Combustion Fine Tuning

energies
Article
Optimization of a 660 MWe Supercritical Power Plant
Performance—A Case of Industry 4.0 in the
Data-Driven Operational Management Part 1.
Thermal Efficiency
Waqar Muhammad Ashraf 1,2 , Ghulam Moeen Uddin 2 , Syed Muhammad Arafat 2,3 ,
Sher Afghan 4,5 , Ahmad Hassan Kamal 1 , Muhammad Asim 2 , Muhammad Haider Khan 1,6 ,
Muhammad Waqas Rafique 2 , Uwe Naumann 4 , Sajawal Gul Niazi 7,8 , Hanan Jamil 1,2 ,
Ahsaan Jamil 1 , Nasir Hayat 2 , Ashfaq Ahmad 2 , Shao Changkai 1 , Liu Bin Xiang 1 ,
Ijaz Ahmad Chaudhary 9 and Jaroslaw Krzywanski 10, *
1 Huaneng Shandong Ruyi (Pakistan) Energy Pvt. Ltd. Sahiwal Coal Power Complex, Sahiwal,
Punjab 57000, Pakistan; engr.waqar986@gmail.com (W.M.A.); ahmad11b08@smme.edu.pk (A.H.K.);
mhkuet@gmail.com (M.H.K.); hanan.jamil08@gmail.com (H.J.); ahsaan.jamil@gmail.com (A.J.);
mingxi0429@gmail.com (S.C.); lbxzhzhx@163.com (L.B.X.)
2 Department of Mechanical Engineering, University of Engineering & Technology, Lahore,
Punjab 54890, Pakistan; ghulammoeenuddin@uet.edu.pk (G.M.U.);
syedmuhammadarafat@outlook.com (S.M.A.); masim381@uet.edu.pk (M.A.);
waqasrafique474@yahoo.com (M.W.R.); nasirhayat@uet.edu.pk (N.H.); ashfaqahmad82@yahoo.com (A.A.)
3 Department of Mechanical Engineering, Faculty of Engineering & Technology, The University of Lahore,
Lahore 54000, Pakistan
4 Software and Tools for Computational Engineering, RWTH Aachen University, 52074 Aachen, Germany;
afghan@stce.rwth-aachen.de (S.A.); naumann@stce.rwth-aachen.de (U.N.)
5 Department of Computer Science, Khawaja Fareed University of Engineering and Information Technology,
Rahim Yar Khan, Punjab 64200, Pakistan
6 Institute of Energy & Environment Engineering, University of the Punjab, Lahore, Punjab 54000, Pakistan
7 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China,
Chengdu 611731, China; sajawalgul@outlook.com
8 Center for System Reliability and Safety, University of Electronic Science and Technology of China,
Chengdu 611731, China
9 Department of Industrial Engineering, University of Management and Technology, Lahore,
Punjab 54770, Pakistan; ie.cod@umt.edu.pk
10 Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15,
42-200 Czestochowa, Poland
* Correspondence: j.krzywanski@ajd.czest.pl

Received: 27 September 2020; Accepted: 22 October 2020; Published: 26 October 2020
Abstract: This paper presents a comprehensive step-wise methodology for implementing industry
4.0 in a functional coal power plant. The overall efficiency of a 660 MWe 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
Energies 2020, 13, 5592; doi:10.3390/en13215592 www.mdpi.com/journal/energies

Energies 2020, 13, 5592 2 of 33
identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s
overall thermal efficiency.
Keywords: combustion; supercritical power plant; industry 4.0 for the power sector; artificial
intelligence; thermal efficiency
1. Introduction
With the development of information and communication technology (ICT) in the last decade,
the industrial sectors generate large volumes of data that possess an undiscovered source of information.
The key challenges industries are facing today are data collection, storage, integration, processing,
and analysis. The scientific literature addressing these problems is scarce [1,2]. The analysis of such raw
industrial data using advanced data analytics and AI algorithms can identify significant operational
savings and suggest areas of useful technological improvements, i.e., the optimum industrial outputs,
better product quality, and sustainable growth of the industries. Such an approach is truly in line with
the industrial revolution we live through that is recently being termed industry 4.0.
The growth rate of electricity consumption is a direct indicator of industrialization, economic growth,
and a country’s gross domestic product. By the end of June 2019, Pakistan’s planned electricity
generation capability was 26,887 MWe (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 [5–8]. 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 [9–25].
A large number of operating parameters govern the ηthermal of a running coal power plant.
The operating parameters have a non-linear, inter-dependent, and complex relationship with the
ηthermal [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].
AI-based process models such as the least square support vector machine (LSSVM) and artificial
neural network (ANN) are widely used to model such ill-defined and complex problems using
real operational data of the process [29–34]. An extensive process of data of high quality, the data
visualization tests, and the validation of AI process models are essential for reliable AI utilization.
Moreover, the response of a useful AI process model under certain operating conditions of an actual
process establishes guidelines for optimizing the industrial performance without conducting the costly
hit and trial technological changes. The incorporation of real industrial data, computational software
tools, and AI algorithms for the improved performance of industrial outputs help realize the real
implementation of industry 4.0.
Researchers have reported a novel Nelder-Mead approach for optimizing the objective function
of a complex process. Three different direct search approaches, i.e., Nelder-Mead, Rosenbrock,
and Hooke-Jeeves, have represented the potential to locate the optimal objective function value against
the influence of several decision variables [35–37]. The Nelder-Mead approach is primarily designed
for the un-constrained nature of the problem. The algorithm is computationally time-consuming to
achieve the optimum results under the influence of many decision variables and the big volume of the
process’s operation data [35]. However, AI process modeling techniques like ANN and LSSVM have
proved to be computationally inexpensive and efficient to model the complex process and find the
optimal solution of the objective function [38–40].
Energies 2020, 13, 5592 3 of 33
The new generation of artificial intelligence, called AI 2.0, had seen a rapid development
worldwide, especially in smart energy and electric power systems (Smart EEPS) and fueling its
enormous applications in operation, optimization, control, and management of Smart EEPS [41,42].
Moreover, a comprehensive review of machine learning tools for energy efficiency objectives was
presented based on the published forty-two research articles. Machine learning tools were potentially
used for the energy utilization improvement demands of petrochemical industries, but limited
applications were reported in other industries [43]. The process data analytics platform was built
around the concept of industry 4.0 for studying the syngas heating values and flue gas temperatures
in waste to energy plants. A neural network-NARX model developed to evaluate the performance
of waste to the energy system well described the dynamic behavior of the system compared with
conventional statistical techniques [1].
Complex data mining methods were utilized to minimize the net coal consumption rate (NCCR)
of a 1000 MWe 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 MWe ultra-supercritical coal
power plant [53].
Power generation from a power complex is governed by the demand and stability of the national
grid. The operating regimes of the key-controllable operation parameters are adjusted according to the
various unit loads defined within the power generation capacity of the power complex offering an
opportunity to optimize the controllable operating parameters for energy-efficient and techno-economic
power generation. The power plant’s efficient operation control can be assessed by the power plant’s
ηthermal . Since a power plant’s ηthermal is defined as the ratio of electric power produced (MWe )
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 ηthermal 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.
In this paper, operational data of the initially selected input control variables were taken from the
Supervisory Information System (SIS) of the 660 MWe supercritical coal-fired power plant under the
continuous power generation mode for developing the AI process models for ηthermal . 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 MWe , 495 MWe,
and 660 MWe 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 ηthermal at 50%,
75%, and 100% unit loads were constructed with a 95% confidence interval. Extensive investigations of
Energies 2020, 13, 5592 4 of 33
the power plant’s various operational strategies were conducted using the ANN approach and the
Monte Carlo technique.
Two fundamental objectives were considered: (a) the pursuit of minimum potential energies
spent (conveniently translatable in various useful measures like the monetary cost of power generation
or fuel cost of power generation), and (b) achieving those above while maintaining or enhancing
the ηthermal of the power plant. The savings in heat input values at 50%, 75%, and 100% unit load
relative to the power plant’s optimal ηthermal were calculated for the power plant’s energy-efficient
operation control.
Big data analytics, industrial internet of things, and simulation were the three technologies
prioritized and incorporated in the study to achieve the power plant’s operational excellence by
embracing the industry 4.0 digital transformation approach. The process modeling based on process
data, process optimization, and data-driven strategy development for the improved process control lead
to an increase in the thermal efficiency of the power plant that offers many benefits, i.e., reduced operating
cost and optimal fuel consumption. The utilization of advanced and sophisticated technologies
dedicated to the implementation of industry 4.0 in the industrial complexes for higher productivity and
effective operation control is in line with the objectives of the industry, innovation, and infrastructure
program of the united nations sustainable development goals and the Paris agreement to fulfill the
nations’ commitment for sustainable growth and environment [54,55].
2. Overview of a Coal Power Plant Operation

