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Optimization of a 660 MW_{e} Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

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## Abstract

**:**

_{e}supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

## 1. Introduction

_{e}(electric power), while 63.96% of the electricity demand was met by thermal power plants [3,4]. The energy conversion efficiency, fuel consumption, and hazardous emissions from thermal power plants on the environment are critical issues of concern in research in industrial and regulatory circles in the last decade [5,6,7,8]. Various retrofits, technology improvements, and state-of-the-art air pollution control devices are integrated at the power complexes to ensure cleaner energy production with minimal emissions from the power plants that comply with various national and international standards [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25].

_{thermal}of a running coal power plant. The operating parameters have a non-linear, inter-dependent, and complex relationship with the η

_{thermal}[26]. An extensive set of assumptions should be made to help derive the analytical equations for such a complicated process analytically, and thereby, the correct response of the process cannot be accurately described [27,28].

_{e}power generation. Such an approach allowed us to determine the benchmark values of the power plant’s key operation parameters. The proposed result served to adjust the critical operating parameters for an energy-efficient plant operation [44]. A cross-feature convolutional neural network was employed for generalizing the boiler load fluctuations behavior to ensure optimal energy utilization efficiency and ethylene production in the petrochemical industry. The average relative generalization error was reduced to 2.86%, and energy utilization efficiency was increased by 6.38% [45]. LSSVM-based hybrid models were developed for forecasting the energy demand of the grid [46] as well as the energy consumption of complex industrial processes to ensure the efficient operation management and control of the cement industry [47]. In other studies, ANN and LSSVM were used for dynamic optimization of a pilot-scale entrained flow gasifier operation [48]. They allow monitoring the stability of a gas combustor [49], improving the energy conversion, optimization, and thermal efficiency of coal-fired utility boiler [50,51], gas turbine operation performance evaluation, and fault diagnosis [52], and for predicting the boiler thermal efficiency of a 660 MW

_{e}ultra-supercritical coal power plant [53].

_{thermal}. Since a power plant’s η

_{thermal}is defined as the ratio of electric power produced (MW

_{e}) to the energy supplied (MW) by the fuel, the improved heat transfer to the heating surfaces and effective operational control of the power plant offers optimal energy spent on the power production. Consequently, the power plant’s η

_{thermal}can be simultaneously improved. The increase in thermal efficiency offers many benefits, i.e., reduced operation cost, optimal fuel consumption, and reduced power plant emissions.

_{e}supercritical coal-fired power plant under the continuous power generation mode for developing the AI process models for η

_{thermal}. A histogram and self-organizing feature map (SOFM) of control variables were constructed to visualize the data’s health, distribution, and quality. Monte Carlo experiments on ANN and LSSVM and an interval adjoint significance analysis (IASA) were performed to eliminate the insignificant variables from the list of control variables. Effective and experimentally validated ANN and LSSVM process models were developed, and the performance of the two models was compared comprehensively. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. The 360 MW

_{e}, 495 MW

_{e,}and 660 MW

_{e}that corresponded to 50%, 75%, and 100% unit load of the power plant were taken into account in the study. Finally, some control variables that affected the power plant’s η

_{thermal}at 50%, 75%, and 100% unit loads were constructed with a 95% confidence interval. Extensive investigations of the power plant’s various operational strategies were conducted using the ANN approach and the Monte Carlo technique.

_{thermal}of the power plant. The savings in heat input values at 50%, 75%, and 100% unit load relative to the power plant’s optimal η

_{thermal}were calculated for the power plant’s energy-efficient operation control.

## 2. Overview of a Coal Power Plant Operation

_{e}supercritical coal-fired boiler model # HG-2118/25.4-HM16 is manufactured by Harbin Boiler Plant Co., Ltd. at Harbin, China and installed at the Sahiwal Coal Power Plant, as shown in Figure 1. The modern and advanced design features of the boiler include a π-shaped structure, once-through technology (no steam drum), an intermediary reheating system, sliding pressure, balanced draft, wet bottom ash with a single furnace, ultra-sonic leakage detection system, full steel frame, full suspension structure arranged in the open air, and is also equipped with two rotary tri-sector air preheaters (APH). The boiler’s sliding pressure ability allows it to continuously provide steam ranging from 330 MW

_{e}to 660 MW

_{e}unit load.

_{x}formation. The boiler’s burning system is equipped with a medium-speed direct-fired pulverizing system with cold and hot primary air. Twenty-four direct-flow fuel burners are arranged, four at the corners in a layer, and a total of six layers for six coal mills. The fuel burner at the bottom of the furnace is provided with a micro oil gun system to save fuel during start-up and support the combustion. In addition to that, three layers of big oil guns are provided, consisting of 12 burners.

