Development of artificial neural network model for a coal-fired boiler using real plant data

Development of artificial neural network (ANN) models using real plant data for the prediction of fresh steam properties from a brown coal-fired boiler of a Slovenian power plant is reported. Input parameters for this prediction were selected from a large number of available parameters. Initial selection was made on a basis of expert knowledge and previous experience. However, the final set of input parameters was optimized with a compromise between smaller number of parameters and higher level of accuracy through sensitivity analysis. Data for training were selected carefully from the available real plant data. Two models were developed, one including mass flow rate of coal and the other including belt conveyor speed as one of the input parameters. The rest of the input parameters are identical for both models. Both models show good accuracy in prediction of real data not used for their training. Thus both of them are proved suitable for use in real life, either on-line or off-line. Better model out of these two may be decided on a case-to-case basis depending on the objective of their use. The objective of these studies was to examine the feasibility of ANN modeling for coal-based power or combined heat and power (CHP) plants.     

Experimental‐ and numerical‐simulation research on the inner–secondary‐air ratio in a 600‐MWe down‐fired boiler

Using a phase Doppler‐anemometer measurement system, the cold gas/particle‐airflow behavior in a 1:40 scale‐model furnace was assessed to study the influences of adjusting the inner–secondary‐air ratio in a 600‐MW_e multi‐injection and multistaging down‐fired boiler. Numerical simulations were also conducted to verify the results of the modeling trials and to provide heat‐state information. The results demonstrate that reducing the inner–secondary‐air ratio from 19.66% to 7.66% gradually enhances the downward velocity decay of the gas/particle airflow, while the inner secondary‐air downward‐entraining effect on the fuel‐rich flow is weakened. Lowering the inner–secondary‐air ratio greatly inhibits the decay of the near burner–particle volume flux. In addition, the fuel rich–flow ignition distance is reduced, from 1.02 to 0.87 m. A lower inner–secondary‐air ratio is harmful to restrain early NO_x formation. Reducing the ratio also causes the fuel‐rich flow to turn upwards ahead, while the penetration depth of this flow gradually decreases and the maximum temperature in the hopper region falls from 1900 to 1800 K. On the basis of these data, an optimal inner–secondary‐air ratio of 13.66% is recommended.     

Exergy analysis of direct expansion solar‐assisted heat pumps using artificial neural networks

Artificial neural network (ANN) is applied for exergy analysis of a direct expansion solar‐assisted heat pump (DXSAHP) in the present study. The experiments were conducted in a DXSAHP under the meteorological conditions of Calicut city in India. An ANN model was developed based on backpropagation learning algorithm for predicting the exergy destruction and exergy efficiency of each component of the system at different ambient conditions (ambient temperature and solar intensity). The experimental data acquired are used for training the network. The results showed that the network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. The results confirmed that the use of an ANN analysis for the exergy evolution of DXSAHP is quite suitable. Copyright © 2009 John Wiley & Sons, Ltd.     

煤粉炉冷态数值试验模拟研究

针对某热电厂燃煤锅炉,建立其物理模型及冷态流场数学模型,以模拟冷态性能试验中的炉内空气动力场.并将试验数据直接与模拟结果进行分析、对比,以达到验证物理、数学模型正确性的目的.     

Performance of ammonia–water refrigeration systems using artificial neural networks

In this paper, a new formulation, based on artificial neural network (ANN) model, is presented for the analysis of ammonia–water absorption refrigeration systems (AWRS). Performance analysis of the AWRS is very complex because of analytic functions used for calculating the properties of fluid couples and simulation programs. Therefore, it is extremely difficult to perform analysis of this system. It is well known that the generator temperature, evaporator temperature, condenser temperature, absorber temperature, poor and rich solution concentration affect the AWRS's coefficient of performance (COP) and circulation ratio (f). In this study, COP and f are estimated depending on the above temperatures and concentration values. Using the weights obtained from the trained network a new formulation is presented for the calculation of the COP and f; the use of ANN is proliferating with high speed in simulation. The R²-values obtained when unknown data were used to the networks was 0.9996 and 0.9873 for the circulation ratio and COP, respectively which is very satisfactory. The use of this new formulation, which can be employed with any programming language or spreadsheet program for the estimation of the circulation ratio and COP of AWRS, as described in this paper, may make the use of dedicated ANN software unnecessary.     

Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks

This paper presents the suitability of artificial neural network (ANN) to predict the performance of a direct expansion solar assisted heat pump (DXSAHP). The experiments were performed under the meteorological conditions of Calicut city (latitude of 11.15 °N, longitude of 75.49 °E) in India. The performance parameters such as power consumption, heating capacity, energy performance ratio and compressor discharge temperature of a DXSAHP obtained from the experimentation at different solar intensities and ambient temperatures are used as training data for the network. The back propagation learning algorithm with three different variants (such as, Lavenberg–Marguardt (LM), scaled conjugate gradient (SCG) and Pola-Ribiere conjugate gradient (CGP)) and logistic sigmoid transfer function were used in the network. The results showed that LM with 10 neurons in the hidden layer is the most suitable algorithm with maximum correlation coefficients (R²) of 0.999, minimum root mean square (RMS) value and low coefficient of variance (COV). The reported results conformed that the use of ANN for performance prediction of DXSAHP is acceptable.     

The black smoke control in a chain‐grate stoker‐fired boiler at the time of ignition and restart of the combustion

The black smoke is always emitted from the chimney in the chain‐grate stoker‐fired boiler at the time of ignition at ambient temperature and restart of the combustion after temporary flameout in China. The purpose of the work is to reduce the black smoke emission. A laboratory fixed‐grate model has been used to simulate the combustion of coal in chain‐grate stoker‐fired boiler. The CO and O₂ concentration in the flue gas have been measured with a flue gas analyser, and the black smoke emitted from the chimney has been screened with Charge Couple Device (CCD) video camera. Power 2^# coal, sized at 5–25 mm, has been fired in the fixed‐grate model. The secondary air has been used to enhance the turbulence in the furnace after the numerical simulation. The results of experiments show that the emission of the black smoke at the time of ignition of the coal at ambient temperature is more serious than that of restart of the combustion after the temporary flameout for the case of the temperature; the secondary air is helpful for reducing the black smoke emission for enhancing the intensity of the turbulence in the furnace; selection of coal particle size is necessary, the smaller the size of the coal is, the more serious the black smoke emission is, and the effect of reducing the black smoke emission with the secondary air is more evident with smaller‐size coal. The industrial test has been employed to study the effect of the air demand. It indicates that rational stoichiometric air/fuel ratio is helpful for reducing the black smoke emission in the restart of the combustion after the temporary flameout. Copyright © 2006 John Wiley & Sons, Ltd.     

A fast simulation model combining with artificial neural networks for fin‐and‐tube condenser

In order to improve predicting precision and increase the computation speed of simulation for a fin‐and‐tube condenser, a novel method integrating the fundamental mathematical model with an artificial neural network (ANN) is presented. A three‐zone model is used as the basic mathematical model. An ANN maps the nonlinear relation between the distributed‐parameter model and the three‐zone one. Another ANN is involved in the present model to reflect the bias between the model and experimental results, and to improve the simulation accuracy. Practical utilization of the present model shows that the computation speed of the present model is two orders of magnitude higher than that of the distributed‐parameter model, while the precision is also improved. © 2002 Wiley Periodicals, Inc. Heat Trans Asian Res, 31(7): 551–557, 2002; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/htj.10054     

Greek long-term energy consumption prediction using artificial neural networks

In this paper artificial neural networks (ANN) are addressed in order the Greek long-term energy consumption to be predicted. The multilayer perceptron model (MLP) has been used for this purpose by testing several possible architectures in order to be selected the one with the best generalizing ability. Actual recorded input and output data that influence long-term energy consumption were used in the training, validation and testing process. The developed ANN model is used for the prediction of 2005–2008, 2010, 2012 and 2015 Greek energy consumption. The produced ANN results for years 2005–2008 were compared with the results produced by a linear regression method, a support vector machine method and with real energy consumption records showing a great accuracy. The proposed approach can be useful in the effective implementation of energy policies, since accurate predictions of energy consumption affect the capital investment, the environmental quality, the revenue analysis, the market research management, while conserve at the same time the supply security. Furthermore it constitutes an accurate tool for the Greek long-term energy consumption prediction problem, which up today has not been faced effectively.     

