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Stacked autoencoder for operation prediction of coke dry quenching process
Control Engineering Practice 88 (2019) 110–118
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Stacked autoencoder for operation prediction of coke dry quenching process Jian-Guo Wang a ,∗, Yu Wang a , Yuan Yao b ,∗, Bang-Hua Yang a , Shi-Wei Ma a a b
School of Mechatronical Engineering and Automation, Shanghai University, Shanghai Key Lab of Power Station Automation Technology, Shanghai 200072, China Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
ARTICLE
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Keywords: Coke dry quenching Stacked autoencoder Deep learning Economic benefit index Process modeling
ABSTRACT Coke dry quenching (CDQ) is widely adopted for waste heat recovery in iron and steel plants. In this work, an economic benefit index was introduced to evaluate the performance of the CDQ system and stacked autoencoder (SAE) based deep neural networks are adopted for CDQ operation prediction. Based on the prediction results, a guidance is provided for online adjustment of the supplementary air flow rate, hence the efficiency and safety of the CDQ system can be improved. The case study on a real plant shows that the proposed method increases the economic efficiency of the CDQ process by 4.39%.
1. Introduction The steel industry is the most energy-intensive industry in the world, in which energy consumption reduction and environmental protection have become a common issue. Coke quenching is an important process in blast furnace steelmaking. In traditional coking plants, coke wet quenching (CWQ), which causes not only a lot of heat loss but also air pollution problems owing to the steam volatiles, is widely used. Compared with CWQ, coke dry quenching (CDQ) is an advanced, energy-saving, and environmentally friendly technology for coke quenching, where the heat recovery is realized by heat exchange between inert circulation gases and hot coke. After the inert gas absorbs heat from hot coke, it enters the boiler and passes the heat to the boiler to produce high-temperature steam for power generation. In addition to reducing energy consumption, the CDQ technology also improves coke quality and reduces the cost of blast furnace, which makes it even more popular (Errera & Milanez, 2000; Lin, Wang, & Huang, 2009; Liu et al., 2002a; Liu, Zhang, Xu, & Wang, 2002b; Sun et al., 2015; Yang et al., 2009). There have been a number of research works related to the CDQ technology. Errera and Milanez (2000) compared the CDQ and CWQ technologies from the perspective of thermodynamics and demonstrated that CDQ is more effective for recovery. Yang et al. (2009) and Lin et al. (2009) demonstrated the specific benefits of CDQ for improving energy efficiency and reducing CO2 emissions through case studies. Liu et al. (2002a) established a mathematical model for the cooling process of CDQ systems and conducted computational and experimental studies. In Liu et al. (2002b), Liu et al. established a mathematical model of a CDQ device for simulating the relationship between circulation gas flow rates and heat exchange.
The above studies (Errera & Milanez, 2000; Lin et al., 2009; Liu et al., 2002a, 2002b; Yang et al., 2009) mainly focus on the relationship between different variables, such as discharge rate of incandescent coke, temperature, flow rate of circulation gas, boiler steam generation, and power generation in CDQ systems. However, there is little research on online operational control. Sun et al. (2015) proposed a statistical modeling method based on which model predictive control was used to achieve online adjustment of the amount of supplemented air to maximize the steam production of quenching waste heat recovery. However, their research does not consider the effect of coke burning loss on the economic benefit of CDQ system during quenching. The excess air reacts with hot coke, resulting in an increase in coke loss rate and a reduction in economic benefit. Different types of regression models (Zhao, 2014; Zhao, Chunhui, Gao, & Wang, 2010) have been developed in various industrial sections. However, the related works on CDQ operation are not sufficient. In recent years, deep learning techniques have been adopted in various industrial applications (Liu, Fan, & Chen, 2017; Liu, Yang, Gao, & Yao, 2018; Xuan et al., 2018). The concept of deep learning originated from artificial neural networks (Deng & Yu, 2013; Ng et al., 2013). Compared with the traditional machine learning methods, deep neural networks often have more powerful model expression ability and better accuracy. Since first proposed in 2006, deep learning has shown its good performance in the modeling of complex data by using the backpropagation algorithm (Bengio, 2009; Chicco, Sadowski, & Baldi, 2014; Deng & Yu, 2013; LeCun, Bengio, & Hinton, 2015; Schmidhuber, 2015; Wang, Huang, Wang, & Wang). This type of techniques usually has three features, namely, a large number of hidden units, better learning algorithms, and better parameter initialization, ensuring its successful applications (Deng & Yu, 2013). To list some examples,
∗ Corresponding authors. E-mail addresses: [email protected] (J.-G. Wang), [email protected] (Y. Yao).
https://doi.org/10.1016/j.conengprac.2019.04.007 Received 17 October 2018; Received in revised form 19 February 2019; Accepted 26 April 2019 Available online 16 May 2019 0967-0661/© 2019 Elsevier Ltd. All rights reserved.
J.-G. Wang, Y. Wang, Y. Yao et al.
Control Engineering Practice 88 (2019) 110–118 Table 1 Variables in CDQ system.
Shang, Yang, Huang, and Lyu (2014) employed the deep learning technique to estimate the heavy diesel 95% cut point of a crude distillation unit; Hu, Zhang, and Zhou (2016) constructed a transferlearning framework for wind speed prediction based on deep neural networks. In this paper, the coupling relationship between coke loss rate and economic benefits in CDQ system is analyzed. Then, a deep learning regression strategy supplemented with stacked autoencoder (SAE) models for operation prediction of CDQ is proposed, which provides a guidance for online process adjustments. The models are trained based by using the process data collected online by distributed control system (DCS). First, an economic benefit index J is defined, based on which the process data are screened. Then, two models are set up. The first SAE model aims to predict J, which is used to evaluate the process operating performance. The second model is used to predict suitable operating conditions of the CDQ system, which provides a guidance to adjust the supplementary air flow rate online. The remaining of this paper is structured as follows. Section 2 describes the CDQ process and its features in detail. Section 3 presents the introduction of SAE. In Section 4, deep learning models with SAE for the operation prediction and adjustment of the CDQ system is proposed. Furthermore, a verification experiment is presented in Section 5 where the results and the validity analysis are provided. Conclusion remarks are made in Section 6.
