96/05572 A neural network based optimization system provides online coal fired furnace air flow balancing for heat rate improvement and NO x reduction

1996 ◽  
Vol 37 (5) ◽  
pp. 386
Keyword(s):  
Neural Network ◽  
Air Flow ◽  
Heat Rate ◽  
Optimization System ◽  
Rate Improvement

scholarly journals Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 167
Author(s):  
Hasan Alimoradi ◽  
Madjid Soltani ◽  
Pooriya Shahali ◽  
Farshad Moradi Kashkooli ◽  
Razieh Larizadeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Temperature ◽  
Water Flow ◽  
Flow Rate ◽  
Cooling Tower ◽  
Air Flow ◽  
Water Flow Rate ◽  
Pso Algorithm ◽  
Outlet Temperature ◽  
Air Flow Rate

In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate, air flow rate, inlet water temperature, and packing compaction on the performance are examined. A new empirical model for the cooling tower performance and efficiency is also developed. Finally, the optimized performance conditions of the cooling tower are obtained by the presented correlations. The results reveal that cooling tower efficiency is increased by increasing the air flow rate, water flow rate, and packing compaction.

A Modified Master Cycle Off-Design Performance and Heat Rate Improvement Optimization

2017 ◽  
Author(s):  
Chenghao Fan ◽  
Dongsheng Pei ◽  
Xiang He ◽  
Wentai Zhou ◽  
Zengtao Wei
Keyword(s):  
Power Generation ◽  
Thermodynamic Simulation ◽  
Heat Rate ◽  
Feed Water ◽  
Partial Load ◽  
Current Cycle ◽  
Rate Improvement ◽  
Pressure Extraction ◽  
Rate Optimization ◽  
Future Work

Coal-fired power generation will continue to be the cornerstone of China’s energy sources in the coming decades and advanced ultra-supercritical technology is the future of coal-fired power generation. This paper selects double reheat cycle design for study and incorporates back pressure extraction steam turbine (BEST) into current cycle design, which used to drive boiler feed water pump and feed regenerative heaters. This design prevailed in US in 1960s and gradually was replaced by condensing turbine due to less efficiency benefits at subcritical steam condition. Reinvention of BEST design in current double reheat cycle is an evitable choice, because the efficiency advantage is improved at USC steam condition. BEST configuration incorporated into current double reheat cycle and advanced cycle is developed to compare with other two conventional systems in this study. Thermodynamic simulation at design and off-design condition shows that BEST configuration has an obvious efficiency advantage at design load, but the advantage decreases at partial load. BEST expansion line and reheat pressure is integrated in cycle heat rate optimization. Genetic algorithm is chosen to implement the optimization and exergy analysis method is utilized to evaluate BEST expansion line optimization results. Finally, BEST design limitation and future work is practically concluded.

Analysis of Fin-Tube Evaporator Performance With Limited Experimental Data Using Artificial Neural Networks

2000 ◽  
Author(s):  
Arturo Pacheco-Vega ◽  
Mihir Sen ◽  
Rodney L. McClain
Keyword(s):  
Neural Network ◽  
Heat Rate ◽  
Network Models ◽  
Activation Function ◽  
Operating Conditions ◽  
Training Data ◽  
Neural Network Models ◽  
The Neural Network ◽  
Artificial Neural ◽  
Fin Tube

Abstract In the current study we consider the problem of accuracy in heat rate estimations from artificial neural network models of heat exchangers used for refrigeration applications. The network configuration is of the feedforward type with a sigmoid activation function and a backpropagation algorithm. Limited experimental measurements from a manufacturer are used to show the capability of the neural network technique in modeling the heat transfer in these systems. Results from this exercise show that a well-trained network correlates the data with errors of the same order as the uncertainty of the measurements. It is also shown that the number and distribution of the training data are linked to the performance of the network when estimating the heat rates under different operating conditions, and that networks trained from few tests may give large errors. A methodology based on the cross-validation technique is presented to find regions where not enough data are available to construct a reliable neural network. The results from three tests show that the proposed methodology gives an upper bound of the estimated error in the heat rates.

