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Combining Support Vector Regression and Ant Colony Optimization to Reduce NOx Emissions in Coal-Fired Utility Boilers Ligang Zheng,†,‡ Hao Zhou,*,† Chunlin Wang,† and Kefa Cen† State Key Laboratory of Clean Energy Utilization, Institute for Thermal Power Engineering, Zhejiang UniVersity, Hangzhou 310027, People’s Republic of China, and School of Safety Science and Engineering, Henan Polytechnic UniVersity, Jiaozuo 454000, People’s Republic of China ReceiVed July 28, 2007. ReVised Manuscript ReceiVed December 27, 2007
Combustion optimization has recently demonstrated its potential to reduce NOx emissions in high capacity coal-fired utility boilers. In the present study, support vector regression (SVR), as well as artificial neural networks (ANN), was proposed to model the relationship between NOx emissions and operating parameters of a 300 MW coal-fired utility boiler. The predicted NOx emissions from the SVR model, by comparing with that of the ANN-based model, showed better agreement with the values obtained in the experimental tests on this boiler operated at different loads and various other operating parameters. The mean modeling error and the correlation factor were 1.58% and 0.94, respectively. Then, the combination of the SVR model with ant colony optimization (ACO) to reduce NOx emissions was presented in detail. The experimental results showed that the proposed approach can effectively reduce NOx emissions from the coal-fired utility boiler by about 18.69% (65 ppm). A time period of less than 6 min was required for NOx emissions modeling, and 2 min was required for a run of optimization under a PC system. The computing times are suitable for the online application of the proposed method to actual power plants.
1. Introduction Coal remains, now and in the near future, the main energy consumed in China. It is estimated that coal will constitute about 67% of primary energy in 2020, dropping from the present 76%.1 As a kind of primary energy, the main utilization of coal is combustion. The formation of nitrogen oxides (NOx) in the coal combustion process is a significant pollutant source in the environment, and the control of NOx emissions is a worldwide concern as the utilization of fossil fuels continues to increase. The consumed energy in China amounts to about 8–9% of that of the world, but NOx emissions are around 10.1% of that of the world, about 67% of which are emitted from coal combustion in power plants.1 The current limit in the People’s Republic of China for the 300 MW and larger capacity dry bottom boilers is 650 mg/Nm3 (at 6 vol % O2 dry), and this limit will be stricter in the future.2 As air emission standards become more stringent in China and around the world, coal-fired power plants face important challenges concerning the methods and technologies they will use to meet these new environmental requirements. Recently, combustion optimization has demonstrated its potential to reduce NOx emissions in high capacity, coal-fired utility boilers.2–5 Combustion optimization includes two impor* Corresponding author. Tel.: +86-571-87952598. Fax: +86-57187951616. E-mail:
[email protected]. † Zhejiang University. ‡ Henan Polytechnic University. (1) Xu, X. C.; Chen, C. H.; Qi, H. Y.; He, R.; You, C. F. Fuel Process. Technol. 2000, 62, 153–160. (2) Zhou, H.; Cen, K. F.; Fan, J. R. Energy 2004, 29, 167–183. (3) Zhou, H.; Cen, K. F.; Fan, J. R. Int. J. Energy Res. 2005, 29, 499– 510. (4) Frenken, R. M. L.; Rozendaal, C. M.; Dijk, H. E.; Knoester, P. C. Development of an advisory system based on a neural network for the operation of a coal fired power plant. Proceedings of the 1996 International Conference on Artificial Neural Networks - ICANN, Bochum, Germany, July 16–19, 1996.
