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Model predictive control for improving waste heat recovery in coke dry quenching processes
Energy 80 (2015) 275e283
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Energy journal homepage: www.elsevier.com/locate/energy
Model predictive control for improving waste heat recovery in coke dry quenching processes Kai Sun a, Chen-Ting Tseng b, David Shan-Hill Wong b, Shyan-Shu Shieh c, **, Shi-Shang Jang b, *, Jia-Lin Kang b, Wei-Dong Hsieh d a
Department of Automation, Qilu University of Technology, Jinan, Shandong 250353, China Department of Chemical Engineering, National Tsing Hua University, Hsin-Chu 30013, Taiwan Department of Occupational Safety and Health, Chang Jung Christian University, Tainan 71101, Taiwan d New Materials Research & Development Department, China Steel Corporation, Kaohsiung 81233, Taiwan b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 31 March 2014 Received in revised form 23 October 2014 Accepted 25 November 2014 Available online 23 December 2014
CDQ (coke dry quenching) is a widely used method for recovering waste heat in the steel industry. We have developed a novel, data driven modeling approach and model based control for a CDQ unit to increase steam generation in a cogeneration system. First, the correlation between steam generation and TCGB (the temperature of circulation gas entering the associated boiler) was confirmed. Subsequently, a nonlinear variable selection method was employed to build models of TCGB and the carbon monoxide concentration of the circulation gas. The models obtained were implemented to achieve MPC (model predictive control) for regulating the supplementary gas to maximize steam generation in an existing steelmaking plant. Upon comparison of the original process and the proposed modified operation, the effectiveness of the implementation of MPC was justified. The results showed that steam generation was increased by 7%. In our approach, the large amount of available operational data stored electronically was used to establish the models. Modification of the established system is not required. Taking into account that no capital investment is required, the process improvement is remarkable in terms of its return on investment. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Coke dry quenching Model predictive control Neural network Waste heat recovery Cogeneration
1. Introduction Steelmaking is a high energy-consumption process. There are several methods for saving energy in steelmaking, with cokequenching being one particular method. In traditional CWQ (coke wet quenching) system, hot coke is cooled by spraying water. The approach results in high CO2 emissions and thermal energy loss. The CDQ (coke dry quenching) system is an energy conserving alternative, in which hot coke is quenched by inert gases instead of spraying water in the quenching tower [1]. The recovered thermal energy from the quenching gas can be used to generate highpressure steam in a downstream boiler. Some articles [1,2] illustrated and made comparison of these two systems. In the case
* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (S.-S. Shieh), [email protected] (S.-S. Jang). http://dx.doi.org/10.1016/j.energy.2014.11.070 0360-5442/© 2014 Elsevier Ltd. All rights reserved.
study of this work, the CDQ unit and its associated boiler are incorporated in a cogeneration system to generate electricity. Over the last two decades, the CDQ system has been gradually accepted, although the CWQ system is still popular within the steelmaking industry. Several articles have advocated the use of the CDQ process. Errera and Milanez [2] presented a thermodynamic analysis for a CDQ unit and reported a comprehensive comparison between the performance of both CDQ and CWQ systems. Their analysis procedure was used in the decision making process towards developing new technology. Lin et al. [3] documented an energy saving calculation based on the operation of an existing CDQ system, and concluded that 85% of the waste heat generated was recovered. Liu et al. [4] developed a mathematical model to simulate the fluid flow and heat transfer in the cooling shaft of a CDQ unit. Feng [5] carried out an experimental investigation of coke descending behavior in a CDQ cooling shaft and developed a viscous model to describe the bulk coke flow. Sun et al. [6] proposed to combine a CDQ system and a gasification system, using coke-oven gas and steam. They used a PRO/II simulator to study the
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effects of the coke-oven gas flow rate, steam consumption on the flow rate and composition of the syngas, and energy efficiency. Wang et al. [7] built a set of rigorous models for a CDQ system and run simulation work. Their study provided static relationships among steam generation, flow rate of circulation gas and discharge rate of incandescent coke. However, the literature regarding the issues of operation or control of a CDQ system is limited. Real time operation of the CDQ process poses several difficult control problems. To tackle the difficulties operators have faced, the operation strategy of an existing CDQ unit was reviewed in this study. We found that the supplementary air flow was not routinely regulated and had occasionally been operated manually. Besides the supplementary air-flow regulation problem, unstable coke discharge from the coke oven is another operational problem, which is caused by the unstable supply of incandescent coke. We consider it as a challenge towards planning and scheduling and it is excluded from the scope of this work. MPC (model predictive control), as an advanced control technology, uses a sufficiently accurate model to predict the future changes in manipulative variables to efficiently reach the setpoint of a controlled variable. The model is established by obtaining explicit or implicit relationships between manipulative variables, controlled variables, and associated process variables from statistical analysis or theoretical development. MPC has been successfully employed in process industries such as chemical plants and oil refineries in recent years [8e14]. The construction of a sufficiently accurate model is one of the key issues for developing an MPC approach. The main theme of operating a CDQ system is to maintain the outlet temperature of the hot circulation gas at certain point. The operation of a CDQ system is usually under high-temperature conditions and involves complicated combustion mechanism. The extend of combustion processes and the subsequent outlet temperature of the hot circulation gas are determined by many operation variables, namely, the inlet temperature and flow rate of the circulation gas, the flow rate of the supplementary air, the pressure in the quenching tower, the concentration of O2 and CO in the combustion chamber, the temperature of the hot incandescent coke from the oven, etc. These variables are interacted with each other and make the system highly nonlinear and difficult to model. It is almost impossible to predict the operation temperature using a linear model. Therefore, a nonlinear empirical model such as ANN (artificial neural network) is the most practical and reasonable approach. Accompanying the use of an ANN models, the modeling complexity and explosive dimensionality problems arise as the number of variables increases. Appropriate variable selection techniques can eliminate redundant variables; reduce the complexity of the model; and present more accurate model. A variety of variable selection techniques for ANN modeling have been studied in recent years [15e18]. The CDQ process has been used commercially for several decades. It has a remarkable performance with regard to energy saving in comparison with the CWQ method. This work is aimed to develop an MPC approach to optimize the on-line operation and advance to further improve waste heat recovery in a CDQ process. The proposed method takes advantage of the existing distributed control system to model the process using the large amount of operational data available. The acquired models were subsequently used to implement a model-based control to regulate the supplementary air-flow rate. The rest of our work is organized as follows: Section 2 presents the process description of the CDQ system, and Section 3 describes the model development and the mathematical formulation of the MPC approach used for the CDQ system. The results of implementing the proposed MPC approach in the existing steelmaking plant are documented and discussed in Section 4. Conclusive remarks are given in the last Section.
2. Process description This research takes the commercial installation of a CDQ process as a case study, and the schematic flow diagram of the CDQ system is shown in Fig. 1. The entire system is similar to a cogeneration system, except that a fuel combustion unit is replaced with a CDQ chamber. The system consists of a coke quenching tower (prechamber and cooling chamber), waste heat recovery boiler, and steam turbine-generator set. Incandescent coke from a coke-oven plant is discharged into the pre-chamber located at the top of the coke quenching tower by a crane. The pressure, PCGB at this location is kept below atmospheric pressure to prevent hot gases from being released into the environment. The cooled circulation gas from the boiler is blown into the tower from the bottom. The counter flow between the incandescent coke and the cooled circulation gas facilitates heat exchange within the tower. When the coke is discharged from the bottom of the tower, its temperature drops to ~200 C. After receiving heat and attaining a high temperature of ~980 C, the circulation gas leaves the tower and heads to the boiler. The hot circulation gas then transfers its heat energy to the feed-water in the boiler and is cooled. Subsequently, the circulation gas passes through a sub-economizer and returns to the tower for the next cycle. There are three control loops regulating the operation in regard of the dry quenching tower. At first, the circulation gas likely damages the pipelines if its temperature continuously exceeds 980 C for more than 10 min. Therefore, the temperature of the hot circulation gas has to be kept under 980 C. A by-pass stream coming out of the cold circulation return gas line is led to the hot circulation stream. A control loop is installed to regulate the bypass flow rate to keep the temperature of the hot circulation gas <980 C. Another control loop is installed to regulate the cold circulation return gas flow rate to keep the pressure at the top of the tower below the atmospheric pressure by releasing excess gas into the atmosphere. Both excess gas line and by-pass line are illustrated in Fig. 1. During the contact between incandescent coke and the cooled circulation gas within the tower, some carbon particles are carried out by the circulation gas. These carbon particles are combusted towards the top of the dry quenching chamber. A flow of supplementary air is added to complete the combustion of the carbon particles. The third control loop regulates the flow rate of the supplementary air to maintain low concentrations of CO and O2. A
Fig. 1. A schematic flow diagram of the studied CDQ system.
