Computational intelligence approach for NOx

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Dec 9, 2009 - 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 ...
Energy Conversion and Management 51 (2010) 580–586

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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman

Computational intelligence approach for NOx emissions minimization in a coal-fired utility boiler Hao Zhou *, Ligang Zheng, Kefa Cen State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, PR China

a r t i c l e

i n f o

Article history: Received 26 August 2008 Received in revised form 7 April 2009 Accepted 7 November 2009 Available online 9 December 2009 Keywords: Low NOx combustion modification Boiler Support vector regression Particle swarm optimization

a b s t r a c t The current work presented a computational intelligence approach used for minimizing NOx emissions in a 300 MW dual-furnaces coal-fired utility boiler. The fundamental idea behind this work included NOx emissions characteristics modeling and NOx emissions optimization. First, an objective function aiming at estimating NOx emissions characteristics from nineteen operating parameters of the studied boiler was represented by a support vector regression (SVR) model. Second, four levels of primary air velocities (PA) and six levels of secondary air velocities (SA) were regulated by using particle swarm optimization (PSO) so as to achieve low NOx emissions combustion. To reduce the time demanding, a more flexible stopping condition was used to improve the computational efficiency without the loss of the quality of the optimization results. The results showed that the proposed approach provided an effective way to reduce NOx emissions from 399.7 ppm to 269.3 ppm, which was much better than a genetic algorithm (GA) based method and was slightly better than an ant colony optimization (ACO) based approach reported in the earlier work. The main advantage of PSO was that the computational cost, typical of less than 25 s under a PC system, is much less than those required for ACO. This meant the proposed approach would be more applicable to online and real-time applications for NOx emissions minimization in actual power plant boilers. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Coal remains and will be the main energy resources around the world, especially in China, due to low-cost and its abundance. Coal combustion in modern power plant boilers generating most of global electricity emanates a large amount of NOx to the ambient surroundings, subsequently results in severe hazards such as acid rain. Therefore, the control of NOx is one of the most important problems to be solved in the operation of a coal-fired utility boiler. At present, the technologies for reduction of NOx emissions from various combustion processes have been attracted much research, such as combustion modification [1], ozone injection [2], Selective Catalytic Reduction (SCR) [3], high temperature air combustion [4] and pulsed corona discharge [5]. Of studied techniques for NOx emissions control, low NOx combustion optimization (or modification) by regulating operating conditions of combustion process to the optimum has received much attention as an effective way to reduce NOx emissions due to its cost-free and easy-to-handle characteristics. Zhou et al. [6] presented low NOx pulverized coal combustion by using the computational intelligence, i.e. the artificial neural network (ANN) and genetic algorithm (GA). The application * Corresponding author. Tel./fax: +86 571 87952598. E-mail address: [email protected] (H. Zhou). 0196-8904/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.enconman.2009.11.002

on a 600 MW capacity boiler showed a NOx reduction from 746.34 mg/m3 to 581.77 mg/m3. ANN was also employed to model the co-firing of coal and sewage sludge in a 500-kW pilot-scale combustion rig equipped with a swirl stabilised low-NOx burner [7] and to model a waste incineration plant [8]. As a powerful search tool, GA was also widely used to optimize the operating parameters of the combustion process. For example, genetic algorithm was connected with computational fluid dynamics (CFD) code to find the optimum settings for nitric oxide (NO) emission minimization in a bubbling fluidized bed boiler. The result showed the predicted NO emission was reduced approximately 15% with respect to the current operating point by the application of GA and CFD [9]. More recently, NO and NH3 emissions in flue gas was simultaneously minimized at the same fluidized bed boiler by using genetic algorithms and CFD [10]. Engineering applications of computational-intelligence-based combustion optimization software packages reported a 10.5% reduction of NOx emissions from a coal-fired boiler by Pegasus technologies, Inc. [11] and NOx reductions 15–35% by Emersion process management [12]. These research and applications demonstrated that computational intelligence is a promising design tool for the reduction of pollution emission from combustion process. The fundamental idea of this method included NOx emissions characteristics modeling and NOx emissions optimization. First,