Power plants can be classified into sub-critical, critical, supercritical, and ultra-supercritical based
on the steam conditions [56]. At critical condition, water is directly converted to steam and no longer
exists in separate phases. The critical state of water is defined at 22.1 MPa, and 374 ◦ C. Steam conditions
in sub-critical power plants are generally around 16.5 MPa and 538 ◦ C, while the steam parameters in
supercritical power plants are generally maintained between 22.1 MPa to 28.9 MPa, and below 600 ◦ C.
There is no uniform definition for ultra-super-critical steam conditions. However, steam conditions of
28.9 MPa or higher and 600 ◦ C or higher are generally maintained in ultra-supercritical power plants.
Supercritical power plants have higher thermal efficiency, stable combustion, better fuel economy,
reduced emissions, faster load following response than the sub-critical power plants, and are generally
employed for electrical power generation systems [57].
The 660 MWe 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 MWe to 660 MWe unit load.
The boiler furnace consists of diaphragm walls designed to improve the wall water tightness
and bear the high structural loads. The lower furnace water walls and hopper adopt spiral coils and
have enough cooling capacity under different boiler loads to effectively compensate for the thermal
deviation of furnace circumambient. Above the spiral tubes, vertical tubes are used to ensure the
heat transfer for fast response characteristics. The intermediary mixing header used for the transition
between spiral tubes and vertical tubes also balances the pressure across four sides of water walls.
The furnace and fuel burners’ operation is synchronized to ensure the flame burning until burnout,
higher combustion efficiency, and minimal NOx 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
Energies 2020, 13, 5592 5 of 33
fuel during start-up and support the combustion. In addition to that, three layers of big oil guns are
provided, consisting of 12 burners.
Energies 2020, 13, x FOR PEER REVIEW 5 of 35
Figure
Figure 1.
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coal combustion in the furnace with the combustion chamber’s uniform temperature field. The
tangential combustion system ensures uniform heat distribution and stable combustion under
varying boiler loads.
The fuel burners adopt a low NOx, 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
combustion and implement efficient combustion to reduce furnace temperature level and to control
NOx 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
Energies the
2020, 13, 5592burners melting by high temperature. Moreover, infrared flue gas temperature 6 of 33
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
is also equipped
system, and the withflamethe ultrasoniccameras
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operation for the power generation. The manufacturer designed the boiler’s operating
manufacturer designed the boiler’s operating parameters at boiler maximum continuous rating parameters at
boiler maximum continuous
(BMCR), listed in Table 1. rating (BMCR), listed in Table 1.
Table1.1. Designed
Table Designed operating
operating parameters
parameters of
of the
the boiler.
boiler.
Parameters
Parameters UnitUnit BMCR
BMCR LoadLoad
Superheated steam flow
Superheated steam flow t/h t/h 21182118
Superheater outlet steam pressure
Superheater outlet steam pressure MPa MPa 25.425.4
Superheater outlet steam temperature
Superheater outlet steam temperature ◦ C °C 571571
Reheat steam
Reheat steam flow
flow t/h t/h 17521752
Reheater
Reheater steam
steam inlet
inlet pressure
pressure MPa MPa 5.6 5.6
Reheater
Reheater steam
steam outlet
outlet pressure
pressure MPa MPa 5.4 5.4
Reheater ◦C
Reheater steam
steam inlet
inlet temperature
temperature °C 345345
Reheater ◦C
Reheater steam
steam outlet
outlet temperature
temperature °C 569569
Feed-water pressure MPa 29
Feed-water pressure ◦ CMPa 29
Feed-water temperature 300
Feed-water temperature °C 300
The
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schematic diagram
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coal power
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Figure2.2. Coal
Coal isis supplied
supplied
by
by a coal feeder, while primary air is provided by the primary air fan (PAF) to the boiler. Coal and
a coal feeder, while primary air is provided by the primary air fan (PAF) to the boiler. Coal and
primary
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coal mill
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boiler via
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coal burners.
burners.
Figure 2. Schematic diagram of coal power plant operation.

Figure 2. Schematic diagram of coal power plant operation.
The pulverized coal burns in the boiler with the secondary air provided by the forced draft fan
(FDF). The hot flue gas produced by the pulverized coal combustion is used for heat transfer to the
heating surfaces in the boiler, as shown in Figure 2. An induced draft fan (IDF) is used for the flue
gas suction from the boiler, which helps to maintain the desired furnace pressure and discharges
flue gas to the ambient environment through the stack. Upon leaving the boiler, flue gas is passed
through the APH to heat the primary and secondary air and enter the electrostatic precipitator (ESP)
and flue gas desulphurization (FGD) system particulate matter and sulfur oxides removal, respectively.
Finally, through strict control of emission parameters, flue gas is discharged to the ambient environment
through the stack.
Energies 2020, 13, 5592 7 of 33
Feed-water can be considered the power plant’s blood and is converted to superheated steam in
the boiler. The condensate pump directs feed-water, now also called condensate water in the condenser,
to the low-pressure heaters (LPH) for the feed-water heating. After passing through the LPH, feed-water
is passed through a deaerator where deoxygenation is applied to the feed-water. The feed-water pump
builds up the feed-water pressure and forces it to the series of high-pressure heaters (HPH) for further
feed-water heating. The steam extractions heat feed-water in the HPH and LPH from high-pressure
(HP) and intermediate-pressure (IP) turbines. After passing through the HPH, feed-water passes
through a series of heating surfaces like the economizer (ECO), low-temperature superheater (LTSH),
division platen superheater (DPSH), and the final superheater (FSH), etc. for producing superheated
main steam by the heat transfer from the flue gas. The superheated main steam is expanded in the
HP turbine, where its temperature and pressure are dropped during expansion. After leaving the
HP turbine, steam is directed to the low-temperature re-heater (LT REHEATER) and final re-heater
(FRH) for reheating the steam. Attemperation water flow is used to control the temperature of the
main steam and reheat steam. The reheat steam is expanded in the IP turbine and is directed to low-
pressure (LP) turbines A and B for further expansion. The steam, after expanding in LPA and LPB
turbines, is condensed to condensate water in the condenser, and the cycle continues. The expansion of
superheated and reheated steam in HP, IP, and LP turbines is used for the rotor rotation, coupled with
the generator (G) for electricity generation.
The Sahiwal coal power plant was commissioned in 2017 and has been operational and integrated
with the national grid since then. It is equipped with state-of-the-art measuring sensors as well as
an SIS data storage system. All the sensors measuring the control variables involved in this study
are hard sensors measuring the power plant’s different operating parameters. The data generated
constitute a large number of variables, with each variable having randomly distributed values. It has
been extensively reported in the literature that mining causal relationships out of such data is beyond
the capability of any type of multi-variate regression technique. AI-based data analytic techniques
perform significantly better for modeling such scenarios [39,58]. The sensors’ location for measuring
the power plant’s different operating parameters is shown with numbers in Figure 2. The sensors
make and model numbers are mentioned in Table 2.
Table 2. Details of sensors measuring the control variables.
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)
% O2 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)
Siemens
Condenser vacuum
STTRANS D PS III (Germany) 7MF4233-1GA50-2AB6-Z
Attemperation water flow rate Siemens (Germany) 7MF4533-1FA32-2AB6-Z
Turbine speed Braun (Germany) A5S
3. AI-Based Data Visualization and Process Modeling

AI-based process models are developed to learn the complex, non-linear, and interacting
relationship between the system’s input and output variables [59–61]. ANN and LSSVM are considered
the most efficient approximation tools of AI and can generalize the relationship between input and
output variables. These AI tools also have the proven ability to mine the hidden details in the training
data, thereby ensure their reliable applications in real-world problems [40,62,63].
Energies 2020, 13, 5592 8 of 33
3.1. Variables Selection for AI Process Modeling

In this paper, thirteen control variables were initially selected to model the 660 MWe supercritical
coal power plant’s ηthermal in Sahiwal, Pakistan. The variables were selected based upon the
recommendation of experienced plant managers of the power plant and a comprehensive literature
review [33,64–68]. 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
(O2 ). 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.
Table 3. Properties of coal (air-dried basis).
LHV
Properties of Coal/wt.%
MJ/kg
Moisture Volatile Mater Ash Sulfur Fixed Carbon
24.23
2.5 23.73 16.6 0.55 57.66
A total number of 27,267 data points were taken from the SIS of the power plant. Each data point
constitutes thirteen of the control variables’ numerical values and the output variable’s corresponding
values. All the variables have continuous numeric values, and the respective distributions are shown
in Figure 3. The data points were taken at intervals of one minute during the power plant’s controlled
continuous operation. It is important to note here that the power plant’s load varied from 50% to 100%
rated load of the unit as per the requirement and stability of the national grid of Pakistan to which the
power plant is connected to by a 500 kV transmission line. The statistics of training data of the initially
selected control variables for the development of AI process models are shown in Table 4. The operation
parameters that were essential for the stability and effective control of combustion include the coal
flow rate (Mc ), the air flow rate (Ma ), furnace pressure (Pf ), the APH air outlet temperature (Ta ), % O2
in flue gas at the boiler outlet (O2 ), the APH outlet flue gas temperature (Tfg ), feed water temperature
(FWT), main steam pressure (MSP), main steam temperature (MST), reheat steam temperature (RST),
condenser vacuum (Pvac ), 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.
The histograms of the control variables are shown in Figure 3a–m. Mc 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 Pf , while Figure 3d illustrates the
distribution of Ta . Ta 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 ηthermal .
Figure 3e represents the % O2 in the flue gas exhaust from the boiler. Tfg 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 Tfg is shown in Figure 3f.
The increase in Tfg 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.
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5000 6000
(a) (b)
5000
4000
4000
3000
Count
Count
3000
2000
2000
1000
1000
0 0
120 150 180 210 240 1500 1800 2100 2400
Mc (t/h) Ma (t/h)
4000
6000 (c) (d)
5000 3000
4000
Count
Count
2000
3000
2000
1000
1000
0 0
-200 -150 -100 -50 0 50 312 325 338
Pf (Pa) Ta (oC)
3000 3000
(e) (f)
2000 2000
Count
Count
1000 1000
0 0
3.5 4.0 4.5 5.0 5.5 6.0 120 130 140 150
O2 (%) Tfg (oC)
(g) (h)
5000
4000
4000
3000
Count
3000
Count
2000
2000
1000
1000
0 0
260 270 280 290 300 15 20 25
FWT ( C) o MSP (MPa)
Figure 3. Cont.
Energies 2020,
Energies 13, x5592
2020, 13, FOR PEER REVIEW 10 of
10 of 35
33
5000
(i) (j)
6000 4000
3000
Count
Count
4000
2000
2000
1000
0 0
550 560 570 552 558 564 570
MST (oC) RST (oC)
3000 (l)
(k)
6000
2000
Count
Count
4000
1000
2000
0 0
-94 -93 -92 -91 -90
0 15 30 45 60 75 90
Pvac (kPa)
AWF (t/h)
(m)
4000
3000
Count
2000
1000
0
2970 2980 2990 3000 3010 3020 3030
N (Rpm)
Figure 3.
Figure Histogramsofofinitially
3. Histograms initiallyselected
selected control
control variables,
variables, (a)(a)
coal coal
flowflow
raterate
(Mc(M c ), the
), (b) (b) air
theflow
air flow
rate
rate (M ), (c) furnace pressure (P ), (d) the APH air outlet temperature
(Ma), (c)a furnace pressure (Pf), (d)f the APH air outlet temperature (Ta), (e) a% O2 in flue (T ), (e) % O in flue gas at the
2 gas at the boiler
boiler (O
outlet outlet (O2 ),APH
2), (f) the
(f) the APHflue
outlet outlet
gas flue gas temperature
temperature (Tfg), (g) (T fg ),water
feed (g) feed water temperature
temperature (FWT), (h)(FWT),
main
(h) main steam pressure (MSP), (i) main steam temperature (MST), (j)
steam pressure (MSP), (i) main steam temperature (MST), (j) reheat steam temperature (RST), reheat steam temperature (RST),
(k)
(k) condenser
condenser vacuum
vacuum (Pvac(P (l)),attemperation
), vac (l) attemperationwaterwater
flowflow
rate rate (AWF),
(AWF), (m) (m) turbine
turbine speed speed(N).(N).
Table 4. Statistics of data for artificial neural network (ANN) process modeling of ηthermal of the power
plant.
Parameters Unit Min Avg Max