K. Sun et al. / Energy 80 (2015) 275e283
single control loop scheme is adopted to perform on-line control of these three control loops. Amongst these three control loops, the last control loop is the most complicated. The combustion principle is to bring in the optimal amount of supplementary air to oxidize the carry-out carbon particles. The measurements of CO and O2 concentrations allow operators to make judgments on the performance of the combustion process and to make necessary adjustments to the supplementary air-flow rate accordingly. However, this process is not as simple as it seems. The nature of the combustion process is dynamic and complex. The fluctuations in the descending rate of incandescent coke, amount of carry-out carbon particles, and unloading cycles of incandescent coke bucket, make the combustion process unsteady. Consequently, the measurements of CO and O2 concentrations vary and often, the tracks of their variation are inconceivable. The operators in the steelmaking plant used in our case study had experienced repeated difficulties and thereby left the control loop open and fixed the setting of the control valve. It should be noted that the operation objective in this CDQ unit was to maximize steam generation in the boiler. The high-pressure steam passes through a turbine to generate electricity and the lowpressure steam generated continues to serve elsewhere in the plant as a thermal energy resource. To generate more high-pressure steam, according to the first law of thermal-dynamics, if the circulation gas flow rate is kept constant, it is desirable to maintain the hot air temperature at the highest level of 980 C. Notably, for the purpose of convenience, the operators tend to keep the circulation gas flow rate constant. At the first stage of this study, operational data were collected and analyzed. Operational data from January 1, 2013 to January 31, 2013 were used. The data were recorded every minute during the operation and stored electronically. Before the analysis of the operational data, the consistency of the data was checked by mass and energy balances, and the measurement noises were diminished by data-smoothing. To make the operation simple and practical, it was necessary to have a surrogated objective instead of steam generation. Further studies revealed that there was a strong and positive correlation between MST (steam generation) and TCGB (the temperature of circulation gas entering the associated boiler) as shown in Fig. 2. The R2 (coefficient of determination) value of 0.7658 is sufficient to make TCGB a reasonable surrogated objective in the rest of this study to achieve the aim of maximizing steam generation. The MC (discharge rate of incandescent coke) represents the mass flow of incandescent coke at a time, and MST/MC represents the steam production per MC. Fig. 3 shows the distribution chart
Fig. 2. Regression figure between MST and TCGB.
277
Fig. 3. Distribution chart for MST/MC and MC of all operational data.
between MC and MST/MC using operational data from January 1, 2013 to January 31, 2013. It is can be seen in Fig. 3 that there exists an operation barrier (marked as a red square) at each MC. The operation barrier is the best operation that has the maximum MST/MC at a particular MC in the historical data. A shorter distance to the operation barrier means the operation is better, and a longer distance from the operation barrier means the operation can be improved. In addition, we separated all the operational data into four groups on the basis of their residuals to the operation barrier: 20%, 10%e20%, 5%e10%, and 5%. Fig. 4 presents the distribution chart of all operation data with TCGB and MC. Obviously, operations closer to the operation barrier have a higher TCGB. Fig. 3 shows that the steam generation is almost independent with the discharge rate of incandescent coke. Fig. 4 reveals that TCGB is almost independent with the discharge rate of incandescent coke. These phenomena formulate the basic operation philosophy i.e., keeping TCGB at the curtain setpoint, 980 C in the studied case. In the next section of this study, the prediction model on TCGB will be established and used for MPC to substitute the third control loop and regulate the supplementary air-flow rate. Fig. 5 demonstrates a 12-h period of data for TCGB on January 1, 2013. There are two different operational modes: stable and unstable coke discharging from the coke oven. The delivery of incandescent coke from the coke oven to the quenching tower is done with an automatically moving crane. In consideration of the safety reason, operators have to be present in the field to monitor the delivery operation. In a 24-h period of operation, there are at least six occasions that the delivery of incandescent coke stops for a short period of time due to operators taking meals/breaks or
Fig. 4. Distribution chart for TCGB and MC of all operational data.
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Fig. 5. Measured data of TCGB in the first 12 h of January 1, 2013.
operation troubles. The disruption of delivering incandescent coke causes the drop of temperature, supplementary air flow rate and makes the process unstable. There are three occasions of unstable discharging cases as shown in Fig. 5. Because some upsets in the unstable discharging operations are too great to build a stable and reliable model, this study focuses on improving the cases of stable discharging operations. However, providing possible solutions to the cases of unstable coke discharging involves further complicated mathematical programming and management actions and this was not considered within the scope of this work.
3. Development of MPC for a CDQ system The objective of MPC using neural network predictors is to minimize a cost function based on the error between the predicted output and setpoints of the process. Usually, the MPC algorithm utilizes a quadratic cost function that is shown as [13]:
min Du
XN1 i¼1
XN1 ½rðk þ iÞ yðk þ iÞ2 þ w$ i¼1 Du2 ðk þ i 1Þ
subject to : yðk þ 1Þ ¼ FðuðkÞÞ and Dulb DuðkÞ Duub
(1)
y¼g
Xq j¼1
woj f
Xp i¼1
þ bo wij xj þ bhj
where x ¼ fx1 ; x2 ; …; xp g denotes the candidate input variables of the network, y denotes the output of the network, the hidden layer has q nodes represented as h ¼ fh1 ; h2 ; …; hq g, and the weight wij ði ¼ 1; 2; …; p; j ¼ 1; 2; …; qÞ denotes the input weight between input variable xi and the jth hidden neuron hj, bhj is the bias of the jth neuron of the hidden layer, bo is the bias of the output layer, g represents the activation function of the output layer, f represents the activation function of the hidden layer, and woj ðj ¼ 1; 2; …; qÞ represents the jth output weight between the hidden layer and the output layer. Breiman [20] proposed a new shrinkage method called NNG (nonnegative garrote). The mechanism of this shrinkage method involves variable selection by shrinking or setting some coefficients of a “greedy” model to zero. In brief, NNG is a two-step shrinkage algorithm. In the first step the initial coefficients are obtained using an OLS (ordinary least squares) method. In the second step, the magnitude shrinkage of the initial coefficients is conducted using the “garrote” constraints that can be formulated as follows:
where N1 is the prediction horizon, y is the system output, r is the setpoint of the process, and the system start from time k, w is the weight factor which is often set to zero, D is the differentiation operator, u is the input vector and Du has the amplitude constraints [Dulb,Duub]. An MPC approach towards the control of TCGB was developed in the study, and the development process of the method and its results will be demonstrated in the following sections of the paper.
3.1. NNG-ANN algorithm for modeling ANNs are powerful tools used to model complex multivariable processes. Haykin [19] has presented a milestone textbook on neural networks, which provides comprehensive information of neural networks from an engineer's perspective. Fig. 6 shows the architecture of an MLP (multi-layer perceptron) neural network that consists of three layers: an input layer, an output layer, and a hidden layer. The MLP neural network has the mathematical formulation:
(2)
Fig. 6. Architecture of ANN.
K. Sun et al. / Energy 80 (2015) 275e283
o nXn Xp yk c* ðsÞ ¼ argmin cbx k¼1 i¼1 i i ik subject to :
ci 0;
Xp
c i¼1 i
s
Table 1 Candidate input variables of CDQ system.