H. Zhou et al. / Energy Conversion and Management 51 (2010) 580–586

an objective function aiming at estimating NOx emissions characteristics from operating conditions was established by an empirical model. Secondly, the operating condition which is responsible for low NOx emissions were optimized by using global search tools such as genetic algorithm [6]. Computational intelligence-based techniques, e.g., various types of neural networks, were recently employed to model NOx emissions from various industrial combustion processes [6–10,13,14], as reviewed by Kalogirou [15]. Moreover, large amounts of process measurement data in modern power plants are often available through distributed control system (DCS), which could be used to analyze the problems. However, there exist some drawbacks with respect to neural network modeling, such as troublesomeness of numerous controlling parameters, difficulty in obtaining a stable solution and the danger of over-fitting. Most recently, Zheng et al. introduced a method based on support vector regression (SVR) and an ant colony optimization (ACO) for solving low NOx emission combustion optimization problems for a coal-fired utility boiler [16]. Another computational intelligence-based technique, SVR, was employed as an alternative to neural network to model NOx emissions and a better agreement between the predicted and measured NOx concentration was achieved. By comparison, ACO has shown better performance than GA method in terms of quality of solutions and convergence speed. However, its computational efficiency is yet to be improved (about 2 min computational time). Hence, the aim of this work was to develop a more computationally efficient method for low NOx emissions combustion optimization of the same coal-fired utility boiler. As a novel evolutionary technique, particle swarm optimization (PSO) has received increasing attention and wide applications in a variety of fields such as the electricity load dispatch [17] and low exhaust emission diesel engine design [18]. In this work, PSO was tested for low NOx combustion optimization of a boiler with the steam capacity of 300 MW. A total of 670 tests have been carried out at this boiler to investigate the effects of boiler load, primary air (PA) and secondary air (SA) distribution on NOx emissions characteristics behavior. The relationships between NOx emission and operating parameters as well as the coal properties were obtained by using the established SVR model. Then, PSO was employed as an optimizing tool to determine the optimal operating conditions for various types of conditions, to obtain the minimum NOx emissions for a given boiler load. Since the purpose is to reduce the time demanding, a more flexible stopping condition was used to improve the computational efficiency. By comparing with the authors’ reported results, the quality of the optimization results and especially the computational efficiency were greatly improved.

2. Field tests The experiments have been performed in a dual-furnaces tangentially fired full scale boilers. A brief description of the test facilities is given below. Unit 8 of JianBi power station is a 300 MWe power tangentially fired boiler. The boiler is with a large furnace of 14.08  11.858 m2 cross section and 53 m high. Six secondary air ports and four primary air ports are arranged on each corner of the furnace. Coal is pulverized by four medium-speed pulverizers with rotating classifiers. The burner arrangement is shown in Fig. 1. To obtain a wide range of operating conditions, a total of 670 tests have been carried out at this boiler to investigate the effects of the boiler load, primary air (PA) and secondary air (SA) distribution on NOx emissions characteristics behavior. During the experiments, coal quality was kept constant, and NOx concentrations were continuously monitored in the boiler outlet prior to the air

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heater by using continuous emission monitoring system (Rosemount, Emerson Process Ltd.). The measurements were performed a week later after the boiler switched to the pure test coal, to make the boiler conditions constant. The operating conditions during field tests are summarized in Table 1. The NOx emissions characteristics of this studied boiler were plotted as a function of the boiler load, as shown in Fig. 2. Each open cycle in Fig. 2 was responsible for a field test. Though scatter points existed in this graph, the NOx emissions increased with the boiler load, as shown by the solid line (least squared fit between NOx emissions and the boiler load). The correlation factor between the solid line and the scatter points is 0.2674. The positive correlation factor also meant an increasing NOx emissions with the increasing boiler load. However, the averaged deviation of the scatter points from the solid line is 19.44 ppm, thus leading to insufficient prediction of NOx emissions by the boiler load alone. 3. Objective The main aim was to minimize NOx emissions of the 300 MW coal-fired utility boiler. This is performed by changing the settings of operating conditions (e.g., PA and SA) with the aid of computational intelligence. Therefore, an optimization problem can be described as follows:

min f ðxÞ;

x ¼ ðx1 ; x2 ; . . . ; xN Þ

s:t: xi 2 ðai ; bi Þ; i ¼ 1; 2; . . . ; N:

ð1Þ ð2Þ

gðxÞ < 0 where f(x) represents the objective function through which NOx emissions characteristics will be indirectly predicted from operating conditions (PA and SA as well as other conditions such as the load, speed of pulverizers, coal quality). x represent a vector which will be optimized, conceptualized as design variables. In the present work, 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. Hence, x consists of PA and SA. The objective is to regulate design variables so as to obtain the optimal operating conditions which will lead to the minimum NOx emissions for a given load. 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. a and b restricted the design variables to bounds. g(x) is an inequality constraint, which was described in the previous study [16]. In brief, 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. With regard to inequality constraint, those solutions that violated the inequality constraint were directly discarded during the optimization. The new solutions, which were randomly generated within the variables bounds and at the same time satisfied the inequality constraint, were used to replace the violated solutions. It is obvious that determination of f(x) is an important and critical problem for low NOx combustion optimization. The input–output characteristics of NOx emissions in coal-fired utility boilers were featured with a highly nonlinear function. The analytical model is very difficult to be derived. CFD seems to be an effective tool [11,12,19,20], but is very time-consuming. Recently, data-driven modeling employing artificial intelligence and machine learning methods are finding increasing relevance and importance in NOx emissions modeling. The goal of data-driven modeling is to build a prototype that can adapt and learn from practical data.

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Fig. 1. Boiler schematic: (a) furnace cross-view and (b) burner arrangement [16].

Table 1 Operating conditions of the boiler during field tests. Operating conditions

Variation range

Primary air velocities (m/s) A level B level C level D level

[22.9, 29.3] [24.3, 29.2] [2.4, 28.3] [23.2, 29.1]

Secondary air velocities (m/s) A level B level C level D level E level F level

[24.7, 35.8] [23.8, 31.7] [23.4, 36.9] [20.9, 35.3] [19.7, 39.0] [2.1, 9.9]

Speed of pulverizers (rpm) A level B level C level D level

[497.7, 651.5] [237.2, 505.0] [0.0, 626.3] [300.9, 635.8]

Load (MW)

[239.6, 331.0]

Coal quality Volatile content (%) Moisture content (%) Ahs content (%) Low calorific value (MJ/kg)

31.96, constant 11.06, constant 13.6, constant 23.49, constant

NOx emissions (ppm)

[258.7, 407.2]

ANN is the most commonly used modeling tool under this category. Over the last decade, computational intelligence-based techniques, such as neural network, have been extensively employed to predict NOx emissions [6–8,13,14,21–24]. Most recently, Zheng et al. [16] introduced support vector regression to estimated NOx emissions in a 300 MW coal-fired boiler, and indicated that the predictive accuracy of SVR was better than that of neural network. Moreover, SVR can also overcome the drawbacks of neural network [25]. Briefly, SVR, introduced by Vapnik and his co-workers, is a data-driven modeling tool to derive the relationship between input and output. In the past decade, SVR have become increasingly popular due to their good generalization performance. Moreover, the SVR approach can use a small subset of the training data, namely the support vectors (SVs), to approximate the unknown functions within a tolerance epsilon band. NOx emissions characteristics model is probably the most important key to low NOx combustion optimization in coal-fired power plants. SVR was also employed to predict NOx emissions of the boiler in this work. Fig. 3 presents the modeling error between the predicted and the measured NOx emissions. The open circle presents the modeling error on the training subset, while solid inverse-triangle represents the modeling error on the testing subset. The 224th case in the testing subset had the maximum modeling error of 16%. Also, 97% (647 cases) of the total 670 cases had modeling error less than 5%. The mean modeling error and the correlation factor were 1.08% and 0.95, respectively. As a whole, the predicted values showed rather good agreement with the mea-

20

450

Testing subset Training subset 16

400

modeling error (%)

NOx emissions (ppm)

Cases Linear Fit (y=0.3857x+227.1705)

350

300

12

8

4

250

0 240

260

280

300

320

340

Boiler load (MW) Fig. 2. Relationship between the boiler load and NOx emissions.

0

100

200

300

400

500

600

Sample indices Fig. 3. Modeling error of the SVR model.

