Furnace Temperature Control Research Articles

Hybrid self-learning model for the prediction and control of sintering furnace temperature

Ternary cathode materials are important components of lithium-ion batteries. However, the sintering process during manufacturing is challenging to control due to the inaccessibility of key dynamic variables and the frequent fluctuations in operating conditions. These lead to high energy consumption and inconsistent product quality. In this paper, we propose a hybrid self-learning prediction model and control method for sintering furnace temperature based on both first-principle and process data. Firstly, a mechanism model with temperature time delay is established based on energy flow analysis in the furnace. To capture the tail gas temperature dynamic in the mechanism model, a Ventingformer-based prediction data-driven model is proposed. In this model, a memory updating technique and an autoregressive module based on the Transformer framework are developed to identify long-time dependencies and respond to variations in input sequences. Then, a hybrid self-learning modeling framework is designed. Based on the established hybrid model, a multiscale objective function-based nonlinear model predictive control (MSCF-NMPC) method is proposed to achieve precise tracking control of the internal temperature in the furnace. A multiscale objective function with short-term cost in terms of energy consumption and tracking accuracy as well as long-term cost in terms of energy loss is constructing in the control optimization problem. Finally, the proposed hybrid self-learning model and MSCF-NMPC method are verified using the actual process data from a sintering furnace, demonstrating the effectiveness of the proposed method. The results offer practical guidance for industrial applications.

VD furnace discharge temperature control method study

This paper designs a set of VD furnace temperature control decision-making system. In the refining process, the harsh environment and harsh conditions can be overcome, and the control and prediction of VD furnace outlet temperature can be realised. According to the known parameters of the current smelting information, a matching model is established, and a GM (1, N) model based on grey theory is designed to predict the temperature. At the same time, the control parameter optimisation model is designed, and the local optimisation method is used to optimise the control parameters to calculate the prediction accuracy. The experimental results show that the system can accurately control the refining temperature of the VD furnace and effectively improve the quality of steel. This is of great significance to improve steelmaking production efficiency and reduce costs.

Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization

Furnace temperature (FT) is a key variable in municipal solid waste incineration (MSWI) processes, influenced by many manipulated variables and directly impacting pollutant concentrations in exhaust gas. Domain experts cannot achieve the optimal FT setpoint value under manual control, leading to abnormal pollutant emission concentrations. To address this, we propose an intelligent optimal control framework for FT aiming to minimize pollutant emission concentration. First, the FT controlled object model is established using the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm. Then, based on the experience of domain experts, a multi-loop controller is developed using an improved single neuron adaptive PID (ISNA-PID) algorithm to stabilize FT. Next, after establishing NOx and CO2 indicator models, the particle swarm optimization (PSO) algorithm is employed to determine the FT setpoint value in terms of minimum pollutant emission concentration. Finally, the FT intelligent optimal control framework is verified. Experimental results indicate that the optimal FT setpoint value can reduce NOx and CO2 emission concentrations by 19.93 % and 6.99 %, respectively.

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.

Temperature Control in Solar Furnaces Using Nonlinear PID-based Control Approaches

Temperature Control in Solar Furnaces Using Nonlinear PID-based Control Approaches

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.

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.

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

<abstract> <p>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.</p> </abstract>

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.

Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network for Furnace Temperature Control

Bayesian Optimization-Based Interval Type-2 Fuzzy Neural Network for Furnace Temperature Control

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

Design of vacuum annealing furnace temperature control system based on GA-Fuzzy-PID algorithm.

As is well known, the metal annealing process has the characteristics of heat concentration and rapid heating. Traditional vacuum annealing furnaces use PID control method, which has problems such as high temperature fluctuation, large overshoot, and long response time during the heating and heating process. Based on this situation, some domestic scholars have adopted fuzzy PID control algorithm in the temperature control of vacuum annealing furnaces. Due to the fact that fuzzy rules are formulated through a large amount of on-site temperature data and experience summary, there is a certain degree of subjectivity, which cannot ensure that each rule is optimal. In response to this drawback, the author combined the technical parameters of vacuum annealing furnace equipment, The fuzzy PID temperature control of the vacuum annealing furnace is optimized using genetic algorithm. Through simulation and comparative analysis, it is concluded that the design of the fuzzy PID vacuum annealing furnace temperature control system based on GA optimization is superior to fuzzy PID and traditional PID control in terms of temperature accuracy, rise time, and overshoot control. Finally, it was verified through offline experiments that the fuzzy PID temperature control system based on GA optimization meets the annealing temperature requirements of metal workpieces and can be applied to the temperature control system of vacuum annealing furnaces.

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.

Automated systems for monitoring the consumption of metal charge at factories by conversion metallurgy

The domestic factories engaged in conversion metallurgy use scrap as the main raw material for steel production (at least 70 % of the total charge). In difficult economic and geopolitical situation factories are cautiously considering the possibility of introducing new technologies that require high capital expenditures and modification of existing units. In this article automated control systems are proposed that help reduce the consumption of metal charge and energy resources during steel smelting: an electromagnetic scanner with an automatic control system that determines the presence of concentrations of non-magnetic substances in scrap at each point of the wagon or trailer of the road train and the bulk density of scrap; a neural network system for temperature control in electric furnaces and out-of-furnace processing (installation ladle-furnace and units for vacuuming). These systems will allow enterprises of conversion metallurgy (on the example of Pervouralsk Novotrubny Plant): to improve the quality of scrap metal by tightening the requirements for the quality of raw materials of scrap suppliers according to the interstate standard for secondary fer-rous metals; to increase the productivity of the electric furnace by reducing the time under current by 3 minutes, and to reduce the consumption of metal charge by 4.66%, electricity by 500 kWh for 1 melting cycle, the release temperature by 10 ℃ to reduce vacuuming time for 2 minutes. The proposed automated control systems require insignificant capital costs for installation and commissioning, and do not require the shutdown of existing production.