(3)
where x and y are input and output variables respectively, bi ði ¼ 1; 2; …; pÞ denotes the OLS estimate and s is the garrote parameter. X2Rnp is the input data matrix, in which each column represents a candidate explanatory variable, and Y2Rn is a vector of the response variable. Sun et al. [18] developed a new variable selection method which is known as NNG-ANN for inferential modeling using the NNG and ANN. In the first step, the proposed method trains an ANN and obtains the initial input weights of the network, wij ði ¼ 1; 2; …; p; j ¼ 1; 2; …; qÞ. In the second step, magnitude coefficient shrinkage on the input weights is performed as follows:
X Xq Xp n yk g c ðsÞ ¼ argmin wo f cw x k¼1 j¼1 j i¼1 i ij j þ bo 2 þ bhj *
Variable name
Description
Unit
1 2 3 4 5 6
TCGB TC FSA MC TEC TCGT
7 8 9 10 11 12 13
FCGT FBG FEG PCGT PCGB PT CCO
Temperature of circulation gas heading to the boiler Temperature of incandescent cokes Supplementary air flow rate Discharge rate of incandescent coke Temperature of extinguished coke Temperature of circulation gas returning to the CDQ tower Flow rate of circulation gas returning to the CDQ tower Flow rate of by-pass circulation gas Flow rate of relieved excess gas to the atmosphere Pressure of circulation gas returning to the CDQ tower Pressure of circulation gas heading to the boiler Pressure of the tower measured at the top CO concentration of the circulation gas entering the tower H2 concentration of the circulation gas entering the tower CO2 concentration of the circulation gas entering the tower O2 concentration of the circulation gas entering the tower Coke level in the pre-chamber
14 CH2 15 CCO2 16 CO2 17 LC
subject to :
ci 0;
Xp
c i¼1 i
s
279
(4)
Compared to other state-of-the-art methods, the NNG-ANN results in a more compact model with fewer false selections and improved selection ratio. In this study, we take NNG-ANN to construct the model, and the results are compared to three other methods: i) the linear stepwise method [21], ii) the nonlinear ANN method [22], and iii) an effective nonlinear variable selection method, called SBS-MLP [16]. The simulation results are reported in terms of the following statistics: (1) M.S (model size): the number of input variables in the final model. A low M.S. value indicates better efficiency of the variable selection algorithm. (2) R2 (coefficient of determination): the square of the sample correlation coefficient between the outcomes and their predicted values. (3) PMSE (prediction mean square error): the mean square error between the predicted and desired output, which is based on a test dataset that is not used during the overall modeling process.
3.2. Model of TCGB(k þ 1) The model of TCGB(k þ 1) with respect to other process variables at time k is presented, where TCGB(k þ 1) denotes the value of TCGB at time k þ 1. The study takes production data with intervals of 1 min from May 1, 2013 to May 2, 2013 as the training data, and data from May 3, 2013 to May 4, 2013 as the testing data. The candidate input variables at time k are listed in Table 1. The average prediction performance over 100 runs is shown in Table 2. Obviously, the nonlinear methods present improved prediction accuracy than the linear method, which means the CDQ process displays highly nonlinear characteristics. In addition, the PMSE of NNG-ANN is considerably better than those of SBS-MLP and ANN; however, their R2 values are very similar. Therefore, the NNG-ANN shows improved prediction accuracy in the model with less model size. Fig. 7 presents the variable selection frequency over 100 runs by our proposed method. It shows that the selection probability of
C C Nm3/h ton/h C C
Nm3/h Nm3/h Nm3/h mmAq mmAq mmAq % % % % %
variables TCGB, FSA, CCO, TCGT is higher than 80%, while that of the others is less than 40%. Firstly, TCGB(k) has the most significant influence on TCGB(k þ 1) and is selected with 100% probability. After consulting the field operator, FSA is the amount of supplementary air, which can change the CO and O2 concentrations and therefore influence the combustion intensity of the tower. The cold supplementary air also can change the temperature of the circulation gas in the tower when they mix. The CCO of the circulation gas is highly correlated with TCGB. A high CCO means the combustion was incomplete, and a low CCO means the combustion was complete. TCGT is the temperature of circulation gas returning to the CDQ tower, and apparently can influence the TCGB(k þ 1). Notably, CO2 is not selected by this model. In any typical combustion system, e.g., a coal fired boiler, the oxygen concentration in the off-gas is an important indication of thermal efficiency. However, in most combustion cases, fresh air is brought into the combustion chamber and the oxygen concentration of the entering gas is ~21%. In our case study, the supplementary air is mixed with the circulation gas when it enters the CDQ chamber, and the oxygen concentration of the mixed gas varies in the range of 1.5%e3.0%, whose value depends on the ratio of the supplementary air-flow rate and the circulation gas flow rate. This difference explains why CO2 is not used in the model. Fig. 8 presents the measured and predicted values of TCGB using the NNG-ANN algorithm. It is obvious that the proposed method can successfully track the dynamics of TCGB both in the training and testing processes. 3.3. Model of CCO(k þ 1) The CCO(k þ 1) of the circulation gas is an important variable for the process as discussed above. The candidate input variables for CCO(k þ 1) in the prediction model are the same as the variables shown in Table 1. Table 3 summarizes the average prediction Table 2 Statistical prediction performance for the TCGB(k þ 1) over 100 runs.
2
R PMSE M.S
Stepwise
ANN
SBS-MLP
NNG-ANN
0.8851 56.12 7
0.9790 31.33 17
0.9811 24.78 8.03
0.9865 19.16 6.57
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Fig. 7. Variable selected frequency over 100 runs for TCGB(k þ 1) model.
Fig. 8. Measured and predicted TCGB(k þ 1) of the training and testing data.
performance over 100 runs for four methods. The NNG-ANN can build a more accurate model using lesser number of variables than the stepwise, ANN and SBS-MLP approaches. Fig. 9 shows the prediction and measured values of CCO(k þ 1), and demonstrates that the developed model using the NNG-ANN can predict the CCO(k þ 1) successfully.
achieved by providing a great amount of extra oxygen in the combustion chamber. It is not worthy of doing so. According to the experience of the plant operators, the lower bound of the CCO is set to 0.20. Therefore, a penalty function of CCO is added in the MPC model. The penalty function of CCO is denoted as:
PCO ðuðkÞ; xðkÞÞ ¼
3.4. Mathematical model of MPC
0 0:2 GðuðkÞ; xðkÞÞ
if GðuðkÞ; xðkÞÞ 0:2 else (7)
The study utilizes FSA at time k as the control variable, which is denoted as u(k). Other measured variables in Table 1 are denoted as the vector x(k). The TCGB prediction model presented by NNG-ANN can be denoted as:
TCGB ðk þ 1Þ ¼ FðuðkÞ; xðkÞÞ
(5)
Consequently, a one-step MPC model of the CDQ system can be formulated as:
argmin ½rðk þ 1Þ FðuðkÞ þ DuðkÞ; xðkÞÞ2 þ l$PCO ðuðkÞ DuðkÞ
þ DuðkÞ; xðkÞÞ
The CO prediction model can be denoted as:
CCO ðk þ 1Þ ¼ GðuðkÞ; xðkÞÞ
(6)
Zero value of CCO means complete combustion, but it can only be Table 3 Statistical prediction performance for the CCO(k þ 1) over 100 runs.
2
R PMSE M.S
Stepwise
ANN
SBS-MLP
NNG-ANN
0.8733 0.031 8
0.9581 0.019 17
0.9635 0.015 8.49
0.9650 0.011 7.05
subject to :
Dulb DuðkÞ Duub
(8)
where r(k þ 1) is the setpoint of TCGB and set to 980 C, [Dulb,Duub] are the boundary of Du(k) in consideration of the field operation conditions and is set to [3000, 3000], l is the penalty parameter which can be determined by field experience and experiments. Equation (8) is a nonlinear constrained quadratic problem. The study takes the trust region reflective optimization algorithm, which is a subspace trust region method based on the interior reflective Newton method proposed by Refs. [23] and [24].
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281
Fig. 9. Measured and predicted CCO(k þ 1) of the training and testing data.
4. Results and discussions In this section, we examine the dynamic operation data in a short period of hourly base, and compare the results between the routine operation and the experimental operation in the long period of weekly base. Before implementing the acquired model in the experimental operation, we illustrate how it could help
improve the routine operation by examining the dynamic profiles of TCGB, FSA and CCO. The operational data in a 90-min period of operation were used to document the detailed dynamic variation in a routine operation on May 5, 2013. Fig. 10 shows the dynamic profiles of FSA, CCO, and TCGB. The blue-colored dots represent the real operation while the red-colored dots represent the suggested operation based on the
Fig. 10. a. Comparison of the real FSA versus MPC proposed FSA. b. Comparison of the real CCO versus MPC proposed CCO. c. Comparison of the real versus predicted TCGB using MPC.
