Image-Based Characterization of Alternative Fuel Combustion With Multifuel Burners

Abstract

Many industrial high-temperature processes such as e. g. cement production employ multi-fuel burners in order to achieve the required energy input with low-cost alternative fuel. So far, a constant operation of multi-fuel burners with high fractions of alternative fuel (>70%) is not possible due to inherent fluctuating fuel properties. Energy input and product quality are directly affected by varying points of combustion time, different scattering of fuel and insertion of unburned fuel or chemical substances into the product. We propose an image processing system based on infrared images that detects the alternative fuel streakline and derives parameters for the characterization of the flight and burning behavior. Using these parameters, an adjustment of the burner settings depending on the fluctuating fuel properties can be carried out. This automatic monitoring and control of the combustion process allows an increased use of alternative fuels in constant operation. Experimental data from a rotary kiln for cement clinker production are used to validate the image processing system.

Two- and Three-Dimensional Benchmarks for Particle Detection from an Industrial Rotary Kiln Combustion Chamber Based on Light-Field-Camera Recording

“…We consider the detection of small fuel particles in a special combustion chamber, the rotary kiln, which is used industrially, e.g., for cement production or hazardous waste incineration but is also widely used for research purposes. Particle detection is used, for example, to determine the trajectory or the landing point of the fuel and thus to characterize the fuel and contribute to an optimization of the combustion process in comparison with CFD simulations [1,2]. Due to the difficult environmental conditions at the combustion chamber, the 2D and 3D data acquisition is not performed with a stereo camera system, which requires two access points to the combustion chamber, but with a light field camera.…”

Cholesky Factorization Based Online Sequential Multiple Kernel Extreme Learning Machine Algorithm for a Cement Clinker Free Lime Content Prediction Model

“…According to the requirements of different cement production lines, the time interval of laboratory testing fCaO content varies from 1 to 4 h. However, there is a certain time difference between each process in the cement clinker firing process, which makes the measurement results of fCaO content have a great lag for the production guidance in the clinker firing process. The measurement results can not meet the requirements of real-time optimization control in the cement clinker firing process [7]. Therefore, the essential work of estimating fCaO content is to set up a high precision online prediction model which is very important for monitoring and optimizing the operations of cement plants.…”

Infrared Measurements and Estimation of Temperature in the Restrictive Scope of an Industrial Cement Plant

“…Due to the more accurate insight into e.g. the kiln, an improvement of product quality and increase of energy efficiency can be achieved (Vogelbacher et al, 2018). Especially in the cement industry, as one major branch of industry depending on efficient and smooth burning processes, the monitoring of the kiln temperatures gains considerable benefits.…”