700

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sured values. The reliability and the accuracy of this SVR model has also demonstrated by in situ field tests. Once the objective function f(x) was obtained, optimization algorithm can be employed to search the optimal design variables (xopt), resulting in low NOx emissions. 4. PSO used for NOx minimization 4.1. Overview of PSO The particle swarm optimization (PSO) is an evolutionary computation technique developed by Kennedy and Eberhart [26] based on the social behave. PSO is initialized with a population of random candidate solutions. Each particle in the population is a point in an N-dimension space (N is the dimension of the space, which equals the number of design variables), which is a potential solution of the optimization problem. Each particle is assigned a randomized velocity and is iteratively moved through the problem space. It is attracted towards the location of the best fitness achieved so far by the particle itself (pbest) and towards the location so far by the best fitness achieved so far across the whole swarm (gbest). Let x and v denote a particle position (solution) and its corresponding flight velocity in a search space, respectively. The best previous position of a particle is recorded as pbest, while the best particle among all the particles in the group is represented by gbest. Finally, the modified velocity and position of each particle can be calculated as shown in the following formulas [17,27]:

v tþ1 ¼ xv t þ c1 r1 ðtÞðpbest  xt Þ þ c2 r2 ðtÞðgbest  xt Þ

ð3Þ

xtþ1 ¼ xt þ v tþ1 ( w w

ð4Þ

max

wðtÞ ¼

min

q

wmin ;

ðt  1Þ þ wmin ;

if iter 6 q

if iter > q

ð5Þ

where vt, the current velocity of the particle at iteration t; xt, the current position of the particle at iteration t; vt+1, the modified velocity; xt+1, the position of the particle at iteration t + 1. xt will be replaced with xt+1, if xt+1 is better according to the evaluation of the objective function. x is a inertia weight; A large inertia means that the velocity is very much influenced by its previous value, so rapid changes are impossible. A small inertia results in a very nervous behavior, which is suited for further convergence towards the optimum if the region where this optimum is located is already found. Experiments have been carried out with a linearly decreasing inertia over time. c1 and c2, are positive constant coefficients, also known as cognitive acceleration constant and social acceleration constant, respectively; r1(t) and r2(t), are uniformly distributed random numbers in (0, 1). The variants of PSO mainly lie in the update rule (Eqs. (3)–(5)) for particles’ velocity.

Clerc and Kennedy recommended v = 0.729 and c1 = c2 = 2.05. Values of v = 0.6 and c1 = c2 = 2.83 was proposed by Trelea [29] through convergence analysis and a better performance was achieved when compared to those by Clerc and Kennedy. The main advantage of the above update rules is that the number of control parameters for algorithms is very small. To make the computation more efficient, parameters selection in a trial-and-error manner had better be avoided. A few-parameters algorithm is preferred. Therefore, the methods by Clerc and Kennedy or by Trelea rather than those by choosing the optimal parameters through time-consuming attempts were tried here to check its applicability to low NOx emissions combustion optimization. 4.3. Stopping criteria In most applications of engineering optimization, the algorithm will be terminated until a ‘‘maximum iterations” is achieved (here named the simplex stopping criterion). For example, the maximum iteration G = 500 was set for optimization algorithm as the stopping condition in our earlier work [16]. This meant the optimization procedure would not be terminated until the maximum iteration G was completed, and this is not a good choice as an actual application. For operators of power plants, they do not have sufficient knowledge about the convergence rate of the optimization algorithm embedded in combustion optimization software package. The excessive iterations will be time-consuming, while too small iterations will be not sufficient to make the optimization algorithm converge and subsequently can not get the optimal operating conditions. To make the stop conditions more flexible, the iterative procedure can be terminated when any of the following criteria (here named the multiple stopping criteria) is met: (i) an acceptable solution has been reached (e.g., the expected NOx emissions expGoal = 250 ppm), (ii) a state with no further improvement in solution within the successive specified iteration is reached (e.g., no_change_in_iterations = 30), (iii) a predefined maximum cpu time has been reached (e.g., max_allowed_time = 120 s), or (iv) a predefined maximum number of iterations have been completed (e.g., G = 500). Of above four kinds of stopping conditions, the third one (the predefined maximum running time which one can tolerate) is probably the more useful for operators of power plants. The maximum running time can be set to a relatively small value when the optimal solution is expected to be obtained very soon. However, the risk of obtaining bad solutions will also be increased. Hence, the stopping conditions presented by this work provide a way for operators (or users) to make a tradeoff between the computational cost and the success rate of finding an optimal solution (here refers to PA and SA distribution). The flowchart of the proposed method was given in Fig. 4.