_{x,}horizontal rich, and lean burner. The fuel burner’s secondary air is fired in the furnace 5° away from the primary air to form the air-enclosure-coal arrangement and oxidation near the water wall zone. Fuel burners are divided into upper and lower groups within a specific distance to reduce the burner zone’s thermal load and slagging. Separated over-fire air (SOFA) nozzles are arranged near the furnace’s exit and above the burners to supply air for the late combustion and implement efficient combustion to reduce furnace temperature level and to control NO

_{x}emissions. The flame detection system is installed with every fuel burner to prevent flameout phenomenon, which detects flame strength both by digital and analog signals. Two sets of temperature sensing thermocouples are installed at a high-temperature position for each fuel burner to prevent the burners melting by high temperature. Moreover, infrared flue gas temperature measuring devices are set on the right and left sides of the furnace outlet to monitor the furnace outlet’s flue gas temperature. The boiler is also equipped with the ultrasonic leakage detection system, and the flame observation cameras on both sides of the boiler monitor the combustion. It is necessary to mention that the advanced and reliable combustion control systems installed at the power plant ensure the boiler’s stable and reliable operation for the power generation. The manufacturer designed the boiler’s operating parameters at boiler maximum continuous rating (BMCR), listed in Table 1.

## 3. AI-Based Data Visualization and Process Modeling

#### 3.1. Variables Selection for AI Process Modeling

_{e}supercritical coal power plant’s η

_{thermal}in Sahiwal, Pakistan. The variables were selected based upon the recommendation of experienced plant managers of the power plant and a comprehensive literature review [33,64,65,66,67,68]. Some control variables were controllable by the operator, e.g., the main steam temperature (MST), reheat steam temperature (RST), and oxygen content in flue gas at the boiler outlet (O

_{2}). On the other hand, some control variables were uncontrollable during the power plant operation, e.g., turbine speed (N). The coal properties measured under the air-dried basis are listed in Table 3.

_{c}), the air flow rate (M

_{a}), furnace pressure (P

_{f}), the APH air outlet temperature (T

_{a}), % O

_{2}in flue gas at the boiler outlet (O

_{2}), the APH outlet flue gas temperature (T

_{fg}), feed water temperature (FWT), main steam pressure (MSP), main steam temperature (MST), reheat steam temperature (RST), condenser vacuum (P

_{vac}), attemperation water flow rate (AWF), and turbine speed (N). It is evident from Table 4 that control variables possess wide operating ranges of the power plant control parameters, which contain not only all possible operating modes of the power plant but also possess the detailed and comprehensive information of the plant operation required for the development of a generalized AI process model.

_{c}values in the operating range, as mentioned in Table 4, correspond to the various unit load generation from the power complex and are shown in Figure 3a. Figure 3b represents air consumption in the boiler during different load generation. Figure 3c shows a nearly normal distribution of P

_{f}, while Figure 3d illustrates the distribution of T

_{a}. T

_{a}was selected as the training parameter to better account for the heat recovery from the flue gas after its exhaust from the boiler and the subsequent benefit for improving the power plant’s η

_{thermal}.

_{2}in the flue gas exhaust from the boiler. T

_{fg}was a very critically controlled and sensitive parameter for the boiler operation as it was one of the critical parameters of the boiler operation for ensuring its effective operation control. The variation in T

_{fg}is shown in Figure 3f. The increase in T

_{fg}indicated that heat transfer from flue gas to the heating surfaces decreased, which might be caused due to soot accumulation on the heating surfaces, high flame center, or large access air coefficient of combustion re-burning of un-burnt carbon in the tail of the boiler.

_{thermal}of the power plant. The reheat system positively influenced the η

_{thermal}of the power plant. The distribution of RST is shown in Figure 3j. Figure 3k represents the variation in vacuum maintained in the condenser, while Figure 3l shows the distribution of AWF data in the input space used to control the MST and RHT. Lastly, Figure 3m represents the variation in N during power plant operation. It is evident from Figure 3a–m that the distribution of control variables data points across their operating ranges was wide and meaningful and thus can be confidently used to develop AI process models of the η

_{thermal}of the power plant.

#### 3.2. Elimination of Insignificant Control Variables

#### 3.2.1. Monte Carlo Experimentation for Significance Analysis

- A control variable is represented as x
_{i}where, i = 1,2,…,13. - Each input vector containing all control variables x
_{i}can estimate output variable y_{o}where, o = 1,2,3,….,m, where m is equal to the number of input vectors in the training data set. - The Monte Carlo experimentation can be illustrated by considering a control variable x
_{i}and all other control variables as x_{j}_{,}were ($\mathrm{i}\ne $ j). As an example, let x_{i}= x_{1}and x_{j}= x_{2},_{…..}, x_{13}, where ($\mathrm{i}\ne $ j). - Create n equal divisions (k) for x
_{i}between its range (x_{imax}–x_{imin}) where, k = 1,2,…,n. - Generate M random values for each division k (k = 1,2, 3, …, n) by keeping x
_{ik}, at a constant value. All other input control variables for these M replications are generated so that the probability (P) of any value (u) between x_{jmin}and x_{jmax}is equal. The Mth input vector will be [x_{1kM}, x_{2u}, x_{3u}, x_{4u}…, x_{13u}], and the corresponding output will be y_{okM}. - The output value y
_{okM}is obtained by ANN and LSSVM prediction for the Mth input vector [x_{1kM}, x_{2u}, x_{3u}, x_{4u}, …, x_{13u}]. Compute a mean value (μ) for each y_{okM}having M replications, which will give y_{ok}for each x_{ik}_{.} - Repeat step number iii to step number vi for all remaining control variables
- Compute Δy
_{i}where Δy_{i}= y_{okmax}− y_{okmin}for all control variables x_{i}and compute the summation value Y for all Δy_{i}$$\mathrm{Y}={\sum}_{i=1}^{13}\mathsf{\Delta}{y}_{i}$$ - Compute the percentage significance (r
_{i}) of each x_{i}by dividing Δy_{i}with Y and multiplying it by 100$${r}_{i}=\left(\frac{\mathsf{\Delta}{y}_{i}}{Y}\right)\times 100$$

#### 3.2.2. Interval Adjoint Significance Analysis (IASA)

_{2}, N, and P

_{f}are the least significant and impact on the network’s overall performance is negligible. The significance ranking is given in Figure 5c.

