Modelling the Relationships among the Key Factors Affecting the Performance of Coal-Fired Thermal Power Plants: Implications for Achieving Clean Energy

Abstract

Most countries depend on coal-fired thermal power plants (CTPPs) to meet energy demands. However, the adverse environmental impacts of CTPPs also remain a major concern. As the energy generations from renewable energy resources are still in the developing stage, reliance on CTPPs is inevitable. Hence, the efficiency of CTPPs has to be improved, while decreasing carbon emissions. This study aims to identify and evaluate the key factors that need to be addressed in improving the performance and minimizing the carbon emission of CTPPs. With the literature review and industrial interaction, twenty-four key factors are identified. Next, an integrated approach of the fuzzy analytic hierarchy process (FAHP) and fuzzy decision-making and trial laboratory (FDEMATEL) is used to evaluate the key factors. FAHP prioritizes the key factors and FDEMATEL reveals the relationship among the key factors. Results indicate air preheater leakage, plugging by ash, high levels of air ingress, air preheater secondary fire, and high levels of corrosion as the top five key factors affecting CTPP performance. Based on the outcome, the study offers some implications that may assist the industrial management in taking timely actions in improving the performance of CTPPs.

1. Introduction

An increase in energy demand is directly proportional to the rise in industrial and developmental activities to improve people’s living conditions [1]. The pace of industrial activities accelerates with the introduction of globalization. Both developing and developed countries are faced with increasing energy demand. Being an active player in globalization, the industrial activities in India reached a great height (and so did the energy demand). As a result, countries started investing more in power plants for energy generation. However, most power plants are operated using non-renewable energy resources, mainly coal. As a result, an acute resource scarcity occurred [2]. Resource scarcity and disadvantages, such as the adverse environmental impact of fossil fuels, has led to the search for renewable energy resources [3]. The role of energy is very crucial in the sustainable development of the country. Hence, to meet the energy demand and lower the environmental burden caused by conventional fossil fuels, the search for renewable resources intensifies. Further, the need to meet the sustainable development goals (SDGs), a set of seventeen goals proposed by the United Nations (UN) covering the overall wellness of humankind, which forced the nations to identify and invest more in renewable energy resources [4].

In response to the request of the UN, research communities and the industrial managements of all countries streamlined their business, with the intent to identify renewable energy resources that meet the energy demand and are harmless to the environment [5]. By changing from non-renewable to renewable energy resources, it is possible to reduce the carbon footprints [6]. Bearing this positive impact in mind, the global nations amplified the search and development of renewable energy resources. As a result, the number of possible sources (e.g., tidal, solar, and wind) of energy generation were identified, and enhancements were carried on in [7]. However, these alternatives came with several shortcomings. For instance, energy generation from solar power plants is non-reliable. The energy generation is based on the quality of sunlight received, which is non-uniform throughout the year and differs from place to place [8]. Likewise, every renewable energy resource alternative has its limitations. However, by varying the above facts, with only environment preservation and sustainable performance in mind, increasing investment is being made on sustainable renewable energy resources [9].

Another energy resource that the research community relied on is nuclear energy. The search for clean energy results in nuclear energy, and it is regarded as the low-carbon energy resource behind hydropower [10]. Besides low-carbon emission, the wastes generated by the nuclear power plant are also low. Moreover, the power generating capacity of the nuclear power plant is also higher than other renewable energy resources [11]. Although the nuclear power plant largely benefits society, it has some serious shortcomings. The radiation from the nuclear power plant poses a health threat to the people, and it depends on the non-renewable material, i.e., uranium. The raw material that nuclear power plants depend on is available only in a limited quantity [12]. Further, the construction and maintenance cost of the nuclear power plant is also high. Hence, reliance on the nuclear power plant is unrealistic.

It must be realized that the complete dependence on renewable energy resources will not meet the ever-raising energy demand. Additionally, it is hard to refute the need for sustainable renewable energy resources [13]. When it is possible to develop a new system for energy generation, the same is applicable for enhancing the existing systems. Most countries, especially developing countries, depend on coal for energy generation [14]. Additionally, among the existing energy generation system, the coal-fired thermal power plants (CTPPs) are widely criticized for their adverse environmental impact, such as fly ash and carbon emissions [15]. The question arises here: why can the performance efficiency of the CTPPS not be improved with technological progress, while reducing carbon emission. It has to be understood that renewable energy resources are still in developing stages, and it takes time to become completely reliable. The same was underscored by Maradin [16], in a study that states that renewable energy resources are not still the predominant energy resources. However, they contribute to energy demand to some extent. It has also been estimated that the global energy consumption from renewable energy resources is above 20% and expected to increase in the future. So, as far as we are concerned, compared to reliance on renewable energy resources, it is now necessary to improve the efficiency of the thermal power plants that are dependent on conventional energy resources.

From the above information, it is clear that the amount of energy generated using renewable energy resources is insufficient in meeting the energy demands. At the same time, it is difficult to rule out the role of renewable energy resources, in terms of SDGs. However, the existing sources of energy generations can be improved by enhancing the performance efficiency and reducing carbon emission. Moreover, developing countries account for a large share of carbon emissions. So, there is a need to minimize the carbon emission from the CTPPs located in developing countries.

