Model predictive control for improving waste heat recovery in cokedry 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, Chinab Department of Chemical Engineering, National Tsing Hua University, Hsin-Chu 30013, Taiwanc Department of Occupational Safety and Health, Chang Jung Christian University, Tainan 71101, Taiwand New Materials Research & Development Department, China Steel Corporation, Kaohsiung 81233, Taiwan
a r t i c l e i n f o
Article history:Received 31 March 2014Received in revised form23 October 2014Accepted 25 November 2014Available online 23 December 2014
CDQ (coke dry quenching) is a widely used method for recovering waste heat in the steel industry. Wehave developed a novel, data driven modeling approach and model based control for a CDQ unit toincrease steam generation in a cogeneration system. First, the correlation between steam generation andTCGB (the temperature of circulation gas entering the associated boiler) was confirmed. Subsequently, anonlinear variable selection method was employed to build models of TCGB and the carbon monoxideconcentration of the circulation gas. The models obtained were implemented to achieve MPC (modelpredictive control) for regulating the supplementary gas to maximize steam generation in an existingsteelmaking plant. Upon comparison of the original process and the proposed modified operation, theeffectiveness of the implementation of MPC was justified. The results showed that steam generation wasincreased by 7%. In our approach, the large amount of available operational data stored electronically wasused to establish the models. Modification of the established system is not required. Taking into accountthat no capital investment is required, the process improvement is remarkable in terms of its return oninvestment.
Steelmaking is a high energy-consumption process. There areseveral methods for saving energy in steelmaking, with coke-quenching being one particular method. In traditional CWQ (cokewet quenching) system, hot coke is cooled by spraying water. Theapproach results in high CO2 emissions and thermal energy loss.The CDQ (coke dry quenching) system is an energy conservingalternative, in which hot coke is quenched by inert gases instead ofspraying water in the quenching tower [1]. The recovered thermalenergy from the quenching gas can be used to generate high-pressure steam in a downstream boiler. Some articles [1,2] illus-trated and made comparison of these two systems. In the case
study of this work, the CDQ unit and its associated boiler areincorporated in a cogeneration system to generate electricity.
Over the last two decades, the CDQ system has been graduallyaccepted, although the CWQ system is still popular within thesteelmaking industry. Several articles have advocated the use of theCDQ process. Errera and Milanez [2] presented a thermodynamicanalysis for a CDQ unit and reported a comprehensive comparisonbetween the performance of both CDQ and CWQ systems. Theiranalysis procedure was used in the decision making process to-wards developing new technology. Lin et al. [3] documented anenergy saving calculation based on the operation of an existing CDQsystem, and concluded that 85% of the waste heat generated wasrecovered. Liu et al. [4] developed a mathematical model to simu-late the fluid flow and heat transfer in the cooling shaft of a CDQunit. Feng [5] carried out an experimental investigation of cokedescending behavior in a CDQ cooling shaft and developed aviscous model to describe the bulk coke flow. Sun et al. [6] pro-posed to combine a CDQ system and a gasification system, usingcoke-oven gas and steam. They used a PRO/II simulator to study the
Fig. 1. A schematic flow diagram of the studied CDQ system.
K. Sun et al. / Energy 80 (2015) 275e283276
effects of the coke-oven gas flow rate, steam consumption on theflow rate and composition of the syngas, and energy efficiency.Wang et al. [7] built a set of rigorous models for a CDQ system andrun simulation work. Their study provided static relationshipsamong steam generation, flow rate of circulation gas and dischargerate of incandescent coke. However, the literature regarding theissues of operation or control of a CDQ system is limited.
Real time operation of the CDQ process poses several difficultcontrol problems. To tackle the difficulties operators have faced, theoperation strategy of an existing CDQ unit was reviewed in thisstudy. We found that the supplementary air flow was not routinelyregulated and had occasionally been operated manually. Besidesthe supplementary air-flow regulation problem, unstable cokedischarge from the coke oven is another operational problem,which is caused by the unstable supply of incandescent coke. Weconsider it as a challenge towards planning and scheduling and it isexcluded from the scope of this work.
