This study presents a methodology to estimate the instantaneous pulverised coal flow rate of the E-type vertical spindle coal mills. Modifications over a six-segment coal mill model …
This paper presents a multisegment coal mill model that covers the whole milling process from mill startup to shutdown. This multisegment mathematical model is derived through analysis of energy ...
The online stage includes a coal change detection algorithm to detect transition of coal and a SSCC-based coal classifier that enables real-time classification of coal using only the live sensor data.
In practical engineering applications, coal seam thick-ness prediction and modeling is the focus of geological modeling of coal-bearing rock formations. In the ordinary kriging interpolation process, the PSO-SVR automatic fit-ting model proposed in this paper is used to form a three-dimensional visualization model of coal seam thickness by ...
Constructing high quality three-dimensional (3D) geological models of coal seams from multi-source data, such as boreholes and geological maps, is a crucial task in seam distribution analysis and production planning of coal mine. However, widely used interpolation methods in geology, such as inverse distance weighting (IDW) and ordinary …
Zachariades, Paschalis ; Wei, Jianlin ; Wang, Jihong. / Development of a Tube-ball coal mill mathematical model using Particle Swarm Optimization (PSO).Paper presented at World Congress on Engineering 2008, Vols I-Ii.6 p.
Semantic Scholar extracted view of "Modeling and Optimization of NOX Emission in a Coal-fired Power Plant using Advanced Machine Learning Methods" by Tan Peng et al.
The researchers from India optimized the availability field for generators, feed-water systems, and coal handling systems of subcritical power generation using PSO and …
A nonlinear support vector machine (SVM) uses engineered features to classify the quality of currently combusting coal as it is fired in an operating electric utility generator. The SVM classification result selects a unique neural network regression model to predict NOx emission rate. A two-part exhaustive grid-search and 5-fold cross …
This non-linear model with 73 unknown parameters in 42 model equations, provides relatively good results for the complete milling process. The optimisation of those model …
In order to coordinate the global and local optimization ability of PSO, inertia weight and simulated annealing algorithm are introduced to improve the optimization ability and forced local optimal trap probability of PSO. The improved prediction model combined with PSO and FSVM is used to predict coal and gas outburst.
This paper presents a mathematical model for Tube-ball mills which is developed based on the previous work. The Particle Swarm Optimization (PSO) method is used to identify the …
Coal handling system comprises of five subsystems, namely Wagon Tippler, Crusher, Bunker, Feeder and Coal Mill. The partial differential equations are derived on the behalf …
Since it was first presented, particle swarm optimization (PSO) has experienced numerous improvements as a traditional optimization approach. PSO becomes more complex as a …
Semantic Scholar extracted view of "A calibration framework for the microparameters of the DEM model using the improved PSO algorithm" by Min Wang et al.
The test show that the variogram of SVM-Kriging can avoid the subjectivity of selecting the type of variogram models and the SVM is better than the general Kriging based on other variogram model as a whole and is a good and adaptive interpolation method.
In order to make practical use of the trained models, a handy freely-accessible software platform was designed using the selected ANFIS−PSO models for approximating biomass pyrolysis kinetics.
Due to the complexity of boiler combustion model, it is difficult to establish a precise mathematical model for it. However, artificial intelligence methods may be applied on alternative models [7]. Domestic common modeling methods are based on state estimation, fuzzy theory and artificial neural networks [8].
This paper presents a mathematical model for Tube-ball mills which is developed based on the previous work. The Particle Swarm Optimization (PSO) method is used to identify the unknown parameters of the coal mill model with the on-line measurement data provided by EDF Energy. Simulation studies are carried out and the results are encouraging although …
The paper presents development and validation of a coal mill model to be used for improved mill control, which may lead to a better load following capability of power plants fired by pulverized coal. The model is relatively simple, yet it captures all significant mill dynamics. The model is validated using data from four mills of a similar type ...
This paper presents a multisegment coal mill model that covers the whole milling process from mill startup to shutdown. This multisegment mathematical model is derived through analysis of energy transferring, heat exchange, and mass flow balances. The work presented in the paper focuses on modeling E-type vertical spindle coal mills that are …
The researchers from India optimized the availability field for generators, feed-water systems, and coal handling systems of subcritical power generation using PSO and Simulated Annealing to ...
This paper presents a mathematical model for Tube-ball mills which is developed based on the previous work. The Particle Swarm Optimization (PSO) method is used to identify the …
The Particle Swarm Optimization (PSO) algorithm was put forth by Kennedy and Eberhart in the year 1995. It is widely known for the ease with which it can be implemented and its …
This paper also presents a novel approach for modeling cutting forces and surface roughness in milling PA-6/NC nanocomposite materials, by using particle swarm optimization-based neural network (PSONN) and the training capacity of PSONN is compared to that of the conventional neural network.
This non-linear model with 73 unknown parameters in 42 model equations, provides relatively good results for the complete milling process. The optimisation of those model parameters by using coal mill data takes a considerable amount of computational time.
Using PSO optimization, the problem of abnormal detection and identification in coal and gas outburst monitoring is developed and studied, and a CSA coal and gas outburst risk anomaly detection and identification model based on PSO optimization variation is established. The model uses the coal and gas outburst index data as a collection of ...
Coal mills are used to grind and dry the raw coal containing moisture and to transport the pulverized coal air mixture to the boiler. Poor dynamic performance of coal mill causes difficulties in maintaining the frequency, boiler pressure and temperature of the plant. This paper presents development and validation of coal mill model for improved coal mill …
It then calculates the key parameters of the combined model using the Particle Swarm Optimization (PSO) algorithm and finally compares this model with conventional …
The theory and the algorithm of Particle swarm optimization (PSO) based for neural network training were applied in the research of the modeling of milling force in high speed machining and showed that PSO-BP not only effectively shortens the time of training networks, but also greatly improves the accuracy of prediction and universal …
Therefore, this paper derives a gas–solid coupling model considering pore sorption strain. Based on this model and a hybrid optimization algorithm, which combines particle swarm optimization (PSO) and Levenberg–Marquardt (LM) algorithm, a novel method for determining coal seam permeability, namely the GP method, is proposed.
This study presents a methodology to estimate the instantaneous pulverised coal flow rate of the E-type vertical spindle coal mills. Modifications over a six-segment coal mill model have been proposed, and particle swarm optimisation technique with Kalman correction has been used to optimise the unknown parameters of the modified …
The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in north-ern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset …
Semantic Scholar extracted view of "Prediction model of goaf coal temperature based on PSO-GRU deep neural network" by Jun Guo et al.
To this end, a combined prediction model based on PSO-LSTM is proposed for pre-cise research on coal mine gas concentration prediction, using the gas concentration data collected from a mine in Guizhou as an example.
Finally, the soft sensing models based on improved PSO-SVM, SVM, long short term memory (LSTM) and back propagation (BP) neural network are trained and debugged.
These procedures include building a database with real coal power plant operating data, modeling the database and using the genetic algorithm to optimize the operating conditions. The operation is optimized with respect to the steam temperature, selective catalyst reactor, and separated over-fire air conditions.
Pulverised coal preparation system (Coal mills) is the heart of coalfired power plants. The complex nature of a milling process, together with the complex interactions between coal quality and mill conditions, would lead to immense difficulties for obtaining an effective mathematical model of the milling process. In this paper, vertical spindle coal mills …
In order to get the mill load modeling of combined grinding system in normal working condition, this paper proposes a method based on the RBF neural network. The neural network uses three kinds of kernel functions that are Gauss kernel function, multiquadric kernel function and inverse multinuclear kernel function. Using the gradient descent …