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proposed MPC operation, where the MPC operation at time k þ 1 is calculated based on the real operation data at time k. It is clear that FSA is insufficient to complete combustion and to keep TCGB close to the setpoint of 980 C. The suggested FSA is shown using red-colored dots and these rates are usually higher than the real ones. However, there is an exception as shown Fig. 10. In the period between 16:10 and 16:40, the proposed values for FSA are almost equal to those of the real FSA values, even though the values of TCGB are 50 C below the setpoint (980 C) as shown in Fig. 10. The low values of CCO in that time period indicate that the combustion was almost complete. Further studies revealed that a low discharge rate of incandescent coke in that period cause a low carry-over carbon and low TCGB. After the models for predicting TCGB and CCO were obtained, experiments were conducted in the week between May 6, 2013 and May 13, 2013. These data were used as the case of the experimental operational data. A comparison between the routine operation (as the control group) and the experimental operation (as the treatment group) was made to illustrate the effectiveness of implementing MPC. The routine operation is to regulate the supplementary air flow using the conventional method, i.e., manual control, whilst the experimental operation is to regulate it using MPC. To make a meaningful comparison, the operational conditions regarding energy input of the CDQ must be equal or close enough to be deemed equal. TC and MC are the variables that determine energy input. The historical data were reviewed and those in the period of between February 3, 2013 and February 10, 2013 were considered suitable for the comparison. These data were used as the routine operation. Both cases are real plant data and have the similar values in the operation variables as shown in Table 4. Most of the differences of the operation variables, i.e., TCGT, FCGT, FBG, FEG, PCGT, PCGB, PT, TC and MC between two cases are around 1%. The values between two cases are close enough to justify the fairness of the comparison. Among those variables, temperature TC and discharge rate MC of incandescent cokes as mentioned above determine the energy input of incandescent coke, i.e., the potential for waste heat recovery. The average values of TC for the routine operation and for the experimental operation were 1085 C and 1087.8 C respectively, whilst the average value of MC were 133.3 and 133.7 respectively. The differences in TC and MC were 0.3% and the energy input of these two groups can be deemed as equal. Therefore, the comparison between these two groups can be used to verify effectiveness of our approach in terms of steam production, which is the recovered heat of the CDQ system. Table 4 Weekly performance comparison between manual control and MPC. Variables
Routine operation
MPC implemented
TCGB( C) TC( C) FSA(Nm3/h) MC(ton/h) TEC( C) TCGT( C) FCGT(Nm3/h) FBG(Nm3/h) FEG(Nm3/h) PCGT(mmAq) PCGB(mmAq) PT(mmAq) CCO(%) CH2 (%) CCO2 (%) CO2 (%) LC(%) MST(ton/h)
946.4 1085.0 16,097 133.3 154.6 120.9 188,700 5100 17,980 401.9 58.3 2.02 1.630 0.40 16.93 0.192 26.8 83.9
961.2 1087.8 20,306 133.7 159.6 120.4 190,620 4885 18,197 388.2 60.4 2.01 0.461 0.053 12.26 0.177 27.8 89.8
In the MPC implemented case, the weekly average value of FSA is 26% higher than that of the routine case. This reflects the fact that the oxygen supply in the MPC implemented case is much more than the routine case. The concentrations of CO, H2, and O2 in the MPC implemented case are much lower than those in the routine case. The values show that the combustible species in the MPC implemented case are fully combusted and that the supply of oxygen is sufficient to complete the combustion process, but not excess. Consequently, the weekly average TCGB in the MPC implemented case is 15 C higher than that found in the routine case. It means more heat is recovered in the MPC implemented case. As a result, the weekly average steam generation increased from 84.0 ton/h of the routine case to 89.8 ton/h of the MPC implemented case, which corresponds to a 7% improvement. In addition to the above comparisons, we examine the dynamic variation of FSA, CCO, and TCGB in the experimental case when implementing MPC. Fig. 11a shows that the values of FSA are between 17 103 and 21 103, which are much higher than those shown in Fig. 10a. Fig. 11b indicates that the values of CCO are always <0.7, which are much lower than those shown in Fig. 10b. Fig. 11c illustrates that the values of TCGB are always controlled at ~980 C. The dynamic profiles shown in Fig. 11 as well as the comparisons shown in Table 4 conclude that the MPC-implemented operation has smoother regulation of supplementary fresh air flow, achieves more stable temperature control, and as a result, has better combustion performance and waste heat recovery than the routine case. 5. Conclusion Many researchers have demonstrated the advantages of CDQ over CWQ in their thermal analysis studies. However, the operational difficulties associated with the control of the supplementary air flow have long existed without being noticed. The abundance of historical operational data stored electronically and the availability of several data analysis tools make the operation modelbuilding convenient. In our study, we took an existing largescale coke-oven plant as the case study subject. We proposed a neural-network-based MPC approach and implemented it in the plant site. The effectiveness of the proposed MPC approach was verified upon comparison with the routine operation. The results showed that the supplementary air flow was controlled perfectly. It provided sufficient oxygen to make the combustion process complete, but not in excess. Consequently, the steam generation increased by 7%. The performance of the proposed MPC approach in the studied case was impeccable. The implementation of the MPC approach in an existing CDQ is simple and only requires a plant to collect its existing operational data stored electronically and configure in the distributed control system in the plant. In other words, the implementation does not need any extra hardware or staff training. However, uncorrupted data are essential. The acquired models, which are the most important aspects in this study, must be accurate; otherwise, effective control is impossible. Our study estimates to achieve an economic gain of 1.3 million USD, with an environmental gain with regard to CO2 reduction by 2.5 103 tons annually in the studied coke-ovens, for which the annual coke production is currently 1 106 tons. Acknowledgments The work is partially supported by Ministry of Economic Affairs through the grant 102-EC-17-A-09-S1-198, and National Science Council through the grant NSC 100-2221-E-007-058-MY2, Advanced Manufacturing and Service Management Research
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283
Fig. 11. a. FSA profile in the MPC implemented operation. b. CCO profile in the MPC implemented operation. c. TCGB profile in the MPC implemented operation.
Center, National Tsing Hua University, Taiwan (Grant 101N2072E1) and the Shandong Provincial Natural Science Foundation of China (Grant No. ZR2010FQ009).
References [1] Bisio G, Rubatto G. Energy saving and some environment improvements in coke-oven plants. Energy 2000;25(3):247e65. [2] Errera MR, Milanez LF. Thermodynamic analysis of a coke dry quenching unit. Energy Convers Manag 2000;41(2):109e27. [3] Lin PH, Wang PH, Huang A. Exergy analysis of a coke dry quenching system. China Steel Tech Rep 2009;22:63e7. [4] Liu H, Zhang X, Xu L, Wang M. Mathematical model for fluid flow and heat transfer in the cooling shaft of coke dry quenching unit. J Therm Sci 2002;11(1):65e73. [5] Feng Y, Zhang X, Yu Q, Shi Z, Liu Z, Zhang H, et al. Experimental and numerical investigations of coke descending behavior in a coke dry quenching cooling shaft. Appl Therm Eng 2008;28(11):1485e90. [6] Sun LY, Liu YD, Zhai C, Zhou H. Process simulation of an integrated coke dry quenching combined gasification. Petroleum Coal 2012;54(1):34e41. [7] Wang D, Yang T, Wen Z, Feng J-X, Kong N, Wang Q, et al. A mathematical model for optimized operation and control in a CDQ-boiler system. Int J Minerals Metallurgy Mater 2005;12(5):390e3. [8] Camacho EF, Bordons C, Camacho EF, Bordons C. Model predictive control. London: Springer; 2004. € wer F. An introduction to nonlinear model predictive control. [9] Findeisen R, Allgo 21st Benelux Meeting on Systems and Control, vol. 11; 2002. p. 119e41. [10] Hajimolana S, Tonekabonimoghadam S, Hussain M, Chakrabarti M, Jayakumar N, Hashim M. Thermal stress management of a solid oxide fuel cell using neural network predictive control. Energy 2013;62:320e9.