tant and separate steps, i.e. NOx emission modeling and NOx emission optimizing. The first key problem is NOx emission modeling. Once a NOx emission model is built, NOx emissions can be to a certain extent controlled by regulating the inputs of the model. Unfortunately, the theoretical model is extremely difficultly determined so far. Other alternative models are preferably desirable. At present, the majority of coal-fired power plants are equipped with a distributed control system (DCS) and continuous emissions monitoring system (CEMS) which supply a great deal of information on what is happening in the plant.6 As a result, many operating parameters and NOx emissions can be obtained by requiring daily records in the DCS database. These will provide a way to establish the data-based model. With the progress of the computational capability and the development of the artificial intelligence (AI), a large amount of literature covers the application of neural networks to NOx modeling. The radial basis function (RBF),7 back propagation (BPNN),2–5,8–10 and time delay neural networks11 were developed to model pollution emissions from coal-fired power plants,2–5,8,11 a simulated coal-fired boiler10 and internal engine.7 The pollution emissions monitoring approach using ANN-based software (5) Zhou, H.; Cen, K. F.; Mao, J. B. Fuel 2001, 80, 2163–2169. (6) Copado, A.; Rodriguez, F. Fuel 2002, 81, 619–626. (7) Karonis, D.; Lois, E.; Zannikos, F.; Alexandridis, A.; Sarimveis, H. Energy Fuels 2003, 17, 1259–1265. (8) Reinschmidt, K. F.; Ling, B. Neural networks with multiple-state neurons for nitrogen oxide (NOx) emissions modeling and advisory control. IEEE World Congress on Computational Intelligence, Orlando, FL, June 27-July 2, 1994. (9) Chan, C. W.; Huang, G. H. Eng. Appl. Artif. Intel. 2003, 16, 75–90. (10) Chu, J. Z.; Shieh, S. S.; Jang, S. S.; Chien, C. I.; Wan, H. P.; Ko, H. H. Fuel 2003, 82, 693–703. (11) Adali, T.; Bakal, B.; Sonmez, M. K.; Fakory, R. J. Integr. Comput.Aid. Eng. 1999, 6, 27–39.
10.1021/ef700451v CCC: $40.75 2008 American Chemical Society Published on Web 02/20/2008
Reducing NOx Emissions Via SVR and ACO
sensors have been developed.12 Kalogirou13,14 presented extensive and excellent reviews on recent applications of AI to combustion modeling and pollutant control. Successful application of several commercial combustion optimization software packages to actual power plants demonstrated the potential of ANN to online model NOx emissions of utility boilers.15,16 However, there are some unresolved issues regarding the current application of ANN-based NOx emissions modeling. First, the neural network suffers from a number of weaknesses, which include the need for numerous controlling parameters, uncertainty in solution (network weights), and the danger of overfitting.17 The current common practice that calls for a repetitive trial-and-error process is time-consuming and produces uncertain results, which are the most detrimental factor affecting the online application of neural networks. Second, experience reveals that, for a good ANN model, the most important factor is data available, their sufficiency and representativeness.18 However, due to much time and tremendous costs required for obtaining the operation data of the boiler, the application reported in open references used either only few samples,2,3,5 simulated samples,19,20 or laboratory scale test samples21 to train NOx emission model. The generalization of the model must be further examined when considering an extension of the model to actual power plants. Another key problem in combustion optimization is the optimization algorithms. The commonly used optimization method is genetic algorithms (GA), which follows the principles first presented by Charles Darwin in terms of the survival of the fittest. GA has been successfully applied to optimize NOx emissions in combustion system. These applications include optimizing waste incineration plant operation and providing a decision support tool for plant operators,22 finding the optimum settings for NOx emission minimization in the bubbling fluidized bed boiler,23 providing a viable way to realize low NOx emissions in engine,24 the palm oil mill,25 and utility boilers.2,3,5 Nevertheless, despite its benefits, GA may require long processing time for a near optimum solution to evolve.26 Therefore, efforts must be devoted to testing alternative approaches for modeling and optimizing NOx emissions from coal-fired utility boilers. More recently, support vector regression (SVR) has been proposed by Vapnik in order to overcome the drawbacks of ANN. SVR has already been successfully used in mapping the (12) Tronci, S.; Baratti, R.; Servida, A. Neurocomputing 2002, 43, 3– 15. (13) Kalogirou, S. A. Energy ConVers. Manage. 