No. Variable Description
Unit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Nm3 /h kg/h Nm3 /h Pa Nm3 /h Nm3 /h ◦ C ◦ C ◦C ◦C ◦C % % % % MW MW
𝐹𝑆𝐴 𝑀𝐶 𝐹𝐶𝐺𝑇 𝑃𝐶𝐺𝑇 𝐹𝐵𝐺 𝐹𝐵𝐺 𝑇𝐶 𝑇𝐸 𝑇𝐶𝐺𝐵 𝑇𝐸𝐺𝐵 𝑇𝐶𝐺𝑇 𝐶𝐶𝑂 𝐶𝐻2 𝐶𝐶𝑂2 𝐶𝑂2 𝐸𝑓 𝑎𝑛 E
Supplementary air flow rate Discharge rate of incandescent coke Flow rate of circulation gas returning to the CDQ tower Pressure of circulation gas returning to the CDQ tower Flow rate of bypass circulation gas Flow rate of relieved excess gas to the atmosphere Temperature of incandescent coke Exit temperature of CDQ tower Temperature of circulation gas heading to the boiler Temperature of circulation gas out of the boiler Temperature of circulation gas returning to the CDQ tower 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 Power consumption of circulating fan Power energy production
2.3. Coke burning loss rate 𝑅𝐶𝐿 The circulation inert gas enters the dry quenching furnace from the bottom of the cooling chamber. The gas composition is mostly of nitrogen. Because of the volatilization of the coke and the sealing of the system, the circulation gas also includes CO2 , CO, and H2 . During the operation of the CDQ system, flammable gases in the circulation gas mixture may gradually accumulate and affect the safety of the system. Therefore, the CDQ system needs supplementary air from the air inlet pipe to solve the problem. In addition, heat recovery of flammable gas combustion increases the power generated by the CDQ system. However, if the amount of air is too high, the oxygen in the air may react with the coke, resulting in an increase of coke burning loss rate (𝑅𝐶𝐿 ) and a reduction in the economic efficiency of the CDQ system. The coke burning loss is mainly caused by the chemical reaction between hot coke and O2 or the small amount of water vapor in the furnace. The ratio of the coke burning loss to the total amount of coke is defined as the coke burning loss rate (𝑅𝐶𝐿 ), which is an important indicator for the efficiency of the CDQ system. According to the variables selected in Table 1, 𝑅𝐶𝐿 is calculated by the carbon conservation method. (1) The temperature of the circulation gas exhausted to the atmosphere is approximately 130 ◦ C at the standard atmospheric pressure. The flow rate of the discharging circulation gas to the atmosphere is converted into the gas flow rate at the standard temperature:
2. CDQ system 2.1. Process description The CDQ system, as shown in Fig. 1, comprises a dry quenching furnace (including a pre-store room and a cooling room), a powergenerating boiler, an annular flue, a dust-extraction unit, a circulating fan, and several other units. During the operation of the CDQ system, the red-hot coke (also called incandescent coke, about 1150 ◦ C) produced by a coke-oven plant is charged into the pre-store room from the ceiling of the CDQ furnace through the crane. Then, it is moved from the pre-store room into the cooling room where convective heat exchange takes place with the circulating inert gas entering the bottom of the furnace. In this step, the CDQ system needs to control the red-hot gas pressure in the pre-store room which should be lower than the atmospheric pressure to prevent the gas in the furnace from overflowing into the external environment. Next, the cooled coke (about 200 ◦ C) is discharged from the bottom of the system. The inert gas (about 960 ◦ C) carrying heat energy flows out of the annular flue and passes through the dust catcher device. Finally, the secondary heat exchange happens in the boiler to generate steam, while the cooled inert gas is blown into the dry quenching furnace by the circulation fan for the next circle (Sun et al., 2015). In the CDQ system, if the temperature of the circulation gas flowing from the dry quenching furnace exceeds 980 ◦ C for a long time, the high temperature may cause damage to the annular flue. To avoid this, a bypass gas path is needed to allow the cool gas to flow into the flue at the gas exit of the dry quenching furnace, which keeps the annular flue temperature below 980 ◦ C. In addition, to control the pressure of the circulation gas, a pipeline is used to relieve excess gas to the atmosphere.
𝑉𝑁 = 𝑉𝑜𝑢𝑡 ×
273.15 . 273.15 + 130
(1)
(2) The amount of carbon oxides discharged from the CDQ system to the atmosphere per unit time is as follows: ( ) ( ) 1000 𝑛𝑓 𝑐𝑜 + 𝑐𝑜2 = 𝑉𝑁 × 𝐶𝐶𝑂 + 𝐶𝐶𝑂2 × . (2) 22.4 (3) The amount of carbon dioxide entering the CDQ system from the atmosphere with the supplementary air per unit time is: 𝑛𝑘 𝑐𝑜2 = 𝑉𝑖𝑛 × 0.039% ×
1000 273.15 × . 22.4 273.15 + 20
(3)
(4) Let 𝛿% be the ash content of coke (12.5% in this study); then, the coke burning loss rate is: ( ( ) ) 𝑛𝑓 𝑐𝑜 + 𝑐𝑜2 − 𝑛𝑘 𝑐𝑜2 × 12∕ (1 − 𝛿%) 𝑅𝐶𝐿 = × 100%. (4) 𝑀𝐶 × 10−3
2.2. Variables in CDQ system In the studied CDQ system, 45 process variables measured by DCS in real time are selected as the candidates for modeling, which are then screened by their physical significances and statistical analysis. The information of the selected variables (i.e. variables 1–17) which are marked in Fig. 1 is shown in Table 1, where variables 1–6 are controlled variables and variables 7–17 are state variables.
2.4. Ratio between supplementary air flow rate and coke discharge rate It is noted that both 𝑀𝐶 and 𝐹𝑆𝐴 listed in Table 1 are important operating variables. A single variable 𝐹𝑆𝐴 cannot tell whether the amount 111
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Fig. 1. Schematic flow diagram of the CDQ system.
of supplemental air is reasonable or not. Therefore, in this paper, a derived variable 𝐹𝑆𝐴 ∕𝑀𝐶 is introduced, whose unit is Nm3 ∕1000 kg. The relationships between different variables are shown in Fig. 2. In the CDQ system, the supplementary air flow rate should be positively correlated with the carbon discharge rate. However, such a pattern is not observed in Fig. 2(a), indicating that the control of the supplement airflow rate should be improved. Fig. 2(b) depicts the relationship between 𝐶𝐶𝑂 and 𝐹𝑆𝐴 ∕𝑀𝐶 . 𝐶𝐶𝑂 is high when 𝐹𝑆𝐴 ∕𝑀𝐶 is low, which indicates that 𝐹𝑆𝐴 ∕𝑀𝐶 is a key factor to 𝐶𝐶𝑂 . When 𝐹𝑆𝐴 ∕𝑀𝐶 increases, flammable gases (CO and H2 ) are burned more efficiently, and the power (E) per unit flow of coke increases. At the same time, some of the coke may be burned, resulting in an increase of 𝑅𝐶𝐿 . Figs. 2(c) and (d) demonstrate this relationship. For steel coking plants, the increase of the amount of the supplemental air can generate more power, but, at the same time, the increase in coke burning loss inevitably leads to a reduction in coke acquisition, which negatively affects the overall benefit of the CDQ system. This phenomenon indicates the contradiction between the amount of electricity generated and the rate of coke burning loss.
which are considered to be 86.4 dollar/kWh and 172.8 dollar/ton, respectively, in the case study presented in this paper. The correlation analysis was conducted as shown in Fig. 3. Fig. 3(a) shows the correlation between economic benefit index J and some process variables, while the correlation between J and a number of derived variables is demonstrated in Fig. 3(b). From the comparison between these two figures, it is observed that the correlation among the variables shown in Fig. 3(b) is significantly higher than that shown in Fig. 3(a), indicating the validity of using the derived variable 𝐹𝑆𝐴 ∕𝑀𝐶 in the following modeling step. 3. SAE for CDQ operation prediction In this section, the SAE-based deep learning models for CDQ operation prediction and adjustment are introduced. An SAE is a neural network consisting of multiple layers of sparse autoencoders. In SAE model training, two steps are conducted. (1) The SAE is pre-trained with a greedy layer-wise training algorithm. (2) Then, the network is fine-tuned to find the optimal weights. (Baldi, 2011; Gehring, Miao, Metze, & Waibel, 2013; Hinton & Salakhutdinov, 2006; Hong, Yu, Wan, Tao, & Wang, 2015; Shin, Orton, Collins, Doran, & Leach, 2013; Suk, Lee, & D. Shen, 2015)
2.5. Economic benefit index 𝐽 The analysis above illustrates that the derived variable 𝐹𝑆𝐴 ∕𝑀𝐶 plays an important role in the CDQ system, while 𝑅𝐶𝐿 is also an important indicator. When 𝐹𝑆𝐴 ∕𝑀𝐶 is large, power generation increases, but the corresponding 𝑅𝐶𝐿 increases as well, which is undesirable for the CDQ plant; when 𝐹𝑆𝐴 ∕𝑀𝐶 is small, the corresponding 𝑅𝐶𝐿 decreases, but the incomplete combustion may cause energy dissipation and potential risk during operation. Therefore, keeping 𝐹𝑆𝐴 ∕𝑀𝐶 in a reasonable state has become an important yet difficult task in the operation of the CDQ system. Here, an economic efficiency index (J ) is defined below, which is used to screen the historical process data. In Section 4.3, a prediction model of 𝐹𝑆𝐴 ∕𝑀𝐶 will be developed based on the screened data, according to which suggestions on process operation can be provided. In addition, the index J will be used to evaluate the performance of process operation. Based on Eqs. (1), (2), (3), and (4), the economic benefit per unit mass of coke can be calculated as: ( ) 𝐸 − 𝐸𝑓 𝑎𝑛 × 𝑃𝐸 − 𝑅𝐶𝐿 × 𝑀𝐶 × 10−3 × 𝑃𝐶 (5) 𝐽= 𝑀𝐶 × 10−3
3.1. Autoencoder An autoencoder aims to learn high-level features from its input by minimizing the reconstruction error. Moreover, it is an unsupervised learning algorithm that applies backpropagation. The structure of an autoencoder network is shown in Fig. 4 (Liu et al., 2018; Ng et al., 2013). Usually, an autoencoder is defined by three layers: input layer, hidden layer, and output layer. The input layer is fully connected to the hidden layer which is further fully connected to the output layer. The autoencoder maps the input vector 𝑥 ∈ R𝐼×1 to a latent representation ℎ ∈ R𝐻×1 as follows: ℎ = 𝑓 (𝑊 𝑥 + 𝑏1 )
(6)
where • H and I denote the numbers of hidden and input neurons, respectively.
where 𝐸𝑓 𝑎𝑛 is the electricity consumption of the circulating fan, and 𝑃𝐸 and 𝑃𝐶 can be determined according to the market floating prices,
• W ∈ R𝐻×𝐼 is a weight matrix with H features. 112
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Fig. 2. Scatter plots between variables.