_{2}, N, and P

_{f}were relatively insignificant variables and could be eliminated from the data set of control variables for the sake of decreasing the computational time, eliminating the redundant control variables, and achieving accurate results [69]. Therefore, ten control variables out of thirteen initially selected control variables highlighted in blue in Figure 5a–c were finalized for training the AI process modeling of the power plant’s η

_{thermal}.

#### 3.3. Self-Organizing Feature Map

_{thermal}of the power plant.

#### 3.4. Development of ANN Process Model

_{thermal}. It has a well-established ability to dig and learn the complex nonlinearities and interactions out of high dimensional and complex input space data [82,83,84]. Gradient descent with momentum was employed as a training function, and tangent hyperbolic was used as a transfer function between the layers of MLP for the neural network model development [64,85].

#### 3.5. Development of LSSVM Process Model

#### 3.6. Evaluation Criteria

_{thermal}of the power plant. The definitions of the error criteria are given below:

#### 3.7. External Validation Case of Trained AI Process Models

_{thermal}, and the corresponding residuals are shown in Figure 8a,b. Comparing the ANN and LSSVM models’ responses, as shown in Figure 8a,b, the ANN model had more effectively predicted the external validation data set than the LSSVM model. The spread of residuals for the ANN model was comparatively smaller than the one for the LSSVM model. The performance comparison of ANN and LSSVM models for predicting the power plant’s η

_{thermal}, in terms of the evaluation criteria, is presented in Table 6.

_{thermal}concerning the control variables compared to LSSVM. The ANN presented a good generalization ability to model the complex power plant operation with better network robustness, confirming its superior efficacy for data analysis and decision making.

## 4. Results and Discussion

_{thermal}was evaluated.

- Let x
_{i}be the control variable(s) where i = 1,2,3….,10, whose effect is to be studied, and x_{j}(i $\ne $ j) represents the remaining control variables. - Let yo represent the output value corresponding to x
_{i}where o = 1. - Divide the range of x
_{i}(x_{imax}–x_{imin}) in n equal step size (d) where d = 1,2,….,n - Keep the remaining control variables xj constant at the selected value at every step size d. The input vector with x
_{i}say, i = 1, step size d and remaining input control variables x_{j}is represented as [x_{1d}, x_{2d}, x_{3d}, x_{4d}, x_{5d}, x_{6d}, x_{7d}, x_{8d}, x_{9d}, x_{10d}]. Create “m” replications for the input vector for every step size d. - Generate “m” Gaussian noise values (g) from the 1% range value of control variables x
_{i}and x_{j}. Add g with the input vector for all step size d. - Predict the developed ANN process model from an input vector and compute mean (µ) and standard deviation (σ) of the predicted values y
_{od}against x_{id}input vector, which is represented as µ_{yod}and σ_{yod}relative to x_{id}input vector, respectively. - Calculate upper control limit (UCL = µ
_{yod}+ 2* σ_{yo}_{d}) and lower control limit (LCL = µ_{yod}− 2* σ_{yo}_{d}) and plot mean, UCL and LCL against x_{i}.

#### 4.1. Effect of MST and RST on η_{thermal} of Power Plant

_{thermal}at 50%, 75%, and 100% unit load, MST and RST were varied from 550 to 570 °C. The remaining control parameters were set at the corresponding average values at 50%, 75%, and 100% unit load, as mentioned in Table 7. Thus, the experiments were used to evaluate the combined effect of MST and RST on the power plant’s η

_{thermal}.

_{1}) and RST (x

_{2}) was divided into ten equal divisions (d) in order to conduct experiments according to the procedure described in Section 4. MST and RST remained constant in each division, while the other control variables (x

_{j}= 3,4,5,6,7,8,9,10) were set at their average values, as mentioned in Table 7. A total of 100 replications (m) of the control variables were created and added with the Gaussian noise values (g). The constructed experiment was simulated using the ANN process model of η

_{thermal}of the power plant and mean (µ

_{yod}), and the standard deviation (σ

_{yo}

_{d}) of the predicted values of η

_{thermal}(y

_{o}) was calculated. The procedure was repeated for remaining division values (d), and mean UCL and LCL trend lines against MST and RST were plotted for 50%, 75%, and 100% unit load and represented in Figure 9a–c.

_{thermal}. A general increasing trend of the power plant’s η

_{thermal}was observed when MST and RST increased from 550 °C to 570 °C. The power plant’s η

_{thermal}at 50%, 75%, and 100% unit load had, on average, a relative increase of 1.50%, 1.50%, and 1.32%, respectively, with every 10 °C rise in MST and RST. The upper control limits of the temperatures were restricted by material properties [93].