Given the importance of enhancing the performance of CTPPs, this study addresses the critical research gaps in the literature. First, it lists key factors known to affect the functioning of CTPPs via literature review and interviews with industrial management. Second, the key factors are prioritized using the fuzzy analytic hierarchy process (FAHP) and relationships are revealed using the fuzzy decision-making trial and evaluation laboratory (FDEMATEL). This study aims to identify and evaluate the key factors that affect the CTPP in India, as India’s energy demand CTPPs meet nearly 60%. Finally, this study would like to answer the following research questions:

• What are the key factors that need to be addressed in improving the performance of CTPPs?
• How to prioritize the key factors in improving the CTPPs performance?
• How to reveal the relationship among the identified key factors in improving the CTPPs performance?

The purpose of this study is to prioritize and picture the relationship among the key factors affecting the performance of CTPPs using the multi-criteria decision-making (MCDM) technique. Here, FAHP and FDEMATEL are used to answer the above research questions. The AHP method, developed by Saaty [17], is used to prioritize the key factors, while DEMATEL, developed by Gabus and Fontela [18], is used to reveal the relationship among the key factors. The prioritization and interrelationship of key factors helps the industrial management in taking proactive and appropriate actions in improving the performance of CTPPs.

The remainder of the paper is pursued in the following manner: Section 2 illustrates the role of CTPPs in meeting the energy demands, as well as the adverse environmental impacts needed for SDGs. The research methodology used in this study is explained in Section 3. The application of the research methodology in evaluating the key factors is given in Section 4. The outcome of the research methodology applied is discussed thoroughly in Section 5. Conclusions and Policy Implications of the study are provided in Section 6.

2. Literature Review

This section provides an overview of the role of the CTPPs and their adverse environmental impact, which necessitated the transition towards SDGs, particularly SDG 7 (affordable and clean energy) and SDG 13 (climate action). Finally, the research gap identified, and the contribution of this study is provided.

2.1. Role of Coal Based Thermal Power in Meeting Energy Demands

Rapid industrialization and developmental activities have enormously increased the energy demand in India and other countries. According to the International Energy Agency (IEA), India is the third-largest energy-consuming county, and its energy demand is expected to increase exponentially in the coming years [19]. In meeting the energy demands, the global nations generate energy from coal, nuclear, diesel, solar, wind, and tidal sources. According to Rawat and Yadav [20], India has 155 CTPPs with a capacity of 157,377.00 MW. India’s current energy demands are met by coal (69%), hydro (14%), natural gas (10%), oil (4%), nuclear (2%), and renewable (1%) resources [21]. Other than India, countries such as China, Bangladesh, and many others are also largely (nearly up to 50%) dependent on CTPPs to meet domestic needs [22]. As a result, the global demand for coal is expected to rise by 2.6% in the coming years [23]. From this information, it is apparent that the role of CTPPs is crucial in meeting the energy demands of the countries. Though the CTPPs play a substantial role in meeting the energy demand, they seriously negatively impact the environment. Carbon and fly ash emission by the CTPPs impacts both the environment and humans [24,25]. So, the CTPPs offer both benefits and hazards. In addition to the industrial and transportation sector, the CTPPs also faced many challenges in mitigating the adverse environmental impact. Realizing the need to curb the CO₂ emission and lower the global warming effect, the United Nations (UN) has called all the member nations to take the required actions to lower carbon emissions. Regarding this, the UN proposed a set of seventeen goals, together called the sustainable development goals (SDGs), and appealed to the nations to work in attaining the goals [26]. Similarly, the recently concluded Paris Agreement also insisted that the global nations transform towards sustainable energy, bearing the agenda of lowering carbon emissions and temperature. It is difficult for India and other countries to completely adhere to the Paris Agreement, which insists on a complete transition to sustainable renewable sources for energy, while the demand is huge. However, it is possible to optimize the functioning of CTPPs by lowering carbon and another particulate emission.