1999, 40, 1073–1087. (14) Kalogirou, S. A. Prog. Energy Combust. Sci. 2003, 29, 515–566. (15) Jia, J. H. Applying Artificial Intelligence, Advanced Control, and Optimization Technologies In Coal Fired Generating Units. http://www. netl.doe.gov. (16) Plant Optimization & Performance Software. http://www.netl.doe. gov. (17) Chen, K. Y. Reliab. Eng. Syst. Safe. 2007, 92, 423–432. (18) Jiang, D.; Zhang, Y.; Hu, X.; Zeng, Y.; Tan, J. G.; Shao, D. M. Atmos. EnViron. 2004, 38, 7055–7064. (19) Slanvetpan, T.; Barat, R. B. Combust. Sci. Technol. 2003, 175, 1761–1782. (20) Elshafei, M.; Habib, M. A.; Al-Dajani, M. Prediction of Boilers Emission using Polynomial Networks. Canadian Conference on Electrical and Computer Engineering, Ottawa, ON, Canada, May, 2006. (21) Ferretti, G.; Piroddi, L. J. Eng. Gas Turb. Power 2001, 123, 465– 471. (22) Anderson, S. R.; Kadirkamanathan, V.; Chipperfield, A.; Sharifi, V.; Swithenbank, J. Comput. Chem. Eng. 2005, 29, 1121–1130. (23) Saario, A.; Oksanen, A.; Ylitalo, M. Combust. Theor. Model 2006, 10, 1037–1047. (24) Kesgin, U. Fuel 2004, 83, 885–895. (25) Ahmad, A. L.; Azid, I. A.; Yusof, A. R.; Seetharamu, K. N. Comput. Chem. Eng. 2004, 28, 2709–2715. (26) Elbeltagi, E.; Hegazy, T.; Grierson, D. AdV. Eng. Inform. 2005, 19, 43–53.
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Figure 1. Boiler schematic of burner arrangement.
Figure 2. NOx emissions monitored at the studied coal-fired power plant.
complex and highly nonlinear relationship between the system inputs and output(s).27,28 In the present study, SVR was introduced to model NOx emissions characteristics of a 300 MW utility boiler. Compared to few samples used by the previous work by our group,2,3,5 a number of experimental tests (670 cases) have been carried out in an actual power plant to improve the generalization of the SVR model for NOx emissions. Ant colony optimization (ACO) has attracted many interests from many industrial fields29–33 and has shown good performance. However, there is no literature that reports the (27) Dong, B.; Cao, C.; Lee, S. E. Energy Buildings 2005, 37, 545– 553. (28) Li, X.; Cao, G. Y.; Zhu, X. J. Energy ConVers. Manage. 2006, 47, 1032–1050. (29) Toksari, M. D. Appl. Math. Comput. 2006, 176, 308–316. (30) Dorigo, M.; Blum, C. Theor. Comput. Sci. 2005, 344, 243–278. (31) Liu, Y. P.; Wu, M. G.; Qian, J. X. Thermochim. Acta 2007, 454, 64–68. (32) Toksari, M. D. Energy Policy 2007, 35, 3984–3990. (33) Xiao, J.; Li, J.; Xu, Q.; Huang, Y. L.; Lou, H. H. AIChE J. 2006, 52, 1410–1422.
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Table 1. Cross-Correlation of NOx Emissions with Operating Parameters primary air velocities A
B
C
D
secondary air velocities A
B
C
D
E
speed of mills F
O2
A
B
C
D
coal quality total air load flowrate Aar Vdaf Qnet.ar Mt
R 0.0237 0.3338 0.3338 0.0334 0.4199 0.4142 0.4487 0.3819 -0.0006 0.2270 0.1618 R 0.0591 -0.2018 0.4464 0.0074 0.2674 0.4003
application of ACO to reduce NOx emissions in coal-fired utility boilers. The main aim of this work was to investigate the applicability of ACO on NOx emission reduction of coal-fired utility boilers. 2. Experimental Section The experiments have been carried out in a 300 MW tangentially fired dry bottom boiler with large dual furnaces of 17 × 8.475 m2 cross section and 45.5 m high (the boiler was manufactured by Shanghai Boiler Co.Ltd. in the 1980s), which is one unit of the JianBi power plants located in the Jiangsu Province of China. The fuel and primary air streams were directed at the circumference of an imaginary circle of 500 mm diameter at the center of the furnace. The arrangement of the burners was illustrated in Figure 1. As shown in Figure 1, the burners were equipped with six levels of secondary air (represented by A, B, C, D, E, and F) and four levels of primary air. There were eight inlets at each level that corresponded respectively to eight corners of this dual-furnace boiler. Coal for combustion was supplied by four coal pulverizers. Coal-air two-phase flows were fed to the burners on the A-D levels. Operation experiences showed that NOx emissions were high due to the old design technology of the boiler. The concentric firing system was employed to combust bituminite. The characteristics of the combusted coal as received were listed as follows: volatile content of 31.96 wt %, ash content of 11.06 wt %, moisture content of 13.60 wt %, and heating value of 23.49 MJ/kg. NOx and O2 concentrations were monitored continuously by CEMS (Rosemount, Emerson process management) in the boiler outlet prior to the air heater. The NOx concentrations reported here were average values over several hours of stable operation, and they were obtained under dry gas conditions. The measurements were performed a week later after the boiler switched to the pure testing coal to make the boiler conditions constant. On this boiler, 670 tests were carried out, by changing the boiler load in the range of 239.64-331.03 MW, primary air velocities in the range of 2.4–29.3 m/s, A-E elevations of secondary air velocities in the range of 19.7–39.0 m/s, and the F level of secondary air velocity in the range of 2.0–10.0 