Cloud–edge collaborative control for roaster furnace: A hybrid first-principles and data-driven method

Temperature stability control of the roaster furnace is an important yet challenging problem due to the fluctuation of feed compositions. To solve this problem, a first-principles and data-driven cloud–edge collaborative control (CECC) method is proposed. Specifically, through first-principles analysis, the influencing factors of temperature fluctuation are determined, and then the feed fluctuation is divided into two parts: the batch difference (BD) caused by changes in production objectives or process conditions, and the random fluctuation (RF) caused by uncertain factors of component content or ore source mixing process. For the problem of BD, a first-principles model of the roasting process is established by integrating multisource data in the cloud. Through timely response to the batch differences, the appropriate feed rate is sent to the edge controller to address the temperature unstable problem induced by the sudden component batch changes. For the problem of RF, a fuzzy controller based on trend analysis is adopted at the edge to fine-tune the feed rate to further reduce the negative impact of component fluctuation on the temperature. Accordingly, the proposed CECC method can realize stable control of the roaster furnace temperature synergistically. Both the numerical simulation experiment and hardware-in-the-loop platform experiment results verify the effectiveness of the proposed method.

A Temperature Control Method of Electric Heating Furnace Based on Fuzzy PID

The traditional PID control method has a series of problems such as overshoot or static error. This paper takes the electric heating furnace temperature control system as the background, combines the fuzzy algorithm to design the fuzzy PID system, and uses the MATLAB fuzzy control box to set up the fuzzy reasoning system and the fuzzy rule table., and use the Simulink platform to build the temperature control system simulation model of ordinary PID and fuzzy PID, and verify the influence of parameters on the system.

Logic adaptive optimization model for dynamic temperature compensation of heating furnace based on proportion‐integral‐derivative position algorithm

AbstractIn order to improve the adaptive compensation control ability of the furnace dynamic temperature compensation logic, an adaptive optimal control model of the furnace dynamic temperature compensation logic based on proportion‐integral‐derivative (PID) position algorithm is proposed. Firstly, the constraint index model of the dynamic compensation logic control of the heating furnace temperature is constructed, and the target structure analysis of the electric heating furnace temperature control is realized by using the PID control mode. The platinum resistor is used to collect the temperature in the feedback link, and the controlled equipment converts electrical energy into heat energy for release. As the temperature rises, the difference between the two is sent to the fuzzy controller. The model fuzzy controller outputs an analog value, and the PID control system outputs different control parameters, thus realizing the parameter analysis and optimization design of the control law of the heating furnace temperature dynamic compensation logic control. The test results show that the method has good accuracy, with an error of 0.01 to 0.02, and has certain application value.

Features of bismuth telluride based ternary alloys for thermoelectric applications

The presented article provides a comparative analysis of the methods of manufacturing technology for semiconductor branches of p- and n-type conductivity. For comparative analysis, a method is considered for obtaining ternary alloys based on the Bi2Te3 base material by pressing and zone melting. The sequence of the manufacturing process, the principles of operation and design of devices used to obtain alloyed substances, as well as methods for studying and measuring the electrophysical parameters of the thermoelement legs included in the assembled thermopile are described. The principles of automatic temperature control of an electric furnace by a two-position method are indicated. The advantages of the zone melting method are determined and the best values of the figure of merit of a thermoelectric material are shown. It is proved that the original values of the parameters of the branches are preserved after they are assembled into a battery.

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 process is explained here and the different methods of achieving emissivity independence are examined.

Modern Temperature Control of Electric Furnace in Industrial Applications Based on Modified Optimization Technique

In this paper, an enhanced version of whale optimization algorithm (EWOA) is presented to be applied in adaptive control techniques as a parameter tuner. One weakness point in this control scheme is the low efficiency of its objective function. Balloon effect (BE) is a modification introduced to increase the efficiency of the objective function of the optimization method and the ability of the controller to deal with system problems increase consequently. Controlling of the temperature of electric furnaces is considered as one of the important issues in several industrial applications. Conventional controllers such as PID controller cannot deal efficiently with the problem of parameters variations and step disturbance. This paper proposes an adaptive controller, in which the gain of the temperature controller is tuned online using EWOA supported by balloon effect. System responses obtained by the proposed adaptive control scheme using EWOA + BE have been compared with an electric furnace temperature control (EFTC) scheme response using both the PID controller-based modified flower pollination algorithm (MoFPA) and PID-accelerated PIDA-based MoFPA. From the results, it can be observed that the proposed controller tuned by the EWOA + BE method improves the time performance compared with the other techniques (PID and PIDA-based MoFPA) in case of EFTC application.