2.2. Need of Improving CTPPs and SDGs

As per the Paris Conference (2015), the global nations have agreed to reduce global warming by below 2 °C and, subsequently, to 1.5 °C [27]. To act according to the Paris Agreement, global nations are working tirelessly to find sustainable renewable energy sources. However, it must be realized that it would be difficult to meet the demand [28]. Hence, by improving the functioning of CTPPs or other conventional energy resources, it is possible to reduce carbon emissions and increase output efficiency. According to Malik and Tewari [29], frequent inspection and required maintenance of the condenser, boiler feed pump, and deaerator will enrich the overall performance of the power plants. It was also stated that it is possible to avoid breakdown by doing proper maintenance and reducing carbon emissions. A similar study by Jović et al. [30] emphasizes that, by improving the cooling tower system, the overall energy efficiency of the thermal power plants can be improved by 1.5%. The quality of the coal consumed and equipment used in the energy generation largely determines the output efficiency of thermal power plants [31]. It was highlighted by Li et al. [32] that improving the thermodynamic cycle can enhance the power plant’s efficiency by 50.2%. Advanced ultra-supercritical (USC) technology has been suggested as the best technology to mitigate carbon emission from the CTPPs [33]. Geete [34] recommended the use of smooth inner surfaces to extract the fly ash deposited in the linings. The timely removal of fly ash increases the incoming and outgoing air, thus improving the boiler’s performance. Hence, most of the research on the CTPPs recommends and explores improving the performances. Further, the CTPPs act as the main source of energy generation in many countries, so it is difficult to shut down completely. Only the efficiency of the thermal power plants can be improved while reducing carbon emissions. Further, most of the SDGs are mainly focused on environmental issues. In this regard, improving the CTPPs may help the nations in meeting the number of SDGs. When CTPPs are considered, with improvement, it is possible to meet SDG 7 (affordable and clean energy) and SDG 13 (climate action). By doing so, the energy demand is fulfilled, and the SDGs are also attained.

2.3. Research Gaps and Contributions

To counter the scarcity of non-renewable resources and mitigate the adverse environmental impact, the research communities are focusing on identifying renewable resources for energy generation. Additionally, some research studies claim that it is possible to meet the energy demands with renewable energy resources [35,36]. At the same time, some studies turn down the observations of earlier studies. In line with the arguments, some of the studies explored the possibilities of enhancing the boiler’s performance, while minimizing carbon emissions [33,34]. From the above information, it is clearer that the total reliance on renewable resources will not meet the energy demands. Hence, a mix of renewable and non-renewable resources is suggested. Although some studies focused on improving the boiler’s performance, no studies are dealing with identifying the factors affecting the boiler’s performance. Listing the factors affecting the performance of boilers may act as a guide in improving the boiler’s performance. To fill this existing research gap in the literature, this study identifies the factors affecting the boiler’s performance. The following are the significant contributions of this study:

✓

A list of key factors affecting the performance of the boilers is identified by combining the literature review process and expert interaction.

✓

The key factors are prioritized using the FAHP technique, as the prioritization of factors helps in sequentially addressing the factors.

✓

The relationships between the factors are revealed using FDEMATEL, categorizing factors into cause-and-effect groups.

The next section explains the steps involved in FAHP and FDEMATEL.

3. Methodology and Data

This section explains the steps involved in the integrated MCDM approach, comprising of fuzzy AHP (FAHP) and fuzzy DEMATEL (FDEMATEL).

3.1. Fuzzy Analytic Hierarchy Process (FAHP)

The analytic hierarchy process, developed by Saaty [17], is the most widely used multi-criteria decision-making (MCDM) method. AHP handles both quantitative and qualitative data, which are converted into numbers. In AHP, pairwise comparisons between the criteria/alternatives are made to evaluate and determine the significance of one criterion over the other. Pairwise comparisons are made using an absolute judgment scale that represents the prominence of one criterion over the other. Most of the time, while making pairwise comparisons, the decision-makers face the problem of uncertainty and difficulty in giving precision ratings. To overcome this uncertainty and fuzziness in decision-making, the concept of fuzzy set theory was developed by Zadeh [37]. Generally, in the AHP approach, crisp values (scale) are used to make pairwise comparisons. However, these crisp values fail to provide the option of clearing, making pairwise comparison. To solve the problem related to uncertainty and clearness in expressing the opinion, fuzzy set theory is being incorporated with the decision-making methods. Various studies, such as risk assessment [38], sustainability issues [39], and aircraft selection [40], have been carried out using FAHP. So, in this study, the FAHP method is used. Here, the extent analysis method, developed by Chang [41], is preferred. The extent analysis method is preferred for its robustness and the simplicity of calculations. The triangular fuzzy numbers (TFNs) used in this study are given in

Table 1. Linguistic scale [42].

Linguistic Scale	Triangular Fuzzy Numbers
Just important (JI)	(1, 1, 1)
Equally important (EI)	(1/2, 1, 3/2)
Weakly important (WI)	(1, 3/2, 2)
Strongly important (SI)	(3/2, 2, 5/2)
Very strongly important (VSI)	(2, 5/2, 3)
Absolutely important (AI)	(5/2, 3, 7/2)

.

The following are the steps involved in FAHP using extent analysis method:

Consider a triangular fuzzy number $\widehat{F}=(l,m,u)$, with the following membership function:

$${\mu }_f(X)=\left\{\begin{array}{ccc}\frac{X-l}{m-l}& l\le X\le m& \\ \frac{u-X}{u-m}& m\le X\le u& \\ 0& Otherwise& \end{array}\right.$$

(1)

Some of the operational laws of the two triangular numbers ${\widehat{F}}_1$ and ${\widehat{F}}_2$ are as follows:

$${\widehat{F}}_1+{\widehat{F}}_2=(l_1+l_2,m_1+m_2,u_1+u_2)$$

(2)

$${\widehat{F}}_1\otimes {\widehat{F}}_2=(l_1{l}_2,m_1{m}_2,u_1{u}_2)$$

(3)

$$\widehat{F}=\left(\frac{1}{u_1},\frac{1}{m_1},\frac{1}{l_1}\right)$$

(4)

Step 1: Construct pairwise comparison matrix.