m/s, respectively. The aim was to analyze the characteristics of NOx emissions of the tangentially fired system. In all tests, coal quality remained constant. Due to the air duct arrangement restriction, only a very narrow air velocity range of the F level of secondary air could be achieved. The conducted experimental tests showed that the boiler operated in a wide range. The measured NOx emissions and operating parameters provided a rich enough data set for developing an empirical model. The monitored NOx emissions were shown in Figure 2. The measured NOx emissions varied from 259 to 407 ppm. Statistical analysis showed that more than 88% of cases in all tests emitted NOx concentrations higher than legislation requirements in China, 316 ppm or 650 mg/Nm3. Therefore, measures must be taken to achieve low NOx emissions so as to meet the limit. The obtained operating conditions with NOx emissions of 259 ppm for the 309th case demonstrated the potential of the boiler to achieve low NOx emission by modifying the operating parameters, which is also the basis on which we performed the current work. Table 1 presents the cross-correlation characteristics between operating variables and NOx emissions, which reflect the physical nature of the process. It shows that the increase in the oxygen content in the flue gas will result in more NOx emissions due to the positive cross-correlation. The basic factors that increase the rate of NOx formation reaction are excess oxygen concentration and the temperature in the furnace. Therefore, O2 and furnace temperature should not be allowed to exceed certain levels.11 NOx
production is weakly negatively correlated with the E elevation of the secondary air velocity, the positive variation of which will lead to lower NOx emissions. The primary air velocities and other secondary air velocities have positive cross-correlation characteristics with NOx emissions. The boiler load and the coal properties are also important parameters that affect NOx emissions. The increase in the boiler load leads to the higher furnace peak temperature. Therefore, formation of the thermal NOx increases dramatically because there is an exponentially rising relation between the thermal NOx emissions and the temperature according to the Arrenihus reaction rate equation. The positive cross-correlation coefficient as shown in Table 1 agrees with the physical analysis. Certainly, coal quality especially the nitrogen content has pronounced effect on the emissions in practice. However, the influence cannot be determined in the present work due to the unchanged combusted coal.
3. Methodology 3.1. SVR for NOx Emission Modeling. As indicated in the Introduction, SVM methodologies are modeling techniques that produce mathematical expressions using a set of inputs-output data. SVM is an algorithm introduced by Vapnik and his co-workers.34,35 With the introduction of the ε- insensitive loss function, SVM has been extended to solve a nonlinear regression estimation problem, called SVR. Moreover, the SVR approach with the ε- insensitive loss function can use a small subset of the training data, namely the support vectors (SVs), to approximate the unknown functions within a tolerance epsilon band. With regard to the complete SVR equations, refs 34 and 35 can be referred to. In the present study, the 19 inputs of the SVR model for NOx emissions were 4 levels of primary air velocities, 6 levels of secondary air velocities, speeds of 4 mills, and the boiler load and coal quality, while the 1 output was NOx emissions. Oxygen concentration in the flue gas and the total air flowrate were not considered as the inputs because they were not the independent design variables and their contributions to NOx emissions were already reflected by the primary and secondary air velocities. The training and testing data for the SVR model were 670 cases, which were downloaded from DCS and CEMS equipped on the boiler, as described in section 2. To classify these cases into the training subset and the testing subset, they were indexed by the arabic numeral, i.e., i ) 1, 2, ..., 670. Two hundred and twenty-four cases with the index, i ) 1, 4, 7,..., 670, were chosen as the testing subset, while the remaining 446 cases were used to train the SVR model. In fact, the variables such as coal quality, did not contribute to the SVR model due to the lack of change in the experiments. However, this does not mean that the coal quality has no effects on NOx emissions. Therefore, without a loss of generality, coal quality was also fed to the SVR mdoel as inputs. To eliminate the unit influence of various operating parameters of the boiler, some necessary preprocessing of the raw training data before feeding them into the SVR model is needed. In this study, all the inputs and the outputs were scaled so that (34) Smola, A. J.; Schölkopf, B. Stat. Comput. 2004, 14, 199–222. (35) Ustun, B.; Melssen, W. J.; Buydens, L. M. C. Anal. Chim. Acta 2007, 595, 299–309.