[11] Hovgaard TG, Larsen LF, Edlund K, Jørgensen JB. Model predictive control technologies for efficient and flexible power consumption in refrigeration systems. Energy 2012;44(1):105e16. [12] Kittisupakorn P, Thitiyasook P, Hussain M, Daosud W. Neural network based model predictive control for a steel pickling process. J Process Control 2009;19(4):579e90. [13] Lazar M, Pastravanu O. A neural predictive controller for non-linear systems. Math Comput Simul 2002;60(3):315e24. [14] Zhang J, Zhou Y, Wang R, Xu J, Fang F. Modeling and constrained multivariable predictive control for ORC (Organic Rankine Cycle) based waste heat energy conversion systems. Energy 2014;66(1):128e38. [15] Rani A, Singh V, Gupta J. Development of soft sensor for neural network based control of distillation column. ISA Trans 2013;52(3):438e49. [16] Romero E, Sopena JM. Performing feature selection with multilayer perceptrons. IEEE Trans Neural Netw 2008;19(3):431e41. [17] Souza FA, Araújo R, Matias T, Mendes J. A multilayer-perceptron based method for variable selection in soft sensor design. J Process Control 2013;23(10):1371e8. [18] Sun K, Liu J, Kang JL, Jang SS, Wong DSH, Chen DS. Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote. J Process Control 2014;24(7):1068e75. [19] Haykin S. Neural networks: a comprehensive foundation. Prentice Hall PTR; 1994. [20] Breiman L. Better subset regression using the nonnegative garrote. Technometrics 1995;37(7):373e84. [21] Hocking RR. A biometrics invited paper. The analysis and selection of variables in linear regression. Biometrics 1976;32(1):1e49. [22] Hagan MT, Demuth HB, Beale MH. Neural network design. Boston: Pws Pub; 1996. [23] Coleman TF, Li Y. On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds. Math Program 1994;67(1e3): 189e224. [24] Coleman TF, Li Y. An interior trust region approach for nonlinear minimization subject to bounds. SIAM J Optim 1996;6(2):418e45.
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Model predictive control for improving waste heat recovery in coke dry quenching processes Kai Sun a, Chen-Ting Tseng b, David Shan-Hill Wong b, Shyan-Shu Shieh c, **, Shi-Shang Jang b, *, Jia-Lin Kang b, Wei-Dong Hsieh d a
Department of Automation, Qilu University of Technology, Jinan, Shandong 250353, China Department of Chemical Engineering, National Tsing Hua University, Hsin-Chu 30013, Taiwan Department of Occupational Safety and Health, Chang Jung Christian University, Tainan 71101, Taiwan d New Materials Research & Development Department, China Steel Corporation, Kaohsiung 81233, Taiwan b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 31 March 2014 Received in revised form 23 October 2014 Accepted 25 November 2014 Available online 23 December 2014
CDQ (coke dry quenching) is a widely used method for recovering waste heat in the steel industry. We have developed a novel, data driven modeling approach and model based control for a CDQ unit to increase steam generation in a cogeneration system. First, the correlation between steam generation and TCGB (the temperature of circulation gas entering the associated boiler) was confirmed. Subsequently, a nonlinear variable selection method was employed to build models of TCGB and the carbon monoxide concentration of the circulation gas. The models obtained were implemented to achieve MPC (model predictive control) for regulating the supplementary gas to maximize steam generation in an existing steelmaking plant. Upon comparison of the original process and the proposed modified operation, the effectiveness of the implementation of MPC was justified. The results showed that steam generation was increased by 7%. In our approach, the large amount of available operational data stored electronically was used to establish the models. Modification of the established system is not required. Taking into account that no capital investment is required, the process improvement is remarkable in terms of its return on investment. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Coke dry quenching Model predictive control Neural network Waste heat recovery Cogeneration
1. Introduction Steelmaking is a high energy-consumption process. There are several methods for saving energy in steelmaking, with cokequenching being one particular method. In traditional CWQ (coke wet quenching) system, hot coke is cooled by spraying water. The approach results in high CO2 emissions and thermal energy loss. The CDQ (coke dry quenching) system is an energy conserving alternative, in which hot coke is quenched by inert gases instead of spraying water in the quenching tower [1]. The recovered thermal energy from the quenching gas can be used to generate highpressure steam in a downstream boiler. Some articles [1,2] illustrated and made comparison of these two systems. In the case
* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (S.-S. Shieh), [email protected] (S.-S. Jang). http://dx.doi.org/10.1016/j.energy.2014.11.070 0360-5442/© 2014 Elsevier Ltd. All rights reserved.
study of this work, the CDQ unit and its associated boiler are incorporated in a cogeneration system to generate electricity. Over the last two decades, the CDQ system has been gradually accepted, although the CWQ system is still popular within the steelmaking industry. Several articles have advocated the use of the CDQ process. Errera and Milanez [2] presented a thermodynamic analysis for a CDQ unit and reported a comprehensive comparison between the performance of both CDQ and CWQ systems. Their analysis procedure was used in the decision making process towards developing new technology. Lin et al. [3] documented an energy saving calculation based on the operation of an existing CDQ system, and concluded that 85% of the waste heat generated was recovered. Liu et al. [4] developed a mathematical model to simulate the fluid flow and heat transfer in the cooling shaft of a CDQ unit. Feng [5] carried out an experimental investigation of coke descending behavior in a CDQ cooling shaft and developed a viscous model to describe the bulk coke flow. Sun et al. [6] proposed to combine a CDQ system and a gasification system, using coke-oven gas and steam. They used a PRO/II simulator to study the
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effects of the coke-oven gas flow rate, steam consumption on the flow rate and composition of the syngas, and energy efficiency. Wang et al. [7] built a set of rigorous models for a CDQ system and run simulation work. Their study provided static relationships among steam generation, flow rate of circulation gas and discharge rate of incandescent coke. However, the literature regarding the issues of operation or control of a CDQ system is limited. Real time operation of the CDQ process poses several difficult control problems. To tackle the difficulties operators have faced, the operation strategy of an existing CDQ unit was reviewed in this study. We found that the supplementary air flow was not routinely regulated and had occasionally been operated manually. Besides the supplementary air-flow regulation problem, unstable coke discharge from the coke oven is another operational problem, which is caused by the unstable supply of incandescent coke. We consider it as a challenge towards planning and scheduling and it is excluded from the scope of this work. MPC (model predictive control), as an advanced control technology, uses a sufficiently accurate model to predict the future changes in manipulative variables to efficiently reach the setpoint of a controlled variable. The model is established by obtaining explicit or implicit relationships between manipulative variables, controlled variables, and associated process variables from statistical analysis or theoretical development. MPC has been successfully employed in process industries such as chemical plants and oil refineries in recent years [8e14]. The construction of a sufficiently accurate model is one of the key issues for developing an MPC approach. The main theme of operating a CDQ system is to maintain the outlet temperature of the hot circulation gas at certain point. The operation of a CDQ system is usually under high-temperature conditions and involves complicated combustion mechanism. The extend of combustion processes and the subsequent outlet temperature of the hot circulation gas are determined by many operation variables, namely, the inlet temperature and flow rate of the circulation gas, the flow rate of the supplementary air, the pressure in the quenching tower, the concentration of O2 and CO in the combustion chamber, the temperature of the hot incandescent coke from the oven, etc. These variables are interacted with each other and make the system highly nonlinear and difficult to model. It is almost impossible to predict the operation temperature using a linear model. Therefore, a nonlinear empirical model such as ANN (artificial neural network) is the most practical and reasonable approach. Accompanying the use of an ANN models, the modeling complexity and explosive dimensionality problems arise as the number of variables increases. Appropriate variable selection techniques can eliminate redundant variables; reduce the complexity of the model; and present more accurate model. A variety of variable selection techniques for ANN modeling have been studied in recent years [15e18]. The CDQ process has been used commercially for several decades. It has a remarkable performance with regard to energy saving in comparison with the CWQ method. This work is aimed to develop an MPC approach to optimize the on-line operation and advance to further improve waste heat recovery in a CDQ process. The proposed method takes advantage of the existing distributed control system to model the process using the large amount of operational data available. The acquired models were subsequently used to implement a model-based control to regulate the supplementary air-flow rate. The rest of our work is organized as follows: Section 2 presents the process description of the CDQ system, and Section 3 describes the model development and the mathematical formulation of the MPC approach used for the CDQ system. The results of implementing the proposed MPC approach in the existing steelmaking plant are documented and discussed in Section 4. Conclusive remarks are given in the last Section.