Make pairwise comparison between the criteria under consideration, as shown below:

$$\widehat{F}=\left[\begin{array}{ccc}{\widehat{F}}_{11}& \cdots & {\widehat{F}}_{1n}\\ ⋮& \ddots & \\ {\widehat{F}}_{m1}& \cdots & {\widehat{F}}_{mn}\end{array}\right]$$

(5)

$${\widehat{F}}_{ij}=(l_{ij},m_{ij},u_{ij})$$

(6)

$${\widehat{F}}_{ji}={\left({\widehat{F}}_{ij}\right)}^{-1}=(u_{ij}^{-1},m_{ij}^{-1},l_{ij}^{-1})$$

(7)

Step 2: Consistency check.

Once the pairwise comparison is made, the consistency has to be checked. For verifying the consistency of the pairwise matrix, the fuzzy numbers are converted into crisp numbers using defuzzification Equation (8).

$$F_{11}=\frac{l_{11}+m_{11}+u_{11}}{3}$$

(8)

The result of the defuzzied crisp matrix is:

$$F=\left[\begin{array}{ccc}{F}_{11}& \cdots & F_{1n}\\ ⋮& \ddots & ⋮\\ F_{m1}& \cdots & F_{mn}\end{array}\right]$$

(9)

To check the consistency of the pairwise comparison matrix, consistency index (CI), ${\lambda }_{\max }$, and $W^{\prime }$ has to be calculated using the following equations.

$$W=\left[\begin{array}{c}{W}_1\\ W_2\\ ⋮\\ W_n\end{array}\right]\mathrm{and}{W}_i=\frac{{\displaystyle \sum _{i=1}^n{\alpha }_{ij}}}{n}$$

(10)

$$W^{\prime }=FW=\left[\begin{array}{c}{W}_1^{\prime }\\ W_2^{\prime }\\ ⋮\\ W_n^{\prime }\end{array}\right]$$

(11)

$${\lambda }_{\max }=\frac{1}{n}\left(\frac{W_1^{\prime }}{W_1}+\frac{W_2^{\prime }}{W_2}+\dots +\frac{W_n^{\prime }}{W_n}\right)$$

(12)

$$ConsistencyIndex(CI)=\frac{{\lambda }_{\max }-n}{n-1}$$

(13)

$$ConsistencyRatio(CR)=\frac{CI}{RI(RandomIndex)}$$

(14)

Step 3: Weight calculation.

The weights of the criteria under consideration are calculated using the following equations:

$${\alpha }_j={\left[{\displaystyle \prod }_{j=1}^n{l}_{ij}\right]}^{1/n}$$

(15)

$${\beta }_j={\left[{\displaystyle \prod }_{j=1}^n{m}_{ij}\right]}^{1/n}$$

(16)

$${\chi }_j={\left[{\displaystyle \prod }_{j=1}^n{u}_{ij}\right]}^{1/n}$$

(17)

Fuzzy weights are computed as:

$${\widehat{w}}_j=({\alpha }_j{\chi }^{-1},{\beta }_j{\alpha }^{-1},{\chi }_j{\beta }^{-1})$$

(18)

Wight vector $w$ is computed as:

$$W=(w_1,w_2,\dots ,w_n)$$

(19)

3.2. Fuzzy DEMATEL

The DEMATEL method was introduced by Gabus and Fontela [18] in the Geneva Research Centre. It is the method that focuses on developing and building relationships among the criteria under consideration. In DEMATEL, the criteria after evaluation are categorized under the cause-and-effect group. Hence, it is based on the digraph model. Digraphs are useful in representing the contextual interrelationship among the criteria under consideration. Making a decision under uncertainty has been a problem for the decision-makers. Hence, to overcome the difficulty, the fuzzy set theory is being incorporated with the DEMATEL and used as fuzzy DEMATEL (FDEMATEL). Since uncovering the relationship among the factors is crucial, many of the earlier studies have been carried out using FDEMATEL. In studies such as sustainable supplier selection [43], evaluation of barriers to the blockchain [44], and critical factors to cloud computing [45], FDEMATEL has been used.

The following are the steps involved in FDEMATEL:

Step 1: Establishment of initial direct relationship matrix $(\widehat{F})$.

Step 2: Construct normalized direct relationship matrix $(\widehat{S})$ using Equations (20) and (21).

$$K=\frac{1}{{\max }_{1\le i\le n}{\displaystyle \sum _{j=1}^n{f}_{ij}}}$$

(20)

$$\widehat{S}=\widehat{K}\times \widehat{F}$$

(21)

Step 3: Formation of total-relationship matrix $(\widehat{M})$ using Equation (22).

$$\widehat{M}=\widehat{S}(I-\widehat{S})$$

(22)

where $I$ is identity matrix.