Coal flow rate (Mc) t/h 122 167 239
Air flow rate (Ma) t/h 1315 1850 2590
Furnace pressure (Pf) Pa −229 79 69
APH air outlet temperature (Ta) °C 307 325 350
% O2 in flue gas at boiler outlet (O2) % 3.5 4.8 5.9
APH outlet flue gas temperature (Tfg) °C 110 132 154
Feedwater temperature (FWT) °C 259 276 298
Energies 2020, 13, 5592 11 of 33
Table 4. Statistics of data for artificial neural network (ANN) process modeling of ηthermal of the
power plant.
Parameters Unit Min Avg Max

Coal flow rate (Mc ) t/h 122 167 239
Air flow rate (Ma ) t/h 1315 1850 2590
Furnace pressure (Pf ) Pa −229 79 69
APH air outlet temperature (Ta ) ◦C 307 325 350
% O2 in flue gas at boiler outlet (O2 ) % 3.5 4.8 5.9
APH outlet flue gas temperature (Tfg ) ◦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 (Pvac ) 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 (ηthermal) % 37.25 40.39 42.75
A regeneration system was installed at the power plant. Three HPH and four LPH utilized the
steam extractions from the HP and IP turbines for heating the feed-water. The distribution of FWT
achieved by regenerative heating is shown in Figure 3g. Figure 3h represents the operating range of
the MSP. The MSP was a critical parameter for the unit load generation. Similarly, the MST controllable
range was critically controlled as it affected the thermal efficiency, safety, and stable power plant
operation. The distribution of the MST is depicted in Figure 3i. As mentioned in Table 4, the MST
working range was a good operating range for evaluating its effect on the ηthermal of the power plant.
The reheat system positively influenced the ηthermal 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 ηthermal of
the power plant.
3.2. Elimination of Insignificant Control Variables

Each selected control variable’s significance on the output variable ought to be investigated to
train effective AI models. For physical system modeling through equations representing a process,
many input variables are required to describe an output variable accurately. In contrast, AI process
models have an excellent, proven ability to model such a system with fewer significant variables.
The selection of significant variables for AI process models development is crucially important,
and therefore, sensitivity analysis was performed to eliminate insignificant control variables that had a
minimal or negligible effect on the output variable [64].
3.2.1. Monte Carlo Experimentation for Significance Analysis

Monte Carlo experimentation was used to determine each variable’s effect on the output variables
considering the uncertainties in the control variables’ data set. Monte Carlo experiments operate by
generating the random values between the control variables [69,70]. The detailed description of the
working of Monte Carlo experimentation for the variable elimination purpose is described below.
1. A control variable is represented as xi where, i = 1,2, . . . ,13.

2. Each input vector containing all control variables xi can estimate output variable yo where,
o = 1,2,3, . . . .,m, where m is equal to the number of input vectors in the training data set.
Energies 2020, 13, 5592 12 of 33
3. The Monte Carlo experimentation can be illustrated by considering a control variable xi and
all other control variables as xj, were (i , j). As an example, let xi = x1 and xj = x2 , . . . .. , x13 ,
where (i , j).
4. Create n equal divisions (k) for xi between its range (ximax –ximin ) where, k = 1,2, . . . ,n.
5. Generate M random values for each division k (k = 1,2, 3, . . . , n) by keeping xik , 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 xjmin and xjmax is equal. The Mth input vector will be [x1kM , x2u , x3u , x4u
. . . , x13u ], and the corresponding output will be yokM .
6. The output value yokM is obtained by ANN and LSSVM prediction for the Mth input vector
[x1kM , x2u , x3u , x4u , . . . , x13u ]. Compute a mean value (µ) for each yokM having M replications,
which will give yok for each xik.
7. Repeat step number iii to step number vi for all remaining control variables
8. Compute ∆yi where ∆yi = yokmax − yokmin for all control variables xi and compute the summation
value Y for all ∆yi
X13
Y= ∆yi (1)
i=1
9. Compute the percentage significance (ri ) of each xi by dividing ∆yi with Y and multiplying it
by 100
∆yi
!
ri = × 100 (2)
Y
The least insignificant variables obtained from Monte Carlo experimentation performed on ANN
and LSSVM are shown later in the paper. The elimination of insignificant control variables is essential
to develop a useful process model based on ANN and LSSVM. This elimination of insignificant control
variables is usually obtained by coupling an algorithm (in our case, Monte Carlo experimentation)
to ANN and LSSVM. Therefore, the significance of control variables obtained by Monte Carlo
experimentation is confirmed with the Interval Adjoint Significance Analysis (IASA) method.
3.2.2. Interval Adjoint Significance Analysis (IASA)

The sensitivity-based method is advantageous in finding out the significance of control variables
in a given sample [71,72]. The deviation in output ∆y caused by the deviations in the input is defined
as sensitivity [71]. By finding out the sensitivities of given control variables, we can find out their
significance. In this sub-section, intervals are represented by uppercase letters (e.g., A, B, C, . h. .) andi
scalars are represented by lower case letters (e.g., a, b, c, . . .). The interval is defined as X = xl , xu ,
where l and u represent the lower and upper limit of the interval, respectively.
Interval arithmetic (IA) [73] is used to evaluate a function f [X] in a given range over a domain

and it gives us a guaranteed enclosure f [X] ⊇ f [x]x 3 [X] that contains all possible values of f (x)

for x 3 [X]. Similarly, interval evaluation yield enclosures [Vi ] for all intermediate variables Vi .
Reverse mode (also; adjoint mode) of algorithmic differentiation (AD) [74,75] can be applied here to
evaluate this interval function. Because reverse mode AD not only computes the primal values of
δY δY
intermediate and output variables, but it also computes their derivatives ( δX , ). Significance can
i δVi
also be calculated here by taking the absolute maximum of first-order derivative max∇[xi ] [ y] of an
input interval Xi and multiplying it by the width w[Xi ] = xui − xli of that interval [76].

SY (Xi ) = w[Xi ] ∗ max∇[xi ] [ y] (3)
To illustrate the basic working of the interval adjoint method, here is an example of significance
analysis on a very simple interval function f (X) = log(X0 · X1 )/10 + X1 /100. Let X = {X0 , X1 } ∈ R2 ,
where X0 = [1, 36, 000], X1 = [1, 36, 000]. The computational graph for function f is given in Figure 4a,
and a possible code list of f is listed in Table 5.
Energies 2020, 13, 5592 13 of 33
Energies 2020, 13, x FOR PEER REVIEW Table 5. A possible code list. 13 of 35
V 0 Table 5.=A possible code

X 0 list.
· X1
V1 == log(V0 ) ⋅
Y == V1 /10 + X1 /100( )
= /10 + /100
Afterthe
After thereverse
reverse mode,
mode, ADAD significance
significancevalues
valuesare
aregenerated
generated based
based onon
Equation
Equation(3) (3)
(see(see
the the
computational graph given in Figure 4b). Note the significance value of the intermediate
computational graph given in Figure 4b). Note the significance value of the intermediate node node is V1
equaltoto2.098,
isequal 2.098, which
whichisisway
waybelow
belowthethesignificance
significancevalues ofof
values thethe
other
othernodes. This
nodes. node
This nodeandandthe the
preceding nodes connecting to
preceding nodes connecting to V1 are removed and replaced with the mean value of interval as
are removed and replaced with the mean value of interval V1 as
shown in Figure 4c.
shown in Figure 4c.
Figure4.4.Computational
Figure Computational graphs for f (X)) == log((X0⋅ · X)/10
graphs for 1 ) /10++ X /100;
1 /100;
(a)(a) computational
computational graphgraph
of of
( )with primal values (the forward mode of AD), (b) computational graph of with first-order
f (X ) with primal values (the forward mode of AD), (b) computational graph of with first-order
derivatives
derivatives(reverse
(reversemode AD),and
mode of AD), andsignificance
significancevalues,
values, (c)(c)
newnew computational
computational graph
graph ( )f (after
of of X) after
thesignificance
the significanceanalysis.
analysis.
SomeEnergies
authors used
2020, 13, 5592IASA to determine the significance of the trained network’s input14parameters of 33
and hidden parameters based on Equation (3) [77]. After selecting insignificant nodes/parameters,
insignificant nodes can beused
Some authors removed
IASA to from the the
determine network, andofbias
significance for thenetwork’s
the trained next layer parameters/nodes
input parameters
is updated.
andUsing
hiddenthe IASA method
parameters ofEquation
based on finding(3) out theAfter
[77]. ranking of significant
selecting insignificantnodes defined in [77] of
nodes/parameters,
a trained insignificant
network, we nodesfind
canout variables
be removed from Othe
2, N, and Pand
network, f are theforleast
bias significant
the next and impact on the
layer parameters/nodes
network’s overall performance is negligible. The significance ranking is given in Figure in
is updated. Using the IASA method of finding out the ranking of significant nodes defined 5c.[77]
of a trained network, we find out variables O2 , N, and Pf are the least significant and impact on the
network’s overall performance is negligible. The significance ranking is given in Figure 5c.
25 (a) Monte Carlo-ANN Variables Elimination