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Figure 3. Ants finding the shortest path around an obstacle.29
they fall into the range of [-1, 1]. When using the model, the computed target value should be converted back into the same scales that were used for the original target values. SVR parameters (C, γ) must be set carefully in order to construct the SVR model efficiently. GA17 and grid-search36 are two popular methods to determine these two parameters. They were both tried in this work so as to select an appropriate one. In the grid-search method, pairs of (C, γ) are tried and the one with the maximum R (correlation coefficient) or the minimum MRE (mean relative error) is picked. Lin et al.36 found that trying exponentially growing sequences of C and γ is a practical method to identify good parameters (for example, C ) 20, 21, ..., 210, γ ) 2-10, 2-9, ..., 23). Here, in the optimization of parameters (C, γ), MRE and R between the measured and predicted NOx emissions were used as an evaluation criterion. The smaller the MRE, the more accurate is the prediction. R is used as derivation measurements between measured and predicted values. The R ) 1 gives results of the predicted value equal to the measured value. 3.2. Ant Colony Optimization. ACO draws its inspiration from the behavior of real ants as they move from their nest toward a food source.30 ACO has been successfully applied to solve some complex combinatorial optimization problems with NP-hard characteristic, such as traveling salesman problems, quadratic assignment problems, vehicle routing problems, and scheduling problems.31 It has been found that real ants are always capable of finding the shortest path from a food source to the nest without using visual cues. Ants can also adapt to environmental changes. For example, they can find the new shortest path once the original one is no longer the shortest because of the appearance of an obstacle. It is known that ants can deposit a substance called “pheromone” and use its trail as a medium for communication among them. Each ant prefers probabilistically to follow the direction rich in pheromone. This explains why and how they can find the shortest path that reconnects a broken path after a sudden appearance of an unexpected obstacle has interrupted the initial path. As shown in Figure 3,29 there is an equal probability for every ant to choose the left or right path when facing an obstacle. As the right trail is shorter than the left one, it required less travel time, and it will end up with a higher level of pheromone. The more that ants take the right path, the higher the pheromone trail. The process will be intensified in the evaporation stage. The procedure of the ACO algorithm includes three activities: The first step consists mainly of the initialization of the pheromone trail. In the iteration (second) step, each ant constructs a complete solution to the problem according to a probabilistic state transition rule. The state transition rule depends mainly on the state of the pheromone. The third step (36) Chang, C. C.; Lin, C. J. LIBSVM: a library for support vector machines, 2001; software is available at http://www.csie.ntu.edu.tw/cjlin/ libsvm.
Figure 4. Flowchart for NOx emissions optimization by ACO.