MPC (model predictive control), as an advanced control tech-nology, uses a sufficiently accurate model to predict the futurechanges in manipulative variables to efficiently reach the setpointof a controlled variable. The model is established by obtainingexplicit or implicit relationships between manipulative variables,controlled variables, and associated process variables from statis-tical analysis or theoretical development. MPC has been success-fully employed in process industries such as chemical plants and oilrefineries in recent years [8e14].
The construction of a sufficiently accurate model is one of thekey issues for developing an MPC approach. The main theme ofoperating a CDQ system is tomaintain the outlet temperature of thehot circulation gas at certain point. The operation of a CDQ systemis usually under high-temperature conditions and involvescomplicated combustion mechanism. The extend of combustionprocesses and the subsequent outlet temperature of the hot cir-culation gas are determined by many operation variables, namely,the inlet temperature and flow rate of the circulation gas, the flowrate of the supplementary air, the pressure in the quenching tower,the concentration of O2 and CO in the combustion chamber, thetemperature of the hot incandescent coke from the oven, etc. Thesevariables are interacted with each other and make the systemhighly nonlinear and difficult to model. It is almost impossible topredict 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 theuse of an ANN models, the modeling complexity and explosivedimensionality problems arise as the number of variables increases.Appropriate variable selection techniques can eliminate redundantvariables; reduce the complexity of the model; and present moreaccurate model. A variety of variable selection techniques for ANNmodeling have been studied in recent years [15e18].
The CDQ process has been used commercially for several de-cades. It has a remarkable performance with regard to energysaving in comparison with the CWQmethod. This work is aimed todevelop an MPC approach to optimize the on-line operation andadvance to further improve waste heat recovery in a CDQ process.The proposed method takes advantage of the existing distributedcontrol system to model the process using the large amount ofoperational data available. The acquired models were subsequentlyused to implement a model-based control to regulate the supple-mentary air-flow rate. The rest of our work is organized as follows:Section 2 presents the process description of the CDQ system, andSection 3 describes the model development and the mathematicalformulation of the MPC approach used for the CDQ system. Theresults of implementing the proposedMPC approach in the existingsteelmaking 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 processas a case study, and the schematic flow diagram of the CDQ systemis shown in Fig. 1. The entire system is similar to a cogenerationsystem, except that a fuel combustion unit is replaced with a CDQchamber. The system consists of a coke quenching tower (pre-chamber and cooling chamber), waste heat recovery boiler, andsteam turbine-generator set. Incandescent coke from a coke-ovenplant is discharged into the pre-chamber located at the top of thecoke quenching tower by a crane. The pressure, PCGB at this locationis kept belowatmospheric pressure to prevent hot gases from beingreleased into the environment. The cooled circulation gas from theboiler is blown into the tower from the bottom. The counter flowbetween the incandescent coke and the cooled circulation gas fa-cilitates heat exchange within the tower. When the coke is dis-charged 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 hotcirculation gas then transfers its heat energy to the feed-water inthe boiler and is cooled. Subsequently, the circulation gas passesthrough a sub-economizer and returns to the tower for the nextcycle.
There are three control loops regulating the operation in regardof the dry quenching tower. At first, the circulation gas likelydamages the pipelines if its temperature continuously exceeds980 �C for more than 10 min. Therefore, the temperature of the hotcirculation gas has to be kept under 980 �C. A by-pass streamcoming out of the cold circulation return gas line is led to the hotcirculation stream. A control loop is installed to regulate the by-pass flow rate to keep the temperature of the hot circulation gas<980 �C. Another control loop is installed to regulate the cold cir-culation return gas flow rate to keep the pressure at the top of thetower below the atmospheric pressure by releasing excess gas intothe atmosphere. Both excess gas line and by-pass line are illustratedin Fig. 1.
During the contact between incandescent coke and the cooledcirculation gas within the tower, some carbon particles are carriedout by the circulation gas. These carbon particles are combustedtowards the top of the dry quenching chamber. A flow of supple-mentary air is added to complete the combustion of the carbonparticles. The third control loop regulates the flow rate of thesupplementary air to maintain low concentrations of CO and O2. A
Fig. 3. Distribution chart for MST/MC and MC of all operational data.
K. Sun et al. / Energy 80 (2015) 275e283 277
single control loop scheme is adopted to perform on-line control ofthese three control loops.