Percentage Significance (%)
20
15
10
0
a
c
M
c
2
a
SP
P
M
T
N
O
f
fg
T
F
ST
va
T
FW
W
H
M
M
R
20
(b) Monte Carlo-LSSVM Variables Elimination
Percentage Significance (%)
15
10
0
c
2
SP
M
T
O
fg
a
N
f
M
F
T
ST
T
T
va
FW
W
H
P
M
M
R
Figure 5. Cont.
Energies 2020, 13, 5592 15 of 33
(c) IASA-Variables Elimination

15
Significance Ranking
10
0
a
c
M
c
2
M
P
T
SP
f
T
T
F
ST
va
fg
FW
T
P
H
W
M
R
M
A
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
Figure 5. Comparison of variables elimination techniques (a) Monte Carlo-ANN variables elimination
significance analysis (IASA)-variables elimination.
(b) Monte Carlo- least square support vector machine (LSSVM) variables elimination (c) interval
adjointThe results of both
significance Monte
analysis Carlo experimentation
(IASA)-variables for variables elimination and IASA indicated
elimination.
that the control variables O2 , N, and Pf were relatively insignificant variables and could be eliminated
fromresults
The the dataofsetboth
of control
Monte variables
Carloforexperimentation
the sake of decreasing
forthe computational
variables time, eliminating
elimination and IASA the indicated
redundant control variables, and achieving accurate results [69]. Therefore, ten control variables out of
that the control variables O2, N, and Pf were relatively insignificant variables and could be eliminated
thirteen initially selected control variables highlighted in blue in Figure 5a–c were finalized for training
from the
the data set ofmodeling
AI process controlofvariables
the power for theηsake
plant’s of decreasing the computational time, eliminating
thermal .
the redundant control variables, and achieving accurate results [69]. Therefore, ten control variables
out of 3.3. Self-Organizing Feature Map
thirteen initially selected control variables highlighted in blue in Figure 5a–c were finalized for
training the A self-organizing feature map,
AI process modeling of also
the known
powerasplant’s
the Kohonen feature
ηthermal. map, is primarily used in data
visualization techniques. An unsupervised learning machine maps the underlying possible statistical
features in training data on to the nodes in the two-dimensional lattice. Owing to its excellent ability
3.3. Self-Organizing Feature Map
to distribute the control variables data on the nodes in the form of homogenous groups, SOFM is
Aused in many real-life
self-organizing applications
feature map, [78–80]. In this as
also known work,
thea Kohonen
two-dimensional
featureoutput
map,layer carrying
is primarily used in
10 × 10 nodes was created, and the distribution of control variables data points are
data visualization techniques. An unsupervised learning machine maps the underlying possible shown in Figure 6.
The z-axis represents the frequency of occurrence of data points on a node. It is evident from Figure 6,
statistical features in training data on to the nodes in the two-dimensional lattice. Owing to its
that control variables data points were well-distributed on the 10 × 10 nodes on the output layer and
excellent ability directed
confidently to distribute the control
the construction of the variables data on
AI process models for the
the ηnodes in the form of homogenous
thermal of the power plant.
groups, SOFM is used in many real-life applications [78–80]. In this work, a two-dimensional output
layer carrying 10 × 10 nodes was created, and the distribution of control variables data points are
shown in Figure 6. The z-axis represents the frequency of occurrence of data points on a node. It is
evident from Figure 6, that control variables data points were well-distributed on the 10 × 10 nodes
on the output layer and confidently directed the construction of the AI process models for the ηthermal
of the power plant.
Energies 2020, 13, 5592 16 of 33
Figure6.6.Self-organizing
Figure Self-organizingfeature
featuremap
mapofofcontrol
controlvariables.
variables.
3.4. Development of ANN Process Model

3.4. Development of ANN Process Model
The multilayer perceptron (MLP) consisted of three layers. The first input layer consisted of
The multilayer perceptron (MLP) consisted of three layers. The first input layer consisted of
neurons, which number corresponded to the number of control variables. The MLP may consist
neurons, which number corresponded to the number of control variables. The MLP may consist of
of one or more hidden layers, depending on its architecture, and the optimum one is determined
one or more hidden layers, depending on its architecture, and the optimum one is determined by hit
by hit and trial methods [58]. It was proved that one hidden layer was enough to approximate the
and trial methods [58]. It was proved that one hidden layer was enough to approximate the
nonlinearity present in the data provided enough number of neurons were present in the hidden
nonlinearity present in the data provided enough number of neurons were present in the hidden
layer [81]. The neurons’ number in the output layer was equal to the number of outputs. The optimal
layer [81]. The neurons’ number in the output layer was equal to the number of outputs. The optimal
ANN, thus trained, had ten neurons in the input layer, 17 neurons in the hidden layer, and one neuron
ANN, thus trained, had ten neurons in the input layer, 17 neurons in the hidden layer, and one
in the output layer. The MLP architecture is represented as [10-17-1] and shown in Figure 7.
neuron in the output layer. The MLP architecture is represented as [10-17-1] and shown in Figure 7.
The feed-forward backpropagation network algorithm was used to develop a process model for
the power plant’s ηthermal . It has a well-established ability to dig and learn the complex nonlinearities
and interactions out of high dimensional and complex input space data [82–84]. 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].
The ANN training was carried out until one of the two stopping criteria was met, i.e., either a
0.0000001 change in convergence error or a maximum number of epochs was reached. The best MLP
architecture is represented as [10-17-1] and shown in Figure 7. The trained ANN achieved a good
correlation coefficient (R) value, i.e., 0.917 for training, 0.911 for validation, and 0.92 for testing purposes
during ANN development.
3.5. Development of LSSVM Process Model

The support vector machine is a powerful machine learning tool and is utilized for non-linear
classification, function approximation, and density estimation [86–88]. LSSVM can be trained
more effectively for modeling a system based on the structural risk minimization (SRM) principle.
The Gaussian kernel function is generally used for mapping the complicated non-linear relationship
between the input and output variables onto the feature space [40]. It is essential to mention here that
the training data set should be standardized for developing a useful LSSVM model. Bayesian optimizer
and expected improvement per second plus acquisition function was used to optimize the regularized
Energies 2020, 13, 5592 17 of 33
constant (C) and epsilon (ε) parameters for LSSVM [89–92]. Thus, the optimal value for C and ε for the
developed LSSVM model was 305.30 and 0.0057, respectively. The R-value for the developed LSSVM
model was2020,
Energies equal
13, xto 0.922.
FOR PEER REVIEW 17 of 35
Figure Structure
7. 7.
Figure ofofMulti-Layer
Structure Multi-LayerPerceptron.
Perceptron.
3.6. Evaluation Criteria

The feed-forward backpropagation network algorithm was used to develop a process model for
the
Thepower plant’s ηof
performance the. Itdeveloped
thermal has a well-established ability to
AI process models candig
beand learn the
assessed complex
based upon nonlinearities
the prediction
and interactions out of high dimensional and complex input space data [82–84].
error against the validation data set that was unseen to the networks during their development Gradient descent
phase.
with momentumerror
Root-mean-square was (RMSE),
employednormalized
as a trainingRMSE
function, and tangent
(NRMSE), hyperbolic
and mean was used
absolute as a transfer
percentage errors
function
(MAPE) werebetween the on
calculated layers
the of MLP for
models the neural
predicted network
values model development
to evaluate [64,85].
their robustness and effectiveness
for modeling the ηthermal of the power plant. The definitions of the error criteria are met,
The ANN training was carried out until one of the two stopping criteria was giveni.e., either a
below:
0.0000001 change in convergence error or a maximum number of epochs was reached. The best MLP
architecture is represented as [10-17-1] and shown t n in Figure 7. The trained ANN achieved a good
v
1X
correlation coefficient (R) value, i.e.,RMSE 0.917
= for training, yi )2 for validation, and 0.92 for testing
( ŷi − 0.911 (4)
purposes during ANN development. n
i=1
3.5. Development of LSSVM ProcessNRMSE

Model = RMSE
∗ 100% (5)
ymax− ymin
The support vector machine is a powerful machine learning tool and is utilized for non-linear
n
ŷi − yi

classification, function approximation, and 1density X estimation [86–88]. LSSVM can be trained more
MAPE = ∗ 100% (6)
effectively for modeling a system based onn the structural yi risk minimization (SRM) principle. The
i=1
Gaussian kernel function is generally used for mapping the complicated non-linear relationship
where n is thethe
between sample
input size, ŷi and variables
and output yi are the onto
predicted and actual
the feature spacevalues, ymax
[40]. It is and ymin
essential are the maximum
to mention here that
and the
minimum
trainingvalue setyi ,should
data of respectively.
be standardized for developing a useful LSSVM model. Bayesian
optimizer and expected improvement per second plus acquisition function was used to optimize the
regularized constant (C) and epsilon (ε) parameters for LSSVM [89–92]. Thus, the optimal value for
C and ε for the developed LSSVM model was 305.30 and 0.0057, respectively. The R-value for the
developed LSSVM model was equal to 0.922.
Energies 2020, 13, 5592 18 of 33
3.7. External Validation Case of Trained AI Process Models