Application of Artificial Neural Network for Modeling of Mechanical Cooling Tower

2011 ◽  
Vol 383-390 ◽  
pp. 7746-7749 ◽  
Author(s):  
Wei Shun Huang ◽  
Ching Wei Chen ◽  
Cheng Wen Lee ◽  
Ching Liang Chen ◽  
Tien Shuen Jan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Flow ◽  
Cooling Tower ◽  
Air Flow ◽  
Cooling Water ◽  
Computation Method ◽  
Cooling Load ◽  
Artificial Neural ◽  
Tower Model

The objective of the study is to focus on the application of the artificial neural network to configure a heat-radiating model for cooling towers within the parameters of fluctuating in air flow or cooling water flow. To achieve the objective, a cooling tower heat balancing equation have been used to instill the correlations between a cooling tower cooling load to the four predefined parameters. Based on the premise established, the parameters of a cooling tower’s air flow and cooling water flow in a modulated process are utilized in an experimental system for collecting relevant operating data. Lastly, the artificial neural network tool derived from the Matlab software is utilized to define the input parameters being – the cooling water temperature, ambient web-bulb temperature, cooling tower air flow, and cooling water flow, with an objective set to instilling a cooling tower model for defining a cooling tower cooling load. In addition, the tested figures are compared to the simulated figures for verifying the cooling tower model. By utilizing the method derived from the model, the mean error of between 0.72 and 2.13% is obtained, with R2 value rated at between 0.97 and 0.99. The experiment findings show a relatively high reliability that can be achieved for configuring a model by using the artificial neural network. With the support of an optimized computation method, the model can be applied as an optimization operating strategy for an air-conditioning system’s cooling water loop.

Review of Control Strategies Employing Neural Network for Main Steam Temperature Control in Thermal Power Plant

2014 ◽  
Vol 66 (2) ◽  
Author(s):  
N. A. Mazalan ◽  
A. A. Malek ◽  
Mazlan A. Wahid ◽  
M. Mailah
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Temperature Control ◽  
Pid Control ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Heat Rate ◽  
Steam Temperature ◽  
Multiple Variables ◽  
Main Steam

Main steam temperature control in thermal power plant has been a popular research subject for the past 10 years. The complexity of main steam temperature behavior which depends on multiple variables makes it one of the most challenging variables to control in thermal power plant. Furthermore, the successful control of main steam temperature ensures stable plant operation. Several studies found that excessive main steam temperature resulted overheating of boiler tubes and low main steam temperature reduce the plant heat rate and causes disturbance in other parameters. Most of the studies agrees that main steam temperature should be controlled within ±5 Deg C. Major factors that influenced the main steam temperature are load demand, main steam flow and combustion air flow. Most of the proposed solution embedded to the existing cascade PID control in order not to disturb the plant control too much. Neural network controls remains to be one of the most popular algorithm used to control main steam temperature to replace ever reliable but not so intelligent conventional PID control. Self-learning nature of neural network mean the load on the control engineer re-tuning work will be reduced. However the challenges remain for the researchers to prove that the algorithm can be practically implemented in industrial boiler control.

Economy of Efficient Air Preheating With Extraction Steam

1962 ◽  
Vol 84 (1) ◽  
pp. 1-6
Author(s):  
M. K. Drewry
Keyword(s):  
Flue Gas ◽  
Heat Rate ◽  
Heat Rejection ◽  
Air Heater ◽  
Fixed Charges ◽  
Rate Improvement

Two per cent heat rate improvement of a 275,000 kw unit results from efficient 2-stage steam air preheaters heating to 190 F with 5 F terminal temperature differences. Condenser heat rejection and turbine leaving losses are reduced substantially. Flue-gas losses are not increased. Air heater cleanliness is improved. Maintenance is reduced. Annual coal savings after fixed charges are one half of the net added investment.

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