4.2. Improved PSO

5. Results and discussion

In the original PSO, the control parameters include the inertia weight x, acceleration coefficients c1 and c2. It is not a trivial task for users to select appropriate values of these parameters. In most cases and/or applications, the best parameters for the PSO algorithm are determined in an empirical manner. In addition, the tuning of the algorithm parameters is specific for the case studied. Therefore, few-parameters algorithm is preferable for use. In Ref. [28], Clerc and Kennedy proposed an improved update rule for particle’s velocity based on convergence analysis,

5.1. Optimization results for the boiler load of 288.45 MW

V i ðt þ 1Þ ¼ vðV i ðtÞ þ r 1 c1 ðpbesti  X i Þ þ r 2 c2 ðgbest  X i ÞÞ ( 1; if / ¼ ðc1 þ c2 Þ 6 4 v¼ p2 ffiffiffiffiffiffiffiffiffiffi ffi ; if />4 2 j2/

/ 4/j

ð6Þ ð7Þ

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In each case study, 100 independent runs were made for each of the optimization methods. One main advantage of the PSO method is that the algorithm is relatively easy to tune, as the number of parameters to be adjusted is limited. The number of particles, K, is the only parameter to be tuned by users when the update rules of particles velocity by Clerc or by Trelea were adopted. One case with boiler load of 288.45 MW and NOx emissions of 318 ppm was chosen for optimization. The process iteration history was shown in Fig. 5. To compare the effects of update rules for particle velocity, parameters set by Clerc and Kennedy, by Trelea, and linearly decreasing weight as shown in Eq. (5) with the initial weight of 0.9 and the end weight of 0.4 were mutually

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Fig. 5a. The influence of the number of particles, K, was also investigated, as shown in Fig. 5b. It is clearly shown that the performance of the proposed method was less dependent on the number of particles, which will help users to choose the appropriate parameters. On the whole, the proposed PSO method was a relatively robust and conveniently-used optimization method.

Start

initilization

Calculate new velocity

5.2. Comparison with other methods Move particle

Calculate NOx emissons (SVR model)

Update personal and global best

Particle counter+1

last particle

iteration counter+1 Particle counter+1

iterations =G? Result

Fig. 4. Flowchart of the simulation.

(a)

330

Parameter set by Trelea Parameter set by Clerc Linearly decreasing weight

NOx emissions (ppm)

320 310 300 290 280 270 260

0

30

60

90

120

150

180

210

Iterations

(b)

330

K=20 K=40 K=60 K=80 K=100

NOx emissions (ppm)

320 310 300

Table 2 Operating conditions and optimization results of Case I (load = 312.08 MW). Operating conditions

290 280 270 260

The proposed method was compared with the GA method by Zhou et al. [6] and the ACO method by Zheng et al. [16]. To make the comparison fair, the control parameters for PSO, ACO and GA were set carefully. In using the PSO algorithm, the population size, K, was set to 80, which was exactly the same as the population size of ACO and GA methods. The maximum iteration number is set to 500 (G = 500) for PSO, 1000 for both ACO and GA. For ACO, the control parameters were given in Ref. [16]. With respect to the GA algorithm, the control parameters, i.e. crossover possibility and mutation possibility were obtained by the trial-and-error and set to pc = 0.625 and pm = 0.275, respectively, which would give better performance than those in Ref. [16]. As well known, NOx emissions concentration of the boiler is closely related to the load, as shown in Fig. 2. The cases with distinct loads probably give different NOx emissions because many other operating conditions must be regulated according to the predetermined load. Generally, the boiler load is affected by the load dispatch scheme. That means the boiler load must be taken into account when considering NOx emissions minimization. The aim of this work was to investigate the effectiveness of the present method on NOx emissions minimization under low load and high load. Hence, we chose two cases as the examples to check the effectiveness of the proposed method. Case I has the maximum NOx emissions concentration of 407.2 ppm among a total of 670 field tests. For a boiler load of 312.08 MW, the predicted NOx emissions concentration indirectly calculated by the SVR model from the design variables as well as other operating conditions was 399.7 ppm. Hence, the performance of the SVR model for Case I showed good predictive accuracy with the modeling error of 1.98%. NOx emissions concentration of Case II with the boiler load of 288.45 MW was 318.5 ppm. The predicted NOx emissions concentration was 318.4 ppm with the modeling error of 0.03%. Tables 2 and 3 list the optimal PA and SA settings (design variables), the optimization results (also standard deviation of repetitive simulation experiments) for Case I and Case II derived from PSO, ACO and GA algorithms. Fig. 6 showed the solution results. These results clearly demonstrate the superiority of the PSO