After training the ANN and LSSVM process models using the control variables dataset consisting
of 27,267 observations, the developed models were externally validated using a new operating
data of the power plant that was “unseen” to ANN and LSSVM models during their development.
An additional 28,460 observations of the control variables that serve as a characteristics data set for
external validation cases were taken from the SIS to compare the ANN and LSSVM models’ predictions
with the power plant’s actual ηthermal. The ANN and LSSVM predicted response for actual vs.
predicted ηthermal , 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 ηthermal , in terms of the evaluation criteria, is presented
in Table 6.
Actual-ηthermal ANN-ηthermal
(a) 4.5
40
3.0
1.5
ηthermal (%)
30
Residual
0.0
20 -1.5
-3.0
10
-4.5
0 5000 10,000 15,000 20,000 25,000
Data Points
Actual-ηthermal (LSSVM-ηthermal)
(b) 4.5
40
3.0
ηthermal (%)
30 1.5
Residual
0.0
20 -1.5
-3.0
10
-4.5
0 5000 10,000 15,000 20,000 25,000

Data Points
External
Figure 8.Figure validation
8. External of ANN
validation and
of ANN LSSVM
and LSSVMmodels. (a)ANN
models. (a) ANN prediction
prediction (b) LSSVM prediction.
(b) LSSVM
prediction.
Table 6. Comparison of ANN and LSSVM model prediction performance.
RMSE NRMSE MAPE

Model
(%) (%) (%)
ANN 0.5051 8.2159 1.016
LSSVM 0.7164 11.6538 1.2819
It is clear from Table 6 that various error estimations, i.e., RMSE, NRMSE, and MAPE for the
Energies 2020, 13, 5592 19 of 33
Table 6. Comparison of ANN and LSSVM model prediction performance.
RMSE NRMSE MAPE

Model
(%) (%) (%)
ANN 0.5051 8.2159 1.016
LSSVM 0.7164 11.6538 1.2819
It is clear from Table 6 that various error estimations, i.e., RMSE, NRMSE, and MAPE for the ANN
predicted response was 0.5051%, 8.2159%, and 1.016% respectively, which was lower than the ones
for LSSVM model predictions, i.e., 0.7164%, 11.6538%, and 1.2819%, respectively. It confirmed that
the ANN process model had effectively modeled the power plant’s ηthermal 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

Generally, power generation from a power plant is either 50%, 75%, or 100% of unit load
capacity, as determined by the national grid’s demand. The control variables’ operating regimes are
different under the different power generating capacity of the power plant. The minimum, average,
and maximum control variables at 50%, 75%, and 100% unit load are listed in Table 7.
Table 7. Operating ranges of parameters at 50%, 75%, and 100% unit load.
50% Unit Load 75% Unit Load 100% Unit Load

Parameters Unit
Min Avg Max Min Avg Max Min Avg Max
Mc t/h 128 134 140 170 178 185 219 227 235
Ma t/h 1326 1386 1456 1896 1973 2076 2206 2303 2395
Ta ◦C 315 320 330 320 330 335 335 342 350
Tfg ◦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
Pvac 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
It was crucial to evaluate the effect of selected control parameters on the output variable by
keeping remaining control variables at the select value (generally the mean values) corresponding to
the specific unit load to simulate the power plant operation’s actual operating scenario. The individual
or combined effect of the essential control variables on the ηthermal was evaluated.
The detailed procedure for creating the experiments is discussed below:
1. Let xi be the control variable(s) where i = 1,2,3 . . . .,10, whose effect is to be studied, and xj (i , j)
represents the remaining control variables.
2. Let yo represent the output value corresponding to xi where o = 1.
3. Divide the range of xi (ximax –ximin ) in n equal step size (d) where d = 1,2, . . . .,n
4. Keep the remaining control variables xj constant at the selected value at every step size d. The input
vector with xi say, i = 1, step size d and remaining input control variables xj is represented as
[x1d , x2d , x3d , x4d , x5d , x6d , x7d , x8d , x9d , x10d ]. Create “m” replications for the input vector for
every step size d.
5. Generate “m” Gaussian noise values (g) from the 1% range value of control variables xi and xj .
Add g with the input vector for all step size d.
Energies 2020, 13, 5592 20 of 33
6. Predict the developed ANN process model from an input vector and compute mean (µ) and
standard deviation (σ) of the predicted values yod against xid input vector, which is represented
as µyod and σyod relative to xid input vector, respectively.
7. Calculate upper control limit (UCL = µyod + 2* σyod ) and lower control limit (LCL = µyod − 2* σyod )
and plot mean, UCL and LCL against xi .
4.1. Effect of MST and RST on ηthermal of Power Plant

MST and RST are critically and simultaneously controlled power plant operating parameters.
The power production was generally maintained at either 50%, 75%, or 100% unit load, which depends
on the connected national grid’s demand and stability. MST and RST parameters under such an
operation scenario should be effectively controlled within the operating control limits to ensure
economical, safe, and fuel-efficient power production from the power plant.
To evaluate the effect of MST and RST on the power plant’s ηthermal 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 ηthermal .
The operating range of MST (x1 ) and RST (x2 ) 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 (xj = 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 ηthermal of the power plant and mean (µyod ), and the standard deviation (σyod )
of the predicted values of ηthermal (yo ) 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.
Figure 9a–c relates to the effect of the MST and RST on the power plant’s ηthermal . A general
increasing trend of the power plant’s ηthermal was observed when MST and RST increased from 550 ◦ C
to 570 ◦ C. The power plant’s ηthermal 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].
Figure 9d compares the effect of the MST and RST on the power plant’s ηthermal at 50%, 75%,
and 100% unit load. It was apparent from Figure 9d that the ηthermal 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 ηthermal ,
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.
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 ηthermal of the power plant and mean (μyod), and the standard
deviation (σyod) of the predicted values of ηthermal (yo) was calculated. The procedure was repeated for
remaining division values (d), and mean UCL and LCL trend lines against MST and RST were plotted
Energies 2020, 13, 5592 21 of 33
for 50%, 75%, and 100% unit load and represented in Figure 9a–c.
RST (oC)
RST (oC)
550 555 560 565 570
40.0 550 555 560 565 570
(a) 50% Unit Load 41.2 (b) 75% Unit Load
UCL
UCL
39.6 Mean
Mean
LCL 40.8
ηthermal (%)
LCL
ηthermal (%)
39.2
40.4
38.8
40.0
38.4
550 555 560 565 570 39.6
550 555 560 565 570
MST (oC)
MST (oC)
RST (oC) RHT (oC)

550 555 560 565 570 550 555 560 565 570
42.2
(c) 100% Unit Load (d) 1567 MW
1569 MW
UCL 41.6
Mean 1585 MW
42.0
ηthermal (%)
LCL
ηthermal (%)
40.8 1206 MW
1211 MW
41.8 40.0
1242 MW 904 MW
39.2 100% Unit Load
41.6 932 MW 914 MW
75% Unit Load
50% Unit Load
38.4
550 555 560 565 570 550 555 560 565 570
o
MST ( C) MST (oC)
Figure 9.
Figure Effect of main steam temperature
9. Effect temperature (MST)
(MST)and
andreheated
reheatedsteam
steamtemperature
temperature(RST)(RST)on onηη thermal of
thermal
power plant. The parameters of other input control variables: (a) M = 134 t/h,
power plant. The parameters of other input control variables: (a)c Mc =134 t/h,a Ma =1386 t/h, M = 1386 t/h, T =
a T320
◦ C,
a = 320
Tfg T
°C, =fg113 ◦
= 113C,°C,FWTFWT = =260 ◦
260 °C, MSP == 13.6
C, MSP 13.6 MPa, vac= =
MPa, PPvac −94.6
−94.6 kPakPa
andandAWFAWF = t/h
= 18 18 t/h
(b) (b)
Mc M c = 178
= 178 t/h, t/h,
Ma
a = 1973 a =°C, = FWT °C,=MSP MPa,= P18.9 = −92.0
◦ 125 ◦ C, 281 =◦18.9
=M1973 t/h, Tt/h,
a =T330 330Tfg =C,125
Tfg°C, FWT
= 281 C, MSP vac = MPa, Pvacand
-92.0 kPa AWFkPa = 15and
t/h
AWF = 15 t/h (c) M = 227 t/h, M a = 2303 t/h, Ta = 342 ◦ C, T = 123 ◦ C, FWT = 296 ◦ C, MSP = 24.2 MPa,
(c) Mc =227 t/h, Ma = 2303 t/h, Ta = 342 °C, Tfg = 123 °C, FWT = 296 °C, MSP= 24.2 MPa, Pvac = −91.0 kPa
c fg
and = −91.0
Pvac AWF = 15kPa
t/hand ηthermal=comparison
(d) AWF 15 t/h (d) ηthermal
at 50%,comparison
75% and 100% at 50%,
unit75%
load.and 100% unit load.
4.2. Effect of Tfg on the ηthermal of Power Plant

