Temperature control in electric furnaces: Methods, applications, and challenges

Temperature control of electric heating furnaces, a common piece of equipment in industrial production, is essential for assuring product quality and enhancing production effectiveness. In order to better understand the similarities and differences in temperature management between various types of electric heating furnaces, this paper first discusses the fundamental concepts and classifications of electric heating furnaces. Following that, it systematically describes the applications of the various temperature control techniques now used for electric heating furnaces, such as PID control, fuzzy logic control, genetic algorithm control, and model predictive control. This paper further analyzes the difficulties in controlling the temperature of electric heating furnaces and identifies potential future development trends in light of the issues that currently used control methods must deal with in practical applications, such as insufficient control accuracy, slow response speed, and poor stability. Finally, it summarizes the information in the entire paper and considers the direction of temperature control research for electric heating furnaces. This research will focus on new control theories and algorithms, furnace body design optimization, control system integration, and intelligent adaptive temperature control. In order to advance the development and advancement of technology in this area, the goal of this article is to provide a thorough theoretical reference and practical advice for the temperature management of electric heating furnaces.


Introduction
Electric heating methods are used in a variety of industries, including metallurgy, chemistry, and materials, because of their effectiveness, energy-saving features, and environmental friendliness.Temperature management, which assures production process stability and product quality, is an essential part of these technologies [1].The use of more complex temperature control techniques in electric heating systems has been prompted by improvements in control precision and control technology.
One such technique is the use of continuous temperature control, which, when compared to position temperature control in electric furnaces at equivalent temperatures, has been found to increase the lifespan of heaters.The desire for a continuous approach to temperature control in electric ovens [1] is strengthened by this.
New electric furnace designs are also being created, such as the direct current electric arc furnace, which is intended to enhance procedures like the separation of metal and slag.This furnace's mechanical components, including the crucible, electrodes, and lining, have been improved.To enhance thermal insulation effectiveness, the thickness of the lining and bottom has been estimated using furnace heat balancing formulae.Software has also been created to track electrical parameters [2].
This essay attempts to offer a thorough overview of the state of the art and emerging trends in electric furnace temperature control techniques.The paper is organized as follows: Before examining the similarities and differences in temperature management techniques among various types of electric furnaces, we will first go over the fundamental concepts and classifications of electric furnaces.The classifications and general concepts of several temperature control techniques will next be covered, along with examples of each technique's application in various kinds of electric furnaces.Finally, we will explore the difficulties in temperature control for electric furnaces and potential development trends.
In conclusion, this work aims to present a comprehensive assessment of current developments in electric furnace temperature control [2].We believe that this paper will be an invaluable resource for researchers and industry professionals who are interested in learning more about current and emerging developments in electric furnace temperature management.

Basic principle of electric heating furnaces
Electrical energy is converted into thermal energy, which then allows the heating of various materials, as a foundation for the operation of electrically heated furnaces.The author thoroughly explains the fundamental concepts behind the many types of electrically heated furnaces in this section.The Joule heating effect and the magneto-thermal effect are the two basic ideas of electrically heated furnaces, respectively.

Joule heating effect.
The Joule effect turns electrical energy into heat when current passes through an electrical substance.Due to its resistance, a heating element like a metal wire or ceramic heater converts some electric energy into thermal energy in resistance heating furnaces.The substance is heated by releasing this heat energy.P = I^2Rt is the Joule effect, which links current, resistance, and time to electrical energy spent.Figure 1

Magneto-thermal effect.
The magneto-thermal effect is the generation of heat by eddy currents as a result of the movement of electrical materials in an alternating magnetic field.In an induction heating furnace, an alternating magnetic field is created by passing an alternating current through an induction coil.When the material to be heated (usually a metal with good electrical conductivity) is placed in an alternating magnetic field, this field generates eddy currents.Due to the internal resistance of the material, the eddy currents convert electrical energy into heat, thereby heating the material.The magneto-thermal effect is related to the electrical conductivity and permeability of the material, but is also influenced by the frequency of the magnetic field and the distance between the induction coil and the material.Figure 2    Argon, a monoatomic inert gas, is the most common working gas.Plasma furnaces can melt superconductors, steels, titanium, and titanium alloys at 20,000°C.Hollow cathode furnaces, watercooled copper crystalliser furnaces, induction-heated plasma furnaces, refractory-lined plasma furnaces, etc. Plasma furnaces are used in aerospace, metallurgy, and materials research.