Step 4: Sum of rows $(R)$ and columns $(C)$.

$$R={\left[{\displaystyle \sum _{j=1}^n{m}_{ij}}\right]}_{n\times 1}$$

(23)

$$C={\left[{\displaystyle \sum _{i=1}^n{m}_{ij}}\right]}_{1\times n}$$

(24)

A digraph representing cause and effect group is drawn using the dataset of $(R+C)$ and $(R-C)$.

Prioritizing and revealing the relationship among the factors affecting air preheater performance has been of prime importance. Hence, in this study, an integrated approach of FAHP and FDEMATEL is used. The application of the integrated approach is explained briefly in the next section.

4. Application of FAHP-FDEMATEL in Evaluating the Factors Affecting CTPPs

Though, to address the issues concerned with CO₂, the transition towards dependence on renewable energy sources is inevitable; the complete transition seems impossible. Various factors act as impendence to the transition towards renewable energy sources, for instance, when solar power is considered; first, it is not possible to receive the optimum level of required energy, and receiving sunlight throughout the year is not possible. Apart from this natural impendence, the high initial setup and maintenance costs, technological void, and feasibility keep the transition towards renewable energy sources an uphill task. In addition to extending support to this fact, a study by Ghimire and Kim [46] in analyzing the barriers to renewable energy development in Nepal, identified problems at the economic and policy levels as the major hindrance in the transition. These barriers are not only limited to developing economics; even in developed economies, such as Australia, the situation remains the same [47]. The deployment of other renewable energy sources, such as wind energy, hydro energy, and tidal energy, are also in the same situation [48]. Additionally, it should be sensed that it is impossible to meet all the needs of renewable energy sources. Hence, a blend of conventional and renewable energy sources should also be used to meet the energy demand and SDGs [49]. The necessity of moving towards renewable energy resources arises only due to a series of concerns regarding conventional energy sources, especially coal-based energy production. The major concern with coal-based energy production is CO₂ emission and poor performance efficiency. Many factors affect the performance of the CTPPs, which results in poor performance efficiency and high CO₂ emission. Taking this into consideration, this study attempts to identify various factors that reduce the performance efficiency of thermal power plants with increased CO₂ emission. Next, an integrated approach of FAHP and FDEMATEL is used to prioritize and uncover the relationship between the factors affecting the thermal power plant’s performance. The application of FAHP and FDEMATEL in evaluating the factors is shown in Figure 1 and explained as follows:

The first phase of the study focuses on identifying and collecting the list of factors affecting the performance of the CTPPs. To identify the factors affecting the performance of the CTPPs, a review of the existing literature was carried out. The literature for review was collected from well-established science databases, such as GoogleScholar, EBSCO, Web of Science, and Scopus. The following keywords, with Boolean operators, were used for collecting the literature: coal-fired thermal power plants AND barriers, challenges AND coal-fired thermal power plants, underperformance AND coal-fired thermal power plants. While literature collection, some inclusion and exclusion criteria were followed. The inclusion criteria include publication in the English language, published in Scopus; indexed journal and core work focuses on problems with the CTPPs. The exclusion criteria include conference proceedings, doctoral thesis, and project work. Through literature review, a total of twenty factors affecting the performance of CTPPs were collected. However, the problem associated with a real-time problem keeps emerging, and it is necessary to listen to the voices of the people who are directly associated with the problems. Hence, interaction with the people working in the CTPPs is made. In this study, the case thermal power plant considered was located in Tuticorin, India. For this study, a visit was made to the thermal power plant for the of two months from July 2021–August 2021. Twenty factors affecting the CTPP’s performance, collected via a literature review, was discussed with them. In addition to the twenty factors, the workers of CTPPs also suggested four factors. Thus, the total number of factors affecting the CTPPs performance that were collected for this study is twenty-four, as shown in

Table 2. Key factors affecting the coal-fired thermal power plants performance.