• 𝑏1 ∈ R𝐻 is the bias term.
objective function described in Eq. (8) is called sparse autoencoder, which realizes feature extraction of the input information.
• Regarding the activation function, a sigmoid function 𝑓 (𝑧) = 1 is considered, which is the most widely used in the field (1+exp(−𝑧)) of pattern recognition.
4. CDQ system modeling based on deep learning with SAE Prior to modeling, data processing was performed on the 25,000 data points collected by DCS. The values of some data points are outside the normal operating range because of sensor or equipment failures occurred during operation. These data were removed from the data set before modeling. Therefore, totally 20,200 valid data points were used in this case study.
Then, decoding of ℎ is performed as: 𝑥̂ = 𝑓 (𝑈 T ℎ+𝑏2 )
(7)
where 𝑏2 ∈ R𝐼 is the bias term, and U ∈ R𝐻×𝐼 is the decoding matrix. 3.2. Sparse autoencoder
4.1. Evaluation indices
The cost function J that needs to be optimized to train an autoencoder is defined as follows: 𝑛 𝑚 ∑ ∧ 1∑ 𝐽= ‖𝑥̂ 𝑖 − 𝑥𝑖 ‖22 + 𝜆‖𝑊 ‖22 + 𝛽 𝐾𝐿(𝜌 ∥ 𝜌𝑗 ) 2 𝑖=1 𝑗=1
The model accuracy was evaluated with four criteria: mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The expressions for these indices are given below (Shang et al., 2014):
(8)
In Eq. (8), the first term is the reconstruction error. The second term is used to prevent over-fitting, while the third term is the regularization term to sparse the hidden layer through a Kullback–Leibler (KL) divergence over the training samples (Shin et al., 2013): ⎛ ⎞ ⎛ ⎞ 𝜌 1−𝜌 ⎟ 𝐾𝐿(𝜌 ∥ 𝜌𝑗 ) = 𝜌 log ⎜ ∧ ⎟ + (1 − 𝜌) log ⎜ ∧ ⎟ ⎜ ⎟ ⎜ ⎝ 𝜌𝑗 ⎠ ⎝ 1 − 𝜌𝑗 ⎠ ∧
𝑀𝐴𝐸 =
𝑛 1 ∑| 𝑦 − 𝑦′𝑖 || 𝑛 𝑖=1 | 𝑖
1∑ (𝑦 − 𝑦′𝑖 )2 𝑛 𝑖=1 𝑖 √ √ 𝑛 √1 ∑ 𝑅𝑀𝑆𝐸 = √ (𝑦 − 𝑦′𝑖 )2 𝑛 𝑖=1 𝑖
(10)
𝑛
𝑀𝑆𝐸 =
(9)
∧
where 𝜌𝑗 is the average activation of the jth hidden neuron. This term leads to a sparse hidden layer. That is why it is often called ‘‘the sparsity regularization’’. As a result, the autoencoder trained by minimizing the
𝑀𝐴𝑃 𝐸 = 113
′| 𝑛 | 1 ∑ ||𝑦𝑖 − 𝑦𝑖 || × 100% 𝑛 𝑖=1 𝑦𝑖
(11)
(12)
(13)
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Control Engineering Practice 88 (2019) 110–118
Fig. 3. Comparison of variable correlation coefficients.
Fig. 4. Autoencoder neural network.
where n is the sample size, 𝑦𝑖 is the ith true value, and 𝑦′𝑖 is the ith predicted value.
good generalization performance. Fig. 5 shows the prediction results of the above-mentioned four models on the test data set. It can be seen that the SAE model performs best.
4.2. Modeling of economic benefit index 𝐽 4.3. Modeling of 𝐹𝑆𝐴 ∕𝑀𝐶 For the prediction of the economic benefit index J, an SAE model was set up, where the model input includes the 17 variables listed in Table 1 with 𝐹𝑆𝐴 replaced by 𝐹𝑆𝐴 ∕𝑀𝐶 . This model can be used for evaluating the process operating performance. Besides SAE, three different modeling methods were adopted in the comparative analysis, including single hidden layer neural networks (SLFN), non-negative garrote (NNG), and partial least squares (PLS). Among them, PLS is one of the most commonly used linear regression model, NNG is powerful in variable selection, and SLFN is a popular nonlinear model. In this experiment, the deep learning model with SAE (further also called SAE for short) selected two hidden layers, and the numbers of neurons used in different layers were determined to be 17-30-30-1, respectively, by cross-validation. For SLFN, the number of neurons in the hidden layer was selected as 35. The 20,200 data points were divided into two sets, including 18,200 points were used for model training and the remaining 2000 served as test data. Training and prediction results of different modeling methods are shown in Table 2. In terms of training data, nonlinear models (SAE and SLFN) show obviously better fitting ability compared to the linear models (NNG and PLS). For test data, SAE model has a better prediction accuracy than the other three models, which indicates a
From the description in Section 2.4, it can be seen that the ratio between 𝐹𝑆𝐴 and 𝑀𝐶 is important. According to the changes in 𝑀𝐶 , the values of 𝐹𝑆𝐴 have an important impact on the efficiency and safety of the CDQ system. A reasonable control strategy of 𝐹𝑆𝐴 should consider not only the effects of other controlled variables but also the influences by the state variables such as CO and O2 concentrations in the device. As a result, the human experience-based control is usually difficult to ensure that the performance of the CDQ system. Here, a second prediction model is proposed to provide an operating guidance for online adjustment of 𝐹𝑆𝐴 , which takes 𝐹𝑆𝐴 ∕𝑀𝐶 as the model output and the process variables listed in Table 1 expect 𝐹𝑆𝐴 as input. Before modeling, the 20,200 historical data points were screened according to the economic benefit index (J ). According to the expert experience, the time periods with J values larger than 3.25 are considered to be with proper process operation. Hence, 5684 data points collected during these time periods were selected based on such a criterion, which were used as the labeled data for model training and test. Among these 5684 labeled samples, 5184 were included in the training set and the other 500 were adopted for model test. During the model training step, the 14,516 data points corresponding to J smaller 114
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Table 2 Training and prediction results of different modeling methods for J .
Fig. 5. Comparison of original and predicted J with different modeling methods.
than 3.5 were used as the unlabeled data to pre-train the SAE with the
provide a good initialization for fine-tuning. In this sense, the deep
greedy layer-wise training (GLT) algorithm (Suk et al., 2015). Then,
learning model fully utilizes the information contained in the data. In
the deep neural network was fine-tuned with 5184 labeled samples
this case study, the network structure of the SAE is 16-30-30-1, where
by backpropagation. The weights obtained from the pre-training step
the numbers indicate the amounts of neurons used in different layers. 115
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Fig. 6. Comparison of original and predicted 𝐹𝑆𝐴 ∕𝑀𝐶 with different modeling methods.
efficiency index J using the model introduced Section 4.2. By comparing the predicted J values based on the suggested operation with the original J values, the feasibility of the developed method is well illustrated. In the study described in this section, 1000 samples randomly selected from the 20,200 data points were used as the test data for comparative analysis. The effects of the suggested operation by the model proposed in Section 4.3 are shown in Fig. 7. Fig. 7(a) plots the original values and the model suggestions of 𝐹𝑆𝐴 ∕𝑀𝐶 , while Fig. 7(b) compares the J values achieved by the original operating conditions and the predicted J values obtained by following the model-suggested operation. It can be seen that the suggested operation significantly improves the economic efficiency of the CDQ system. The average improvement is about 4.23%.
