the deviations in the air-gas ratio of a reheating furnace. In this study, a production period of 8 hours was evaluated, during which the air-gas ratio presented a significant deviation. Analysis showed that the probable cause of the deviation was a production stoppage where the furnace control was set to manual mode. A reduction potential of 0.26 Gcal/h and a 13 kg/h decrease in CO2 emissions were identified. Viridis Performance would support the process by guiding the decisions of the operator in a way that energy losses would be avoided or minimized. The potential economic savings were also quantified. This task is performed in the system by Viridis Moneybox, which is another application of Viridis Energy & Sustainability Suite. The results showed potential savings of 13 US $/h. In addition to the gains and savings outlined in this study, the utilization of Viridis Performance encompasses numerous other advantages, such as enhanced operational standardization and improved process capability, to name just a few.

A comprehensive study of melt foaming in E‐glass batch‐to‐melt conversion process: Effects of sulfate content and chemistry of raw materials

AbstractControl of sulfate‐induced melt fining without excessive foaming is one of the critical steps in maintaining the stability of E‐glass fiber manufacturing processes. Besides, the efficiency of combustion or energy utilization is directly affected by the extent of the melt‐foaming. A fundamental understanding of key factors affecting melt foaming under the simulated oxy‐fuel combustion environment will enable commercial E‐glass fiber production to optimize both batch chemistry and operation conditions to achieve adequate furnace control. In this study, six types of E‐glass batches with the same target glass composition were prepared by using four different CaO sources; calcined limes with different SO3 contents, limestone, limestone with sodium sulfate, and a mixture of limestone and calcined lime. All batch samples were examined by HTMOS‐EGA system (high temperature melting observation system with evolved gas analysis). HTMOS enables monitoring batch‐to‐melt conversation steps by using a high‐resolution camera and EGA detects the evolved reaction gaseous, such as CO, CO2, and SO2 via an Fourier transform infrared (FTIR) gas analyzer. Gases of water vapor, N2, and O2 were introduced accordingly into the fused quartz crucible to simulate similar oxy‐fuel atmosphere of the furnace operation. This study aimed to investigate the effects of different SO3 contents in batches and different raw material chemistries on the foam formation in E‐glass melts under the oxy‐fuel atmosphere. Different raw materials were characterized by mineralogical analysis, chemical analysis, particle size distribution, chemical oxygen demanding (COD) level, and Brunauer–Emmett–Teller (BET) analysis. Although some of the batches contained the same SO3 content, different foam formations resulted from the effect of the batch chemistry. Our detailed HTMOS‐EGA investigations show that not only SO3 content in the batch affects foam formation in E‐glass melts, but also raw material chemistry and particle size have strong effects on the melt foaming in E‐glass batch melting, especially for those of ingredients having hydroxide phases and/or finer particles with higher specific areas.

Pilot-scale testing on catalytic hydrolysis of carbonyl sulfur combined with absorption-oxidation of H2S for blast furnace gas purification

About 70% of the flue gas in the iron-steel industry has achieved multi-pollutant ultra-low emissions in China until 2023, and then the blast furnace gas purification has become the control step and bottleneck. Our research group has designed and constructed the world's first blast furnace gas desulfurization pilot plant with the scale of 2000 Nm3/h in October 2021. The pilot plant is a two-step combined desulfurization device including catalytic hydrolysis of carbonyl sulfur (COS) and absorption-oxidation of H2S, continuously running for 120 days. In the hydrolysis system, one reason for catalyst deactivation has been verified from the sulfur deposition. HCN in blast furnace gas can be hydrolyzed on the hydrolysis catalyst to produce the nitrogen deposition, which is one of the reasons for catalyst deactivation and has never been found in previous studies. The deposition forms of S and N elements are determined, S element forms elemental sulfur and sulfate, while N element forms -NH2 and NH4+. In the absorption-oxidation system, the O2 loading and the residence time have been optimized to control the oxidation of HS− to produce elemental sulfur instead of by-product S2O32−. The balance and distribution of S and N elements have been calculated for the whole multi-phase system, approximately 84.4% of the sulfur is converted to solid sulfur product, about 1.3% of the sulfur and 19.2% of N element are deposited on the hydrolysis catalyst. The pilot plant provides technical support for multi-pollutant control of blast furnace.

Translucency of chairside monolithic zirconias using different sintering ovens: An in vitro investigation

ObjectiveTo evaluate the translucency of several monolithic zirconias (MZ) processed in various sintering ovens designed for single-visit, chairside use. MethodsDiscs (n = 40) from zirconia blocks were fabricated for each MZ at manufacturer-recommended minimal thicknesses, as provided in each material's instructions for use: IPS e.max ZirCAD LT (ZLT); CEREC Zirconia+ (CZ+); 3M Chairside (3M); KATANA Zirconia (KT). Groups (n = 10) were sintered following manufacturer instructions for each oven: CEREC SpeedFire, Ivoclar CS4, Ivoclar CS6, and Ivoclar S2 (laboratory furnace control). Specimens were highly polished on one side and glazed on the other. Each side was measured with a spectrophotometer against white and black backgrounds to determine translucency parameter (TP) and contrast ratio (CR) values. Results for TP and CR for each material and oven combination were compared with a linear mixed model. Oven precision was evaluated using the Kruskall-Wallis test. ResultsGlazed specimens were more translucent than polished ones (p < 0.001). ZLT and CZ+ were more translucent than 3M and KT regardless of the sintering oven (p < 0.01). Several oven/material combinations reached or exceeded the S2 oven TP: CS4 with CZ+ and 3M; CS6 with ZLT and KT (p < 0.01). SpeedFire was significantly less precise (p < 0.05) and produced lower TP values for ZLT, CZ+, and KT (p < 0.01). Results for TP and CR were highly correlated. ConclusionsMZ surface finish, material thickness, and oven used all had a significant effect on translucency. Some chairside-oriented solutions produced results with translucency equal to conventionally processed zirconia. Clinical significanceThe translucency of a ceramic restoration is an important factor in determining its esthetics. Clinicians desiring the most esthetic outcomes with monolithic zirconia should be aware of the significant effects that surface finishing, material thickness, and the sintering oven used can have on restoration translucency.