Key Factors	Description	References
Excess operating air levels (F1)	It increases the mass flow rates and reduces flue gas inlet temperature.	[29,31]
PA/SA ratio (F2)	It is the ratio between the primary and secondary air. Increased primary air ratios require lower mill inlet temperature and, thus, more tempering air.	[50]
Fluctuation in inlet air/gas temperature (F3)	Changes in entering AIR/GAS temperature result in a change in temperature head, which directly affects the drop in gas temperature.	[51]
High moisture content in coal (F4)	To evaporate the moisture, higher quantity of latent heat is absorbed, thus reducing the flue gas temperature.	[22,24]
High level of air ingress (F5)	It reduces inlet gas temperature and increases the flue gas mass flow rates. It is quantified by the percentage of oxygen increase in the flue gas.	[29,30]
Upstream ash evacuation (F6)	Full ash evacuation through flue gas leads to more ash-flow through air preheater.	[50,52]
Poor soot blowing (F7)	It affects the cleaning performance of the air preheater. It is the high-velocity steam cleaning done online.	[24]
More number of mills in service (F8)	It increases the mass flow rate of flue gas, leading to choking via ash more erosion.	[22]
Poor maintenance practice (F9)	Leakages, choking, and air ingress will happen.	[23]
Air preheater leakages (F10)	There is leakage of high-pressure air into the low-pressure flue gas path (due to differential pressure), high seal clearance (due to hot conditions), and seal erosion/improper seal setting. An increase in air heater leakage increases the station service power requirements of FD and ID fans.	[31,34]
Profile of the heating element (F11)	It varies the retention time of flue gas in the heating element; also, complicated profile leads to air preheater chocking.	[32,52]
Heating element materials (F12)	Specific heat capacity and corrosion resistance are important properties. Therefore, the materials are selected based on the required properties.	[51,53]
Pressure drop across the air preheater (F13)	High-pressure drop is due to the seal leakages and air ingress in the air preheater.	[50]
Increase in tempering air (F14)	It bypasses the air preheaters in the boiler, thus reducing the heat recovery from the flue gases leaving the boiler. Unfortunately, this increases the boiler exit gas temperature, which increases the dry gas loss in the boiler.	[54]
Air preheater element erosion (F15)	The erosion of the tube at the entry side requires frequent cleaning of the air heater and insists on using replaceable inserts at the erosion-prone areas.	[32,34]
High level of ash fouling (F16)	Reduce the heat transfer.	Industrial input
Plugging by ash (F17)	Highly affects air preheater performance.	[30,34]
High level of corrosion (F18)	Reduce the heat transfer area. Leads to sector plate leakage, quick seal damage	[22]
Air preheater secondary fire (F19)	It is due to the burning of unburned heavy oil. deposited in air preheater heating elements. It produce hot zone, uneven expansion of rotor and air preheater heating elements damage.	[23]
Malfunctioning of oil burners (F20)	Leads to carrying unburnt oil deposits in heating elements	[24]
APH motor failures–Only for rotary type (F21)	Leads to uneven expansion of rotor, jamming of rotor, and air preheater firing.	[53,55]
Improper water washing (F22)	Affects the heat transfer rating.	[23]
Boiler operation with high overload (F23)	Increases the mass flow rates and reduces flue gas inlet temperature.	[51]
Low feed water temperature at boiler inlet (F24)	It needs more sensible heating in the boiler, so the flue gas inlet temperature to the air preheater will reduced.	[52,55]

.

Next, after collecting the factors affecting the CTPPs performance, the factors are evaluated, in order to prioritize and reveal their relationship. An expert panel, comprising of seven experts, was formed for evaluating the factors. The size of the panel is acceptable, in comparison with earlier studies. In addition, these seven experts that were approached to evaluate the factors have substantial knowledge and experience on the functioning of the CTPPs. The profile of the experts is given in

Table 3. Profile of the experts.

Characteristics		n = 7	%
Position	Executive engineer	2	28.57
	Assistant executive engineer	1	14.28
	Superintend engineer	2	28.57
	Assistant engineer	2	28.57
Education qualification	Graduate	2	28.57
	Post-graduate	3	42.85
	Doctorate	2	28.57
Experience	5–10 years	3	42.85
	10–15 years	2	28.57
	Above 15 years	2	28.57

.

Fuzzy linguistic scale (

Table A1. Fuzzy Linguistic Scale.

Linguistic Scale	Triangular Fuzzy Numbers
Just important (JI)	(1, 1, 1)
Equally important (EI)	(1/2, 1, 3/2)
Weakly important (WI)	(1, 3/2, 2)
Strongly important (SI)	(3/2, 2, 5/2)
Very strongly important (VSI)	(2, 5/2, 3)
Absolutely important (AI)	(5/2, 3, 7/2)

of Appendix A) along with a questionnaire (

Table A2. Pairwise comparison of the key factors.

	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	F12	F13	F14	F15	F16	F17	F18	F19	F20	F21	F22	F23	F24
F1	JI
F2		JI
F3			JI
F4				JI
F5					JI
F6						JI
F7							JI
F8								JI
F9									JI
F10										JI
F11											JI
F12												JI
F13													JI
F14														JI
F15															JI
F16																JI
F17																	JI
F18																		JI
F19																			JI
F20																				JI
F21																					JI
F22																						JI
F23																							JI
F24																								JI

of Appendix A), consisting of the twenty-four factors, was given to each expert. The experts were asked to make pairwise comparison between the factors using Table 1. The questionnaire was circulated among the experts via email. Frequent reminders were given; upon receiving the experts’ ratings, the average was considered for further evaluation. The average of the pairwise comparison made by the experts is given in

Table 4. Average of pairwise comparison made by experts on factors.