_{thermal}at 50%, 75%, and 100% unit load. It was apparent from Figure 9d that the η

_{thermal}of the power plant at 100% unit load was higher than 50% and 75% unit load efficiencies. It was because the heart rate of the power plant was improved at 100% unit load. Moreover, the power plant operating mode was also supercritical under which the boiler operation was fuel-efficient, stable, and economical [56,93]. The heat inputs to produce 50%, 75%, and 100% unit load at 550 °C, 560 °C, and 570 °C MST and RST are mentioned as MW values on each trend in Figure 9d. The heat inputs required to sustain the highest η

_{thermal}, i.e., 39.78%, 41.05%, and 41.59% at 50%, 75%, and 100% unit load at higher temperature limit (570 °C) of MST and RST were 905 MW, 1206 MW, 1587 MW respectively. Meanwhile, at a lower temperature limit (550 °C) of MST and RST, the overall thermal efficiencies achieved were 38.62%, 39.85%, and 40.51% at 50%, 75%, and 100% unit load. The corresponding energies spent to keep the plant operational were 932 MW, 1242 MW, 1629 MW, respectively, which were comparatively higher heat inputs for the same power production. Therefore, it was advantageous to maintain the MST and RST at a higher temperature limit to ensure the fuel-efficient and optimum power plant operation for sustainable power production.

#### 4.2. Effect of T_{fg} on the η_{thermal} of Power Plant

_{fg}is a critically controlled operating parameter of the boiler operation. It indicates the efficiency of fuel combustion and heat transfer to the heating surfaces inside the boiler, thereby directly influencing the power plant’s η

_{thermal}. The increase in this temperature beyond its normal controllable operating range, as mentioned in Table 7, indicates that heat transfer from the flue gas to the heating surfaces is decreasing, which might be caused by soot accumulation on the heating surfaces, high flame center, large access air coefficient of combustion and burning of unburned carbon in the tail of boiler. This is an indicator of poor control of boiler operation resulting in the power plant’s reduced η

_{thermal}.

_{fg}on the power plant’s η

_{thermal}at 50%, 75%, and 100% unit load is given in Figure 10a–c. The η

_{thermal}of the power plant decreased with the increase in T

_{fg}. With every 5 °C rise in the T

_{fg}, the relative decrease in the power plant’s η

_{thermal}was an average of 0.65%, 0.25%, and 0.28% at 50%, 75%, and 100% unit load respectively.

_{fg}on the power plant’s η

_{thermal}at 50%, 75%, and 100% unit load. At 100% unit load, the power plant’s η

_{thermal}was relatively higher than 75% and 50% unit load due to the improved heat transfer conditions. At 50% unit load, there was a sharp decreasing trend of the power plant’s η

_{thermal}with increased flue gas temperature after APH. Thus, the temperature should be effectively controlled mainly at 50% unit load as it had a relatively more significant adverse impact on the power plant’s η

_{thermal}. This was explained by the fact that the increase in T

_{fg}indicated the decreased heat transfer from flue gas to the heating surfaces, which was responsible for the decrease in the η

_{thermal}of the power plant. The temperature was critically controlled by the significant soot blowing on various heating surfaces in the boiler, improved fuel combustion, lower irreversibility losses in the boiler, and significant operational control. The lower limit of the temperature was strictly controlled as it may cause corrosion to the downstream equipment due to the condensation of flue gas containing various acidic gases [93,94,95].

_{thermal}of the power plant, i.e., 39.97%, 40.96%, and 42.23% at 50%, 75%, and 100% unit load, the corresponding heat inputs were 901 MW, 1208 MW, and 1563 MW, respectively. The possible lowest overall thermal efficiencies, i.e., 39.18%, 40.66%, and 41.87% at 50%, 75%, and 100% unit load, were 919 MW, 1218 MW, and 1576 MW, respectively. Resultantly, heat input values at lower thermal efficiencies caused by the increased temperature of flue gas after APH were comparatively higher for the same unit load generation. It accounted for the effective control of the flue gas temperature after APH near the lower controllable limits, as mentioned in Table 7, to achieve optimal η

_{thermal}under various power plant operating modes.

#### 4.3. Effect of T_{a} and T_{fg} on the η_{thermal} of Power Plant

_{a}and T

_{fg}is an essential pair of power plant control parameters for the effective boiler operation and improved η

_{thermal}under various power plant operating modes. Flue gas leaving the boiler had some thermal energy depending upon the boiler thermal efficiency, which would otherwise be lost if not recovered and decrease the power plant’s η

_{thermal}. A part of this energy was recovered by the air passing through the APH. Thus, the pre-heated air improved the power plant’s η

_{thermal}and helped coal combustion at high air temperature for producing high-quality steam. The combined effect of rising T

_{a}and falling T

_{fg}represented the flue gas’s waste heat recovery system.

_{a}and T

_{fg}. T

_{a}was varied systematically in the ascending order, while T

_{fg}was decreasing systematically. The remaining operating parameters were kept at the average values corresponding to the 50%, 75%, and 100% unit load, as mentioned in Table 7. The further treatment for constructing the experiment’s design for evaluating the effect of T

_{a}and T

_{fg}on the power plant’s η

_{thermal}was executed as per the procedure described in Section 4.