The training and prediction results of different methods are shown in Table 3, while Fig. 6 shows the model predictions on the 500 test data. Clearly, the SAE-based deep neural network model is superior to the traditional modeling methods used in comparison. Although the shallow neural network model, i.e. SLFN, has a strong fitting ability for nonlinear systems, its generalization ability for the test data is not good enough. This is because the SAE uses the greedy layer-wise training method for pre-training. In this process, the utilization of the 14,516 unlabeled samples makes the weights in the network initialized better in the parameter space than random allocation. 4.4. Operating performance analysis According to the 𝐹𝑆𝐴 ∕𝑀𝐶 prediction model proposed in Section 4.3, the suggested value of 𝐹𝑆𝐴 can be obtained at each time point for guiding the adjustment of the supplementary air flow rate. Then, the operating performance can be evaluated by calculating the economic
5. Experimental verification The SAE model proposed in Section 4.3 and the suggested process operation which adjusts 𝐹𝑆𝐴 ∕𝑀𝐶 online (denoted as ‘‘model-guided 116
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Fig. 7. Comparison of original operation and suggested operation.
Fig. 8. Trajectories of 𝐹𝑆𝐴 ∕𝑀𝐶 and J under model-guided control. Table 3 Training and prediction results of different modeling methods for 𝐹𝑆𝐴 ∕𝑀𝐶 .
control’’ in the following of this section) were applied to a real industrial CDQ system. The one-day average of each process variable
collected by DCS is shown in Table 4. Clearly, after conducting the
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Table 4 Performance comparison between manual control and model-guided control. No.
Variables
Manual control
Model-guided control
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
𝐹𝑆𝐴 (Nm3 /h) 𝑀𝐶 (kg/h) 𝐹𝐶𝐺𝑇 (Nm3 /h) 𝑃𝐶𝐺𝑇 (Pa) 𝐹𝐵𝐺 (Nm3 /h) 𝐹𝐸𝐺 (Nm3 /h) 𝑇𝐶 (◦ C) 𝑇𝐸 (◦ C) 𝑇𝐶𝐺𝐵 (◦ C) 𝑇𝐸𝐺𝐵 (◦ C) 𝑇𝐶𝐺𝑇 (◦ C) 𝐶𝐶𝑂 (%) 𝐶𝐻2 (%) 𝐶𝐶𝑂2 (%) 𝐶𝑂2 (%) 𝐸𝑓 𝑎𝑛 (KW) 𝐸 (MW) 𝐹𝑆𝐴 ∕𝑀𝐶 (Nm3 ∕1000𝑘𝑔) J ($/1000 kg)
15,519.4 145900 202,524.3 4192.4 2191.9 14,302.9 1083.7 148.2 967.4 155.6 121.2 1.09 0.21 17.11 0.22 262.8 7.2 106.4 3.1049
16,897.8 145600 216,688.6 4553.1 7641.8 15,897.2 1085.4 189.9 977.3 158.5 122.4 0.75 0.14 15.20 0.19 279.2 7.5 116.1 3.2413
Declaration of competing interest None declared. References Baldi, P (2011). Autoencoders, unsupervised learning and deep architectures. In Proceedings of the 2011 international conference on unsupervised and transfer learning workshop: Vol. 27, (pp. 37–50). Bengio, Y. (2009). Learning deep architectures for AI (pp. 1–127). Now Foundations and Trends. Chicco, D., Sadowski, P., & Baldi, P. (2014). Deep autoencoder neural networks for gene ontology annotation predictions. In Proceedings of the 5th ACM conference on bioinformatics, computational biology, and health informatics (pp. 533–540). Newport Beach, California: ACM. Deng, L., & Yu, D. (2013). Deep learning for signal and information processing (pp. 1–134). Now Publishers. Errera, Marcelo Risso., & Milanez, Luiz Fernando (2000). Thermodynamic analysis of a coke dry quenching unit. Energy Convers Management, 41, 109–127. Gehring, J., Miao, Y., Metze, F., & Waibel, A. (2013). Extracting deep bottleneck features using stacked auto-encoders. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 3377–3381). Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313, 504. Hong, C., Yu, J., Wan, J., Tao, D., & Wang, M. (2015). Multimodal deep autoencoder for human pose recovery. IEEE Transactions on Image Processing, 24, 5659–5670. Hu, Q., Zhang, R., & Zhou, Y. (2016). Transfer learning for short-term wind speed prediction with deep neural networks. Renewable Energy, 85, 83–95. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. Lin, P. H., Wang, P. H., & Huang, A. (2009). Exergy analysis of a coke dry quenching system: China Steel Technical Report 22, (pp. 63–67). Liu, Yi, Fan, Yu, & Chen, Junghui (2017). Flame images for oxygen content prediction of combustion systems using DBN. Energy & Fuels, 31, 8776–8783. Liu, Yi, Yang, Chao, Gao, Zengliang, & Yao, Yuan (2018). Ensemble deep kernel learning model with application to quality prediction in industrial polymerization processes. Chemometrics and Intelligent Laboratory Systems, 174, 15–21. Liu, H., Zhang, X., Wu, M., Feng, Y., Wang, Y., Wang, D., et al. (2002a). Computational and experimental study of cooling process in coke dry quenching experimental shaft. Journal of Thermal Stresses, 11, 121–127. Liu, H., Zhang, X., Xu, L., & Wang, M. (2002b). Mathematical model for fluid flow and heat transfer in the cooling shaft of coke dry quenching unit. Journal of Thermal Stresses, 11, 65–73. Ng, Andrew, Foo, Chuan Yu, Mai, Yifan, Suen, Caroline, Coates, Adam, Maas, Andrew, et al. (2013). UFLDL tutorial. http://deeplearning.stanford.edu/tutorial/. (Accessed 9 August 2018). Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. Shang, C., Yang, F., Huang, D., & Lyu, W. (2014). Data-driven soft sensor development based on deep learning technique. Journal of Process Control, 24, 223–233. Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J., & Leach, M. O. (2013). Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1930–1943. Suk, H.-I., Lee, S.-W., & D. Shen, I. (2015). The Alzheimer’s disease neuroimaging, latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Structure and Function, 220, 841–859. Sun, K., Tseng, C.-T., Wong, D. S. H., Shieh, S.-S., Jang, S.-S., Kang, J.-L., et al. (2015). Model predictive control for improving waste heat recovery in coke dry quenching processes. Energy, 80, 275–283. Wang, W., Huang, Y., Wang, Y., & Wang, L. (2014). Generalized autoencoder: A neural network framework for dimensionality reduction. In 2014 IEEE conference on computer vision and pattern recognition workshops (pp. 496–503). Xuan, Qi, Fang, Binwei, Liu, Yi, Wang, Jinbao, Zhang, Jian, Zheng, Yayu, et al. (2018). Automatic pearl classification machine based on a multistream convolutional neural network. IEEE Transactions on Industrial Electronics, 65, 6538–6547. Yang, W., Xu, T., Chen, G., Guo, Y.-j., Jia, L.-y., & Li, W. (2009). Coke dry quenching and utilization of its waste heat for electricity in iron and steel industry. In 2009 2nd international conference on power electronics and intelligent transportation system, PEITS: Vol. 2, (pp. 227–230). Zhao, C. (2014). A quality-relevant sequential phase partition approach for regression modeling and quality prediction analysis in manufacturing processes. IEEE Transactions on Automation Science and Engineering, 11, 983–991. Zhao, Chunhui, Gao, F., & Wang, F. (2010). An improved independent component regression modeling and quantitative calibration procedure. AIChE Journal, 56, 1519–1526.