	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	F12	F13	F14	F15	F16	F17	F18	F19	F20	F21	F22	F23	F24
F1	JI	EI	VSI	VSI	WI	VSI	EI	EI	WI	WI	EI	WI	EI	WI	EI	EI	WI	EI	EI	EI	VSI	EI	EI	VSI
F2	EI	JI	EI	WI	EI	WI	EI	WI	WI	SI	EI	SI	SI	SI	SI	SI	EI	SI	SI	WI	SI	SI	EI	WI
F3	VSI	EI	JI	VSI	EI	WI	WI	WI	EI	WI	VSI	EI	WI	WI	WI	EI	VSI	WI	VSI	WI	EI	EI	VSI	VSI
F4	AI	WI	EI	JI	AI	WI	EI	WI	WI	WI	EI	WI	WI	AI	WI	EI	WI	AI	WI	WI	AI	EI	WI	AI
F5	SI	SI	VSI	AI	JI	AI	AI	SI	AI	AI	SI	VSI	VSI	VSI	VSI	VSI	VSI	VSI	VSI	VSI	VSI	SI	VSI	SI
F6	EI	WI	SI	EI	WI	JI	WI	SI	EI	EI	WI	SI	EI	WI	WI	WI	WI	WI	VSI	VSI	WI	WI	WI	WI
F7	VSI	SI	WI	SI	EI	SI	JI	EI	SI	WI	AI	WI	EI	SI	SI	SI	WI	AI	AI	WI	AI	AI	AI	WI
F8	EI	SI	VSI	VSI	AI	AI	EI	JI	VSI	VSI	VSI	AI	EI	EI	EI	SI	EI	SI	VSI	VSI	AI	SI	EI	SI
F9	AI	AI	VSI	AI	EI	VSI	VSI	VSI	JI	VSI	VSI	AI	EI	WI	SI	EI	VSI	EI	EI	EI	SI	EI	AI	AI
F10	AI	AI	VSI	VSI	AI	VSI	VSI	AI	VSI	JI	VSI	AI	VSI	AI	VSI	SI	AI	VSI	VSI	AI	VSI	VSI	AI	VSI
F11	VSI	EI	VSI	EI	EI	AI	VSI	AI	EI	AI	JI	SI	SI	VSI	AI	EI	WI	SI	VSI	VSI	SI	AI	SI	SI
F12	AI	EI	EI	EI	VSI	AI	AI	EI	EI	EI	VSI	JI	EI	AI	WI	AI	AI	AI	WI	WI	AI	WI	EI	EI
F13	AI	WI	WI	WI	VSI	VSI	SI	SI	SI	SI	WI	SI	JI	EI	WI	WI	WI	WI	WI	VSI	WI	WI	SI	WI
F14	WI	WI	WI	VSI	VSI	WI	SI	VSI	VSI	WI	AI	SI	SI	JI	SI	EI	AI	WI	WI	WI	VSI	AI	EI	WI
F15	AI	VSI	AI	EI	WI	WI	VSI	VSI	VSI	VSI	WI	WI	EI	EI	JI	VSI	WI	WI	AI	WI	WI	VSI	WI	WI
F16	EI	EI	AI	AI	WI	WI	VSI	SI	WI	SI	SI	WI	WI	AI	EI	JI	VSI	AI	EI	WI	WI	WI	VSI	WI
F17	VSI	SI	AI	SI	VSI	SI	AI	AI	SI	VSI	AI	SI	VSI	SI	AI	SI	JI	AI	AI	SI	VSI	SI	VSI	SI
F18	VSI	AI	EI	VSI	SI	VSI	WI	AI	WI	SI	VSI	VSI	SI	WI	VSI	VSI	WI	JI	WI	SI	SI	EI	VSI	VSI
F19	VSI	AI	AI	WI	AI	EI	AI	WI	WI	VSI	SI	WI	VSI	VSI	VSI	SI	WI	AI	JI	VSI	WI	AI	EI	SI
F20	EI	EI	SI	EI	AI	EI	WI	WI	SI	WI	AI	EI	EI	WI	AI	EI	AI	WI	SI	JI	AI	SI	VSI	WI
F21	AI	EI	EI	WI	WI	AI	WI	SI	SI	AI	AI	EI	SI	SI	SI	AI	SI	WI	WI	VSI	JI	SI	EI	AI
F22	EI	AI	EI	SI	AI	WI	VSI	VSI	SI	EI	EI	WI	AI	EI	SI	SI	SI	WI	WI	AI	VSI	JI	SI	VSI
F23	VSI	EI	VSI	VSI	EI	WI	WI	SI	SI	EI	EI	SI	SI	SI	SI	SI	EI	VSI	WI	AI	EI	VSI	JI	VSI
F24	EI	SI	AI	EI	WI	AI	WI	VSI	VSI	AI	WI	AI	EI	EI	SI	SI	SI	SI	EI	AI	VSI	WI	WI	JI

. The ratings provided by the experts are transformed into fuzzy numbers. Then, using Equation (8), the fuzzy numbers are converted into crisp numbers. Next, using Equations (10)–(14), the consistency ratio (CR) is calculated. As the CR value calculated is 0.056, which meets the satisfactory criteria [56], the factors are eligible for further evaluation. Finally, using Equations (15)–(19), the weight of the importance of the factors is calculated. The weights of the factors are given in

Table 5. Ranking of factors based on weights obtained using FAHP.

Factors	Weight	Rank
F1	0.03136	24
F2	0.03324	22
F3	0.03486	21
F4	0.03750	20
F5	0.05299	2
F6	0.03251	23
F7	0.04287	8
F8	0.04272	9
F9	0.04409	7
F10	0.05718	1
F11	0.04413	6
F12	0.04024	15
F13	0.03803	19
F14	0.04245	10
F15	0.04110	14
F16	0.03960	16
F17	0.05196	3
F18	0.04446	5
F19	0.04643	4
F20	0.03808	18
F21	0.04240	11
F22	0.04139	12
F23	0.03917	17
F24	0.04125	13

.