_{a}and T

_{fg}at 50%, 75%, and 100% unit load on the power plant’s η

_{thermal}is represented in Figure 11a–c. As expected, an increasing trend in the power plant’s η

_{thermal}was observed at 50%, 75%, and 100% unit load with the increase in T

_{a}and fall in T

_{fg}. The relative increase in the η

_{thermal}of the power plant on an average was 0.43%, 0.44%, and 0.42% at 50%, 75%, and 100% unit load respectively against every 5 °C rise in T

_{a}and 5 °C fall in T

_{fg}. It is essential to mention here that the extent of waste heat recovery was limited to the dew point temperature of flue gas and the APH material metallurgy [96,97].

_{a}and T

_{fg}on the power plant’s η

_{thermal}at 50%, 75%, and 100% unit power generation capacity. At 100% unit power generation capacity, the power plant’s η

_{thermal}was relatively higher than 50% and 75% unit power generation capacity because of its improved heat rate.

_{thermal}of the power plant, i.e., 39.77%, 41.08%, and 42.29% at 50%, 75%, and 100% unit load, the corresponding heat inputs were 905 MW, 1205 MW, and 1561 MW, respectively. The possible lowest overall thermal efficiencies at 50%, 75%, and 100% unit load would be 917 MW, 1221 MW, and 1580 MW, respectively. The lowest heat inputs corresponding to the power plant’s highest η

_{thermal}at 50%, 75%, and 100% unit load was possible due to improved heat recovery from the flue gas leaving the boiler. The effective operation control of T

_{a}and T

_{fg}promised the boiler’s efficient operation and the improved η

_{thermal}of the power plant.

#### 4.4. Effect of Change in All Control Variables on the η_{thermal} of Power Plant

_{thermal}. This strategy of adjusting operating parameters can be practically implemented to achieve the maximum overall thermal efficiencies at various power plant operation modes.

_{fg}. In such an operating scenario, FWT would also drop, and the P

_{vac}would be slightly increased. Meanwhile, the M

_{c}, M

_{a}, and AWF were adjusted to recover the plant operation to the stable operating conditions at any unit load. The operational ranges of all the control variables within which the corresponding variables’ values were systematically changed at 50%, 75%, and 100% unit load are s mentioned in Table 7.

_{thermal}of the power plant at 50%, 75%, and 100% unit load, M

_{c}, M

_{a}, T

_{fg}, T

_{a}, MSP, P

_{vac}, and AWF were varied from maximum to average values as mentioned in Table 7. FWT was varied from minimum to average, while MST and RST were changed between the minimum and the maximum operating limits. The operating ranges of all variables were divided into ten equal divisions. To evaluate the response of the power plant’s ηthermal under such an operating scenario, we applied the procedure as described in Section 4.

_{thermal}at 50%, 75%, and 100% unit load is shown in Figure 12a–c. At 50% unit load, the power plant’s η

_{thermal}was initially stagnant (~550 °C ~555 °C MST), and then it started increasing. On the other hand, at 75% and 100% unit load, an increasing trend of the power plant’s η

_{thermal}was observed. For 550 °C to 560 °C and 560 °C to 570 °C of MST and RST with the corresponding changes in remaining control parameters at 50%, 75%, and 100% unit load, the relative increase in the η

_{thermal}of the power plant on an average was equal to 3.36%, 3.21%, and 4.29% respectively.

_{fg}, T

_{a}and T

_{fg,}and all control variables, as discussed in Section 4.1, Section 4.2, Section 4.3 and Section 4.4, respectively. The energy savings or energy losses for any control variable at a specific unit load were calculated from the heat input values corresponding to the control variable’s operating range, as mentioned in Figure 9d, Figure 10d, Figure 11d and Figure 12d. (+) the sign indicates the % energy savings while the (−) sign indicates the % energy losses in heat input values against the control variable in its operating range. At 50%, 75%, and 100% unit load, MST and RST indicated energy savings in heat input values of 2.94%, 2.92%, and 2.59%. T

_{a}and T

_{fg}noted energy savings of 1.27%, 1.31%, and 1.25%, respectively. All control variables showed significant energy savings of 7.20%, 6.85%, and 8.60%, respectively, whereas, T

_{fg}expressed energy losses of 1.99%, 0.70%, and 0.35% in heat input values for the operation of the power plant.

## 5. Conclusions

_{thermal}for sustainable power generation from a power complex.

_{thermal}on average had a relative increase of 1.50%, 1.50%, and 1.32%, respectively.

_{fg}, the relative decrease in the power plant’s η

_{thermal}was an average of 0.65%, 0.25%, and 0.28%, respectively, under 50%, 75%, 100% unit load.

_{a}and 5 °C fall in T

_{fg}, the relative increase in the η

_{thermal}of the power plant on average was 0.43%, 0.44%, and 0.42%, respectively, under 50%, 75%, and 100% unit load.

_{thermal}of the power plant on an average was 3.36%, 3.21%, and 4.29% respectively at 50%, 75%, and 100% unit load.

_{a}and T

_{fg}are 1.27%, 1.31%, and 1.25% respectively, for all control variables were 7.20%, 6.85%, and 8.60% respectively. Energy losses in heat inputs corresponding to the lowest overall thermal efficiencies at 50%, 75%, and 100% unit load, for T

_{fg}were 1.99%, 0.70%, and 0.35%, respectively.