2. Process description This research takes the commercial installation of a CDQ process as a case study, and the schematic flow diagram of the CDQ system is shown in Fig. 1. The entire system is similar to a cogeneration system, except that a fuel combustion unit is replaced with a CDQ chamber. The system consists of a coke quenching tower (prechamber and cooling chamber), waste heat recovery boiler, and steam turbine-generator set. Incandescent coke from a coke-oven plant is discharged into the pre-chamber located at the top of the coke quenching tower by a crane. The pressure, PCGB at this location is kept below atmospheric pressure to prevent hot gases from being released into the environment. The cooled circulation gas from the boiler is blown into the tower from the bottom. The counter flow between the incandescent coke and the cooled circulation gas facilitates heat exchange within the tower. When the coke is discharged from the bottom of the tower, its temperature drops to ~200 C. After receiving heat and attaining a high temperature of ~980 C, the circulation gas leaves the tower and heads to the boiler. The hot circulation gas then transfers its heat energy to the feed-water in the boiler and is cooled. Subsequently, the circulation gas passes through a sub-economizer and returns to the tower for the next cycle. There are three control loops regulating the operation in regard of the dry quenching tower. At first, the circulation gas likely damages the pipelines if its temperature continuously exceeds 980 C for more than 10 min. Therefore, the temperature of the hot circulation gas has to be kept under 980 C. A by-pass stream coming out of the cold circulation return gas line is led to the hot circulation stream. A control loop is installed to regulate the bypass flow rate to keep the temperature of the hot circulation gas <980 C. Another control loop is installed to regulate the cold circulation return gas flow rate to keep the pressure at the top of the tower below the atmospheric pressure by releasing excess gas into the atmosphere. Both excess gas line and by-pass line are illustrated in Fig. 1. During the contact between incandescent coke and the cooled circulation gas within the tower, some carbon particles are carried out by the circulation gas. These carbon particles are combusted towards the top of the dry quenching chamber. A flow of supplementary air is added to complete the combustion of the carbon particles. The third control loop regulates the flow rate of the supplementary air to maintain low concentrations of CO and O2. A
Fig. 1. A schematic flow diagram of the studied CDQ system.
K. Sun et al. / Energy 80 (2015) 275e283
single control loop scheme is adopted to perform on-line control of these three control loops. Amongst these three control loops, the last control loop is the most complicated. The combustion principle is to bring in the optimal amount of supplementary air to oxidize the carry-out carbon particles. The measurements of CO and O2 concentrations allow operators to make judgments on the performance of the combustion process and to make necessary adjustments to the supplementary air-flow rate accordingly. However, this process is not as simple as it seems. The nature of the combustion process is dynamic and complex. The fluctuations in the descending rate of incandescent coke, amount of carry-out carbon particles, and unloading cycles of incandescent coke bucket, make the combustion process unsteady. Consequently, the measurements of CO and O2 concentrations vary and often, the tracks of their variation are inconceivable. The operators in the steelmaking plant used in our case study had experienced repeated difficulties and thereby left the control loop open and fixed the setting of the control valve. It should be noted that the operation objective in this CDQ unit was to maximize steam generation in the boiler. The high-pressure steam passes through a turbine to generate electricity and the lowpressure steam generated continues to serve elsewhere in the plant as a thermal energy resource. To generate more high-pressure steam, according to the first law of thermal-dynamics, if the circulation gas flow rate is kept constant, it is desirable to maintain the hot air temperature at the highest level of 980 C. Notably, for the purpose of convenience, the operators tend to keep the circulation gas flow rate constant. At the first stage of this study, operational data were collected and analyzed. Operational data from January 1, 2013 to January 31, 2013 were used. The data were recorded every minute during the operation and stored electronically. Before the analysis of the operational data, the consistency of the data was checked by mass and energy balances, and the measurement noises were diminished by data-smoothing. To make the operation simple and practical, it was necessary to have a surrogated objective instead of steam generation. Further studies revealed that there was a strong and positive correlation between MST (steam generation) and TCGB (the temperature of circulation gas entering the associated boiler) as shown in Fig. 2. The R2 (coefficient of determination) value of 0.7658 is sufficient to make TCGB a reasonable surrogated objective in the rest of this study to achieve the aim of maximizing steam generation. The MC (discharge rate of incandescent coke) represents the mass flow of incandescent coke at a time, and MST/MC represents the steam production per MC. Fig. 3 shows the distribution chart
Fig. 2. Regression figure between MST and TCGB.
277
Fig. 3. Distribution chart for MST/MC and MC of all operational data.
between MC and MST/MC using operational data from January 1, 2013 to January 31, 2013. It is can be seen in Fig. 3 that there exists an operation barrier (marked as a red square) at each MC. The operation barrier is the best operation that has the maximum MST/MC at a particular MC in the historical data. A shorter distance to the operation barrier means the operation is better, and a longer distance from the operation barrier means the operation can be improved. In addition, we separated all the operational data into four groups on the basis of their residuals to the operation barrier: 20%, 10%e20%, 5%e10%, and 5%. Fig. 4 presents the distribution chart of all operation data with TCGB and MC. Obviously, operations closer to the operation barrier have a higher TCGB. Fig. 3 shows that the steam generation is almost independent with the discharge rate of incandescent coke. Fig. 4 reveals that TCGB is almost independent with the discharge rate of incandescent coke. These phenomena formulate the basic operation philosophy i.e., keeping TCGB at the curtain setpoint, 980 C in the studied case. In the next section of this study, the prediction model on TCGB will be established and used for MPC to substitute the third control loop and regulate the supplementary air-flow rate. Fig. 5 demonstrates a 12-h period of data for TCGB on January 1, 2013. There are two different operational modes: stable and unstable coke discharging from the coke oven. The delivery of incandescent coke from the coke oven to the quenching tower is done with an automatically moving crane. In consideration of the safety reason, operators have to be present in the field to monitor the delivery operation. In a 24-h period of operation, there are at least six occasions that the delivery of incandescent coke stops for a short period of time due to operators taking meals/breaks or
Fig. 4. Distribution chart for TCGB and MC of all operational data.
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Fig. 5. Measured data of TCGB in the first 12 h of January 1, 2013.
operation troubles. The disruption of delivering incandescent coke causes the drop of temperature, supplementary air flow rate and makes the process unstable. There are three occasions of unstable discharging cases as shown in Fig. 5. Because some upsets in the unstable discharging operations are too great to build a stable and reliable model, this study focuses on improving the cases of stable discharging operations. However, providing possible solutions to the cases of unstable coke discharging involves further complicated mathematical programming and management actions and this was not considered within the scope of this work.
3. Development of MPC for a CDQ system The objective of MPC using neural network predictors is to minimize a cost function based on the error between the predicted output and setpoints of the process. Usually, the MPC algorithm utilizes a quadratic cost function that is shown as [13]:
min Du
XN1 i¼1
XN1 ½rðk þ iÞ yðk þ iÞ2 þ w$ i¼1 Du2 ðk þ i 1Þ
subject to : yðk þ 1Þ ¼ FðuðkÞÞ and Dulb DuðkÞ Duub
(1)
y¼g
Xq j¼1
woj f
Xp i¼1
þ bo wij xj þ bhj
where x ¼ fx1 ; x2 ; …; xp g denotes the candidate input variables of the network, y denotes the output of the network, the hidden layer has q nodes represented as h ¼ fh1 ; h2 ; …; hq g, and the weight wij ði ¼ 1; 2; …; p; j ¼ 1; 2; …; qÞ denotes the input weight between input variable xi and the jth hidden neuron hj, bhj is the bias of the jth neuron of the hidden layer, bo is the bias of the output layer, g represents the activation function of the output layer, f represents the activation function of the hidden layer, and woj ðj ¼ 1; 2; …; qÞ represents the jth output weight between the hidden layer and the output layer. Breiman [20] proposed a new shrinkage method called NNG (nonnegative garrote). The mechanism of this shrinkage method involves variable selection by shrinking or setting some coefficients of a “greedy” model to zero. In brief, NNG is a two-step shrinkage algorithm. In the first step the initial coefficients are obtained using an OLS (ordinary least squares) method. In the second step, the magnitude shrinkage of the initial coefficients is conducted using the “garrote” constraints that can be formulated as follows:
where N1 is the prediction horizon, y is the system output, r is the setpoint of the process, and the system start from time k, w is the weight factor which is often set to zero, D is the differentiation operator, u is the input vector and Du has the amplitude constraints [Dulb,Duub]. An MPC approach towards the control of TCGB was developed in the study, and the development process of the method and its results will be demonstrated in the following sections of the paper.