Multi‐objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms

The present work introduces an approach to predict the nitrogen oxides (NO_x) emissions and carbon burnout characteristics of a large capacity pulverized coal‐fired boiler with an artificial neural network (ANN). The NO_x emissions and carbon burnout characteristics are investigated by parametric field experiments. The effects of over‐fire‐air (OFA) flow rates, coal properties, boiler load, air distribution scheme and nozzle tilt are studied. An ANN is used to model the NO_x emissions characteristics and the carbon burnout characteristics. A genetic algorithm (GA) is employed to perform a multi‐objective search to determine the optimum solution of the ANN model, finding the optimal setpoints, which can suggest operators' correct actions to decrease NO_x emissions and the carbon content in the flyash simultaneously, namely, get a good boiler combustion performance with high boiler efficiency while keeping the NO_x emission concentration meet the requirement. Copyright © 2005 John Wiley & Sons, Ltd.     

Application of artificial neural networks for the wind speed prediction of target station using reference stations data

In this study, artificial neural networks (ANNs) were applied to predict the mean monthly wind speed of any target station using the mean monthly wind speeds of neighboring stations which are indicated as reference stations. Hourly wind speed data, collected by the Turkish State Meteorological Service (TSMS) at 8 measuring stations located in the eastern Mediterranean region of Turkey were used. The long-term wind data, containing hourly wind speeds, directions and related information, cover the period between 1992 and 2001. These data were divided into two sections. According to the correlation coefficients, reference and target stations were defined. The mean monthly wind speeds of reference stations were used and also corresponding months were specified in the input layer of the network. On the other hand, the mean monthly wind speed of the target station was utilized in the output layer of the network. Resilient propagation (RP) learning algorithm was applied in the present simulation. The hidden layers and output layer of the network consist of logistic sigmoid transfer function (logsig) and linear transfer function (purelin) as an activation function. Finally, the values determined by ANN model were compared with the actual data. The maximum mean absolute percentage error was found to be 14.13% for Antakya meteorological station and the best result was found to be 4.49% for Mersin meteorological station.     

State-of-charge prediction of batteries and battery-supercapacitor hybrids using artificial neural networks

The state-of-charge (SOC) of batteries and battery-supercapacitor hybrid systems is predicted using artificial neural networks (ANNs). Our technique is able to predict the SOC of energy storage devices based on a short initial segment (less than 4% of the average lifetime) of the discharge curve. The prediction shows good performance with a correlation coefficient above 0.95. We are able to improve the prediction further by considering readily available measurements of the device and usage. The prediction is further shown to be resilient to changes in operating conditions or physical structure of the devices.     

Static and Transient Performance Prediction for CFB Boilers Using a Bayesian—Gaussian Neural Network

INTRODUCTIONCircu1atingFluidizedBed(CFB)boilershavebeeninoperationworldwidewiththetendencytolargerscalebecauseoftheiruniqueadvantagesinenviron-mentalprotection.Thesenewboilersarereallypro-cessreactorswhichbehavedifferentlythanconven-tionalones.Thestrongernonlinearity,moresophis-ticatedvariablescouplingaswellasmoreinertiaofsolidsinventoryandthermalenergymaketheopera-tionandcontrol,especiallyoffdesignoperation,moredifficulttoimplement[1'2].Anefficientpredictionofstaticandtransientperforma…     

Performance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network

Effects of serpentine flow channel having sinusoidal wave at the rib surface on performance of PEMFC having 25 cm² active area are investigated at different flow rates, three different amplitudes changing from 0.25 mm to 0.75 mm and three different cell operation temperatures. A proton exchange membrane fuel cell (PEMFC) is modeled for the prediction of the output current by using artificial neural network (ANN) that is utilized the aforementioned experimental parameters. Effect of hydrogen and air flow rate, the fuel cell temperature, amplitude of channel is tested. The results indicated that model C1 having lowest amplitude is enhanced maximum power output up to 20.15% as compared to indicated conventional serpentine channel (model C4) for 0.7 SLPM H₂ and 1.5 SLPM air and also model C1 has better performance than C2, C3 and C4 models. The maximum power output is augmented with increasing the cell temperature due to raising the fuel and oxidant diffusion ratio. Cell temperature, amplitude, H₂ and air flow rate and input voltage is used as input variables in train and test of the developing ANN model. MAPE of training and testing is determined as 2.89 and 2.059, respectively. Prediction results of developed ANN model including two hidden layer shows similar trend with experimental results. Developed ANN model can be used to both decrease the number of required experiments and find the optimum operation condition within the range of input parameters.     

大容量燃气热水锅炉对流管束的振动分析和预防措施

针对一台58 MW燃气热水锅炉部分对流管束产生的开裂现象进行了深入的原因分析.对断口部分进行了宏观检查、金相组织分析和扫描电镜分析,对烟气冲刷对流管束时气柱的固有(驻波)频率、卡门涡流频率和水管固有振动频率分别进行了计算,并对该锅炉的对流管束进行了振动测试,得出了对流管束产生管子的共振会引发部分对流管脆性断裂的结论.并据此提出了一系列具体的预防和控制措施及建议,通过改变管子的固有频率解决此类问题的发生,为锅炉的安全稳定运行提供保障.     

Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review

In this paper, an attempt has been made to review the applications of artificial neural networks (ANN) for energy and exergy analysis of refrigeration, air conditioning and heat pump (RACHP) systems. The studies reported are categorized into eight groups as follows: (i) vapour compression systems (ii) RACHP systems components, (iii) vapour absorption systems, (iv) prediction of refrigerant properties (v) control of RACHP systems, (vi) phase change characteristics of refrigerants, (vii) heat ventilation air conditioning (HVAC) systems and (viii) other special purpose heating and cooling applications. More than 90 published articles in this area are reviewed. Additionally, the limitations with ANN models are highlighted. This paper concludes that ANN can be successfully applied in the field of RACHP systems with acceptable accuracy.     

Prediction of the physicochemical properties of woody biomass using linear prediction and artificial neural networks

This article aims at using Artificial Neural Networks (ANNs) and linear prediction to predict the physicochemical properties of woody biomass, including gross calorific value, carbon content, and oxygen content. By analyzing 43 data groups, it was found that Multilayer Feedforward Neural Network (MLFN) with 11 nodes is the best model for predicting the gross calorific value, with a root mean square (RMS) error of 0.85; General Regression Neural Network (GRNN) is the best model for predicting the carbon content, with an RMS error of 1.66; and linear prediction is the best model for predicting the oxygen content, with an RMS error of 2.11.     

Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks

The present work introduces an approach to predict the nitrogen oxides (NOx) emission characteristics of a large capacity pulverized coal fired boiler with artificial neural networks (ANN). The NOx emission and carbon burnout characteristics were investigated through parametric field experiments. The effects of over-fire-air (OFA) flow rates, coal properties, boiler load, air distribution scheme and nozzle tilt were studied. On the basis of the experimental results, an ANN was used to model the NOx emission characteristics and the carbon burnout characteristics. Compared with the other modeling techniques, such as computational fluid dynamics (CFD) approach, the ANN approach is more convenient and direct, and can achieve good prediction effects under various operating conditions. A modified genetic algorithm (GA) using the micro-GA technique was employed to perform a search to determine the optimum solution of the ANN model, determining the optimal setpoints for the current operating conditions, which can suggest operators’ correct actions to decrease NOx emission.     

Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends

This study deals with artificial neural network (ANN) modeling of a spark ignition engine to predict the engine brake power, output torque and exhaust emissions (CO, CO₂, NO_x and HC) of the engine. To acquire data for training and testing of the proposed ANN, a four-cylinder, four-stroke test engine was fuelled with ethanol-gasoline blended fuels with various percentages of ethanol (0, 5, 10,15 and 20%), and operated at different engine speeds and loads. An ANN model based on standard back-propagation algorithm for the engine was developed using some of the experimental data for training. The performance of the ANN was validated by comparing the prediction dataset with the experimental results. Results showed that the ANN provided the best accuracy in modeling the emission indices with correlation coefficient equal to 0.98, 0.96, 0.90 and 0.71 for CO, CO₂, HC and NO_x, and 0.99 and 0.96 for torque and brake power respectively. Generally, the artificial neural network offers the advantage of being fast, accurate and reliable in the prediction or approximation affairs, especially when numerical and mathematical methods fail.     

A dynamic compact thermal model for data center analysis and control using the zonal method and artificial neural networks

Full-scale data center thermal modeling and optimization using computational fluid dynamics (CFD) is generally an extremely time-consuming process. This paper presents the development of a velocity propagation method (VPM) based dynamic compact zonal model to efficiently describe the airflow and temperature patterns in a data center with a contained cold aisle. Results from the zonal model are compared to those from full CFD simulations of the same configuration. A primary objective of developing the compact model is real-time predictive capability for control and optimization of operating conditions for energy utilization. A scheme is proposed that integrates zonal model results for temperature and air flow rates with a proportional–integral–derivative (PID) controller to predict and control rack inlet temperature more precisely. The approach also uses an Artificial Neural Network (ANN) in combination with a Genetic Algorithm (GA) optimization procedure. The results show that the combined approach, built on the VPM based zonal model, can yield an effective real-time design and control tool for energy efficient thermal management in data centers.