_{thermal}needs to be evaluated in the future.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Nomenclature

AWF | attemperation water flow rate (°C) |

FWT | Feed-water temperature (°C) |

M_{a} | air flow rate (t/h) |

M_{c} | coal flow rate (t/h) |

MSP | main steam pressure (MPa) |

MST | main steam temperature (°C) |

MW | energy supplied |

MW_{e} | electric power |

N | turbine speed (rpm) |

O_{2} | % O_{2} in flue gas at boiler outlet (%) |

P_{f} | furnace pressure (Pa) |

P_{vac} | condenser vacuum (kPa) |

RST | reheat steam temperature (°C) |

T_{a} | APH air outlet temperature (°C) |

T_{fg} | APH outlet flue gas temperature (°C) |

η_{thermal} | overall thermal efficiency (%) |

ε | epsilon |

## Abbreviations

AD | algorithmic differentiation |

AI | Artificial Intelligence |

ANN | Artificial Neural Network |

APH | air preheater |

C | regularized constant |

DPSH | division platen superheater |

ECO | economizer |

ESP | electrostatic precipitator |

FDF | forced draft fan |

FGD | flue gas desulphurization |

FRH | final re-heater |

FSH | final superheater |

G | generator |

HP | high pressure |

HPH | high-pressure heaters |

IA | interval arithmetic |

IASA | Interval Adjoint Significance Analysis |

ICT | information and communication technology |

IDF | induced draft fan |

IP | intermediate pressure |

LP | low pressure |

LPA | low-pressure turbine A |

LPB | low-pressure turbine B |

LPH | low-pressure heaters |

LSSVM | Least Square Support Vector Machine |

LT REHEATER | low-temperature re-heater |

LTSH | low-temperature superheater |

MAPE | mean absolute percentage error |

MLP | multilayer perceptron |

NCCR | the net coal consumption rate |

NRMSE | normalized RMSE |

PAF | primary air fan |

R | correlation coefficient |

RMSE | root mean square error |

SIS | Supervisory Information System |

Smart EEPS | smart energy and electric power system |

SOFM | self-organizing feature map |

SRM | structural risk minimization |

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**Figure 3.**Histograms of initially selected control variables, (

**a**) coal flow rate (M

_{c}), (

**b**) the air flow rate (M

_{a}), (

**c**) furnace pressure (P

_{f}), (

**d**) the APH air outlet temperature (T

_{a}), (

**e**) % O

_{2}in flue gas at the boiler outlet (O

_{2}), (

**f**) the APH outlet flue gas temperature (T

_{fg}), (

**g**) feed water temperature (FWT), (

**h**) main steam pressure (MSP), (

**i**) main steam temperature (MST), (

**j**) reheat steam temperature (RST), (

**k**) condenser vacuum (P

_{vac}), (

**l**) attemperation water flow rate (AWF), (

**m**) turbine speed (N).

**Figure 4.**Computational graphs for $f\left(X\right)=log\left({X}_{0}\cdot {X}_{1}\right)/10+{X}_{1}/100$; (

**a**) computational graph of $f\left(X\right)$ with primal values (the forward mode of AD), (

**b**) computational graph of with first-order derivatives (reverse mode of AD), and significance values, (

**c**) new computational graph of $f\left(X\right)$ after the significance analysis.

**Figure 5.**Comparison of variables elimination techniques (

**a**) Monte Carlo-ANN variables elimination (

**b**) Monte Carlo- least square support vector machine (LSSVM) variables elimination (

**c**) interval adjoint significance analysis (IASA)-variables elimination.

**Figure 9.**Effect of main steam temperature (MST) and reheated steam temperature (RST) on η

_{thermal}of power plant. The parameters of other input control variables: (

**a**) M

_{c}= 134 t/h, M

_{a}= 1386 t/h, T

_{a}= 320 °C, T

_{fg}= 113 °C, FWT = 260 °C, MSP = 13.6 MPa, P

_{vac}= −94.6 kPa and AWF = 18 t/h (

**b**) M

_{c}= 178 t/h, M

_{a}= 1973 t/h, T

_{a}= 330 °C, T

_{fg}= 125 °C, FWT = 281 °C, MSP = 18.9 MPa, P

_{vac}= −92.0 kPa and AWF = 15 t/h (

**c**) M

_{c}= 227 t/h, M

_{a}= 2303 t/h, T

_{a}= 342 °C, T

_{fg}= 123 °C, FWT = 296 °C, MSP = 24.2 MPa, P

_{vac}= −91.0 kPa and AWF = 15 t/h (

**d**) η

_{thermal}comparison at 50%, 75% and 100% unit load.

**Figure 10.**Effect of T

_{fg}on η

_{thermal}of the power plant. The parameters of other input control variables: (

**a**) M

_{c}= 134 t/h, M

_{a}= 1386 t/h, T

_{a}= 320 °C, FWT = 260 °C, MSP = 13.6 MPa, MST = 567 °C, RHT = 567 °C, P

_{vac}= −94.6 kPa and AWF = 18 t/h (

**b**) M

_{c}= 178 t/h, M

_{a}= 1973 t/h, T

_{a}= 330 °C, FWT = 281 °C, MSP = 18.9 MPa, MST = 562 °C, RHT = 564 °C, P

_{vac}= −92.0 kPa and AWF = 15 t/h (

**c**) M

_{c}= 227 t/h, M

_{a}= 2303 t/h, T

_{a}= 342 °C, FWT = 296 °C, MSP = 24.2 MPa, MST = 567 °C, RHT = 568 °C, P

_{vac}= −91.0 kPa and AWF = 15 t/h (

**d**) η

_{thermal}comparison at 50%, 75% and 100% unit load.

