Rotary Kiln Control Research Articles

Modeling and Control of Rotary Kiln in a Cement Manufacturing Unit

Modeling and Control of Rotary Kiln in a Cement Manufacturing Unit

Modeling and Control of Rotary Kiln in a Cement Manufacturing Unit

Modeling and Control of Rotary Kiln in a Cement Manufacturing Unit

Rotary Kiln, a Unit on the Border of the Process and Energy Industry—Current State and Perspectives

A rotary kiln is a unique facility with widespread applications not only in the process industry, such as building-material production, but also in the energy sector. There is a lack of a more comprehensive review of this facility and its perspectives in the literature. This paper gives a semi-systematic review of current research. Main trends and solutions close to commercial applications are found and evaluated. The overlap between process and energy engineering brings the opportunity to find various uncommon applications. An example is a biogas plant digestate treatment using pyrolysis in the rotary kiln. Artificial intelligence also finds its role in rotary kiln control processes. The most significant trend within rotary kiln research is the waste-to-energy approach in terms of various waste utilization within the process industry or waste pyrolysis in terms of new alternative fuel production and material utilization. Results from this review could open new perspectives for further research, which should be focused on integrated solutions using a process approach. New, complex solutions consider both the operational (mass calculations) and the energy aspects (energy calculations) of the integration as a basis for the energy sustainability and low environmental impact of rotary kilns within industrial processes.

Industrial IoT Enabled Fuzzy Logic Based Flame Image Processing for Rotary Kiln Control

Industrial IoT Enabled Fuzzy Logic Based Flame Image Processing for Rotary Kiln Control

Industrial IoT enabled fuzzy logic based flame image processing for rotary kiln control

Purpose A rotary kiln is a pyro processing device that is used to raise the temperature of materials in cement factories. Temperature monitoring is an essential process in the rotary kiln to yield high quality clinker. Temperature measurement is a challenging task in clinkering process and it is difficult to apply automation techniques. As the pyrometer gives unreliable readings, it is necessary to apply various image processing techniques on the camera images to measure the temperature inside the kiln at different zones. Design/methodology/approach In this paper, a fuzzy logic rule-based analysis is proposed to measure temperature using a burning flame image in which it considers red, green, blue (RGB) magnitude planes. The proposed method uses Mamdani fuzzy inference system for decision-making. The system takes RGB magnitude as an input fuzzified variable and generates temperature as fuzzified output. Findings This paper focuses on the temperature measurement obtained from the images of the camera system. The commands to the valves and actuators are controlled using the center of gravity of the control regime. The fuzzy logic controller detects the temperature of flame zones using color features of burning flame images. Originality/value Precise temperature mapping of flame images helps to control the temperature inside the rotating kiln to produce high quality clinker. The process can be viewed remotely and controlled using various control loops from anywhere.

Application of Serial Communication Technology in Temperature Control of Rotary Kiln

Application of Serial Communication Technology in Temperature Control of Rotary Kiln

Sintering conditions recognition of rotary kiln based on kernel modification considering class imbalance

Accurate sintering condition recognition (SCR) is an important precondition for optimal control of rotary kilns. However, the occurrence probability of abnormal conditions in the industrial field is much lower than normal, resulting in imbalanced class sintering samples in general. This significantly deteriorates the effectiveness of existing recognition models in abnormal condition detection. In this paper, an integrated framework considering class imbalance is proposed for sintering condition recognition. In the proposed framework, after analysing the characteristics of thermal signals by the Lipschitz method, four discriminant features are extracted to comprehensively describe different sintering conditions. In addition, focusing on the class imbalance of sintering samples, the kernel modification method is introduced to enhance the optimal marginal distribution machine (ODM), and a novel recognition model kernel modified the ODM (KMODM) is proposed for SCR. By constructing a new conformal transformation function to modify the ODM kernel function, KMODM optimizes the spatial distribution of training samples in the kernel space, thereby alleviating the detection accuracy deterioration of the minority class. The experimental results on real thermal signals and standard datasets show that the KMODM model can effectively handle imbalanced data. Based on this, the proposed SCR framework can reduce the misjudgement of abnormal conditions and balance the recognition accuracy of each condition.

An alumina rotary kiln monitoring system based on flame image processing

In order to monitor the combustion condition and improve the product material quality, a digital image acquisition and processing method is adopted in alumina-sintering process. Two digital properties of flame grade and material grade are obtained by image data analyzing and smooth filtering, which provide reliable reference for operational optimization and automatic control of rotary kilns. This image acquisition system improves the operation of kiln effectively and reduces the working intensity and the manufacture cost.