Amongst these three control loops, the last control loop is themost complicated. The combustion principle is to bring in theoptimal amount of supplementary air to oxidize the carry-outcarbon particles. The measurements of CO and O2 concentrationsallow operators to make judgments on the performance of thecombustion process and to make necessary adjustments to thesupplementary air-flow rate accordingly. However, this process isnot as simple as it seems. The nature of the combustion process isdynamic and complex. The fluctuations in the descending rate ofincandescent coke, amount of carry-out carbon particles, andunloading cycles of incandescent coke bucket, make the combus-tion process unsteady. Consequently, the measurements of CO andO2 concentrations vary and often, the tracks of their variation areinconceivable. The operators in the steelmaking plant used in ourcase study had experienced repeated difficulties and thereby leftthe control loop open and fixed the setting of the control valve.
It should be noted that the operation objective in this CDQ unitwas to maximize steam generation in the boiler. The high-pressuresteam passes through a turbine to generate electricity and the low-pressure steam generated continues to serve elsewhere in the plantas a thermal energy resource. To generate more high-pressuresteam, according to the first law of thermal-dynamics, if the cir-culation gas flow rate is kept constant, it is desirable tomaintain thehot air temperature at the highest level of 980 �C. Notably, for thepurpose of convenience, the operators tend to keep the circulationgas flow rate constant.
At the first stage of this study, operational data were collectedand analyzed. Operational data from January 1, 2013 to January 31,2013 were used. The data were recorded every minute during theoperation and stored electronically. Before the analysis of theoperational data, the consistency of the data was checked by massand energy balances, and the measurement noises were dimin-ished by data-smoothing. To make the operation simple andpractical, it was necessary to have a surrogated objective instead ofsteam generation. Further studies revealed that there was a strongand 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 of0.7658 is sufficient to make TCGB a reasonable surrogated objectivein the rest of this study to achieve the aim of maximizing steamgeneration.
The MC (discharge rate of incandescent coke) represents themass flow of incandescent coke at a time, and MST/MC representsthe steam production per MC. Fig. 3 shows the distribution chart
Fig. 2. Regression figure between MST and TCGB.
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 thebest operation that has the maximum MST/MC at a particular MC inthe historical data. A shorter distance to the operation barriermeans the operation is better, and a longer distance from theoperation barrier means the operation can be improved.
In addition, we separated all the operational data into fourgroups on the basis of their residuals to the operation barrier: 20%,10%e20%, 5%e10%, and 5%. Fig. 4 presents the distribution chart ofall operation data with TCGB and MC. Obviously, operations closer tothe operation barrier have a higher TCGB. Fig. 3 shows that the steamgeneration is almost independent with the discharge rate of in-candescent coke. Fig. 4 reveals that TCGB is almost independent withthe discharge rate of incandescent coke. These phenomenaformulate the basic operation philosophy i.e., keeping TCGB at thecurtain setpoint, 980 �C in the studied case.
In the next section of this study, the prediction model on TCGBwill be established and used for MPC to substitute the third controlloop 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 un-stable coke discharging from the coke oven. The delivery of in-candescent coke from the coke oven to the quenching tower is donewith an automatically moving crane. In consideration of the safetyreason, operators have to be present in the field to monitor thedelivery operation. In a 24-h period of operation, there are at leastsix occasions that the delivery of incandescent coke stops for ashort period of time due to operators taking meals/breaks or
Fig. 4. Distribution chart for TCGB and MC of all operational data.
Fig. 5. Measured data of TCGB in the first 12 h of January 1, 2013.
K. Sun et al. / Energy 80 (2015) 275e283278
operation troubles. The disruption of delivering incandescent cokecauses the drop of temperature, supplementary air flow rate andmakes the process unstable. There are three occasions of unstabledischarging cases as shown in Fig. 5. Because some upsets in theunstable discharging operations are too great to build a stable andreliable model, this study focuses on improving the cases of stabledischarging operations. However, providing possible solutions tothe cases of unstable coke discharging involves further complicatedmathematical programming and management actions and this wasnot considered within the scope of this work.