Figure 9a–c relates to the effect of the MST and RST on the power plant’s ηthermal. A general
Tfg is atrend
increasing critically controlled
of the operating
power plant’s parameter
ηthermal of the boiler
was observed whenoperation.
MST and RST It indicates the from
increased efficiency of
550 °C
fuel570
to combustion
°C. The powerand heat transfer
plant’s to the
ηthermal at heating surfaces
50%, 75%, inside unit
and 100% the boiler, thereby
load had, on directly
average,influencing
a relative
the power
increase ofplant’s
1.50%, η1.50%,
thermal . The
and increase
1.32%, in this temperature
respectively, with beyond
every 10 °C its normal
rise in MST controllable
and RST. operating
The upper
range, as mentioned in Table 7, indicates that heat transfer from the
control limits of the temperatures were restricted by material properties [93]. 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 ηthermal .
The effect of Tfg on the power plant’s ηthermal at 50%, 75%, and 100% unit load is given in
Figure 10a–c. The ηthermal of the power plant decreased with the increase in Tfg . With every 5 ◦ C rise
in the Tfg , the relative decrease in the power plant’s ηthermal was an average of 0.65%, 0.25%, and 0.28%
at 50%, 75%, and 100% unit load respectively.
Figure 10d compares the effect of Tfg on the power plant’s ηthermal at 50%, 75%, and 100% unit
load. At 100% unit load, the power plant’s ηthermal 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 ηthermal 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 ηthermal . This was explained by the fact that the increase in Tfg indicated the
decreased heat transfer from flue gas to the heating surfaces, which was responsible for the decrease
fuel combustion and heat transfer to the heating surfaces inside the boiler, thereby directly influencing the
power plant’s ηthermal. 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
Energies 2020, 13, 5592 22 of 33
control of boiler operation resulting in the power plant’s reduced ηthermal.
The effect of Tfg on the power plant’s ηthermal at 50%, 75%, and 100% unit load is given in Figure
10a–c.
in the ηThe ηthermal
thermal of the
of the power
power plantThe
plant. decreased with the
temperature wasincrease in controlled
critically Tfg. With every 5 °C
by the rise in thesoot
significant Tfg,
the relative
blowing on decrease in the power
various heating plant’s
surfaces ηthermal
in the boiler,was an average
improved fuelofcombustion,
0.65%, 0.25%, and irreversibility
lower 0.28% at 50%,
75%, and
losses 100%
in the unitand
boiler, load respectively.
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 [93–95].
40.2 (a) 50% Unit Load 41.1

UCL (b) 75% Unit Load
Mean 41.0 UCL
39.9 LCL Mean
LCL
ηthermal (%)
ηthermal (%)
40.9
39.6 40.8
39.3 40.7
40.6
39.0
10813, x FOR
Energies 2020, 112PEER 116
REVIEW 120 124 23 of 35
124 128 132 136 140
Tfg (oC)
Tfg (oC)
1567 MW
42.0 (d) 1563 MW 1576 MW
(c) 100% Unit Load 1571 MW
42.2 UCL
41.4
Mean
LCL 1212 MW
ηthermal (%)
ηthermal (%)
42.1 40.8 1218 MW

1208 MW
1215 MW
42.0 40.2 901 MW
913 MW 100% Unit Load

39.6
41.9 919 MW 75% Unit Load
907 MW
50% Unit Load
39.0
41.8 110 115 120 125 130 135 140
116 120 124 128 132 136 Tfg (oC)
Tfg (oC)
Figure10.
Figure EffectofofTT
10.Effect ononηthermal
fgfg ηthermal of the
of the power
power plant.
plant. TheThe parameters
parameters of of other
other input
input control
control variables:
variables:
(a)MM
(a) = 134
c =c 134 t/h,t/h, a = 1386
Ma =M1386 t/h, Tt/h, = 320
Ta °C,
a = 320 FWT ◦ C, FWT = 260 ◦ C, MSP = 13.6 MPa, MST = 567 ◦ C,
= 260 °C, MSP = 13.6 MPa, MST = 567 °C, RHT = 567
RHT
°C, Pvac==567
−94.6◦ C, P
kPavac
and = AWF
−94.6=kPa18 t/hand
(b)AWF = 18t/h,
Mc =178 t/hM(b) Mc =t/h,
a = 1973 178Tat/h,
= 330 = FWT
Ma°C, 1973 =t/h, a = MSP
281T°C, 330 ◦=C,
FWT
18.9 = 281
MPa, MST ◦ C,=MSP = 18.9
562 °C, RHT MPa,
= 564 °C, =
MST 562
Pvac ◦
= −92.0 kPa =and
C, RHT ◦
564AWF 15 =
C, P=vac t/h−92.0
(c) MkPa and
c =227 Ma == 2303
AWF
t/h, 15 t/h
(c)TM
t/h, = 227
a=c342 t/h, M
°C, FWT a = 2303
= 296 °C, MSP a = MPa,
t/h, =T24.2 342 ◦ C,
MST FWT = °C,
= 567
◦ C, MSP = 24.2 MPa, MST = 567 ◦ C,
296RHT = 568 °C, Pvac = −91.0 kPa and AWF
15 t/h=(d) vac = −91.0 kPa = 100%
◦ C, P
= RHT 568ηthermal comparison and75%
at 50%, AWFand 15 t/hunit
(d) load.
ηthermal comparison at 50%, 75% and 100%
unit load.
Figure 10d compares the effect of Tfg on the power plant’s ηthermal at 50%, 75%, and 100% unit
load. AtThe heatunit
100% inputs
load,required to achieve
the power plant’sthe power
ηthermal wasplant’s overall
relatively thermal
higher efficiencies
than 75% and 50% at unit
50%,load
75%,
and 100% unit load are shown in Figure 10d. At the highest
due to the improved heat transfer conditions. At 50% unit load,thermal η of the power plant, i.e.,
there was a sharp decreasing trend 39.97%,
40.96%, and 42.23% at 50%, 75%, and 100% unit load, the
of the power plant’s ηthermal with increased flue gas temperature after APH.corresponding heat inputs
Thus, the were 901 MW,
temperature
1208 MW, and 1563 MW, respectively. The possible lowest overall thermal efficiencies,
should be effectively controlled mainly at 50% unit load as it had a relatively more significant adverse i.e., 39.18%,
40.66%,
impact onand 41.87%plant’s
the power at 50%, 75%,. This
ηthermal and was
100% unit load,
explained by were 919that
the fact MW, the1218 MW,inand
increase 1576 MW,
Tfg indicated
the decreased heat transfer from flue gas to the heating surfaces, which was responsibleincreased
respectively. Resultantly, heat input values at lower thermal efficiencies caused by the for the
temperature
decrease in theof flue of
ηthermal gas after
the powerAPH were
plant. comparatively
The temperature was higher for thecontrolled
critically same unit byload generation.
the significant
It accounted
soot blowingfor onthevarious
effectiveheating
control of the flue in
surfaces gasthetemperature after APH near
boiler, improved fuel the lower controllable
combustion, lower
limits, as mentioned in Table 7, to achieve optimal η thermal under various
irreversibility losses in the boiler, and significant operational control. The lower power plant operating
limit ofmodes.
the
temperature was strictly controlled as it may cause corrosion to the downstream equipment due to
4.3. Effect of Ta and Tfg on the ηthermal of Power Plant
the condensation of flue gas containing various acidic gases [93–95].
Ta and
The heat Tinputs
fg is an essential
required topair of power
achieve plantplant’s
the power controloverall
parameters forefficiencies
thermal the effective
atboiler operation
50%, 75%, and
and improved
100% unit load areηthermal
shownunder various
in Figure 10d.power
At theplant operating
highest ηthermal ofmodes. Flueplant,
the power gas leaving the boiler
i.e., 39.97%, had
40.96%,
and 42.23% at 50%, 75%, and 100% unit load, the corresponding heat inputs were 901 MW, 1208 MW,if
some thermal energy depending upon the boiler thermal efficiency, which would otherwise be lost
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
Energies 2020, 13, 5592 23 of 33
not recovered and decrease the power plant’s ηthermal . A part of this energy was recovered by the air
passing through the APH. Thus, the pre-heated air improved the power plant’s ηthermal and helped
coal combustion at high air temperature for producing high-quality steam. The combined effect of
corresponding to the 50%, 75%, and 100% unit load, as mentioned in Table 7. The further treatment
rising Ta and falling Tfg represented the flue gas’s waste heat recovery system.
for constructing the experiment’s design for evaluating the effect of Ta and Tfg on the power plant’s
The operating ranges of two temperatures were divided into ten equal divisions to evaluate
ηthermal was executed as per the procedure described in Section 4.
the combined effect of Ta and Tfg . Ta was varied systematically in the ascending order, while Tfg
The combined effect of Ta and Tfg at 50%, 75%, and 100% unit load on the power plant’s ηthermal is
was decreasing systematically. The remaining operating parameters were kept at the average values
represented in Figure 11a–c. As expected, an increasing trend in the power plant’s ηthermal was
corresponding to the 50%, 75%, and 100% unit load, as mentioned in Table 7. The further treatment
observed at 50%, 75%, and 100% unit load with the increase in Ta and fall in Tfg. The relative increase
for constructing the experiment’s design for evaluating the effect of Ta and Tfg on the power plant’s
in the ηthermal of the power plant on an average was 0.43%, 0.44%, and 0.42% at 50%, 75%, and 100%
ηthermal was executed as per the procedure described in Section 4.
unit load respectively against every 5 °C rise in Ta and 5 °C fall in Tfg. It is essential to mention here
The combined effect of T and Tfg at 50%, 75%, and 100% unit load on the power plant’s ηthermal
that the extent of waste heat arecovery was limited to the dew point temperature of flue gas and the
is represented in Figure 11a–c. As expected, an increasing trend in the power plant’s ηthermal was
APH material metallurgy [96,97].
observed at 50%, 75%, and 100% unit load with the increase in Ta and fall in Tfg . The relative increase
Figure 11d compares the combined effect of Ta and Tfg on the power plant’s ηthermal at 50%, 75%,
in the ηthermal of the power plant on an average was 0.43%, 0.44%, and 0.42% at 50%, 75%, and 100%
and 100% unit power generation capacity. At 100% unit power generation capacity, the power plant’s
unit load respectively against every 5 ◦ C rise in Ta and 5 ◦ C fall in Tfg . It is essential to mention here
ηthermal was relatively higher than 50% and 75% unit power generation capacity because of its
that the extent of waste heat recovery was limited to the dew point temperature of flue gas and the
improved heat rate.
APH material metallurgy [96,97].
Tfg (oC)
Tfg (oC)
138 134 130 126 122
124 120 116 112 108 41.2
(a) 50% Unit Load (b) 75% Unit Load
UCL UCL
39.8
Mean 41.0 Mean
ηthermal (%)
LCL
ηthermal (%)
LCL
39.6
40.8
39.4
40.6
39.2
40.4
316 320 324 328 332 320 324 328 332 336
Ta (oC)
Ta (oC)
Tfg (oC)
Tfg (oC) 130 122 114
129 125 121 117 113 138 130 122
125 117 109
(c) 100% Unit Load 42.5 1566 MW
(d)
UCL
42.0 1580 MW 1561 MW
42.2 Mean
1572 MW
LCL
ηthermal (%)
41.5
1205 MW
ηthermal (%)
41.0 1217 MW
42.0
40.5 1212 MW
1221 MW
40.0 905 MW
913 MW 50% Unit Load
41.8
39.5 75% Unit Load
909 MW
100% Unit Load
39.0 917 MW
336 340 344 348 352
o
Ta ( C) 318 324 330 o 336 342 348
Ta ( C)
Figure11.
Figure 11.Effect
EffectofofTTa aand
andTTfgfgononthe
theηη thermal of power plant.
thermal plant. The parameters of of other
other input
input control
control
variables (a) Mc = 134 t/h, Ma = 1386 t/h, MSP = 13.6 MPa, MST = 567 C, RHT = 567 C, Pvac = −94.6 kPa
variables (a) M c = 134 t/h, M a = 1386 t/h, MSP = 13.6 MPa, MST = ◦
567 °C, RHT = 567◦ °C, Pvac = −94.6 kPa
and AWF == 18 t/h (b) M
and AWF Mcc ==178
178t/h,
t/h,MM a =
a = 19731973t/h,t/h,
FWTFWT = 281
= 281 ◦
°C, MSP
C, MSP = 18.9
= 18.9 MPa, MPa,MSTMST= 562=°C, ◦ C,
562RHT
= 564=°C,
RHT ◦
vac =kPa
564PvacC,=P−92.0 −92.0 andkPa
AWF 15 t/h=(c)15Mt/h
and= AWF c = (c)
227M c =M
t/h, a = t/h,
227 a =FWT
2303Mt/h, 2303=t/h,296FWT = 296
°C, MSP ◦ C,
= 24.2
MPa,=MST
MSP 24.2 =MPa,
567 °C,
MSTRHT = 567 ◦
= 567C,°C,
RHTPvac = = −91.0
567 C,◦ and=AWF
kPaPvac −91.0= 15
kPat/hand ηthermal=comparison
(d) AWF 15 t/h (d) ηat 50%,
thermal
75% and 100%
comparison unit 75%
at 50%, load.and 100% unit load.
Trend lines in Figure 11d show the heat inputs to achieve the power plant’s thermal efficiency
at 50%, 75%, and 100% unit load. At the highest ηthermal 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
Energies 2020, 13, 5592 24 of 33
Figure 11d compares the combined effect of Ta and Tfg on the power plant’s ηthermal at 50%,
75%, and 100% unit power generation capacity. At 100% unit power generation capacity, the power
plant’s ηthermal was relatively higher than 50% and 75% unit power generation capacity because of its
improved heat rate.
Trend lines in Figure 11d show the heat inputs to achieve the power plant’s thermal efficiency
at 50%, 75%, and 100% unit load. At the highest ηthermal 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 ηthermal 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 Ta and Tfg promised
the boiler’s efficient operation and the improved ηthermal of the power plant.
4.4. Effect of Change in All Control Variables on the ηthermal of Power Plant
In this sub-section, first, the power plant’s possible worst operating scenario during which overall
thermal efficiencies of the power plant may drop to the lowest value at 50%, 75%, and 100% unit load
was constructed. Further, in the Monte Carlo experiments, certain adjustments in operating parameters
were simulated to recover the power plant’s ηthermal . This strategy of adjusting operating parameters
can be practically implemented to achieve the maximum overall thermal efficiencies at various power
plant operation modes.
AWF controls the temperature of the MST and RST. Low adjustment of AWF, i.e., the exceptionally
high flow rate, may significantly drop the MST and RST. However, the MSP may have a little increase
depending upon the AWF and unit load. Moreover, MST and RST drop may also be linked with
lower heat transfer to the boiler’s heating surfaces, indicated by higher Tfg . In such an operating
scenario, FWT would also drop, and the Pvac would be slightly increased. Meanwhile, the Mc , Ma ,
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.
To conduct the experiments for optimizing the ηthermal of the power plant at 50%, 75%, and 100%
unit load, Mc , Ma , Tfg , Ta , MSP, Pvac , 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.
The effect of changes in control variables (MST and RST are conveniently represented along the
lower and upper x-axes while the operating range of remaining control variables is mentioned in the
description of Figure 12) on the power plant’s ηthermal at 50%, 75%, and 100% unit load is shown in
Figure 12a–c. At 50% unit load, the power plant’s ηthermal 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 ηthermal 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 ηthermal of the power plant on an average was equal to 3.36%, 3.21%,
and 4.29% respectively.
Figure 12a–c. At 50% unit load, the power plant’s ηthermal 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 ηthermal 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
Energies 2020, 13,increase
5592 in the ηthermal of the power plant on an average was equal to 3.36%, 3.21%,
25 ofand
33
4.29% respectively.
RST (oC)
RST (oC)
550 555 560 565 570 550 555 560 565 570
40.8 (a) 50% Unit Load 41.6
(b) 75% Unit Load
UCL UCL
Mean Mean
40.0 40.8
LCL
ηthermal (%)
LCL
ηthermal (%)
39.2
40.0
38.4
39.2
37.6
550 555 560 565 570 550 555 560 565 570 26 of 35
Energies 2020, 13, x FOR PEER REVIEW
MST (oC)
MST (oC)
RST (oC) RST (oC)