model-guided control, some key performance indicators of the CDQ system, such as 𝐹𝑆𝐴 , 𝐹𝐶𝐺𝑇 , and 𝑇𝐶𝐺𝐵 , were improved significantly. Meanwhile, the amounts of 𝐶𝐶𝑂 , 𝐶𝐶𝑂2 , and 𝐶𝐻2 were lower than in the conventional operation. As a result, the security of CDQ system has been enhanced. After changing the control mode, the mean value of 𝐹𝑆𝐴 ∕𝑀𝐶 was increased from 106.4 Nm3 ∕1000 kg to 116.1 Nm3 ∕1000 kg. The trajectory of 𝐹𝑆𝐴 ∕𝑀𝐶 is shown in Fig. 8(a). It is observed that 𝐹𝑆𝐴 ∕𝑀𝐶 is manipulated at around 105 Nm3 ∕1000 kg. Fig. 8(b) shows the economic benefit index J. In the collected 288 sample points, the mean value of the economic benefit index was 3.2413 $/1000 kg. Compared with 3.1049 $/1000 kg achieved without the model-guided control, there was a 4.39% improvement; that is to say, a coke oven with a capacity of 100,000 kg/h may have a total revenue increase of about 120,000 dollars per year. 6. Conclusions This paper introduces an economic benefit index for the CDQ system, proposes a data-driven modeling method based on deep learning with SAE, and applies the prediction model to guide the CDQ control system. The advantages of SAE were analyzed through the comparison with the traditional modeling methods. The energy conversion process of the CDQ system has a coupling relationship between power generation and burning loss rate. Aiming at this characteristic, a method for data screening according to the CDQ economic benefit index J was proposed to select training data set for SAE modeling of 𝐹𝑆𝐴 ∕𝑀𝐶 . Based on this model, suggestions on the manipulation of 𝐹𝑆𝐴 are provided to realize a model-guided control of the CDQ system. Finally, the proposed strategy was applied to a real industrial CDQ system. Experimental results show that the process operating performance was significantly improved. The economic benefit index J was increase by 4.39%, which demonstrates the effectiveness of the proposed method. Acknowledgment Yao was supported in part by Ministry of Science and Technology, ROC under Grant No. MOST 107-2622-8-007-015. Wang was supported by Special Project on Industrial Transformation and Upgrading (Made in China 2025) in 2017 (No. TC17085JH). Yang was supported by Innovation Project of Shanghai Science and Technology Committee (No. 18411952200) and Key Research & Development Project of National Science and Technology Ministry of China (No. 2018YFC1312903).
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Control Engineering Practice journal homepage: www.elsevier.com/locate/conengprac
Stacked autoencoder for operation prediction of coke dry quenching process Jian-Guo Wang a ,∗, Yu Wang a , Yuan Yao b ,∗, Bang-Hua Yang a , Shi-Wei Ma a a b
School of Mechatronical Engineering and Automation, Shanghai University, Shanghai Key Lab of Power Station Automation Technology, Shanghai 200072, China Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
ARTICLE
INFO
Keywords: Coke dry quenching Stacked autoencoder Deep learning Economic benefit index Process modeling
ABSTRACT Coke dry quenching (CDQ) is widely adopted for waste heat recovery in iron and steel plants. In this work, an economic benefit index was introduced to evaluate the performance of the CDQ system and stacked autoencoder (SAE) based deep neural networks are adopted for CDQ operation prediction. Based on the prediction results, a guidance is provided for online adjustment of the supplementary air flow rate, hence the efficiency and safety of the CDQ system can be improved. The case study on a real plant shows that the proposed method increases the economic efficiency of the CDQ process by 4.39%.
1. Introduction The steel industry is the most energy-intensive industry in the world, in which energy consumption reduction and environmental protection have become a common issue. Coke quenching is an important process in blast furnace steelmaking. In traditional coking plants, coke wet quenching (CWQ), which causes not only a lot of heat loss but also air pollution problems owing to the steam volatiles, is widely used. Compared with CWQ, coke dry quenching (CDQ) is an advanced, energy-saving, and environmentally friendly technology for coke quenching, where the heat recovery is realized by heat exchange between inert circulation gases and hot coke. After the inert gas absorbs heat from hot coke, it enters the boiler and passes the heat to the boiler to produce high-temperature steam for power generation. In addition to reducing energy consumption, the CDQ technology also improves coke quality and reduces the cost of blast furnace, which makes it even more popular (Errera & Milanez, 2000; Lin, Wang, & Huang, 2009; Liu et al., 2002a; Liu, Zhang, Xu, & Wang, 2002b; Sun et al., 2015; Yang et al., 2009). There have been a number of research works related to the CDQ technology. Errera and Milanez (2000) compared the CDQ and CWQ technologies from the perspective of thermodynamics and demonstrated that CDQ is more effective for recovery. Yang et al. (2009) and Lin et al. (2009) demonstrated the specific benefits of CDQ for improving energy efficiency and reducing CO2 emissions through case studies. Liu et al. (2002a) established a mathematical model for the cooling process of CDQ systems and conducted computational and experimental studies. In Liu et al. (2002b), Liu et al. established a mathematical model of a CDQ device for simulating the relationship between circulation gas flow rates and heat exchange.
The above studies (Errera & Milanez, 2000; Lin et al., 2009; Liu et al., 2002a, 2002b; Yang et al., 2009) mainly focus on the relationship between different variables, such as discharge rate of incandescent coke, temperature, flow rate of circulation gas, boiler steam generation, and power generation in CDQ systems. However, there is little research on online operational control. Sun et al. (2015) proposed a statistical modeling method based on which model predictive control was used to achieve online adjustment of the amount of supplemented air to maximize the steam production of quenching waste heat recovery. However, their research does not consider the effect of coke burning loss on the economic benefit of CDQ system during quenching. The excess air reacts with hot coke, resulting in an increase in coke loss rate and a reduction in economic benefit. Different types of regression models (Zhao, 2014; Zhao, Chunhui, Gao, & Wang, 2010) have been developed in various industrial sections. However, the related works on CDQ operation are not sufficient. In recent years, deep learning techniques have been adopted in various industrial applications (Liu, Fan, & Chen, 2017; Liu, Yang, Gao, & Yao, 2018; Xuan et al., 2018). The concept of deep learning originated from artificial neural networks (Deng & Yu, 2013; Ng et al., 2013). Compared with the traditional machine learning methods, deep neural networks often have more powerful model expression ability and better accuracy. Since first proposed in 2006, deep learning has shown its good performance in the modeling of complex data by using the backpropagation algorithm (Bengio, 2009; Chicco, Sadowski, & Baldi, 2014; Deng & Yu, 2013; LeCun, Bengio, & Hinton, 2015; Schmidhuber, 2015; Wang, Huang, Wang, & Wang). This type of techniques usually has three features, namely, a large number of hidden units, better learning algorithms, and better parameter initialization, ensuring its successful applications (Deng & Yu, 2013). To list some examples,
∗ Corresponding authors. E-mail addresses: [email protected] (J.-G. Wang), [email protected] (Y. Yao).
https://doi.org/10.1016/j.conengprac.2019.04.007 Received 17 October 2018; Received in revised form 19 February 2019; Accepted 26 April 2019 Available online 16 May 2019 0967-0661/© 2019 Elsevier Ltd. All rights reserved.
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Control Engineering Practice 88 (2019) 110–118 Table 1 Variables in CDQ system.
Shang, Yang, Huang, and Lyu (2014) employed the deep learning technique to estimate the heavy diesel 95% cut point of a crude distillation unit; Hu, Zhang, and Zhou (2016) constructed a transferlearning framework for wind speed prediction based on deep neural networks. In this paper, the coupling relationship between coke loss rate and economic benefits in CDQ system is analyzed. Then, a deep learning regression strategy supplemented with stacked autoencoder (SAE) models for operation prediction of CDQ is proposed, which provides a guidance for online process adjustments. The models are trained based by using the process data collected online by distributed control system (DCS). First, an economic benefit index J is defined, based on which the process data are screened. Then, two models are set up. The first SAE model aims to predict J, which is used to evaluate the process operating performance. The second model is used to predict suitable operating conditions of the CDQ system, which provides a guidance to adjust the supplementary air flow rate online. The remaining of this paper is structured as follows. Section 2 describes the CDQ process and its features in detail. Section 3 presents the introduction of SAE. In Section 4, deep learning models with SAE for the operation prediction and adjustment of the CDQ system is proposed. Furthermore, a verification experiment is presented in Section 5 where the results and the validity analysis are provided. Conclusion remarks are made in Section 6.