0

30

60

90

120

150

180

210

Number of function evaluations Fig. 5. Effects of: (a) update rule and (b) the number of particles on optimization results.

compared. Obviously, parameters set by Clerc and Kennedy outperforms those of Trelea and dynamic weight rules, as shown in

Before optimization

PSO

ACO

GA

PA (m/s) A level B level C level D level

26.8 26.7 27.7 25.9

27.7 26.5 25.4 26.1

27.6 26.6 27.6 26.0

27.3 26.3 25.6 25.2

SA (m/s) A level B level C level D level E level F level

34.4 31.0 33.8 33.4 32.5 4.0

28.1 29.3 34.0 32.2 33.6 1.6

28.1 29.3 34.2 32.4 33.4 1.6

28.3 28.7 34.4 31.9 33.8 1.3

Predicted NOx (ppm)

399.7

269.3(5.8)

269.4(5.7)

271.9(2.1)

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H. Zhou et al. / Energy Conversion and Management 51 (2010) 580–586 Table 3 Operating conditions and optimization results of Case II (load = 288.45 MW). Operating conditions

Before optimization

PSO

ACO

GA

PA (m/s) A level B level C level D level

27.4 27.5 26.4 26.8

28.2 26.6 25.0 27.2

28.3 26.5 25.1 27.1

28.7 26.8 26.0 27.2

SA (m/s) A level B level C level D level E level F level

31.0 28.2 32.6 30.8 32.2 3.0

28.0 27.2 31.0 30.2 32.6 0.6

27.7 27.3 30.9 29.9 32.2 0.65

28.0 26.3 31.2 29.5 32.80 0.35

318.4

Predicted NOx (ppm)

266.6(0)

266.9(0.7)

271.3(3.2)

algorithm to both the ACO and GA algorithms. The result obtained by this PSO corresponds to the optimum found in previous attempts [16], but the convergence is much faster. Fig. 6 was only employed to illustrate the convergence speeds of various methods in which the maximum iterations was solely chosen as the stopping condition. It is clearly shown that there existed a time waste in Fig. 6 resulted from the inappropriate stopping criteria (e.g., the algorithm will not be terminated until the maximum iterations G has been achieved). In the practical applications, a more flexible stopping criterion can be employed. In this work, a stopping criterion consisting of expGoal = 250 ppm, no_change_in _iterations = 30, max_allowed_time = 120 s and G = 500, as defined in Section 4.3, was used. The algorithm will be automatically

320

GA ACO PSO

310 300

(a) NOx emissions (ppm)

NOx emissions (ppm)

(a)

terminated if and only if one of four stopping conditions was matched. The convergence results for 100 runs are given in Fig. 7. For Case I, by comparing the base emissions (399.7 ppm), the mean optimization results derived from PSO, ACO and GA methods were 269.3(5.9, standard derivation) ppm (32.67% reduction), 270.7(6.45) ppm (32.27% reduction) and 294.3(8.5) ppm (26.37% reduction), respectively; while for Case II with the base emissions of 318.4 ppm, the mean optimization results derived from PSO, ACO and GA methods were 266.6(0) ppm (16.3% reduction), 270.9(7.9) ppm (14.92% reduction) and 284.9(7.9) ppm (10.5% reduction), respectively. Obviously, an improvement of solution quality was achieved by using the proposed method. By comparing Fig. 7 and Tables 2 and 3, it is concluded that the optimized results were deteriorated for GA and showed nearly no change for PSO and ACO when the different stopping conditions were adopted. Taking the boiler load of 312.08 MW for example, the averaged optimized NOx emissions given by GA were 271.9 ppm for the simplex stopping criteria and were 294.3 ppm for the multiple stopping criteria. By using the new stopping criterion, another advantage of using the PSO method is its computational efficiency. Computational time is monitored automatically (all the computational times shown were those averaged on 100 repetitive simulation experiments). A single CPU desktop computer (3.1 GHz, 1.5 GB RAM) was used. The optimal solutions (i.e., the objective function at the value of 269.3 ppm for Case I and of 266 ppm for Case II) were identified after 21.6 s and 25.03 s, respectively, as shown in Fig. 8. ACO proved more time demanding, requiring more than 120 s. A direct comparison of computational time makes it clear that the PSO method is very computationally efficient, which will be helpful for online and real-time applications. Moreover, the stopping conditions were more flexible. It is shown in simulation experiments that PSO and GA methods were terminated due to no_change_in_iterations = 30, i.e., no further improvement in solution within 30 successive iterations. However, GA presented poor