550 555 560 565 570 550 555 560 565 570
42.4 42.4
(c) 100% Unit Load (d)
UCL 1602 MW 1564 MW
41.6 41.6
Mean
LCL
ηthermal (%)
ηthermal (%)
40.8 1197 MW
40.8
40.0 889 MW
40.0 1233 MW
1711 MW
39.2
39.2
50% Unit Load
38.4 1285 MW 75% Unit Load
38.4 958 MW 942 MW 100% Unit Load
37.6
550 555 560 565 570 550 555 560 565 570
MST (oC) MST (oC)
Figure
Figure12. 12.Effect
Effect of
of change
change in all operating
operating parameters
parameterson onηηthermal
thermal of power plant
plant (a)
(a) M = 140~133t/h,
Mcc=140~133 t/h,
MMa a==1456~1386
1456~1386 t/h,
t/h, = 330~320
TaT=a 330~320 °C,◦TC, = fg
fg T = 125~113
125~113 ◦ C, FWT
°C, FWT = 259~260
= 259~260 ◦ C, =MSP
°C, MSP = 13.9~13.6
13.9~13.6 MPa, PMPa,
vac =
Pvac = −94.5~−94.4
−94.5~−94.4 kPa and kPaAWF and= AWF = 82~0
82~0 t/h (b) M Mc = 185~178
= 185~178
t/hc (b) t/h, Mat/h, Ma = 2075~1973
= 2075~1973 t/h, Tat/h, Ta = 335~330
= 335~330 °C, Tfg◦ =C,
Tfg = 138~125
138~125 ◦
°C, FWT C, FWT = 280~281
= 280~281 ◦
°C, MSPC, MSP = 19.1~18.9
= 19.1~18.9 Pvac P=vac
MPa,MPa, = −92.1~
−92.1~ −92.0−92.0
kPa and AWF
kPa and = 85~0
= 85~0
AWF t/h (c)
t/h
c = 236~227 a = 2421~2303 Ta = 350~342 Tfg = 130~123
°C, Tfg◦=C,130~123 FWT = 295~296
M(c)c =M236~227 t/h, M a =M
t/h, 2421~2303 t/h, Tat/h,
= 350~342 °C, FWT ◦ C,
= 295~296 °C, MSP◦ C,=
MSP = 24.4~24.2
24.4~24.2 Pvac = −91.1~−91,0
= −91.1~−91,0
MPa, PvacMPa, kPa and AWF kPa= 93~0 t/h (d)=η93~0
and AWF thermal comparison at 50%,
t/h (d) ηthermal 75% andat100%
comparison 50%,
75%load.
unit and 100% unit load.
Figure12d
Figure 12dcompares
comparespowerpowerplant
plantthermal
thermalefficiencies
efficienciesagainst
againstthe thechanges
changesin inall
allcontrol
controlvariables
variables
at50%,
at 50%,75%,
75%,and and100%
100%unit
unitload.
load.AtAt100%
100%unit unitload,
load,thethepower
powerplant’s
plant’sthermal
thermalefficiency
efficiencywas washigher
higher
than75%
than 75%andand 50%50% unit
unit load.
load. ItItexplained
explainedthat thatthe
theheat
heatrate
ratewaswasgenerally
generallyimproved
improvedatatthe thepower
power
plant’ssupercritical
plant’s supercriticaloperating
operating mode.
mode.
Theheat
The heatinput
inputvalues
valuesfor
forachieving
achievingthe thethermal
thermalefficiencies
efficienciesagainst
againstthethechanges
changesin incontrol
controlvariables
variables
at 50%, 75%, and 100% unit load are mentioned as MW values in Figure
at 50%, 75%, and 100% unit load are mentioned as MW values in Figure 12d. It is observed from Figure 12d. It is observed from
Figure 12d that at a specific unit load, heat input values at higher operating limits
12d that at a specific unit load, heat input values at higher operating limits of the control variables were of the control variables
were significantly
significantly reduced reduced as compared
as compared to lower to lower
operatingoperating limits
limits of the of the control
control parameters.
parameters. The heatThe heat
input
input values corresponding to the power plant’s maximum thermal efficiencies,
values corresponding to the power plant’s maximum thermal efficiencies, i.e., 40.48%, 41.35%, and 42.20% i.e., 40.48%, 41.35%,
and
at 50%,42.20%
75%, andat 50%,
100%75%, and 100%
unit load, unitMW,
were 889 load,1197were
MW, 889and
MW,1564 1197
MW, MW, and 1564On
respectively. MW, therespectively.
other hand,
Onpossible
the the otherlowest
hand,thermal
the possible lowest i.e.,
efficiencies, thermal
37.9%, efficiencies,
38.8%, andi.e., 37.9%,
38.8% 38.8%,
at 50%, 75%,andand38.8%
100%at unit
50%,load,
75%,
and 100% unit load, were 958 MW, 1285 MW, and 1711 MW. The difference
were 958 MW, 1285 MW, and 1711 MW. The difference in heat input values at a specific unit load reflects in heat input values at a
specific
the savingsunitinload
heatreflects the savings
input energy ensured in heat
under input energyplant’s
the power ensured underoperating
optimal the powerconditions.
plant’s optimal
Such
operating conditions. Such savings can be safely interpreted as the total energies
savings can be safely interpreted as the total energies spent to produce output electricity and, therefore, spent to produce
output
be electricity
directly related to and, therefore,
savings in thebe directly
cost of power related
planttooperation.
savings in the cost of power plant operation.
Table 88 compares
Table compares the the percentage
percentage savings
savings or or losses
losses in
in the
the heat
heat input
input values
values forfor power
power plant
plant
operation concerning the operating ranges of control parameters at 50%,
operation concerning the operating ranges of control parameters at 50%, 75%, and 100% unit load. 75%, and 100% unit load.
Energies 2020, 13, 5592 26 of 33
Table 8. Energy savings/losses in heat input values for power plant operation.
50% Unit Load 75% Unit Load 100% Unit Load