Electron beam furnaces.
High-speed electron bombardment melts materials in electron beam furnaces.A low-voltage electric filament burns the vacuum furnace shell cathode, releasing electrons.The anode's high-voltage electric field accelerates the electron beam, heating the metal.Electron beam furnaces melt specialized steels, refractories, reactive metals, aerospace, and nuclear materials.

Commonalities and differences in temperature control of electric heating furnace types
There are certain similarities and variances among the various types of electric furnaces when it comes to temperature management.Based on studies in the reference [3] and [4], this article briefly discusses the similarities and differences in temperature regulation between different types of electric heating furnaces.
First, for process stability and product quality, all electrically powered furnaces need to maintain exact temperature control.In various kinds of electrically heated furnaces, basic temperature control techniques, such as PID control and fuzzy logic control, are therefore frequently used.
Second, there are variations in temperature regulation among various models of electric furnaces.For instance, three types of furnaces-electric, resistance, and induction-have minimal needs for temperature control and are appropriate for most industrial heating applications.For manufacturing operations requiring high temperatures, high purity, and high precision, three types of furnace-electric arc furnaces (including vacuum arc furnaces), plasma furnaces, and electron beam furnaces-are more suited since they have higher needs for temperature control.
Additionally, the stability requirements and temperature control reaction times for various types of electrically powered furnaces may vary.For instance, vacuum arc furnaces and electron beam furnaces might need more sophisticated control techniques because they might need faster response times and more stability.On the other hand, induction furnaces need to adjust the magnetic field to provide localized heating, which may call for more complex temperature control algorithms.
Finally, variations in the situations in which each type of electric heater is utilized may also have an impact on the method of temperature control used.For instance, high temperature stability requirements for production processes may necessitate the use of more sophisticated temperature control techniques like model predictive control or neural network control.

Classification of control methods
Electric heating furnaces have three fundamental temperature control methods: regulation, proportional control, integral control, and differential control.
Closed-loop control is when the controller ignores system performance and directly governs it according to setpoints.It is rarely used since it cannot self-regulate.
Closed-loop control involves the controller sending back system output to govern the system.Closedloop control involves a controller-actuator loop.The controller constantly measures system output, compares it to the setpoint, and adjusts the actuator to gradually approach the setpoint.One of the simplest control systems, proportional control adjusts according to error magnitude to improve control but is more vulnerable to system noise and disruption.Integral control eliminates steady state mistakes by accumulating errors, yet system oscillations are easy to produce with system hysteresis.Differential control reduces system oscillations, but system noise and disturbances can create controller instability.
Finally, different control approaches have advantages and disadvantages.Practical applications include selecting appropriate control methods and optimizing controller parameters for best control effects.

Principle and application examples of PID control methods
The PID (Proportional-Integral-Derivative) control method, a feedback control method that may be used to achieve steady-state regulation of system temperature, is currently one of the most used control systems in industrial control.The weighted total of the proportional, integral, and differential components is used to figure out the PID controller's output.The PID control method's fundamentals and different applications have been extensively used and researched in the context of electric furnaces.

Principle of PID control method.
Based on the proportional link between the error signal and the controlled variable, the proportional controller outputs a proportional error signal.The integral controller is based on the integral value of the error signal and outputs proportionately to it.To improve control, the PID controller output is a weighted sum of these three components (Figure 3).

