1-20 ton gas/oil fired boiler
Capacity: 1-20 ton/h
Pressure: 0.7-2 Mpa
Fuel: Nature gas, coke oven gas, biogas, methanol, liquid propane gas, diesel, heavy oil, light oil, crude oil, etc.
Industries: Heat supplying, chemical, food, textile, printing and dyeing, cigarettes and tobacco, fodder, pharmacy, building materials, brewery, rubber, hospital etc.
7-70 MW gas/oil fired hot water boiler
Capacity: 7-70 MW
Pressure: 1.0-1.6 Mpa
Fuel: Nature gas, coke oven gas, blast furnace gas, carbon black off-gas, biogas, methanol, LPG, diesel, heavy oil, light oil, crude oil, etc.
Industries: Heat supplying, hospital, colleges and universities.
2.8-29mw coal fired boiler
Pressure: 1.0-1.25 Mpa
Fuel: Bituminous coal, lean coal, anthracite
Industries: Heating, hotels, schools, hospitals
horizontal thermal fluid heater
Capacity: 700 - 14000 kw
Pressure: 0.8 - 1.0 Mpa
Fuel: natural gas, coke oven gas, bio-gas，liquid propane gas, diesel, heavy oil, light oil, crude oil
Industries: Petroleum, chemical, chemical fiber, pharmaceutical, textile printing and dyeing, building materials, wood processing, vegetable oil processing and other industries
1-20 ton biomass fired boiler
Capacity: 1-20 ton/h
Pressure: 0.7-2.5 Mpa
Fuel: Biomass particles
Industries: Heating, chemical, food, tobacco, textile, printing and dyeing, feed, medicine, building material, wine, rubber, hospital
Artificial neural network - based prediction of hydrogen content of
Artificial neural network - based prediction of hydrogen content of coal in power 4-5-5-1 and 20-50-1 [18, 20, 2] are simulated to compare these two types of data . Improvement of Boiler's Efficiency Using Heat Recovery and Automatic
(PDF) Neural network for evaluating boiler behavior - ResearchGate
26 Oct 2017 technique to analyze the influence of fouling in a biomass boiler is to of Neural Network (NN) design and application for a biomass boiler . Originally the boiler analysed used coal as fuel, but a fuel change the efficiency reduction. depending on the composition and type of fuel, fuel humidity, boiler
(PDF) Use of Neural Networks for modeling and predicting boiler's
The method utilizes a neural network approach to analyze and predict boiler efficiency and also to discover possibilities for enhancing efficiency. The analysis is based on energy surveys of 65 randomly selected boilers located Distribution of boilers in the industrial sector according to occupational intensity and fuel type .
Using Neural Network to Evaluate Boiler - Semantic Scholar
efficiencies , higher CO2 emissions, losses of availability, and finally an uneven A monitoring technique based on Neural Networks (NN) allows Originally the boiler analysed used coal as fuel, but a fuel change was introduced with a different types of biomass (bark and wood of indeterminate species) with 48.5% of
Neural Network Applications in a Power Station - Semantic Scholar
consists of boiler , turbine, generator, power network and loads. Information plays a vital role for the efficient operation and A neural network based Model Predictive Control ( MPC ) is proposed in coal -fired furnaces the blockage from ash and damage to the Contact- type thermocouples are not as accurate as non -.
The Artificial Neural Network On-line Monitoring Model Of Boiler
4 Sep 2013 monitoring model of boiler efficiency can predict the boiler efficiency loss caused by incomplete combustion of residual combustible gas in the gas,as for coal - combustion heat loss as the input of neural network in this paper. intermediate a reheat control cycle furnace, single furnace type half.
Prediction of Performance of Coal - Based KWU Designed Thermal
of power is generated using coal - based thermal power plants in India. The power . analysis of heat exchangers using artificial neural networks . . prediction of the heat rate and boiler efficiency. This predicted heat rate and boiler efficiency can be used to .. Different types of ANN architecture used are feed forward back .
Thermal power plant analysis using artificial neural network - IEEE
Abstract: Coal - based thermal power stations are the leaders in electricity generation as the internal model for prediction of the Heat Rate and Boiler Efficiency .
Artificial neural networking model for the prediction of high efficiency
Development of artificial neural network (ANN) models using real plant data for the pre- diction of fresh steam properties from a brown coal -fired boiler of a Slovenian nected to the new European environmental directive based on a greenhouse calculation of two type solar air collectors using artificial neural network ,
Optimizing pulverized coal combustion performance based on ANN
In this work, an effective method based on artificial neural network (ANN) and genetic boiler and optimizing the operating conditions to achieve the highest boiler heat efficiency and operators to perform clean and efficient utilization of coal . The third involves predicting combustion behavior by using indices associated
Fault Diagnostics on Steam Boilers and Forecasting System Based
The research develops a generic slagging/fouling prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks (FCANN). structures, two types of high temperature ash deposition schemes namely slagging and fouling are defined. .. A 300 MW coal fired utility boiler is considered for the analysis  .
(PDF) Prediction of Excess Air Requirement Using ANN for the
In this paper artificial intelligence Ultimate analysis of Coal used in the boiler is as shown in concept using Artificial Neural Network (ANN) is used to predict Table1&2. The improvement in boiler efficiency will increase the steam input to GCV of Coal Among the types of fuels ,Natural gas requires less and coal TABLE-3
fired boiler using artificial neural networks - Wiley Online Library
5 Sep 2013 Performance prediction of a RPF-fired boiler using artificial neural results observed from this work demonstrate the fact that artificial neural networks can efficiently predict the data on solid nature of coal poses greater difficulties during online based on modified Gaussian model of Pasquille's type and
A neural network model for quantitative prediction of oxide scale in
Application of artificial neural network (ANN) for predicting industrial process behavior has . range (pH of 9 - 11 depending upon the boiler type and . ( a ). (b).
Deep Bidirectional Learning Machine for Predicting NOx Emissions
26 Sep 2017 A precise NOx emission model and a boiler efficiency model are the basis of In ref 9, a generalized regression neural network is employed to set up a NOx up the prediction models of the boiler efficiency and NOx emissions based . are collected under the same kind of coal during the sample period;
Using Neural Network Combustion Optimization for MATS Compliance
1 Feb 2014 The emission limits vary based on the type of coal burned and whether MATS identifies neural network optimization software as a best Today's more sophisticated systems combine neural network based optimization and model predictive There are inherent boiler efficiency improvements that will be
The Artificial Neural Network On-Line Monitoring Model of Boiler
After precise analysis and tracking, the input variable for the artificial neural network At last, based on a 600MW boiler ,the borler efficiency was predicted in this
Challenges of Power Engineering and Environment: Proceedings of - Google Books Result
Modeling Fireside Corrosion Rate in a Coal Fired Boiler Using
In this paper, an efficient artificial neural network (ANN) model using multi-layer to predict the fireside corrosion rate of super heater tubes in coal fire boiler An efficient gradient based network training algorithm has been employed to corrosion types as those from the steam side can be found in the fireside of a tube,
Nitrogen Oxides Emission Prediction & Optimization in - IJERT
Nitrogen Oxides Emission Prediction & Optimization in Thermal Based . Coal Power Plant Using Artifical Neural Network - A Review. Milind S.. Mankar. M.Tech. contol techniques and efficient approach to predict nitrogen flow, boiler load, air distribution scheme, flue gas outlet, temperature and Arrhenius type : Reaction