No. Variable Description
Unit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Nm3 /h kg/h Nm3 /h Pa Nm3 /h Nm3 /h ◦ C ◦ C ◦C ◦C ◦C % % % % MW MW
𝐹𝑆𝐴 𝑀𝐶 𝐹𝐶𝐺𝑇 𝑃𝐶𝐺𝑇 𝐹𝐵𝐺 𝐹𝐵𝐺 𝑇𝐶 𝑇𝐸 𝑇𝐶𝐺𝐵 𝑇𝐸𝐺𝐵 𝑇𝐶𝐺𝑇 𝐶𝐶𝑂 𝐶𝐻2 𝐶𝐶𝑂2 𝐶𝑂2 𝐸𝑓 𝑎𝑛 E
Supplementary air flow rate Discharge rate of incandescent coke Flow rate of circulation gas returning to the CDQ tower Pressure of circulation gas returning to the CDQ tower Flow rate of bypass circulation gas Flow rate of relieved excess gas to the atmosphere Temperature of incandescent coke Exit temperature of CDQ tower Temperature of circulation gas heading to the boiler Temperature of circulation gas out of the boiler Temperature of circulation gas returning to the CDQ tower 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 Power consumption of circulating fan Power energy production
2.3. Coke burning loss rate 𝑅𝐶𝐿 The circulation inert gas enters the dry quenching furnace from the bottom of the cooling chamber. The gas composition is mostly of nitrogen. Because of the volatilization of the coke and the sealing of the system, the circulation gas also includes CO2 , CO, and H2 . During the operation of the CDQ system, flammable gases in the circulation gas mixture may gradually accumulate and affect the safety of the system. Therefore, the CDQ system needs supplementary air from the air inlet pipe to solve the problem. In addition, heat recovery of flammable gas combustion increases the power generated by the CDQ system. However, if the amount of air is too high, the oxygen in the air may react with the coke, resulting in an increase of coke burning loss rate (𝑅𝐶𝐿 ) and a reduction in the economic efficiency of the CDQ system. The coke burning loss is mainly caused by the chemical reaction between hot coke and O2 or the small amount of water vapor in the furnace. The ratio of the coke burning loss to the total amount of coke is defined as the coke burning loss rate (𝑅𝐶𝐿 ), which is an important indicator for the efficiency of the CDQ system. According to the variables selected in Table 1, 𝑅𝐶𝐿 is calculated by the carbon conservation method. (1) The temperature of the circulation gas exhausted to the atmosphere is approximately 130 ◦ C at the standard atmospheric pressure. The flow rate of the discharging circulation gas to the atmosphere is converted into the gas flow rate at the standard temperature:
2. CDQ system 2.1. Process description The CDQ system, as shown in Fig. 1, comprises a dry quenching furnace (including a pre-store room and a cooling room), a powergenerating boiler, an annular flue, a dust-extraction unit, a circulating fan, and several other units. During the operation of the CDQ system, the red-hot coke (also called incandescent coke, about 1150 ◦ C) produced by a coke-oven plant is charged into the pre-store room from the ceiling of the CDQ furnace through the crane. Then, it is moved from the pre-store room into the cooling room where convective heat exchange takes place with the circulating inert gas entering the bottom of the furnace. In this step, the CDQ system needs to control the red-hot gas pressure in the pre-store room which should be lower than the atmospheric pressure to prevent the gas in the furnace from overflowing into the external environment. Next, the cooled coke (about 200 ◦ C) is discharged from the bottom of the system. The inert gas (about 960 ◦ C) carrying heat energy flows out of the annular flue and passes through the dust catcher device. Finally, the secondary heat exchange happens in the boiler to generate steam, while the cooled inert gas is blown into the dry quenching furnace by the circulation fan for the next circle (Sun et al., 2015). In the CDQ system, if the temperature of the circulation gas flowing from the dry quenching furnace exceeds 980 ◦ C for a long time, the high temperature may cause damage to the annular flue. To avoid this, a bypass gas path is needed to allow the cool gas to flow into the flue at the gas exit of the dry quenching furnace, which keeps the annular flue temperature below 980 ◦ C. In addition, to control the pressure of the circulation gas, a pipeline is used to relieve excess gas to the atmosphere.
𝑉𝑁 = 𝑉𝑜𝑢𝑡 ×
273.15 . 273.15 + 130
(1)
(2) The amount of carbon oxides discharged from the CDQ system to the atmosphere per unit time is as follows: ( ) ( ) 1000 𝑛𝑓 𝑐𝑜 + 𝑐𝑜2 = 𝑉𝑁 × 𝐶𝐶𝑂 + 𝐶𝐶𝑂2 × . (2) 22.4 (3) The amount of carbon dioxide entering the CDQ system from the atmosphere with the supplementary air per unit time is: 𝑛𝑘 𝑐𝑜2 = 𝑉𝑖𝑛 × 0.039% ×
1000 273.15 × . 22.4 273.15 + 20
(3)
(4) Let 𝛿% be the ash content of coke (12.5% in this study); then, the coke burning loss rate is: ( ( ) ) 𝑛𝑓 𝑐𝑜 + 𝑐𝑜2 − 𝑛𝑘 𝑐𝑜2 × 12∕ (1 − 𝛿%) 𝑅𝐶𝐿 = × 100%. (4) 𝑀𝐶 × 10−3
2.2. Variables in CDQ system In the studied CDQ system, 45 process variables measured by DCS in real time are selected as the candidates for modeling, which are then screened by their physical significances and statistical analysis. The information of the selected variables (i.e. variables 1–17) which are marked in Fig. 1 is shown in Table 1, where variables 1–6 are controlled variables and variables 7–17 are state variables.
2.4. Ratio between supplementary air flow rate and coke discharge rate It is noted that both 𝑀𝐶 and 𝐹𝑆𝐴 listed in Table 1 are important operating variables. A single variable 𝐹𝑆𝐴 cannot tell whether the amount 111
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Fig. 1. Schematic flow diagram of the CDQ system.
of supplemental air is reasonable or not. Therefore, in this paper, a derived variable 𝐹𝑆𝐴 ∕𝑀𝐶 is introduced, whose unit is Nm3 ∕1000 kg. The relationships between different variables are shown in Fig. 2. In the CDQ system, the supplementary air flow rate should be positively correlated with the carbon discharge rate. However, such a pattern is not observed in Fig. 2(a), indicating that the control of the supplement airflow rate should be improved. Fig. 2(b) depicts the relationship between 𝐶𝐶𝑂 and 𝐹𝑆𝐴 ∕𝑀𝐶 . 𝐶𝐶𝑂 is high when 𝐹𝑆𝐴 ∕𝑀𝐶 is low, which indicates that 𝐹𝑆𝐴 ∕𝑀𝐶 is a key factor to 𝐶𝐶𝑂 . When 𝐹𝑆𝐴 ∕𝑀𝐶 increases, flammable gases (CO and H2 ) are burned more efficiently, and the power (E) per unit flow of coke increases. At the same time, some of the coke may be burned, resulting in an increase of 𝑅𝐶𝐿 . Figs. 2(c) and (d) demonstrate this relationship. For steel coking plants, the increase of the amount of the supplemental air can generate more power, but, at the same time, the increase in coke burning loss inevitably leads to a reduction in coke acquisition, which negatively affects the overall benefit of the CDQ system. This phenomenon indicates the contradiction between the amount of electricity generated and the rate of coke burning loss.