Examples of the use of PID control methods in electric ovens.
Reference [5] covers a real-time STM32-based electric boiler temperature management system that uses a PID algorithm.An ESP8266 WiFi module connects the bottom and higher PCs for remote data collecting, real-time display, and threshold alarm.Stable, reliable, and easy to use, the system meets electric boiler thermostatic control needs.It discusses real-time temperature control of electric boilers using PID algorithms in electric furnaces.References include other PID-based temperature control system designs.
J. H. Chang [6] and his team members concentrate on a roller conveyor furnace's performance investigation and optimization through numerical simulation [6].For the simulations' calculations of energy consumption and production, the scientists employed a temperature control model.In this instance, PID control was utilized to regulate the heater's power in order to maintain the desired temperature range.Compared to other straightforward temperature control techniques, PID control can produce more precise results.Insertion temperature, heat flow values, and switching control are a few of these.
The new control approach for a resistance furnace temperature control system is the main topic of this paper [7].This study provides a fuzzy PID regulator/algorithm based on PSO that develops a mathematical model of a resistance furnace based on experimental data.To boost the effectiveness of the temperature management system, the technique combines a particle swarm optimization (PSO) algorithm with a fuzzy PID controller.Additionally, it explains how to install the PSO-based fuzzy PID algorithm using a PIC16f and create a temperature control board.To verify the efficacy of the two suggested controllers for a resistance furnace tempering system, a simulation model of a resistance furnace tempering system is constructed using Matlab.
Additionally, to obtain exact control of the temperature of electric heating furnaces, several researchers have applied cutting-edge control techniques including neural network control and genetic algorithm control.
In conclusion, PID control algorithms are one of the most extensively used methods for controlling the temperature of electric furnaces, and they have been optimized and enhanced numerous times to obtain a high level of control precision and stability.In addition, more advanced control methods are applied in this industry, which can offer even higher control accuracy and stability in specific circumstances.
Furthermore, to accomplish exact temperature control of electric ovens, several researchers have applied advanced control methods such as neural network control and genetic algorithm control.The author then discusses some common temperature control systems for electrically heated furnaces.

Fuzzy logic control and application examples
When it comes to temperature control techniques for electric ovens and their applications, fuzzy logic control is an essential topic.Fuzzy logic control, an artificially intelligent control technique, is better suited to non-linear and time-varying systems because it can be adjusted online to the real-time state of the system.To increase the precision and stability of the system when controlling the temperature of an electric oven, fuzzy logic control is frequently employed in conjunction with a PID controller.
A fuzzy PID controller based temperature management strategy for industrial heating furnaces is presented by V. Bharath Kumar et al. in a recent study [8].For industrial heating furnaces, the authors suggest a fuzzy PID controller-based temperature control system.The method calculates the output signals to control the furnace temperature in accordance with a preset set of rules after processing the input and output signals using fuzzy logic.
The authors, specifically, employ a Fuzzy Logic Controller (FLC) to convert all input and output values into a range between 0 and 1, and then they use a Fuzzy Inference Structure (FIS) to map the transformed inputs and outputs, which can be understood in connection with the following image.The output signal can then be calculated to manage the furnace temperature in accordance with a preestablished set of rules after the fuzzy PID controller has been tweaked online using real-time temperature data.Figure 4 shows the FIS structure of FLC. Figure 5 shows the system design with FLP controller.In the experiments, the authors contrast the fuzzy PID controller-based proposed approach with a traditional PID controller.A proportional term, an integral term, and a differential term are the three components that make up a traditional PID controller's output signal.In order to obtain optimal control under various operating situations, the coefficients of these terms must be determined in accordance with the transfer function of the system.However, traditional PID controllers might not offer sufficient accuracy and stability for non-linear and timevarying systems.In contrast, fuzzy PID controllers evaluate the input and output signals using fuzzy logic and determine the output signal in accordance with a predetermined set of rules.Fuzzy PID controllers are more suitable for non-linear and time-varying systems since they do not need a transfer function of the system to calculate the gain value, in contrast to traditional PID controllers.Figure 6 demonstrates that the proposed technique has a superior ability to reject disturbances and can be more effectively applied to non-linear and time-varying systems.