3.1. NNG-ANN algorithm for modeling ANNs are powerful tools used to model complex multivariable processes. Haykin [19] has presented a milestone textbook on neural networks, which provides comprehensive information of neural networks from an engineer's perspective. Fig. 6 shows the architecture of an MLP (multi-layer perceptron) neural network that consists of three layers: an input layer, an output layer, and a hidden layer. The MLP neural network has the mathematical formulation:
(2)
Fig. 6. Architecture of ANN.
K. Sun et al. / Energy 80 (2015) 275e283
o nXn Xp yk c* ðsÞ ¼ argmin cbx k¼1 i¼1 i i ik subject to :
ci 0;
Xp
c i¼1 i
s
Table 1 Candidate input variables of CDQ system.
(3)
where x and y are input and output variables respectively, bi ði ¼ 1; 2; …; pÞ denotes the OLS estimate and s is the garrote parameter. X2Rnp is the input data matrix, in which each column represents a candidate explanatory variable, and Y2Rn is a vector of the response variable. Sun et al. [18] developed a new variable selection method which is known as NNG-ANN for inferential modeling using the NNG and ANN. In the first step, the proposed method trains an ANN and obtains the initial input weights of the network, wij ði ¼ 1; 2; …; p; j ¼ 1; 2; …; qÞ. In the second step, magnitude coefficient shrinkage on the input weights is performed as follows:
X Xq Xp n yk g c ðsÞ ¼ argmin wo f cw x k¼1 j¼1 j i¼1 i ij j þ bo 2 þ bhj *
Variable name
Description
Unit
1 2 3 4 5 6
TCGB TC FSA MC TEC TCGT
7 8 9 10 11 12 13
FCGT FBG FEG PCGT PCGB PT CCO
Temperature of circulation gas heading to the boiler Temperature of incandescent cokes Supplementary air flow rate Discharge rate of incandescent coke Temperature of extinguished coke Temperature of circulation gas returning to the CDQ tower Flow rate of circulation gas returning to the CDQ tower Flow rate of by-pass circulation gas Flow rate of relieved excess gas to the atmosphere Pressure of circulation gas returning to the CDQ tower Pressure of circulation gas heading to the boiler Pressure of the tower measured at the top CO concentration of the circulation gas entering the tower H2 concentration of the circulation gas entering the tower CO2 concentration of the circulation gas entering the tower O2 concentration of the circulation gas entering the tower Coke level in the pre-chamber
14 CH2 15 CCO2 16 CO2 17 LC
subject to :
ci 0;
Xp
c i¼1 i
s
279
(4)
Compared to other state-of-the-art methods, the NNG-ANN results in a more compact model with fewer false selections and improved selection ratio. In this study, we take NNG-ANN to construct the model, and the results are compared to three other methods: i) the linear stepwise method [21], ii) the nonlinear ANN method [22], and iii) an effective nonlinear variable selection method, called SBS-MLP [16]. The simulation results are reported in terms of the following statistics: (1) M.S (model size): the number of input variables in the final model. A low M.S. value indicates better efficiency of the variable selection algorithm. (2) R2 (coefficient of determination): the square of the sample correlation coefficient between the outcomes and their predicted values. (3) PMSE (prediction mean square error): the mean square error between the predicted and desired output, which is based on a test dataset that is not used during the overall modeling process.
3.2. Model of TCGB(k þ 1) The model of TCGB(k þ 1) with respect to other process variables at time k is presented, where TCGB(k þ 1) denotes the value of TCGB at time k þ 1. The study takes production data with intervals of 1 min from May 1, 2013 to May 2, 2013 as the training data, and data from May 3, 2013 to May 4, 2013 as the testing data. The candidate input variables at time k are listed in Table 1. The average prediction performance over 100 runs is shown in Table 2. Obviously, the nonlinear methods present improved prediction accuracy than the linear method, which means the CDQ process displays highly nonlinear characteristics. In addition, the PMSE of NNG-ANN is considerably better than those of SBS-MLP and ANN; however, their R2 values are very similar. Therefore, the NNG-ANN shows improved prediction accuracy in the model with less model size. Fig. 7 presents the variable selection frequency over 100 runs by our proposed method. It shows that the selection probability of
C C Nm3/h ton/h C C
Nm3/h Nm3/h Nm3/h mmAq mmAq mmAq % % % % %
variables TCGB, FSA, CCO, TCGT is higher than 80%, while that of the others is less than 40%. Firstly, TCGB(k) has the most significant influence on TCGB(k þ 1) and is selected with 100% probability. After consulting the field operator, FSA is the amount of supplementary air, which can change the CO and O2 concentrations and therefore influence the combustion intensity of the tower. The cold supplementary air also can change the temperature of the circulation gas in the tower when they mix. The CCO of the circulation gas is highly correlated with TCGB. A high CCO means the combustion was incomplete, and a low CCO means the combustion was complete. TCGT is the temperature of circulation gas returning to the CDQ tower, and apparently can influence the TCGB(k þ 1). Notably, CO2 is not selected by this model. In any typical combustion system, e.g., a coal fired boiler, the oxygen concentration in the off-gas is an important indication of thermal efficiency. However, in most combustion cases, fresh air is brought into the combustion chamber and the oxygen concentration of the entering gas is ~21%. In our case study, the supplementary air is mixed with the circulation gas when it enters the CDQ chamber, and the oxygen concentration of the mixed gas varies in the range of 1.5%e3.0%, whose value depends on the ratio of the supplementary air-flow rate and the circulation gas flow rate. This difference explains why CO2 is not used in the model. Fig. 8 presents the measured and predicted values of TCGB using the NNG-ANN algorithm. It is obvious that the proposed method can successfully track the dynamics of TCGB both in the training and testing processes. 3.3. Model of CCO(k þ 1) The CCO(k þ 1) of the circulation gas is an important variable for the process as discussed above. The candidate input variables for CCO(k þ 1) in the prediction model are the same as the variables shown in Table 1. Table 3 summarizes the average prediction Table 2 Statistical prediction performance for the TCGB(k þ 1) over 100 runs.
2
R PMSE M.S
Stepwise
ANN
SBS-MLP
NNG-ANN
0.8851 56.12 7
0.9790 31.33 17
0.9811 24.78 8.03
0.9865 19.16 6.57
280
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Fig. 7. Variable selected frequency over 100 runs for TCGB(k þ 1) model.
Fig. 8. Measured and predicted TCGB(k þ 1) of the training and testing data.
performance over 100 runs for four methods. The NNG-ANN can build a more accurate model using lesser number of variables than the stepwise, ANN and SBS-MLP approaches. Fig. 9 shows the prediction and measured values of CCO(k þ 1), and demonstrates that the developed model using the NNG-ANN can predict the CCO(k þ 1) successfully.
achieved by providing a great amount of extra oxygen in the combustion chamber. It is not worthy of doing so. According to the experience of the plant operators, the lower bound of the CCO is set to 0.20. Therefore, a penalty function of CCO is added in the MPC model. The penalty function of CCO is denoted as:
PCO ðuðkÞ; xðkÞÞ ¼
3.4. Mathematical model of MPC
0 0:2 GðuðkÞ; xðkÞÞ
if GðuðkÞ; xðkÞÞ 0:2 else (7)
The study utilizes FSA at time k as the control variable, which is denoted as u(k). Other measured variables in Table 1 are denoted as the vector x(k). The TCGB prediction model presented by NNG-ANN can be denoted as:
TCGB ðk þ 1Þ ¼ FðuðkÞ; xðkÞÞ
(5)
Consequently, a one-step MPC model of the CDQ system can be formulated as:
argmin ½rðk þ 1Þ FðuðkÞ þ DuðkÞ; xðkÞÞ2 þ l$PCO ðuðkÞ DuðkÞ
þ DuðkÞ; xðkÞÞ
The CO prediction model can be denoted as:
CCO ðk þ 1Þ ¼ GðuðkÞ; xðkÞÞ
(6)
Zero value of CCO means complete combustion, but it can only be Table 3 Statistical prediction performance for the CCO(k þ 1) over 100 runs.