which are considered to be 86.4 dollar/kWh and 172.8 dollar/ton, respectively, in the case study presented in this paper. The correlation analysis was conducted as shown in Fig. 3. Fig. 3(a) shows the correlation between economic benefit index J and some process variables, while the correlation between J and a number of derived variables is demonstrated in Fig. 3(b). From the comparison between these two figures, it is observed that the correlation among the variables shown in Fig. 3(b) is significantly higher than that shown in Fig. 3(a), indicating the validity of using the derived variable 𝐹𝑆𝐴 ∕𝑀𝐶 in the following modeling step. 3. SAE for CDQ operation prediction In this section, the SAE-based deep learning models for CDQ operation prediction and adjustment are introduced. An SAE is a neural network consisting of multiple layers of sparse autoencoders. In SAE model training, two steps are conducted. (1) The SAE is pre-trained with a greedy layer-wise training algorithm. (2) Then, the network is fine-tuned to find the optimal weights. (Baldi, 2011; Gehring, Miao, Metze, & Waibel, 2013; Hinton & Salakhutdinov, 2006; Hong, Yu, Wan, Tao, & Wang, 2015; Shin, Orton, Collins, Doran, & Leach, 2013; Suk, Lee, & D. Shen, 2015)
2.5. Economic benefit index 𝐽 The analysis above illustrates that the derived variable 𝐹𝑆𝐴 ∕𝑀𝐶 plays an important role in the CDQ system, while 𝑅𝐶𝐿 is also an important indicator. When 𝐹𝑆𝐴 ∕𝑀𝐶 is large, power generation increases, but the corresponding 𝑅𝐶𝐿 increases as well, which is undesirable for the CDQ plant; when 𝐹𝑆𝐴 ∕𝑀𝐶 is small, the corresponding 𝑅𝐶𝐿 decreases, but the incomplete combustion may cause energy dissipation and potential risk during operation. Therefore, keeping 𝐹𝑆𝐴 ∕𝑀𝐶 in a reasonable state has become an important yet difficult task in the operation of the CDQ system. Here, an economic efficiency index (J ) is defined below, which is used to screen the historical process data. In Section 4.3, a prediction model of 𝐹𝑆𝐴 ∕𝑀𝐶 will be developed based on the screened data, according to which suggestions on process operation can be provided. In addition, the index J will be used to evaluate the performance of process operation. Based on Eqs. (1), (2), (3), and (4), the economic benefit per unit mass of coke can be calculated as: ( ) 𝐸 − 𝐸𝑓 𝑎𝑛 × 𝑃𝐸 − 𝑅𝐶𝐿 × 𝑀𝐶 × 10−3 × 𝑃𝐶 (5) 𝐽= 𝑀𝐶 × 10−3
3.1. Autoencoder An autoencoder aims to learn high-level features from its input by minimizing the reconstruction error. Moreover, it is an unsupervised learning algorithm that applies backpropagation. The structure of an autoencoder network is shown in Fig. 4 (Liu et al., 2018; Ng et al., 2013). Usually, an autoencoder is defined by three layers: input layer, hidden layer, and output layer. The input layer is fully connected to the hidden layer which is further fully connected to the output layer. The autoencoder maps the input vector 𝑥 ∈ R𝐼×1 to a latent representation ℎ ∈ R𝐻×1 as follows: ℎ = 𝑓 (𝑊 𝑥 + 𝑏1 )
(6)
where • H and I denote the numbers of hidden and input neurons, respectively.
where 𝐸𝑓 𝑎𝑛 is the electricity consumption of the circulating fan, and 𝑃𝐸 and 𝑃𝐶 can be determined according to the market floating prices,
• W ∈ R𝐻×𝐼 is a weight matrix with H features. 112
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Fig. 2. Scatter plots between variables.
• 𝑏1 ∈ R𝐻 is the bias term.
objective function described in Eq. (8) is called sparse autoencoder, which realizes feature extraction of the input information.
• Regarding the activation function, a sigmoid function 𝑓 (𝑧) = 1 is considered, which is the most widely used in the field (1+exp(−𝑧)) of pattern recognition.
4. CDQ system modeling based on deep learning with SAE Prior to modeling, data processing was performed on the 25,000 data points collected by DCS. The values of some data points are outside the normal operating range because of sensor or equipment failures occurred during operation. These data were removed from the data set before modeling. Therefore, totally 20,200 valid data points were used in this case study.
Then, decoding of ℎ is performed as: 𝑥̂ = 𝑓 (𝑈 T ℎ+𝑏2 )
(7)
where 𝑏2 ∈ R𝐼 is the bias term, and U ∈ R𝐻×𝐼 is the decoding matrix. 3.2. Sparse autoencoder
4.1. Evaluation indices
The cost function J that needs to be optimized to train an autoencoder is defined as follows: 𝑛 𝑚 ∑ ∧ 1∑ 𝐽= ‖𝑥̂ 𝑖 − 𝑥𝑖 ‖22 + 𝜆‖𝑊 ‖22 + 𝛽 𝐾𝐿(𝜌 ∥ 𝜌𝑗 ) 2 𝑖=1 𝑗=1
The model accuracy was evaluated with four criteria: mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The expressions for these indices are given below (Shang et al., 2014):
(8)
In Eq. (8), the first term is the reconstruction error. The second term is used to prevent over-fitting, while the third term is the regularization term to sparse the hidden layer through a Kullback–Leibler (KL) divergence over the training samples (Shin et al., 2013): ⎛ ⎞ ⎛ ⎞ 𝜌 1−𝜌 ⎟ 𝐾𝐿(𝜌 ∥ 𝜌𝑗 ) = 𝜌 log ⎜ ∧ ⎟ + (1 − 𝜌) log ⎜ ∧ ⎟ ⎜ ⎟ ⎜ ⎝ 𝜌𝑗 ⎠ ⎝ 1 − 𝜌𝑗 ⎠ ∧
𝑀𝐴𝐸 =
𝑛 1 ∑| 𝑦 − 𝑦′𝑖 || 𝑛 𝑖=1 | 𝑖
1∑ (𝑦 − 𝑦′𝑖 )2 𝑛 𝑖=1 𝑖 √ √ 𝑛 √1 ∑ 𝑅𝑀𝑆𝐸 = √ (𝑦 − 𝑦′𝑖 )2 𝑛 𝑖=1 𝑖
(10)
𝑛
𝑀𝑆𝐸 =
(9)
∧
where 𝜌𝑗 is the average activation of the jth hidden neuron. This term leads to a sparse hidden layer. That is why it is often called ‘‘the sparsity regularization’’. As a result, the autoencoder trained by minimizing the
𝑀𝐴𝑃 𝐸 = 113
′| 𝑛 | 1 ∑ ||𝑦𝑖 − 𝑦𝑖 || × 100% 𝑛 𝑖=1 𝑦𝑖
(11)
(12)
(13)
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Fig. 3. Comparison of variable correlation coefficients.
Fig. 4. Autoencoder neural network.
where n is the sample size, 𝑦𝑖 is the ith true value, and 𝑦′𝑖 is the ith predicted value.
good generalization performance. Fig. 5 shows the prediction results of the above-mentioned four models on the test data set. It can be seen that the SAE model performs best.
4.2. Modeling of economic benefit index 𝐽 4.3. Modeling of 𝐹𝑆𝐴 ∕𝑀𝐶 For the prediction of the economic benefit index J, an SAE model was set up, where the model input includes the 17 variables listed in Table 1 with 𝐹𝑆𝐴 replaced by 𝐹𝑆𝐴 ∕𝑀𝐶 . This model can be used for evaluating the process operating performance. Besides SAE, three different modeling methods were adopted in the comparative analysis, including single hidden layer neural networks (SLFN), non-negative garrote (NNG), and partial least squares (PLS). Among them, PLS is one of the most commonly used linear regression model, NNG is powerful in variable selection, and SLFN is a popular nonlinear model. In this experiment, the deep learning model with SAE (further also called SAE for short) selected two hidden layers, and the numbers of neurons used in different layers were determined to be 17-30-30-1, respectively, by cross-validation. For SLFN, the number of neurons in the hidden layer was selected as 35. The 20,200 data points were divided into two sets, including 18,200 points were used for model training and the remaining 2000 served as test data. Training and prediction results of different modeling methods are shown in Table 2. In terms of training data, nonlinear models (SAE and SLFN) show obviously better fitting ability compared to the linear models (NNG and PLS). For test data, SAE model has a better prediction accuracy than the other three models, which indicates a
From the description in Section 2.4, it can be seen that the ratio between 𝐹𝑆𝐴 and 𝑀𝐶 is important. According to the changes in 𝑀𝐶 , the values of 𝐹𝑆𝐴 have an important impact on the efficiency and safety of the CDQ system. A reasonable control strategy of 𝐹𝑆𝐴 should consider not only the effects of other controlled variables but also the influences by the state variables such as CO and O2 concentrations in the device. As a result, the human experience-based control is usually difficult to ensure that the performance of the CDQ system. Here, a second prediction model is proposed to provide an operating guidance for online adjustment of 𝐹𝑆𝐴 , which takes 𝐹𝑆𝐴 ∕𝑀𝐶 as the model output and the process variables listed in Table 1 expect 𝐹𝑆𝐴 as input. Before modeling, the 20,200 historical data points were screened according to the economic benefit index (J ). According to the expert experience, the time periods with J values larger than 3.25 are considered to be with proper process operation. Hence, 5684 data points collected during these time periods were selected based on such a criterion, which were used as the labeled data for model training and test. Among these 5684 labeled samples, 5184 were included in the training set and the other 500 were adopted for model test. During the model training step, the 14,516 data points corresponding to J smaller 114
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Table 2 Training and prediction results of different modeling methods for J .