updates the quantity of pheromone; a global pheromone updating rule is applied in two phases: first, an evaporation phase where a fraction of the pheromone evaporates and, then, a reinforcement phase where each ant deposits an amount of pheromone which is proportional to the fitness of its solution. This process is iterated until a stopping criterion. The implementation process for NOx emissions optimization by combining SVR and ACO is illustrated in Figure 4. The SVR model for NOx emissions was chosen as the objective function in the ACO algorithm. The “optimum” design variables can be obtained when ACO searched the inputs of the SVR model within variable bounds. Four primary air velocities and six secondary air velocities were considered as the design variables to be optimized. They were determined by operating routine and safety consideration. The variable bounds for the primary air velocities were 25.0–30.0 m/s, which were constrained by pulverized coal transportation. The variable bounds for the A-E levels of secondary air velocities and the F level of secondary air velocity were 25.0–45.0 and 0–25.0 m/s, respectively. This meant that the search space for the ACO was determined. Certainly, the variable bounds for these operating parameters can be easily regulated if needed. There was no any equality constraint between these design variables for our problem. The inequality constraint was based on the basic fact that the air supply for coal combustion must be larger than the theoretical combustion air amount. This means that the total air amount calculated from the primary and secondary air velocities must be larger than the theoretical value for a given boiler load. During the optimization, those solutions that violated the inequality constraint were directly discarded. The new solutions, which were randomly generated within the variables bounds and at the same time satisfied the constraint, were used to replace the violated solutions. A group of m ) 80 ants and the maximum iterations G ) 500 were set for all calculations. All constants (e.g., the evaporation factor of pheromone) in the ACO algorithm have been carefully tuned so as to obtain an “optimum” algorithm performance. Being different from the standard procedure (in which the algorithm called the fitness (37) Luan, F.; Xue, C.; Zhang, R.; Zhao, C.; Liu, M.; et al. Anal. Chim. Acta 2005, 537, 101–110.
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Figure 6. Measured and predicted NOx emissions by the SVR model.
Figure 5. Selection of (C, γ) by the grid-search method.
function on one ant at a time as it looped through the ants), one function evaluation would simultaneously return the fitness of all ants. 4. Results and Discussion 4.1. NOx Prediction by SVR. As pointed out in section 3.1, parameters (C, γ) determine the predictive accuracy of the SVR model. The selection of (C, γ) must be completed prior to obtaining a well-trained SVR model. GA and grid-search methods were respectively used in this work. In this study, all calculations were performed on a 2.4 GHz Intel Pentium IV PC with 1.5 GB of RAM under Windows XP. It took nearly 47 min of CPU time to get the best (C, γ) with an MRE of 1.59%. The optimal pairs of (C, γ) were (88.82, 0.88). Training a SVR model from the data set consisting of 670 samples with 19 inputs may typically take several seconds. For the problem in this study, the simulation experiments showed that GA with a population of 20 individuals required 100 generations to converge for parameters selection of the SVR model. Hence, at least 2000 SVR models were tried. The searching process was extremely CPU demanding. After conducting the grid search on the training data, the optimal (C, γ) was (90.51, 0.84) with an MRE of 1.58% on testing cases. Figure 5 summarizes results of the grid search using MRE as an evaluation criterion. The “gamma” in the y-axis stands for γ. As shown in Figure 5, parameters pairs that fell in the top right area of the graph give smaller MRE. As γ is fixed, one can find that the larger the value of C, the smaller the MRE. It is an intuition that the large C is a good choice. If C is too large, however, the algorithm will overfit the training data.37 Therefore, the selection of C must be carefully made. The grid-search process took about 344 s to get the optimal parameters pair (C, γ), which was much less than that required for GA. Provided that the SVR model is applied to actual power plants for NOx emission modeling, the computing time to build the SVR model will be on the scale of minutes, which is acceptable for the real-time and online operation. This is why the grid search was chosen as the method for establishing the SVR model for NOx emissions. Once (C, γ) was determined, training the SVR model is very easy. Then, the well-trained SVR model can be employed to predict the testing cases. Figure 6 presents the comparison between the predicted and the measured NOx emissions. The solid line connects the measured NOx emissions, while open
Figure 7. Distribution of modeling error (relative error) as produced by SVR and BPNN.