3. Development of MPC for a CDQ system
The objective of MPC using neural network predictors is tominimize a cost function based on the error between the predictedoutput and setpoints of the process. Usually, the MPC algorithmutilizes a quadratic cost function that is shown as [13]:
minDu
XN1
i¼1½rðkþ iÞ � yðkþ iÞ�2 þw$
XN1
i¼1Du2ðkþ i� 1Þ
subject to : yðkþ1Þ¼ FðuðkÞÞ and Dulb �DuðkÞ�Duub (1)
where N1 is the prediction horizon, y is the system output, r is thesetpoint of the process, and the system start from time k, w is theweight factor which is often set to zero, D is the differentiationoperator, u is the input vector and Du has the amplitude constraints[Dulb,Duub].
An MPC approach towards the control of TCGB was developedin the study, and the development process of the method andits results will be demonstrated in the following sections of thepaper.
Fig. 6. Architecture of ANN.
3.1. NNG-ANN algorithm for modeling
ANNs are powerful tools used to model complex multivariableprocesses. Haykin [19] has presented a milestone textbook onneural networks, which provides comprehensive information ofneural networks from an engineer's perspective. Fig. 6 shows thearchitecture of an MLP (multi-layer perceptron) neural networkthat consists of three layers: an input layer, an output layer, and ahidden layer.
The MLP neural network has the mathematical formulation:
y ¼ gXq
j¼1wo
j fXp
i¼1wijxj
� �þ bhj
� �� �þ bo
� �(2)
where x ¼ fx1; x2;…; xpg denotes the candidate input variables ofthe network, y denotes the output of the network, the hidden layerhas q nodes represented as h ¼ fh1;h2;…;hqg, and the weightwijði ¼ 1;2;…; p; j ¼ 1;2;…; qÞ denotes the input weight betweeninput variable xi and the jth hidden neuron hj, bhj is the bias of thejth neuron of the hidden layer, bo is the bias of the output layer, grepresents the activation function of the output layer, f representsthe activation function of the hidden layer, and wo
j ðj ¼ 1;2;…; qÞrepresents the jth output weight between the hidden layer and theoutput layer.
Breiman [20] proposed a new shrinkage method called NNG(nonnegative garrote). The mechanism of this shrinkage methodinvolves variable selection by shrinking or setting some coefficientsof a “greedy” model to zero. In brief, NNG is a two-step shrinkagealgorithm. In the first step the initial coefficients are obtained usingan OLS (ordinary least squares) method. In the second step, themagnitude shrinkage of the initial coefficients is conducted usingthe “garrote” constraints that can be formulated as follows:
Table 1Candidate input variables of CDQ system.
Variablename
Description Unit
1 TCGB Temperature of circulation gas heading to the boiler �C2 TC Temperature of incandescent cokes �C3 FSA Supplementary air flow rate Nm3/h4 MC Discharge rate of incandescent coke ton/h5 TEC Temperature of extinguished coke �C6 TCGT Temperature of circulation gas returning to the CDQ
tower
�C
7 FCGT Flow rate of circulation gas returning to the CDQ tower Nm3/h8 FBG Flow rate of by-pass circulation gas Nm3/h9 FEG Flow rate of relieved excess gas to the atmosphere Nm3/h10 PCGT Pressure of circulation gas returning to the CDQ tower mmAq11 PCGB Pressure of circulation gas heading to the boiler mmAq12 PT Pressure of the tower measured at the top mmAq13 CCO CO concentration of the circulation gas entering the
tower%
14 CH2H2 concentration of the circulation gas entering thetower
%
15 CCO2CO2 concentration of the circulation gas entering thetower
%
16 CO2O2 concentration of the circulation gas entering thetower
%
17 LC Coke level in the pre-chamber %
K. Sun et al. / Energy 80 (2015) 275e283 279
c*ðsÞ ¼ argminnXn
k¼1
�yk �
Xp
i¼1cibixik
�o
subject to : ci � 0;Xp
i¼1ci � s (3)
where x and y are input and output variables respectively,biði ¼ 1;2;…; pÞ denotes the OLS estimate and s is the garroteparameter. X2Rn�p is the input data matrix, in which each columnrepresents a candidate explanatory variable, and Y2Rn is a vectorof the response variable.