Fig. 5. Comparison of original and predicted J with different modeling methods.
than 3.5 were used as the unlabeled data to pre-train the SAE with the
provide a good initialization for fine-tuning. In this sense, the deep
greedy layer-wise training (GLT) algorithm (Suk et al., 2015). Then,
learning model fully utilizes the information contained in the data. In
the deep neural network was fine-tuned with 5184 labeled samples
this case study, the network structure of the SAE is 16-30-30-1, where
by backpropagation. The weights obtained from the pre-training step
the numbers indicate the amounts of neurons used in different layers. 115
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Fig. 6. Comparison of original and predicted 𝐹𝑆𝐴 ∕𝑀𝐶 with different modeling methods.
efficiency index J using the model introduced Section 4.2. By comparing the predicted J values based on the suggested operation with the original J values, the feasibility of the developed method is well illustrated. In the study described in this section, 1000 samples randomly selected from the 20,200 data points were used as the test data for comparative analysis. The effects of the suggested operation by the model proposed in Section 4.3 are shown in Fig. 7. Fig. 7(a) plots the original values and the model suggestions of 𝐹𝑆𝐴 ∕𝑀𝐶 , while Fig. 7(b) compares the J values achieved by the original operating conditions and the predicted J values obtained by following the model-suggested operation. It can be seen that the suggested operation significantly improves the economic efficiency of the CDQ system. The average improvement is about 4.23%.
The training and prediction results of different methods are shown in Table 3, while Fig. 6 shows the model predictions on the 500 test data. Clearly, the SAE-based deep neural network model is superior to the traditional modeling methods used in comparison. Although the shallow neural network model, i.e. SLFN, has a strong fitting ability for nonlinear systems, its generalization ability for the test data is not good enough. This is because the SAE uses the greedy layer-wise training method for pre-training. In this process, the utilization of the 14,516 unlabeled samples makes the weights in the network initialized better in the parameter space than random allocation. 4.4. Operating performance analysis According to the 𝐹𝑆𝐴 ∕𝑀𝐶 prediction model proposed in Section 4.3, the suggested value of 𝐹𝑆𝐴 can be obtained at each time point for guiding the adjustment of the supplementary air flow rate. Then, the operating performance can be evaluated by calculating the economic
5. Experimental verification The SAE model proposed in Section 4.3 and the suggested process operation which adjusts 𝐹𝑆𝐴 ∕𝑀𝐶 online (denoted as ‘‘model-guided 116
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Fig. 7. Comparison of original operation and suggested operation.
Fig. 8. Trajectories of 𝐹𝑆𝐴 ∕𝑀𝐶 and J under model-guided control. Table 3 Training and prediction results of different modeling methods for 𝐹𝑆𝐴 ∕𝑀𝐶 .
control’’ in the following of this section) were applied to a real industrial CDQ system. The one-day average of each process variable
collected by DCS is shown in Table 4. Clearly, after conducting the
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Table 4 Performance comparison between manual control and model-guided control. No.
Variables
Manual control
Model-guided control
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
𝐹𝑆𝐴 (Nm3 /h) 𝑀𝐶 (kg/h) 𝐹𝐶𝐺𝑇 (Nm3 /h) 𝑃𝐶𝐺𝑇 (Pa) 𝐹𝐵𝐺 (Nm3 /h) 𝐹𝐸𝐺 (Nm3 /h) 𝑇𝐶 (◦ C) 𝑇𝐸 (◦ C) 𝑇𝐶𝐺𝐵 (◦ C) 𝑇𝐸𝐺𝐵 (◦ C) 𝑇𝐶𝐺𝑇 (◦ C) 𝐶𝐶𝑂 (%) 𝐶𝐻2 (%) 𝐶𝐶𝑂2 (%) 𝐶𝑂2 (%) 𝐸𝑓 𝑎𝑛 (KW) 𝐸 (MW) 𝐹𝑆𝐴 ∕𝑀𝐶 (Nm3 ∕1000𝑘𝑔) J ($/1000 kg)
15,519.4 145900 202,524.3 4192.4 2191.9 14,302.9 1083.7 148.2 967.4 155.6 121.2 1.09 0.21 17.11 0.22 262.8 7.2 106.4 3.1049
16,897.8 145600 216,688.6 4553.1 7641.8 15,897.2 1085.4 189.9 977.3 158.5 122.4 0.75 0.14 15.20 0.19 279.2 7.5 116.1 3.2413
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model-guided control, some key performance indicators of the CDQ system, such as 𝐹𝑆𝐴 , 𝐹𝐶𝐺𝑇 , and 𝑇𝐶𝐺𝐵 , were improved significantly. Meanwhile, the amounts of 𝐶𝐶𝑂 , 𝐶𝐶𝑂2 , and 𝐶𝐻2 were lower than in the conventional operation. As a result, the security of CDQ system has been enhanced. After changing the control mode, the mean value of 𝐹𝑆𝐴 ∕𝑀𝐶 was increased from 106.4 Nm3 ∕1000 kg to 116.1 Nm3 ∕1000 kg. The trajectory of 𝐹𝑆𝐴 ∕𝑀𝐶 is shown in Fig. 8(a). It is observed that 𝐹𝑆𝐴 ∕𝑀𝐶 is manipulated at around 105 Nm3 ∕1000 kg. Fig. 8(b) shows the economic benefit index J. In the collected 288 sample points, the mean value of the economic benefit index was 3.2413 $/1000 kg. Compared with 3.1049 $/1000 kg achieved without the model-guided control, there was a 4.39% improvement; that is to say, a coke oven with a capacity of 100,000 kg/h may have a total revenue increase of about 120,000 dollars per year. 6. Conclusions This paper introduces an economic benefit index for the CDQ system, proposes a data-driven modeling method based on deep learning with SAE, and applies the prediction model to guide the CDQ control system. The advantages of SAE were analyzed through the comparison with the traditional modeling methods. The energy conversion process of the CDQ system has a coupling relationship between power generation and burning loss rate. Aiming at this characteristic, a method for data screening according to the CDQ economic benefit index J was proposed to select training data set for SAE modeling of 𝐹𝑆𝐴 ∕𝑀𝐶 . Based on this model, suggestions on the manipulation of 𝐹𝑆𝐴 are provided to realize a model-guided control of the CDQ system. Finally, the proposed strategy was applied to a real industrial CDQ system. Experimental results show that the process operating performance was significantly improved. The economic benefit index J was increase by 4.39%, which demonstrates the effectiveness of the proposed method. Acknowledgment Yao was supported in part by Ministry of Science and Technology, ROC under Grant No. MOST 107-2622-8-007-015. Wang was supported by Special Project on Industrial Transformation and Upgrading (Made in China 2025) in 2017 (No. TC17085JH). Yang was supported by Innovation Project of Shanghai Science and Technology Committee (No. 18411952200) and Key Research & Development Project of National Science and Technology Ministry of China (No. 2018YFC1312903).
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