Parameters Operating Heat Input % Energy Operating Heat Input % Energy Operating Heat Input % Energy
Range Range Savings/Losses Range Range Savings/Losses Range Range 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
Tfg 110–125 903–921 −1.99 123–138 1186–1194 −0.75 118–133 1561–1567 −0.85
Ta and Tfg 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
* All control variables are the operating parameters of the power plant, as mentioned in Table 7.
Energies 2020, 13, 5592 27 of 33
The energy savings were achievable by the effective operational control of the combustion that
ultimately influenced power plant operation. The energy savings or energy losses were expressed in %
of heat input values and were mentioned against the operating parameters, i.e., MST and RST, Tfg ,
Ta and Tfg, and all control variables, as discussed in Sections 4.1–4.4, 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 Figures 9d, 10d, 11d
and 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%. Ta and Tfg
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, Tfg expressed energy losses of 1.99%,
0.70%, and 0.35% in heat input values for the operation of the power plant.
5. Conclusions
In this work, industrial operation data, advanced data analytic tools, and AI algorithms are
incorporated to formulate the step-wise methodology in the spirit of industry 4.0-data analytics to
optimize the power plant’s ηthermal for sustainable power generation from a power complex.
The quality of operation data is ensured by employing data visualization techniques like histograms
and SOFM. Monte-Carlo experiments on ANN and LSSVM process models and IASA were performed
to eliminate insignificant variables from the list of initially selected control variables.
At 50%, 75%, and 100% unit load, for every 10 ◦ C rise in MST and RST, the power plant’s ηthermal
on average had a relative increase of 1.50%, 1.50%, and 1.32%, respectively.
For every 5 ◦ C rise in Tfg , the relative decrease in the power plant’s ηthermal was an average of
0.65%, 0.25%, and 0.28%, respectively, under 50%, 75%, 100% unit load.
For every 5 ◦ C rise in Ta and 5 ◦ C fall in Tfg , the relative increase in the ηthermal of the power plant
on average was 0.43%, 0.44%, and 0.42%, respectively, under 50%, 75%, and 100% unit load.
From 550 ◦ C to 560 ◦ C and 560 ◦ C to 570 ◦ C MST and RST with the corresponding changes in
remaining control variables, the relative increase in the ηthermal of the power plant on an average was
3.36%, 3.21%, and 4.29% respectively at 50%, 75%, and 100% unit load.
At 50%, 75%, and 100% unit load: the savings in heat inputs corresponding to the highest overall
thermal efficiencies, for MST and RST were 2.94%, 2.92%, and 2.59% respectively, for Ta and Tfg 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 Tfg were 1.99%, 0.70%, and 0.35%, respectively.
The true implementation of industry 4.0 built by the modern data analytics tools for power
plant operation ensured the increase in the power plant’s thermal efficiency and, therefore,
the techno-economic benefits that offer reduced operation cost, optimal fuel consumption, and effective
operational control.
Big data analytics, industrial internet of things, and simulation were the three technologies
prioritized and incorporated in the study to achieve the power plant’s operational excellence by
embracing the industry 4.0 digital transformation approach.
The process modeling based on process data, process optimization, and data-driven strategy
development for improved process control using sophisticated technologies dedicated to the
implementation of industry 4.0 in the industrial complexes for higher productivity and effective
operation control is in line with the objectives of the industry, innovation, and infrastructure program
of the united nations for sustainable development and the Paris agreement allowing to fulfill the
nations’ commitment to sustainable growth and the environment.
The effect of auxiliaries’ system operation, separately or combined, on the power plant’s ηthermal
needs to be evaluated in the future.
Energies 2020, 13, 5592 28 of 33
Author Contributions: Conceptualization, W.M.A., G.M.U., S.M.A., J.K.; methodology, S.A., A.H.K.; supervision,
G.M.U., L.B.X., S.C.; validation, M.A., M.H.K., J.K.; data curation, W.M.A., A.J., A.A.; formal analysis, W.M.A.,
S.A., M.W.R., U.N.; investigation, S.G.N., H.J.; writing—original draft, W.M.A., A.A.; writing—review and editing,
G.M.U., L.B.X., S.C., J.K.; visualization, I.A.C., N.H., J.K.; project administration, W.M.A., G.M.U. All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments: The research activity is based on the data collection from the Sahiwal Coal Power plant.
The power plant is installed by Huaneng Shandong Ruyi Pakistan Energy Private limited- a joint venture of China
Huaneng Group and China Ruyi group. The author is obliged to the power plant management and professional
engineers to carry out the research activity for their support, supervision, and wisdom.
Conflicts of Interest: The author declares no conflict of interest concerning this work.
Nomenclature
AWF attemperation water flow rate (◦ C)
FWT Feed-water temperature (◦ C)
Ma air flow rate (t/h)
Mc coal flow rate (t/h)
MSP main steam pressure (MPa)
MST main steam temperature (◦ C)
MW energy supplied
MWe electric power
N turbine speed (rpm)
O2 % O2 in flue gas at boiler outlet (%)
Pf furnace pressure (Pa)
Pvac condenser vacuum (kPa)
RST reheat steam temperature (◦ C)
Ta APH air outlet temperature (◦ C)
Tfg APH outlet flue gas temperature (◦ C)
ηthermal 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
Energies 2020, 13, 5592 29 of 33
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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energies

Energies 2020, 13, 5592; doi:10.3390/en13215592 www.mdpi.com/journal/energies

2. Overview of a Coal Power Plant Operation

Figure 2. Schematic diagram of coal power plant operation.

Table 2. Details of sensors measuring the control variables.

Sensors Make Model Number

3. AI-Based Data Visualization and Process Modeling

3.1. Variables Selection for AI Process Modeling

Table 3. Properties of coal (air-dried basis).

Parameters Unit Min Avg Max

Parameters Unit Min Avg Max

3.2. Elimination of Insignificant Control Variables

3.2.1. Monte Carlo Experimentation for Significance Analysis

1. A control variable is represented as xi where, i = 1,2, . . . ,13.

3.2.2. Interval Adjoint Significance Analysis (IASA)

V 0 Table 5.=A possible code

25 (a) Monte Carlo-ANN Variables Elimination

(c) IASA-Variables Elimination

3.4. Development of ANN Process Model

3.5. Development of LSSVM Process Model

3.6. Evaluation Criteria

3.5. Development of LSSVM ProcessNRMSE

3.7. External Validation Case of Trained AI Process Models

0 5000 10,000 15,000 20,000 25,000

Table 6. Comparison of ANN and LSSVM model prediction performance.

RMSE NRMSE MAPE

Table 6. Comparison of ANN and LSSVM model prediction performance.

RMSE NRMSE MAPE

4. Results and Discussion

50% Unit Load 75% Unit Load 100% Unit Load

4.1. Effect of MST and RST on ηthermal of Power Plant

RST (oC) RHT (oC)

4.2. Effect of Tfg on the ηthermal of Power Plant

40.2 (a) 50% Unit Load 41.1

42.1 40.8 1218 MW

913 MW 100% Unit Load

RST (oC) RST (oC)

50% Unit Load 75% Unit Load 100% Unit Load

LPB low-pressure turbine B

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