Sun et al. [18] developed a new variable selection method whichis known as NNG-ANN for inferential modeling using the NNG andANN. In the first step, the proposed method trains an ANN andobtains the initial input weights of the network,wijði ¼ 1;2;…;p; j ¼ 1;2;…;qÞ. In the second step, magnitude co-efficient shrinkage on the input weights is performed as follows:
c*ðsÞ ¼ argmin�Xn
k¼1
�yk � g
��Xq
j¼1wo
j f��Xp
i¼1ciwijxj
�
þ bhj��
þ bo��
2�
subject to : ci � 0;Xp
i¼1ci � s (4)
Compared to other state-of-the-art methods, the NNG-ANNresults in a more compact model with fewer false selections andimproved selection ratio. In this study, we take NNG-ANN toconstruct the model, and the results are compared to three othermethods: i) the linear stepwise method [21], ii) the nonlinear ANNmethod [22], and iii) an effective nonlinear variable selectionmethod, called SBS-MLP [16]. The simulation results are reported interms of the following statistics:
(1) M.S (model size): the number of input variables in the finalmodel. A low M.S. value indicates better efficiency of thevariable selection algorithm.
(2) R2 (coefficient of determination): the square of the samplecorrelation coefficient between the outcomes and theirpredicted values.
(3) PMSE (prediction mean square error): the mean square errorbetween the predicted and desired output, which is based ona test dataset that is not used during the overall modelingprocess.
Table 2Statistical prediction performance for the TCGB(k þ 1) over 100 runs.
The model of TCGB(k þ 1) with respect to other process variablesat time k is presented, where TCGB(k þ 1) denotes the value of TCGBat time k þ 1. The study takes production data with intervals of1min fromMay 1, 2013 toMay 2, 2013 as the training data, and datafrom May 3, 2013 to May 4, 2013 as the testing data. The candidateinput variables at time k are listed in Table 1.
The average prediction performance over 100 runs is shown inTable 2. Obviously, the nonlinear methods present improved pre-diction accuracy than the linear method, which means the CDQprocess displays highly nonlinear characteristics. In addition, thePMSE of NNG-ANN is considerably better than those of SBS-MLPand ANN; however, their R2 values are very similar. Therefore, theNNG-ANN shows improved prediction accuracy in the model withless model size.
Fig. 7 presents the variable selection frequency over 100 runs byour proposed method. It shows that the selection probability of
variables TCGB, FSA, CCO, TCGT is higher than 80%, while that of theothers is less than 40%. Firstly, TCGB(k) has the most significant in-fluence on TCGB(k þ 1) and is selected with 100% probability. Afterconsulting the field operator, FSA is the amount of supplementaryair, which can change the CO and O2 concentrations and thereforeinfluence the combustion intensity of the tower. The cold supple-mentary air also can change the temperature of the circulation gasin the tower when they mix. The CCO of the circulation gas is highlycorrelated with TCGB. A high CCO means the combustion wasincomplete, and a low CCO means the combustion was complete.TCGT is the temperature of circulation gas returning to the CDQtower, and apparently can influence the TCGB(k þ 1). Notably, CO2
isnot selected by this model. In any typical combustion system, e.g., acoal fired boiler, the oxygen concentration in the off-gas is animportant indication of thermal efficiency. However, in mostcombustion cases, fresh air is brought into the combustion chamberand the oxygen concentration of the entering gas is ~21%. In ourcase study, the supplementary air is mixed with the circulation gaswhen it enters the CDQ chamber, and the oxygen concentration ofthe mixed gas varies in the range of 1.5%e3.0%, whose value de-pends on the ratio of the supplementary air-flow rate and the cir-culation gas flow rate. This difference explains why CO2
is not usedin the model.
Fig. 8 presents the measured and predicted values of TCGB usingthe NNG-ANN algorithm. It is obvious that the proposed methodcan successfully track the dynamics of TCGB both in the training andtesting processes.
3.3. Model of CCO(k þ 1)
The CCO(k þ 1) of the circulation gas is an important variable forthe process as discussed above. The candidate input variables forCCO(k þ 1) in the prediction model are the same as the variablesshown in Table 1. Table 3 summarizes the average prediction
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.