Matlab Simulation of System with Fuzzy Control in Kiln Temperature

Temperature control requires a lot of control regulator. It has wide applications in cement, metallurgy and other industrial fields. The key equipment in metallurgy , cement, refractory materials production is Rotary kiln, of which temperature is one of the important parameters of process control .The kiln of calcination temperature in rotary kiln is an important factor affecting the quality and output of the kiln, and even cause the collapse of the kiln, affecting the normal operation of production. Based on the analysis for several important factors affecting the kiln temperature, the paper presents the application of fuzzy control in temperature control of rotary kiln. A fuzzy controller was established using the matlab fuzzy toolbox. A simulink rotary kiln fuzzy control system simulation was carried out on the kiln temperature control simulation. The simulation results show that the system response speed is faster and has better robustness compared with the traditional PID control system.

Burning state recognition of rotary kiln using ELMs with heterogeneous features

Image based burning state recognition plays an important role in sintering process control of rotary kiln. Although many efforts on dealing with this problem have been made over the past years, the recognition performance cannot be satisfactory due to the disturbance from smoke and dust inside the kiln. This work aims to develop a reliable burning state recognition system using extreme learning machines with heterogeneous features. The recorded flame images are firstly represented by various low-level features, which characterize the distribution of the temperature field and the flame color, the local and global configurations. To learn the merits of our proposed flame image-based burning state recognition system, four learner models (ELM, MLP, PNN and SVM) are examined by a typical flame image database with 482 images. Simulation results demonstrate that the heterogeneous features based ELM classifiers outperform other classifiers in terms of both recognition accuracy and computational complexity.

Application of Fuzzy Neural Network Controller for Cement Rotary Kiln Control System

This paper presents the design and application of fuzzy neural network control system for the rotary cement kiln system. Due to the dynamic characteristics and reaction process parameters are with features of large inertia, pure hysteresis, nonlinearity and strong coupling, the fuzzy neural network controller combining both the advantages of neural network and fuzzy control was applied. The fuzzy neural network is an adaptive control process whose parameters can be adjusted by learning algorithms automatically which is aimed to eliminate the shortcomings of conventional control methods. The main control system structure includes mainly the pressure control loop, the burning zone control loop and the back-end of kiln temperature control loop. Simulation results show the effectiveness of the control scheme with satisfied dynamic performances on response time and overshoot.

Application of Fuzzy Neural Network Controller for Cement Rotary Kiln Control System

This paper presents the design and application of fuzzy neural network control system for the rotary cement kiln system. Due to the dynamic characteristics and reaction process parameters are with features of large inertia, pure hysteresis, nonlinearity and strong coupling, the fuzzy neural network controller combining both the advantages of neural network and fuzzy control was applied. The fuzzy neural network is an adaptive control process whose parameters can be adjusted by learning algorithms automatically which is aimed to eliminate the shortcomings of conventional control methods. The main control system structure includes mainly the pressure control loop, the burning zone control loop and the back-end of kiln temperature control loop. Simulation results show the effectiveness of the control scheme with satisfied dynamic performances on response time and overshoot.

Prediction Model for Rotary Kiln Coal Feed Based on Hybrid SVM

A SVM (Support Vector Machine) classification for rotary kiln based on time series trend characteristics was presented in this paper. The amount of coal feed depended on its trends which were predicted by the dynamic rules of rotary kiln data. The original sample was cleaned by data preprocessing, and the piecewise linear representation based on key points was used to extract the trend characteristics of rotary kiln time series data. The parameters of SVM were decided by Particle Swarm Optimization method. It was proved that the proposed model with high prediction level which enhanced the robustness of the control of rotary kiln could be applied on the actual situation.

Observer-Based Model Predictive Control of Rotary Kilns - Application to Limestone Calcination

This paper presents an observer-based model predictive control approach of rotary kilns. A one-dimensional, object-oriented modeling approach of rotary kilns is used as the basis for the control scheme. After linearization of the nonlinear modelica model at an operating point, the generated LTI model is reduced by applying a balanced truncation method. The reduced LTI system, i.e. the result of an equivalence transformation of the original LTI system, is then used for the observer design. The process observer is enhanced by disturbance models to cope with input and output disturbances. Using the estimates of all immeasurable process and disturbance variables, a linear model predictive controller is applied. With this design, disturbances, stemming in particular from the input, are dealt with. Also, the economically needed weighting of the actuated inputs is dealt with implicitly by the MPC algorithm. The main goals of this approach are stable operation, robustness against model uncertainty and reduced energy consumption.

Application of a digital computer to the cement-making process

This paper describes the use of a digital control computer to supervise and control the process on-line in certain important areas. Among the functions covered are: priority scanning of critical points including off-limit checking; primary raw material control; final raw material control through the raw grinding and homogenizing systems; rotary kiln control including cold start-up procedures; and automatic calculation and typeout routines for production log, chemist's log, and alarm log. Electronic instrumentation and control with computer operated set point provides the basic level of control. This is fully coordinated with the computer, which provides automatic continuous supervisory and documentary functions. Some comments are given on sixteen months of operating experience.