Genetic algorithm control and application cases
A promising method of temperature control for electric furnaces and its applications is genetic algorithms.For instance, the authors suggest a genetic algorithm-based PID controller for controlling the temperature of electric furnaces in a study titled "Optimal PID tuning for temperature control of electric furnaces with genetic algorithms [9]." Figure 7 shows the Genetic Algorithm (GA).The gain settings of the PID controller are optimized with the help of a genetic algorithm (GA).GA is an optimization algorithm that continuously iterates to discover the best solution by simulating the process of natural selection and genetic mechanism.The objective function that minimizes the absolute error integral (IAE) and produces the ideal values for the gain parameters (Kp, Ki, and Kd) for the PID controller is sought after by the GA in this study.Instead of using the time-tested method of manually modifying the PID controller's gain parameters, GA can identify the ideal solution more quickly and with higher performance and stability.In order to improve temperature control outcomes, GA plays a role in the temperature control of electric heating furnaces by optimizing the PID controller gain parameters.Figure 8 is a proposed PID controller based on genetic algorithm.Figure 9 shows the temperature control system.The usage of a genetic algorithm-based PID controller to regulate the temperature of an electric furnace is demonstrated through the use of an application case.MATLAB/Simulink is used to simulate the situation, and the outcomes are compared to those of a traditional PID controller.The outcomes demonstrate that the genetic algorithm-based PID controller performs better and can reach steady state more quickly under the same circumstances.Therefore, an effective method for controlling the temperature of electric heating furnaces is to use PID controllers based on genetic algorithms.Figure 10 shows the step response of the system under different methods.This research compares the Cohen-Coon, direct synthesis, and Nelder-Mead methods with other control methods to assess the performance of the proposed genetic algorithm based PID controller in section 4.3 Performance comparison [9].Rising time, overshoot, and settling time are among the evaluation metrics.Figure 10 shows that the suggested GA-PID controller performs better than the other approaches with a minimum rising time of 1.14 seconds, a regulation time of 7.21 seconds, and an overshoot of 3.27%.As a result, the genetic algorithm-based PID controller is a useful temperature control technique [9].This paper also discusses additional techniques for controlling the temperature of electric heaters, such as fuzzy control and neural network control.PID controllers based on genetic algorithms, in contrast, offer higher performance and a larger range of applications.Changes or degradation of the physical qualities of the processed material can be prevented because to the exact and immediate temperature control available.Therefore, a variety of intriguing applications for temperature control in electric heating ovens, particularly in the food and pharmaceutical industries, exist for genetic algorithmbased PID controllers.To assure product quality in industries, precise and immediate temperature control is crucial.Other disciplines like mechanical engineering, chemical engineering, and environmental engineering can also employ the technique.

Model predictive control and application examples
Electric oven temperature management is a crucial component of industrial production and helps to increase product quality while lowering production costs.PID control algorithms have been one of the most popular ways to regulate the temperature of electric ovens during the last few decades.However, dead zones in conventional PID algorithms make accurate and consistent temperature regulation challenging.As computer simulation and mathematical modeling capabilities have advanced in recent years, an increasing number of academics have begun to test model predictive control approaches to address this issue.
In the article "Mathematical Model for Predicting Mechanical Properties and Structure of the Finished Part and Model for Controlling Electrical Heating System Based on PID Adjustment Algorithms," the literature on temperature control techniques for electric heating furnaces is presented, along with examples of their use [10].This book proposes and applies a model predictive control strategy to the steel heat treatment process based on mathematical models and finite element analysis techniques.The two primary components of the technology are the employment of mathematical models to forecast the mechanical characteristics and structural makeup of the material following heat treatment and the application of PID control principles to regulate furnace temperature for optimal heat treatment outcomes.
The PID control algorithm used in this literature combines a one-step prediction algorithm, which manages the system dead time better than traditional PID algorithms, leading to more precise and stable temperature control.The STEP ARX model, a finite element analysis technique, is also used in the literature to analyze the temperature and heat flow of the component versus time and to obtain better temperature control.The technique has been directly shown to greatly enhance the steel's mechanical characteristics and structural uniformity, lower the expenses associated with trial and error, and boost production efficiency.Additionally, a variety of electrically heated equipment can be heated using this technique, including industrial resistance furnaces and induction heating equipment.The superiority of their method is also demonstrated in the following figure 11.

Figure 11.
Normalizing heat treatment applied to piece in furnace with PID control system.Other model predictive control applications for the temperature regulation of electric heating furnaces exist in addition to the ones given above.Model predictive control, for instance, can be utilized to resolve unique situations where accurate control is challenging to obtain with traditional PID algorithms.In circumstances when quick reaction and high precision control are required, adaptive control techniques based on model predictive control can also be applied.
In conclusion, model predictive control is a highly promising approach for managing the temperature of electric furnaces.More precise, reliable, and effective temperature management may be obtained by combining mathematical modeling and computer simulation approaches, which will increase the economic and social advantages of industrial production.