K. Sun et al. / Energy 80 (2015) 275e283280
performance over 100 runs for four methods. The NNG-ANN canbuild a more accurate model using lesser number of variables thanthe 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-ANNcan predict the CCO(k þ 1) successfully.
3.4. Mathematical model of MPC
The study utilizes FSA at time k as the control variable, which isdenoted as u(k). Other measured variables in Table 1 are denoted asthe vector x(k). The TCGB prediction model presented by NNG-ANNcan be denoted as:
TCGBðkþ 1Þ ¼ FðuðkÞ; xðkÞÞ (5)
The CO prediction model can be denoted as:
CCOðkþ 1Þ ¼ GðuðkÞ;xðkÞÞ (6)
Zero value of CCOmeans complete combustion, but it can only be
Table 3Statistical prediction performance for the CCO(k þ 1) over 100 runs.
achieved by providing a great amount of extra oxygen in thecombustion chamber. It is not worthy of doing so. According to theexperience of the plant operators, the lower bound of the CCO is setto 0.20. Therefore, a penalty function of CCO is added in the MPCmodel. The penalty function of CCO is denoted as:
PCOðuðkÞ; xðkÞÞ ¼�
0 if GðuðkÞ; xðkÞÞ � 0:20:2� GðuðkÞ;xðkÞÞ else
(7)
Consequently, a one-step MPC model of the CDQ system can beformulated as:
argminDuðkÞ
½rðkþ 1Þ � FðuðkÞ þ DuðkÞ;xðkÞÞ�2 þ l$PCOðuðkÞ
þ DuðkÞ;xðkÞÞ
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 operationconditions and is set to [�3000, 3000], l is the penalty parameterwhich can be determined by field experience and experiments.
Equation (8) is a nonlinear constrained quadratic problem. Thestudy takes the trust region reflective optimization algorithm,which is a subspace trust region method based on the interiorreflective Newton method proposed by Refs. [23] and [24].
Fig. 9. Measured and predicted CCO(k þ 1) of the training and testing data.
K. Sun et al. / Energy 80 (2015) 275e283 281
4. Results and discussions
In this section, we examine the dynamic operation data in ashort period of hourly base, and compare the results between theroutine operation and the experimental operation in the longperiod of weekly base. Before implementing the acquired model inthe experimental operation, we illustrate how it could help
Fig. 10. a. Comparison of the real FSA versus MPC proposed FSA. b. Comparison of the real CC
improve the routine operation by examining the dynamic profilesof TCGB, FSA and CCO.
The operational data in a 90-min period of operation were usedto document the detailed dynamic variation in a routine operationon May 5, 2013. Fig. 10 shows the dynamic profiles of FSA, CCO, andTCGB. The blue-colored dots represent the real operation while thered-colored dots represent the suggested operation based on the
O versus MPC proposed CCO. c. Comparison of the real versus predicted TCGB using MPC.
K. Sun et al. / Energy 80 (2015) 275e283282
proposed MPC operation, where the MPC operation at time k þ 1 iscalculated based on the real operation data at time k.
It is clear that FSA is insufficient to complete combustion and tokeep TCGB close to the setpoint of 980 �C. The suggested FSA isshown using red-colored dots and these rates are usually higherthan 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 FSAare almost equal to those of the real FSA values, even though thevalues of TCGB are 50 �C below the setpoint (980 �C) as shown inFig. 10. The low values of CCO in that time period indicate that thecombustion was almost complete. Further studies revealed that alow discharge rate of incandescent coke in that period cause a lowcarry-over carbon and low TCGB.
After the models for predicting TCGB and CCO were obtained,experiments were conducted in the week betweenMay 6, 2013 andMay 13, 2013. These data were used as the case of the experimentaloperational data. A comparison between the routine operation (asthe control group) and the experimental operation (as the treat-ment group) was made to illustrate the effectiveness of imple-menting MPC. The routine operation is to regulate thesupplementary air flow using the conventional method, i.e., manualcontrol, whilst the experimental operation is to regulate it usingMPC. Tomake ameaningful comparison, the operational conditionsregarding energy input of the CDQmust be equal or close enough tobe deemed equal. TC and MC are the variables that determine en-ergy input. The historical data were reviewed and those in theperiod of between February 3, 2013 and February 10, 2013 wereconsidered suitable for the comparison. These data were used asthe routine operation.