290 280 270 260 0

200

400

600

800

420

399.7

390 360 330

307.6

Base

(b) NOx emissions (ppm)

NOx emissions (ppm)

PSO

AC

273.9

GA

Methods

GA ACO PSO

310

267.9 270.7

240

1000

base emissions

320

267.8 269.3

270

Number of function evaluations

(b)

294.3

293.9

293.3

300

300 290 280 270

min emissions

mean emissions

max emissions

340 318.4

318.3

320 300

291.5 284.9

280

266.7

266.6 266.6 266.6

270.9

272.6

260 240 Base

260 0

200

400

600

800

Number of function evaluations Fig. 6. Optimization processes of: (a) Case I, load = 312.08 MW and (b) Case II, load = 288.45 MW.

PSO

ACO

GA

Methods

1000 base emissions

min emissions

mean emissions

max emissions

Fig. 7. Convergence results analysis of: (a) Case I, load = 312.08 MW and (b) Case II, load = 288.45 MW.

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cpu time (sec)

(a) 140 120 100 80 60 40 20 0

References

120.11

25.3

21.6

PSO

ACO

GA

Methods

cpu time (sec)

(b) 120

109.7

100 80 60 40

29.5

25.03

20 0 PSO

ACO

GA

Methods Fig. 8. Time responses of methods for: (a) Case I, load = 312.08 MW and (b) Case II, load = 288.45 MW.

solutions. With respect to ACO, the maximum allowed computational time becomes the main stopping condition. 6. Conclusions In this work, the effects of operating parameters on NOx emissions were experimentally investigated. The settings of operating conditions for coal-fired utility boiler must be studied carefully, as a set of inappropriate combustion parameters can lead to very high NOx emissions. An empirical model calculating NOx emissions from combustion parameters was represented by the SVR model, in which the 19 operating parameters of the boiler was chosen as the inputs of the model, the NOx emissions as the output. A much simpler optimization tool, particle swarm optimization, was employed to regulate the inputs of the model, resulting in low NOx emissions. The simulation experiments have shown that the proposed method was less influenced by the inherent control parameter, thus facilitating the design of optimization process. The performance of PSO was extensively studied by comparing its performance with GA and ACO methods. The result obtained by this PSO method reduced NOx emissions by 32.67% at the load of 312.08 MW and by 16.3% at the load of 288.45 MW, respectively. With the typical computational cost of less than 25 s under a PC system, the running time of PSO was about one fifth of those required for ACO. Acknowledgements This work was supported by the National Natural Science Foundation of China (60534030), Zhejiang Provincial Natural Science Foundation of China (R107532), Program for New Century Excellent Talents in University (NCET-07-0761), a Foundation for the Author of National Excellent Doctoral Dissertation of China (200747) and Zhejiang University K.P. Chao’s High Technology Development Foundation (2008RC001), National Basic Research Program of China (2009CB219802).

