In the cement industry, a rotary kiln is a pyro-processing device that is used to measure temperature. Measuring and maintaining a certain range of temperature in the rotary kiln is important to ensure the production of quality clinker granules. The assessment of consuming zone temperature is acquired using radiation pyrometers from the temperature of a hotspot. However, it is a difficult task to measure the burning zone temperature due to the very high temperature developed in the turning furnace sintering process. Existing pyrometer and camera based techniques are not able to provide accurate temperature and temperature variations developed in the burning zone. This research work considers flame image processing using region of interest (ROI), fuzzy logic, and neural networks for efficient temperature measurement. Various temperature measurement and control techniques are utilized in the existing conventional (Prasanna and Bojja in ESCI (helix-the scientific explorer) 4843-4849, 2019) rotary kiln control techniques. In pyrometer-based measurements, the standard of radiation may lead to errors and inaccurate readings. Hence, the consuming zone temperature estimation got from the radiation pyrometer isn't solid and it is hard to get temperature data for a particular location. A colorimetric device-based intelligent control system measures the burning temperature of a specific point, but reading fluctuations are seen because of smoke and dust developed in the combustion process. In ROI based flame image processing, many factors, such as turbulent flame, brightness of flame zone, and dust, affect identifying the boundary for ROI based flame image analysis. In neural network models, variable selection plays a crucial role in designing effective systems with learning capabilities, but it is not an easy task to accomplish without certain rules. Hence, it is highly necessary to develop an improved control system. In view of the issues in variable and feature selection, a few neuro fuzzy systems are adopted in measurement and control. The consuming zone temperature estimation needs a lot of attention due to the very high temperature developed in the rotary kiln sintering process. Existing techniques have to be improved upon using advanced algorithms and intelligent approaches. A sintering state recognition system has been developed with features of flame images and fusion methodologies. In this approach, various flame image features and texture (Ren and Wang in Int J Autom Compu 11(1):72-77, 2014) features are extracted from the burning zone region. Though these methods address a few issues in flame image processing, the acquired image is largely affected by blurring and internal parameters of the sintering process. Charge coupled device (CCD) camera images and videos are applied to many image processing algorithms for better feature extraction and region extraction. The region of interest-based analysis is mainly focused on temperature assessment in this work. Intelligent control techniques are applied to measure the burning zone temperature in a rotary kiln. Fuzzy logic-based inference systems are combined with neural network algorithms in the development of neuro-fuzzy systems. The fuzzy surmising framework in light of mathematical models is the successful manner to anticipate the temperature esteems utilizing power measures. The fire pictures caught by the CCD cameras are handled utilizing fluffy rule-based picture investigation, which estimates temperature from a fire picture by contemplating RGB power planes. The arrangement of result temperature esteems is wanted to be a participation work. The Mamdani fluffy induction model is used to give planning of fluffy fire temperature. Exact temperature planning of fire pictures is performed to control the temperature inside the going stove to make top notch clinker. The fire picture examination is completed in different edges of three unique datasets, and temperature is estimated for various crude supper feed rates and coal feed rates. However there is a slight distinction in the acquired temperature, the general temperature evaluation process doesn't show a huge contrast as per the dataset.