2
R PMSE M.S
Stepwise
ANN
SBS-MLP
NNG-ANN
0.8733 0.031 8
0.9581 0.019 17
0.9635 0.015 8.49
0.9650 0.011 7.05
subject to :
Dulb DuðkÞ Duub
(8)
where r(k þ 1) is the setpoint of TCGB and set to 980 C, [Dulb,Duub] are the boundary of Du(k) in consideration of the field operation conditions and is set to [3000, 3000], l is the penalty parameter which can be determined by field experience and experiments. Equation (8) is a nonlinear constrained quadratic problem. The study takes the trust region reflective optimization algorithm, which is a subspace trust region method based on the interior reflective Newton method proposed by Refs. [23] and [24].
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Fig. 9. Measured and predicted CCO(k þ 1) of the training and testing data.
4. Results and discussions In this section, we examine the dynamic operation data in a short period of hourly base, and compare the results between the routine operation and the experimental operation in the long period of weekly base. Before implementing the acquired model in the experimental operation, we illustrate how it could help
improve the routine operation by examining the dynamic profiles of TCGB, FSA and CCO. The operational data in a 90-min period of operation were used to document the detailed dynamic variation in a routine operation on May 5, 2013. Fig. 10 shows the dynamic profiles of FSA, CCO, and TCGB. The blue-colored dots represent the real operation while the red-colored dots represent the suggested operation based on the
Fig. 10. a. Comparison of the real FSA versus MPC proposed FSA. b. Comparison of the real CCO versus MPC proposed CCO. c. Comparison of the real versus predicted TCGB using MPC.
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proposed MPC operation, where the MPC operation at time k þ 1 is calculated based on the real operation data at time k. It is clear that FSA is insufficient to complete combustion and to keep TCGB close to the setpoint of 980 C. The suggested FSA is shown using red-colored dots and these rates are usually higher than the real ones. However, there is an exception as shown Fig. 10. In the period between 16:10 and 16:40, the proposed values for FSA are almost equal to those of the real FSA values, even though the values of TCGB are 50 C below the setpoint (980 C) as shown in Fig. 10. The low values of CCO in that time period indicate that the combustion was almost complete. Further studies revealed that a low discharge rate of incandescent coke in that period cause a low carry-over carbon and low TCGB. After the models for predicting TCGB and CCO were obtained, experiments were conducted in the week between May 6, 2013 and May 13, 2013. These data were used as the case of the experimental operational data. A comparison between the routine operation (as the control group) and the experimental operation (as the treatment group) was made to illustrate the effectiveness of implementing MPC. The routine operation is to regulate the supplementary air flow using the conventional method, i.e., manual control, whilst the experimental operation is to regulate it using MPC. To make a meaningful comparison, the operational conditions regarding energy input of the CDQ must be equal or close enough to be deemed equal. TC and MC are the variables that determine energy input. The historical data were reviewed and those in the period of between February 3, 2013 and February 10, 2013 were considered suitable for the comparison. These data were used as the routine operation. Both cases are real plant data and have the similar values in the operation variables as shown in Table 4. Most of the differences of the operation variables, i.e., TCGT, FCGT, FBG, FEG, PCGT, PCGB, PT, TC and MC between two cases are around 1%. The values between two cases are close enough to justify the fairness of the comparison. Among those variables, temperature TC and discharge rate MC of incandescent cokes as mentioned above determine the energy input of incandescent coke, i.e., the potential for waste heat recovery. The average values of TC for the routine operation and for the experimental operation were 1085 C and 1087.8 C respectively, whilst the average value of MC were 133.3 and 133.7 respectively. The differences in TC and MC were 0.3% and the energy input of these two groups can be deemed as equal. Therefore, the comparison between these two groups can be used to verify effectiveness of our approach in terms of steam production, which is the recovered heat of the CDQ system. Table 4 Weekly performance comparison between manual control and MPC. Variables
Routine operation
MPC implemented
TCGB( C) TC( C) FSA(Nm3/h) MC(ton/h) TEC( C) TCGT( C) FCGT(Nm3/h) FBG(Nm3/h) FEG(Nm3/h) PCGT(mmAq) PCGB(mmAq) PT(mmAq) CCO(%) CH2 (%) CCO2 (%) CO2 (%) LC(%) MST(ton/h)
946.4 1085.0 16,097 133.3 154.6 120.9 188,700 5100 17,980 401.9 58.3 2.02 1.630 0.40 16.93 0.192 26.8 83.9
961.2 1087.8 20,306 133.7 159.6 120.4 190,620 4885 18,197 388.2 60.4 2.01 0.461 0.053 12.26 0.177 27.8 89.8
In the MPC implemented case, the weekly average value of FSA is 26% higher than that of the routine case. This reflects the fact that the oxygen supply in the MPC implemented case is much more than the routine case. The concentrations of CO, H2, and O2 in the MPC implemented case are much lower than those in the routine case. The values show that the combustible species in the MPC implemented case are fully combusted and that the supply of oxygen is sufficient to complete the combustion process, but not excess. Consequently, the weekly average TCGB in the MPC implemented case is 15 C higher than that found in the routine case. It means more heat is recovered in the MPC implemented case. As a result, the weekly average steam generation increased from 84.0 ton/h of the routine case to 89.8 ton/h of the MPC implemented case, which corresponds to a 7% improvement. In addition to the above comparisons, we examine the dynamic variation of FSA, CCO, and TCGB in the experimental case when implementing MPC. Fig. 11a shows that the values of FSA are between 17 103 and 21 103, which are much higher than those shown in Fig. 10a. Fig. 11b indicates that the values of CCO are always <0.7, which are much lower than those shown in Fig. 10b. Fig. 11c illustrates that the values of TCGB are always controlled at ~980 C. The dynamic profiles shown in Fig. 11 as well as the comparisons shown in Table 4 conclude that the MPC-implemented operation has smoother regulation of supplementary fresh air flow, achieves more stable temperature control, and as a result, has better combustion performance and waste heat recovery than the routine case. 5. Conclusion Many researchers have demonstrated the advantages of CDQ over CWQ in their thermal analysis studies. However, the operational difficulties associated with the control of the supplementary air flow have long existed without being noticed. The abundance of historical operational data stored electronically and the availability of several data analysis tools make the operation modelbuilding convenient. In our study, we took an existing largescale coke-oven plant as the case study subject. We proposed a neural-network-based MPC approach and implemented it in the plant site. The effectiveness of the proposed MPC approach was verified upon comparison with the routine operation. The results showed that the supplementary air flow was controlled perfectly. It provided sufficient oxygen to make the combustion process complete, but not in excess. Consequently, the steam generation increased by 7%. The performance of the proposed MPC approach in the studied case was impeccable. The implementation of the MPC approach in an existing CDQ is simple and only requires a plant to collect its existing operational data stored electronically and configure in the distributed control system in the plant. In other words, the implementation does not need any extra hardware or staff training. However, uncorrupted data are essential. The acquired models, which are the most important aspects in this study, must be accurate; otherwise, effective control is impossible. Our study estimates to achieve an economic gain of 1.3 million USD, with an environmental gain with regard to CO2 reduction by 2.5 103 tons annually in the studied coke-ovens, for which the annual coke production is currently 1 106 tons. Acknowledgments The work is partially supported by Ministry of Economic Affairs through the grant 102-EC-17-A-09-S1-198, and National Science Council through the grant NSC 100-2221-E-007-058-MY2, Advanced Manufacturing and Service Management Research
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Fig. 11. a. FSA profile in the MPC implemented operation. b. CCO profile in the MPC implemented operation. c. TCGB profile in the MPC implemented operation.
Center, National Tsing Hua University, Taiwan (Grant 101N2072E1) and the Shandong Provincial Natural Science Foundation of China (Grant No. ZR2010FQ009).
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