circles represent the predicted NOx emissions from the SVR model. The modeling errors for all cases (e.g., the absolute error and the relative error) were calculated very easily from Figure 6. The 224th case in the testing subset had the maximum modeling error of 16%, while the maximum absolute error was about 44 ppm, also presented by 224th case. Also, 95% (213 cases) of the testing cases had modeling error less than 5%. The mean modeling error and the correlation factor were 1.58% and 0.94, respectively. As a whole, the predicted values showed rather good agreement with the measured values. The superiority of the SVR model can be seen when compared to the BPNN model, which was widely used to model NOx emissions. The BPNN model was the “optimized” one using the conventionally used “trial-and-error” method in which control parameters were carefully tuned. The particular control parameter settings of the BPNN model were as follows: a single hidden layer with 20 neurons, the Levenberg–Marquardt algorithm, and a learning rate of 0.05. It took about 6000 s (CPU time) to repeat 1000 times so as to construct a “best” BPNN model as possibly as we can. It is believed that the direct comparison between the SVR and BPNN models is quite fair because they were both the optimal ones. The maximum modeling error of the BPNN model was 20.97%, while the maximum absolute error was about 52 ppm, both presented by the 224th case. A total of 88% (198 cases) of testing cases had modeling error less than 5%. The mean modeling error and the correlation factor were 2.47% and 0.889, respectively. Figure 7 compares the modeling error case by case as produced by the SVR and BPNN models. The modeling error of 151 cases in the SVR model was smaller than that in the BPNN model, which is responsible for 67.41% of the testing cases. It is hence concluded that the predictive accuracy of SVR as a whole
Reducing NOx Emissions Via SVR and ACO
Energy & Fuels, Vol. 22, No. 2, 2008 1039 Table 2. Operating Parameters of the Boiler for the Sixth Case before and after Optimization optimization inputs
primary air velocities (m/s) A
B
C
secondary air velocities (m/s) D
A
B
C
D
E
F
before 26.8 26.7 27.7 25.9 34.4 31.0 33.8 33.4 32.5 4.8 optimization after 27.5 26.5 26.7 25.8 28.3 29.2 34.2 32.0 34.1 1.6 optimization
Figure 8. Searching processes of ACO and GA.
outperformed that of BPNN although it was not necessarily true that in all testing cases the SVR model had relatively smaller modeling error than the BPNN model. 4.2. NOx Emission Reduction by ACO. This section presents NOx emissions reduction by ACO based on aforementioned ideas and the implementation flowchart as illustrated in Figure 4. Because the sixth case of a total of 670 cases has the maximum NOx emissions concentrations of 407 ppm, we executed the ACO procedure on this case. As shown in Figure 8, ACO can reduce NOx emissions as the iteration process progresses. At the first iteration of ACO, NOx emissions were 318 ppm, and then, they decreased to 270 ppm after less than 200 iterations. Compared to 400 ppm (predicted NOx emissions before executing optimization), the optimized NOx emissions reduced by about 32.5% (reduction percentage) provided that primary and secondary air velocities followed the optimized ones while the other nine design variables such as boiler load, speeds of four mills, and coal quality remained unchanged. It is interesting to compare GA and ACO. To make the comparison fair, GA’s control parameters were carefully tuned in advance in order to achieve optimum performance. Realvalued GA with a population of 80 individuals and 500 generations was employed. Four sets of crossover possibility (single-point crossover) and mutation possibility were tried: (1) pc ) 0.72, pm ) 0.05; (2) pc ) 0.72, pm ) 0.15; (3) pc ) 0.8, pm ) 0.05; (4) pc ) 0.8, pm ) 0.15. The best control parameters were proved to be pc ) 0.8, pm ) 0.15. As also shown in Figure 8, ACO converged to a stable objective value faster than GA. The calculation results were averaged on 50 repetitive runs of simulation experiments so as to alleviate the stochastic nature of both algorithms. The CPU time required for GA and ACO was 92 and 119 s, respectively. However, the practical computing time required for ACO can be further saved by setting the maximum iterations of 200-300 other than 500 due to the rapid convergence rate (see Figure 8). If so, the computing time required for ACO will be shorter than that for GA. Moreover, ACO obtained a smaller objective value than GA (270 vs 276 ppm). It is noteworthy that the optimized result in Figure 8 was only the “potential” or “calculated” emissions rather than the “real” or “experimental” emissions. The computing time to execute a run of optimization was on the scale of 2 min, which is suitable for the real-time and online application to actual power plants. Optimized operating parameters of the boiler before and after optimization are listed in Table 2. The optimized primary air velocity for the A elevation of burner port increased compared to that before executing ACO optimization. The other primary air velocities decreased slightly. The absolute velocity variations in all primary air ports were less than 1 m/s. The reason that the primary air velocities changed a little as a whole is possibly that they were mainly used to transport the pulverized coal, which may be restricted by the mill feeder and boiler load. The A, B, D, and E levels of the secondary air velocities changed