Both cases are real plant data and have the similar values in theoperation variables as shown in Table 4. Most of the differences ofthe operation variables, i.e., TCGT, FCGT, FBG, FEG, PCGT, PCGB, PT, TC andMC between two cases are around 1%. The values between twocases are close enough to justify the fairness of the comparison.Among those variables, temperature TC and discharge rate MC ofincandescent cokes as mentioned above determine the energyinput of incandescent coke, i.e., the potential for waste heat re-covery. The average values of TC for the routine operation and forthe experimental operation were 1085 �C and 1087.8 �C respec-tively, whilst the average value of MC were 133.3 and 133.7respectively. The differences in TC and MC were �0.3% and theenergy input of these two groups can be deemed as equal. There-fore, the comparison between these two groups can be used toverify effectiveness of our approach in terms of steam production,which is the recovered heat of the CDQ system.
Table 4Weekly performance comparison between manual control and MPC.
In the MPC implemented case, the weekly average value of FSA is26% higher than that of the routine case. This reflects the fact thatthe oxygen supply in theMPC implemented case ismuchmore thanthe routine case. The concentrations of CO, H2, and O2 in the MPCimplemented case are much lower than those in the routine case.The values show that the combustible species in the MPC imple-mented case are fully combusted and that the supply of oxygen issufficient to complete the combustion process, but not excess.
Consequently, theweekly average TCGB in theMPC implementedcase is 15 �C higher than that found in the routine case. It meansmore heat is recovered in the MPC implemented case. As a result,the weekly average steam generation increased from 84.0 ton/h ofthe routine case to 89.8 ton/h of the MPC implemented case, whichcorresponds to a 7% improvement.
In addition to the above comparisons, we examine the dynamicvariation of FSA, CCO, and TCGB in the experimental case whenimplementing MPC. Fig. 11a shows that the values of FSA are be-tween 17 � 103 and 21 � 103, which are much higher than thoseshown 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. 11cillustrates that the values of TCGB are always controlled at ~980 �C.The dynamic profiles shown in Fig. 11 as well as the comparisonsshown in Table 4 conclude that the MPC-implemented operationhas smoother regulation of supplementary fresh air flow, achievesmore stable temperature control, and as a result, has better com-bustion performance and waste heat recovery than the routinecase.
5. Conclusion
Many researchers have demonstrated the advantages of CDQover CWQ in their thermal analysis studies. However, the opera-tional difficulties associated with the control of the supplementaryair flow have long existed without being noticed. The abundanceof historical operational data stored electronically and the avail-ability of several data analysis tools make the operation model-building convenient. In our study, we took an existing large-scale coke-oven plant as the case study subject. We proposed aneural-network-based MPC approach and implemented it in theplant site.
The effectiveness of the proposed MPC approach was verifiedupon comparison with the routine operation. The results showedthat the supplementary air flow was controlled perfectly. It pro-vided sufficient oxygen to make the combustion process complete,but not in excess. Consequently, the steam generation increased by7%. The performance of the proposed MPC approach in the studiedcase was impeccable. The implementation of the MPC approach inan existing CDQ is simple and only requires a plant to collect itsexisting operational data stored electronically and configure in thedistributed control system in the plant. In other words, theimplementation 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 ac-curate; otherwise, effective control is impossible. Our study esti-mates to achieve an economic gain of 1.3 million USD, with anenvironmental gain with regard to CO2 reduction by 2.5 � 103 tonsannually in the studied coke-ovens, for which the annual cokeproduction is currently 1 � 106 tons.
Acknowledgments
The work is partially supported by Ministry of Economic Affairsthrough the grant 102-EC-17-A-09-S1-198, and National ScienceCouncil through the grant NSC 100-2221-E-007-058-MY2,Advanced Manufacturing and Service Management Research
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.
K. Sun et al. / Energy 80 (2015) 275e283 283
Center, National Tsing Hua University, Taiwan (Grant 101N2072E1)and the Shandong Provincial Natural Science Foundation of China(Grant No. ZR2010FQ009).
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