A new control scheme for temperature adjustment of electric furnaces using a novel modified electric eel foraging optimizer

&lt;abstract&gt; &lt;p&gt;In this study, we present a comprehensive framework for enhancing the temperature control of electric furnaces, integrating three novel components: a proportional-integral-derivative controller with a filter (PID-F), a customized objective function, and a modified electric eel foraging optimization (mEEFO) algorithm. The PID-F controller, introduced for the first time in the literature for temperature control of electric furnaces, leverages a filter coefficient to effectively mitigate the kick effect, improving transient and frequency responses. To further optimize the PID-F controller, we employed the mEEFO, a recently proposed metaheuristic inspired by the social predation behaviors of electric eels, with tailored modifications for electric furnace temperature control. The study also introduces a new objective function, based on the modification of the integral of absolute error (IAE) performance index. The proposed framework was evaluated through extensive comparisons with established metaheuristic algorithms, including statistical analysis, Wilcoxon signed-rank test, and time and frequency domain analyses. Comparative assessments with reported methods, such as genetic algorithms and Ziegler–Nichols-based PID controllers, validated the efficacy of the proposed approach, highlighting its transformative impact on electric furnace temperature regulation. The non-ideal conditions such as measurement noise, external disturbance, and saturation at the output of the controller were also evaluated in order to demonstrate the superior performance of the proposed approach from a wider perspective. Furthermore, the robustness of the proposed approach against variations in system parameters was also demonstrated.&lt;/p&gt; &lt;/abstract&gt;

Fuzzy Adaptive PID Algorithm in Temperature Control System of Lead Patenting Furnace

In order to enhance the accuracy of temperature control of the patenting furnace, a new temperature control system (TCS) based on the fuzzy adaptive (FA)–proportion integral differential (PID) control algorithm is proposed. The system takes temperature error value and error rate as input and automatically adjusts the PID control parameter value according to the signal of the furnace temperatures to realize adaptive adjustment. Based on the parameter of the FA from simulations, the results of online application showed the temperature fluctuation reduced from 14 to 4°C in Zone I, and it decreased from 15 to 5°C in Zone II. Thus, the accuracy control of operational ability of the TCS was improved obviously. Following practical application, the microstructure analysis of the steel wire postpatenting demonstrated the creation of a more consistently distributed sorbite with enhanced properties by leveraging the new FA‐PID system.

Building a 5D Database of Heliostats Flux Distributions: Toward High Flexibility High Accuracy Flux Control for Odeillo's Big Solar Furnace

In order to increase the flexibility and the control of the concentrated solar flux provided to test processes and materials at the big solar furnace in Odeillo (up to 1000 kW and 10000 KW/m2), extensive and accurate knowledge of the individual contribution of each of the heliostats is required, for all their aiming points. For a solar furnace, with double mirror reflection and heavily changing shadow of the solar beams, this would require too much experimental time to record this behaviour systematically, either directly (flux measurements of all the configurations) or indirectly (simulation by ray tracing based on the real optical configuration measured, both at macroscopic and microscopic scale). This work here presents a trade-off method based on real flux measurement, but at sampled configurations, then interpolated to fill in missing data. 5 dimensions have been sampled: heliostats (1D) + aiming directions (2D) + flux distributions (2D). An evaluation of the method is presented based on actual experimental data, with a discussion of the experience.

Increasing the Level of Autonomy of Control of the Electric Arc Furnace by Weakening Interphase Interactions

Steelmaking is one of the most energy-intensive industries, so improving control efficiency helps to reduce the energy used to produce a tonne of steel. Mutual influences between the phases of an electric arc furnace in available electrode movement control systems cause unproductive electrode movements as a reaction to the redistribution of currents among the phases of a three-phase power supply system due to changes in arc length in one of the phases. The nonlinearity of the characteristics of an electric arc furnace significantly complicates the ability to provide autonomous electrode movement control. The approach proposed in this paper, based on the formation of a matrix of mutual influences with variable coefficients, significantly improves the per-phase autonomy of the electrode movement control system. Nonlinear dependences of the mutual influence coefficients as a function of the current increment in the phase in which the disturbance occurred are obtained. Thus, it is possible to practically eliminate unproductive electrode movements in existing control systems by avoiding the traditional use of a dead zone, which reduces the control quality in the zone of small disturbances. The complex of experiments performed using the mathematical model demonstrate that the mutual influence improves the dynamic properties of the electrode movement system in certain operating modes.

Temperature Control of Electric Furnace Using Adaptive Lag Compensator Based on Improved Gorilla Troops Optimization: Towards Energy Efficiency

Temperature Control of Electric Furnace Using Adaptive Lag Compensator Based on Improved Gorilla Troops Optimization: Towards Energy Efficiency