Problems in temperature control
The following issues are present in the temperature regulation of electric heating furnaces: (1) Physical limitations of the apparatus: The furnace's design, the placement of its heating components, the airflow within it, and other factors may result in uneven temperature distribution within the furnace, making temperature control more challenging.(2) Parameter uncertainty: During the operation of the electric heating furnace, a number of factors, including the heat load inside the furnace and the ambient temperature, may fluctuate.These changes may have an impact on the effectiveness of temperature control.(3) Algorithm limitations: The current control algorithm might not be able to properly adjust to the complexity of the electric furnace's temperature management, leading to insufficient control accuracy and reaction time.(4) High real-time and stability demands: Electric heating furnaces in industrial production processes frequently require real-time, quick, and stable responses, making it challenging for present control approaches to match these demands.

Development trends
The following future development trends for temperature management of electric heating furnaces are based on issues in current research and practical applications: (1) Studying more sophisticated control algorithms: Future studies might use cutting-edge control theories from other disciplines, such deep learning and neural networks, to boost the precision and responsiveness of temperature management.For instance, Mahmoud M. Hussein developed a contemporary temperature control approach based on enhanced optimisation techniques in which the gain parameters of the PID controller are tuned using an enhanced whale optimisation algorithm (EWOA).The approach, which combines EWOA and the balloon effect to enhance algorithm performance and efficiency, has been proven in trials.Future research of a comparable nature will continue [11].
(2) Intelligent control: utilizing big data and IoT technology to remotely monitor and intelligently schedule electric heaters to reduce emissions and enhance production efficiency Using several data models for analysis and comparison, Goran Andonovski and his colleagues collected EAF batch data to address the issue of streamlining production procedures in the steel sector to conserve resources.Due to this research, more energy will be saved in the future [12].
(3) Furnace design optimization: By making the furnace's construction and heating element arrangement more efficient, the furnace's temperature uniformity is increased and the difficulty of temperature management is decreased.
(4) Control system integration: Integrate other process equipment with the electric heating furnace to achieve optimal control over the whole process, boost output effectiveness, and enhance product quality.

Conclusion
This paper first discusses the fundamental concepts and categories of electric heating furnaces before going into great depth on the various approaches to temperature control and providing instances of their application.It also examines the current issues with temperature regulation and how those issues could evolve in the future.When inspecting and comparing various control systems, it can be seen that the control systems already in use are still lacking in certain fields and require further research and development.The authors seek to discover more effective and reliable strategies to regulate the temperature of electric heating furnaces in further research by having more in-depth conversations in these areas.They also seek to support the development and advancement of electric heating furnace technology.
is a schematic diagram of the Joule heating effect.
is a schematic diagram of the Magneto-thermal effect.

Figure 4 .
Figure 4. FIS structure of FLC.Figure5shows the system design with FLP controller.In the experiments, the authors contrast the fuzzy PID controller-based proposed approach with a traditional PID controller.A proportional term, an integral term, and a differential term are the three components that make up a traditional PID controller's output signal.In order to obtain optimal control under various operating situations, the coefficients of these terms must be determined in accordance with the transfer function of the system.However, traditional PID controllers might not offer sufficient accuracy and stability for non-linear and timevarying systems.In contrast, fuzzy PID controllers evaluate the input and output signals using fuzzy logic and determine the output signal in accordance with a predetermined set of rules.Fuzzy PID controllers are more suitable for non-linear and time-varying systems since they do not need a transfer function of the system to calculate the gain value, in contrast to traditional PID controllers.Figure6demonstrates that the proposed technique has a superior ability to reject disturbances and can be more effectively applied to non-linear and time-varying systems.

Figure 6 .
Figure 6.Comparison of various traditional PID methods for the system.

Figure 10 .
Figure 10.Step response of the system in different methods.

Plasma furnaces. Plasma melts or heats plasma furnaces. Plasma furnaces generate plasma.
Simple and adaptable electric furnaces use metallic or non-metallic heat generators.Heat treatments include annealing, normalising, quenching, tempering, carburising, and carburising-nitriding.Ni-Cr hot wires, Mo-Si alloys, and pure metals like W and Mo are the main metallic heat generators.SiC, LaCrO3, and graphite rods are non-metallic.