**Figure 11.**Effect of T

_{a}and T

_{fg}on the η

_{thermal}of power plant. The parameters of other input control variables (

**a**) M

_{c}= 134 t/h, M

_{a}= 1386 t/h, MSP = 13.6 MPa, MST = 567 °C, RHT = 567 °C, P

_{vac}= −94.6 kPa and AWF = 18 t/h (

**b**) M

_{c}= 178 t/h, M

_{a}= 1973 t/h, FWT = 281 °C, MSP = 18.9 MPa, MST = 562 °C, RHT = 564 °C, P

_{vac}= −92.0 kPa and AWF = 15 t/h (

**c**) M

_{c}= 227 t/h, M

_{a}= 2303 t/h, FWT = 296 °C, MSP = 24.2 MPa, MST = 567 °C, RHT = 567 °C, P

_{vac}= −91.0 kPa and AWF = 15 t/h (

**d**) η

_{thermal}comparison at 50%, 75% and 100% unit load.

**Figure 12.**Effect of change in all operating parameters on η

_{thermal}of power plant (

**a**) M

_{c}= 140~133 t/h, M

_{a}= 1456~1386 t/h, T

_{a}= 330~320 °C, T

_{fg}= 125~113 °C, FWT = 259~260 °C, MSP = 13.9~13.6 MPa, P

_{vac}= −94.5~−94.4 kPa and AWF = 82~0 t/h (

**b**) M

_{c}= 185~178 t/h, M

_{a}= 2075~1973 t/h, T

_{a}= 335~330 °C, T

_{fg}= 138~125 °C, FWT = 280~281 °C, MSP = 19.1~18.9 MPa, P

_{vac}= −92.1~ −92.0 kPa and AWF = 85~0 t/h (

**c**) M

_{c}= 236~227 t/h, M

_{a}= 2421~2303 t/h, T

_{a}= 350~342 °C, T

_{fg}= 130~123 °C, FWT = 295~296 °C, MSP = 24.4~24.2 MPa, P

_{vac}= −91.1~−91,0 kPa and AWF = 93~0 t/h (

**d**) η

_{thermal}comparison at 50%, 75% and 100% unit load.

Parameters | Unit | BMCR Load |
---|---|---|

Superheated steam flow | t/h | 2118 |

Superheater outlet steam pressure | MPa | 25.4 |

Superheater outlet steam temperature | °C | 571 |

Reheat steam flow | t/h | 1752 |

Reheater steam inlet pressure | MPa | 5.6 |

Reheater steam outlet pressure | MPa | 5.4 |

Reheater steam inlet temperature | °C | 345 |

Reheater steam outlet temperature | °C | 569 |

Feed-water pressure | MPa | 29 |

Feed-water temperature | °C | 300 |

Sensors | Make | Model Number |
---|---|---|

Coal flow rate | Vishay Precision Group (USA) | 3410 |

Air flow rate | Siemens (Germany) | 7MF4433-1BA22-2AB6-Z |

Furnace pressure | Siemens (Germany) | 7MF4433-1DA22-2AB6 |

APH air outlet temperature | Anhui Tiankang (China) Thermocouple | WRNR2 (K type) |

% O_{2} in flue gas at boiler outlet | Walsn (Canada) | 0AM-800-R |

APH outlet flue gas temperature | Anhui Tiankang (China) Thermocouple | WRNR2 (K type) |

Feed-water temperature | WIKAI (China) Thermocouple | TC10-3(IEC 60584) (K type) |

Main steam pressure | Siemens (Germany) | 7MF4033-1GA50-2AB6-Z |

Main steam temperature | Anhui Tiankang (China) Thermocouple | WRNK2 (K type) |

Reheat steam temperature | Anhui Tiankang (China) Thermocouple | WRNK2 (K type) |

Condenser vacuum | Siemens | |

STTRANS D PS III (Germany) | 7MF4233-1GA50-2AB6-Z | |

Attemperation water flow rate | Siemens (Germany) | 7MF4533-1FA32-2AB6-Z |

Turbine speed | Braun (Germany) | A5S |

LHV MJ/kg | Properties of Coal/wt.% | ||||
---|---|---|---|---|---|

24.23 | Moisture | Volatile Mater | Ash | Sulfur | Fixed Carbon |

2.5 | 23.73 | 16.6 | 0.55 | 57.66 |

**Table 4.**Statistics of data for artificial neural network (ANN) process modeling of η

_{thermal}of the power plant.