[1] Teng HS, Huang TS. Control of NOx emissions through combustion modifications for reheating furnaces in steel plants. Fuel 1996;75:149–56. [2] Wang ZH, Zhou JH, Zhu YQ, Wen ZC, Liu JZ, Cen KF. Simultaneous removal of NOx, SO2 and Hg in nitrogen flow in a narrow reactor by ozone injection: experimental results. Fuel Process Technol 2007;88:817–23. [3] Benson SA, Laumb JD, Crocker CR, Pavlish JH. SCR catalyst performance in flue gases derived from subbituminous and lignite coals. Fuel Process Technol 2005;86:577–613. [4] Flamme M. Low NOx combustion technologies for high temperature applications. Energy Convers Manage 2001;42:1919–35. [5] Onda K, Kasuga Y, Kato K, Fujiwara M, Tanimoto M. Electric discharge removal of SO2 and NOx from combustion flue gas by pulsed corona discharge. Energy Convers Manage 1997;38:1377–87. [6] Zhou H, Cen KF, Mao JB. Combining neural network and genetic algorithms to optimize low NOx pulverized coal combustion. Fuel 2001;80:2163–9. [7] Tan CK, Wilcox SJ, Ward J. Use of artificial intelligence techniques for optimisation of co-combustion of coal with biomass. J Energy Inst 2006;79:19–25. [8] Anderson SR, Kadirkamanathan V, Chipperfield A, Sharifi V, Swithenbank J. Multi-objective optimization of operational variables in a waste incineration plant. Comput Chem Eng 2005;29:1121–30. [9] Saario A, Oksanen A, Ylitalo M. Combination of genetic algorithm and computational fluid dynamics in combustion process emission minimization. Combust Theor Modell 2006;10:1037–47. [10] Saario A, Oksanen A. Computational fluid dynamics and interactive multiobjective optimization in the development of low-emission industrial boilers. Eng Optimiz 2008;40:869–90. [11] Jia JH. Applying artificial intelligence, advanced control, and optimization technologies in coal fired generating units; 2005. [accessed 15.07.07]. [12] Plant Optimization & Performance Software; 2005. [accessed 16.07.07]. [13] Adali T, Baka B, Sönmez MK, Fakory R. NOx and CO prediction with variable tapped delay line neural networks. J Integr Comput-Aid Eng 1999;6:27–39. [14] Ionescu A, Candau Y. Air pollutant emissions prediction by process modelling – application in the iron and steel industry in the case of a re-heating furnace. Environ Modell Softw 2007;22:1362–71. [15] Kalogirou SA. Artificial intelligence for the modeling and control of combustion processes: a review. Prog Energy Combust 2003;29:515–66. [16] Zheng LG, Zhou H, Wang CL, Cen KF. Combining support vector regression and ant colony optimization to reduce NOx emissions in coal-fired utility boilers. Energy Fuel 2008;22:1034–40. [17] Panigrahi BK, Ravikumar Pandi V, Das S. Adaptive particle swarm optimization approach for static and dynamic economic load dispatch. Energy Convers Manage 2008;49:1407–15. [18] Jeong S, Obayashi S, Minemura Y. Application of hybrid evolutionary algorithms to low exhaust emission diesel engine design. Eng Optimiz 2008;40:1–16. [19] Díez LI, Cortés C, Pallarés J. Numerical investigation of NOx emissions from a tangentially-fired utility boiler under conventional and overfire air operation. Fuel 2008;87:1259–69. [20] Bris TL, Cadavid F, Caillat S, Pietrzyk S, Blondin J, Baudoin B. Coal combustion modelling of large power plant, for NOx abatement. Fuel 2007;86:2213–20. [21] Zhou H, Cen KF, Fan JR. Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks. Energy 2004;29:167–83. [22] Li K, Thompson S, Peng J. Modelling and prediction of NOx emission in a coalfired power generation plant. Control Eng Pract 2004;12:707–23. [23] Wang F, Wang XJ, Ma ZY, Yan JH, Chi Y, Wei CY, et al. The research on the estimation for the NOx emissive concentration of the pulverized coal boiler by the flame image processing technique. Fuel 2002;81:2113–20. [24] Copado A, Rodríguez F. CESAR-SIRE: advanced software for boiler efficiency and NOx optimisation. Fuel 2002;81:619–26. [25] Xi XC, Poo AN, Chou SK. Support vector regression model predictive control on a HVAC plant. Control Eng Pract 2007;15:897–908. [26] Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE international conference on neural networks. Perth, WA, Australia; 1995. p. 1942–8. [27] Zhao B, Guo CX, Bai BR, Cao YJ. An improved particle swarm optimization algorithm for unit commitment. Int J Electr Power 2006;28:482–90. [28] Clerc M, Kennedy J. The particle swarm – explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evolut Comput 2002;6:58–73. [29] Trelea IC. The particle swarm optimization algorithm: convergence analysis and parameter selection. Inform Process Lett 2003;85:317–25.