Consolidated design of the HCPB Breeding Blanket for the pre-Conceptual Design Phase of the EU DEMO and harmonization with the ITER HCPB TBM program

Recycling of electric arc furnace dust through dissolution in deep eutectic ionic liquids and electrowinning.

Maintaining a normal burning temperature is essential to ensuring the quality of nonferrous metals and cement clinker in a rotary kiln. Recognition of the temperature condition is an important component of a temperature control system. Because of the interference of smoke and dust in the kiln, the temperature of the burning zone is difficult to be measured accurately using traditional methods. Focusing on blurry images from which only the flame region can be segmented, an image recognition system for the detection of the temperature condition in a rotary kiln is presented. First, the flame region is segmented employing a region-growing method with a dynamic seed point. Seven features, comprising three luminous features and four dynamic features, are then extracted from the flame region. Dynamic features constructed from luminous feature sequences are proposed to overcome the problem of mis-recognition when the temperature of the flame region changes rapidly. Finally, classifiers are trained to recognize the temperature state of the burning zone using its features. Experimental results using real datasets demonstrate that the proposed image-based systems for recognizing the temperature condition are effective and robust.

Recognition of the Temperature Condition of a Rotary Kiln Using Dynamic Features of a Series of Blurry Flame Images

Free lime (f-CaO) content is a crucial quality parameter for cement clinkers in rotary cement kiln. Due to lack of hardware sensors, f-CaO content in cement clinker is mostly obtained by offline laboratory measurement, making timely control rather difficult and even impossible. In this work, a soft sensor approach named as support vector machine ensemble (ESVM) model is proposed to estimate f-CaO content. The process data employed to train and test the model were collected from a cement plant in China, covering a time span of about 30 days. The raw data were preprocessed by filters and time-series matching. The processed data were then clustered by fuzzy c-means clustering algorithm to capture process features at different operating conditions. For each individual cluster, a base SVM regressor was trained to estimate f-CaO content. Finally, an ensemble model consisting of four base SVM regressors was established to estimate f-CaO content at multifarious process conditions. The effectiveness of the proposed ESVM model was investigated by comparing it with manual measurements and other models available in literature. The results demonstrate that the proposed ESVM model achieves improvements in model accuracy as well as generalization capability. The proposed ESVM model has a broad application space in cement production process for automatic monitoring of f-CaO content.

A novel support vector machine ensemble model for estimation of free lime content in cement clinkers.

Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches

A new infrared camera-based technology for the optimization of the Waelz process for zinc recycling

Fitting an ellipse to given data points is a common optimization task in computer vision problems. However, the possibility of incorporating the prior constraint "the ellipse's center is located on a given line" into the optimization algorithm has not been examined so far. This problem arises, for example, by fitting an ellipse to data points representing the path of the image positions of an adhesion inside a rotating vessel whose position of the rotational axis in the image is known. Our new method makes use of a constrained algebraic cost function with the incorporated "ellipse center on given line"-prior condition in a global convergent one-dimensional optimization approach. Further advantages of the algorithm are computational efficiency and numerical stability.

/pdf/constrained-ellipse-fitting-with-center-on-a-line-qyrycsfhsb.pdf

Constrained Ellipse Fitting with Center on a Line

A high-speed camera based approach for the on-line analysis of particles in multi-fuel burner flames

A new camera-based method for measuring the flame stability of non-oscillating and oscillating combustions

Many industrial high-temperature processes such as cement production employ multifuel burners in order to achieve the required energy input with low-cost alternative fuel. So far, a constant operation of multifuel 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.

/pdf/image-based-characterization-of-alternative-fuel-combustion-4ff504bxy2.pdf

Patrick Waibel

Papers

A new infrared camera-based technology for the optimization of the Waelz process for zinc recycling

Constrained Ellipse Fitting with Center on a Line

A high-speed camera based approach for the on-line analysis of particles in multi-fuel burner flames

A new camera-based method for measuring the flame stability of non-oscillating and oscillating combustions

Image-Based Characterization of Alternative Fuel Combustion With Multifuel Burners