Parameters | Unit | Min | Avg | Max |
---|---|---|---|---|

Coal flow rate (M_{c}) | t/h | 122 | 167 | 239 |

Air flow rate (M_{a}) | t/h | 1315 | 1850 | 2590 |

Furnace pressure (P_{f}) | Pa | −229 | 79 | 69 |

APH air outlet temperature (T_{a}) | °C | 307 | 325 | 350 |

% O_{2} in flue gas at boiler outlet (O_{2}) | % | 3.5 | 4.8 | 5.9 |

APH outlet flue gas temperature (T_{fg}) | °C | 110 | 132 | 154 |

Feedwater temperature (FWT) | °C | 259 | 276 | 298 |

Main steam pressure (MSP) | MPa | 12.9 | 17.6 | 24.5 |

Main steam temperature (MST) | °C | 543 | 563 | 572 |

Reheat steam temperature (RST) | °C | 550 | 565 | 572 |

Condenser vacuum (P_{vac}) | kPa | −95.5 | −92.6 | −89.5 |

Attemperation water flow rate (AWF) | t/h | 0 | 20 | 98 |

Turbine speed (N) | Rpm | 2969 | 3007 | 3033 |

Overall thermal efficiency (η_{thermal)} | % | 37.25 | 40.39 | 42.75 |

V_{0} | = | X_{0} ⋅ X_{1} |
---|---|---|

${V}_{1}$ | $=$ | $log\left({V}_{0}\right)$ |

$Y$ | $=$ | ${V}_{1}/10+{X}_{1}/100$ |

Model | RMSE | NRMSE | MAPE |
---|---|---|---|

(%) | (%) | (%) | |

ANN | 0.5051 | 8.2159 | 1.016 |

LSSVM | 0.7164 | 11.6538 | 1.2819 |

Parameters | Unit | 50% Unit Load | 75% Unit Load | 100% Unit Load | ||||||
---|---|---|---|---|---|---|---|---|---|---|

Min | Avg | Max | Min | Avg | Max | Min | Avg | Max | ||

M_{c} | t/h | 128 | 134 | 140 | 170 | 178 | 185 | 219 | 227 | 235 |

M_{a} | t/h | 1326 | 1386 | 1456 | 1896 | 1973 | 2076 | 2206 | 2303 | 2395 |

T_{a} | °C | 315 | 320 | 330 | 320 | 330 | 335 | 335 | 342 | 350 |

T_{fg} | °C | 110 | 113 | 125 | 123 | 125 | 138 | 118 | 123 | 130 |

FWT | °C | 259 | 260 | 261 | 280 | 281 | 282 | 295 | 296 | 297 |

MSP | MPa | 13.1 | 13.6 | 13.9 | 18.4 | 18.9 | 19.1 | 24.0 | 24.2 | 24.4 |

MST | °C | 550 | 567 | 570 | 550 | 562 | 570 | 550 | 567 | 570 |

RHT | °C | 550 | 567 | 570 | 550 | 564 | 570 | 550 | 567 | 570 |

P_{vac} | kPa | −94.7 | −94.6 | −94.5 | −92.1 | −92.0 | −91.9 | −91.1 | −91.0 | −89.9 |

AWF | t/h | 0 | 18 | 82 | 0 | 15 | 85 | 0 | 15 | 87 |

Parameters | 50% Unit Load | 75% Unit Load | 100% Unit Load | ||||||
---|---|---|---|---|---|---|---|---|---|

Operating Range | Heat Input Range | % Energy Savings/Losses | Operating Range | Heat Input Range | % Energy Savings/Losses | Operating Range | Heat Input Range | % Energy Savings/Losses | |

Unit | °C | MW | % | °C | MW | % | °C | MW | % |

MST and RST | 550–570 | 934–907 | 2.94 | 550–570 | 1219–1183 | 2.95 | 550–570 | 1598–1557 | 1.16 |

T_{fg} | 110–125 | 903–921 | −1.99 | 123–138 | 1186–1194 | −0.75 | 118–133 | 1561–1567 | −0.85 |

T_{a} and T_{fg} | 315–330 | 919–907 | 1.27 | 320–335 | 1196–1183 | 1.31 | 335–350 | 1567–1554 | 1.24 |

* All Control Variables | 550–570 | 952–901 | 7.20 | 550–570 | 1252–1185 | 6.85 | 550–570 | 1643–1521 | 8.60 |

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## Share and Cite

**MDPI and ACS Style**

Muhammad Ashraf, W.; Moeen Uddin, G.; Muhammad Arafat, S.; Afghan, S.; Hassan Kamal, A.; Asim, M.; Haider Khan, M.; Waqas Rafique, M.; Naumann, U.; Niazi, S.G.;
et al. Optimization of a 660 MW_{e} Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management *Part 1. Thermal Efficiency*. *Energies* **2020**, *13*, 5592.
https://doi.org/10.3390/en13215592

**AMA Style**

Muhammad Ashraf W, Moeen Uddin G, Muhammad Arafat S, Afghan S, Hassan Kamal A, Asim M, Haider Khan M, Waqas Rafique M, Naumann U, Niazi SG,
et al. Optimization of a 660 MW_{e} Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management *Part 1. Thermal Efficiency*. *Energies*. 2020; 13(21):5592.
https://doi.org/10.3390/en13215592

**Chicago/Turabian Style**

Muhammad Ashraf, Waqar, Ghulam Moeen Uddin, Syed Muhammad Arafat, Sher Afghan, Ahmad Hassan Kamal, Muhammad Asim, Muhammad Haider Khan, Muhammad Waqas Rafique, Uwe Naumann, Sajawal Gul Niazi,
and et al. 2020. "Optimization of a 660 MW_{e} Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management *Part 1. Thermal Efficiency*" *Energies* 13, no. 21: 5592.
https://doi.org/10.3390/en13215592