significantly. The distribution of the A-E levels of secondary velocity after optimization was inverse towerlike or inverse trianglelike. In this kind of air distribution scheme, the velocities or air amounts decease gradually from the upper burner ports to the lower burners ports (the order is E > D > C > B > A). An inverse towerlike secondary air distribution provides a fuelrich environment (high fuel/air stoichiometric ratio) at the lower zone of the furnace. As a result, the furnace peak temperature and NOx emissions are lower. The oxygen-rich and fuel-lean condition which resulted from a greater amount of combustion air at the upper zone of furnace is helpful for the coal burnout. In general, the larger the height of the burner group, the greater the NOx emission reduction that can be achieved by regulating the distribution scheme of secondary air velocities. The F level of secondary air velocity also decreased slightly. The optimized operation condition agrees with the physical analysis as described in section 2, namely, the decrease in the A, B, D, and F levels of secondary air velocities or/and the increase in the E level of secondary air velocity will lead to decrease in NOx emissions. Compared to the A-E level, the F level of secondary air contributed a small share to low NOx emissions due to very low velocity. The optimized secondary air velocities were reasonable with the physical nature of low NOx coal combustion in utility boilers. The influence of the optimized air velocity distribution scheme on the boiler combustion efficiency was not taken into account due to very low carbon content in the fly ash for this boiler. In order to verify its actual performance through field test, a combustion optimization software package based on the proposed approach was developed by the authors’ group and was tested on the boiler studied in this work. NOx emissions before optimization (base case), the “potential” emissions (calculated from ACO) and “real” emissions were 346, 283, and 281 ppm, respectively, as shown in Table 3. The real emissions were the field data derived from the air velocities set according to optimization results. There was a slight difference between parameters (see the last two rows in Table 3) due to the operating routine. The relative error between the potential and the real NOx emissions was only 0.71%. The reduction percentage was 18.69% (65 ppm) when comparing field data before and after optimization (i.e., 346 vs 281 ppm). It is therefore concluded that the proposed approach showed reliable and good effect. 5. Conclusions Combustion optimization has been proved to be an effective way to reduce NOx emissions in high capacity coal-fired utility boilers. In the current study, SVR was employed for NOx emissions modeling. ACO was presented to search the optimal inputs of SVR model so as to achieve low NOx emissions. The results show that SVR, compared to BPNN, can predict NOx emissions with better accuracy as well as within an acceptable time demand that meets the real time response requirement. The ACO-based optimization method has been successfully used to reduce NOx emissions below the legislative requirement of
1040 Energy & Fuels, Vol. 22, No. 2, 2008
Zheng et al.
Table 3. Field Verification of the Proposed Approach primary air velocities (m/s) field data before optimization predicted results by ACO field data after optimization
secondary air velocities (m/s)
NOx (ppm)
load (MW)
A
B
C
D
A
B
C
D
E
F
346 283 281
310 310 309
28.6 26.6 27.0
26.8 29.2 25.9
27.9 27.6 27.7
26.0 26.7 26.5
35.0 29.4 28.6
29.8 28.9 28.0
32.6 26.3 26.0
31.4 29.2 29.0
31.7 33.0 33.4
2.9 6.9 6.9
China for a 300 MW coal-fired utility boiler. It is believed that some improvements to the previous study and others are made in this work, in terms of predictive accuracy of NOx emission modeling as well as quality of solution and convergence rate of optimization algorithms. Most of important, a number of training samples obtained from an actual power plant will be helpful for improving generalization ability of NOx emission modeling. The experimental results showed that NOx emissions decreased from the original 346 ppm to the optimized 281 ppm. The encouraging results demonstrated that a combination of ACO and SVR is a promising technology that can contribute
to NOx emission reduction in the environment in actual power plants. Moreover, the computing time to execute a run of optimization is in the scale of 2 min, which is suitable for the real-time and online applications in actual power plants. Acknowledgment. This work was finically supported by NSFC (Nos. 60534030, 50576081), Zhejiang Provincial Natural Science Foundation of China (R107532), and NCET-07-0761 and partly funded by NSF of HPU (No. 646102). EF700451V