After calculating the weight of the factors, the relationship among them was calculated using FDEMATEL. The first step in the FDEMATEL is the formation of pairwise comparison matrix. Since a pairwise comparison between the factors is already made in FAHP method, the same pairwise comparison matrix is used in FDEMATEL. Then, using Equations (20) and (21), the normalized decision matrix $(\widehat{S})$ (

Table A3. Normalized decision matrix.

	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	F12	F13	F14	F15	F16	F17	F18	F19	F20	F21	F22	F23	F24
F1	0.02	0.02	0.05	0.06	0.03	0.05	0.02	0.02	0.03	0.03	0.02	0.03	0.02	0.03	0.02	0.02	0.05	0.02	0.02	0.02	0.05	0.02	0.02	0.05
F2	0.02	0.02	0.02	0.03	0.02	0.03	0.02	0.03	0.03	0.04	0.02	0.04	0.05	0.05	0.04	0.05	0.02	0.04	0.04	0.03	0.04	0.04	0.02	0.03
F3	0.05	0.02	0.02	0.06	0.02	0.03	0.03	0.03	0.02	0.03	0.05	0.02	0.04	0.03	0.03	0.02	0.05	0.03	0.06	0.03	0.02	0.02	0.06	0.05
F4	0.06	0.03	0.02	0.02	0.07	0.03	0.02	0.03	0.03	0.03	0.02	0.03	0.04	0.07	0.03	0.02	0.03	0.06	0.03	0.03	0.06	0.02	0.03	0.06
F5	0.04	0.05	0.05	0.07	0.02	0.06	0.07	0.04	0.07	0.07	0.04	0.05	0.06	0.06	0.05	0.06	0.05	0.05	0.06	0.05	0.05	0.04	0.06	0.04
F6	0.02	0.03	0.04	0.02	0.03	0.02	0.03	0.04	0.02	0.02	0.03	0.04	0.02	0.03	0.03	0.03	0.03	0.03	0.06	0.05	0.03	0.03	0.03	0.03
F7	0.05	0.05	0.03	0.04	0.02	0.04	0.02	0.02	0.05	0.03	0.06	0.03	0.02	0.05	0.04	0.05	0.03	0.06	0.07	0.03	0.06	0.07	0.07	0.03
F8	0.02	0.05	0.05	0.06	0.07	0.06	0.02	0.02	0.06	0.05	0.05	0.07	0.02	0.02	0.02	0.05	0.02	0.04	0.06	0.05	0.06	0.04	0.02	0.04
F9	0.06	0.07	0.05	0.07	0.02	0.05	0.05	0.05	0.02	0.05	0.05	0.07	0.02	0.03	0.04	0.02	0.05	0.02	0.02	0.02	0.04	0.02	0.07	0.06
F10	0.06	0.07	0.05	0.06	0.07	0.05	0.05	0.06	0.06	0.02	0.05	0.07	0.06	0.07	0.05	0.05	0.07	0.05	0.06	0.06	0.05	0.06	0.07	0.05
F11	0.05	0.02	0.05	0.02	0.02	0.06	0.05	0.06	0.02	0.07	0.02	0.04	0.05	0.06	0.07	0.02	0.03	0.04	0.06	0.05	0.03	0.07	0.05	0.04
F12	0.06	0.02	0.02	0.02	0.05	0.06	0.07	0.02	0.02	0.02	0.05	0.02	0.02	0.07	0.03	0.07	0.07	0.06	0.03	0.03	0.06	0.03	0.02	0.02
F13	0.06	0.03	0.03	0.03	0.05	0.05	0.04	0.04	0.05	0.04	0.03	0.04	0.03	0.02	0.03	0.03	0.03	0.03	0.03	0.05	0.03	0.03	0.05	0.03
F14	0.03	0.03	0.03	0.06	0.05	0.03	0.04	0.05	0.06	0.03	0.06	0.07	0.05	0.02	0.04	0.02	0.07	0.03	0.03	0.03	0.05	0.07	0.02	0.03
F15	0.06	0.06	0.06	0.02	0.03	0.03	0.05	0.05	0.06	0.05	0.03	0.03	0.02	0.02	0.02	0.06	0.03	0.03	0.07	0.03	0.03	0.06	0.03	0.03
F16	0.02	0.02	0.06	0.07	0.03	0.03	0.05	0.04	0.03	0.04	0.04	0.03	0.04	0.07	0.02	0.02	0.05	0.06	0.02	0.03	0.03	0.03	0.06	0.03
F17	0.04	0.06	0.06	0.04	0.05	0.04	0.07	0.06	0.05	0.05	0.06	0.04	0.06	0.05	0.07	0.05	0.02	0.06	0.07	0.04	0.05	0.04	0.06	0.04
F18	0.05	0.07	0.02	0.06	0.04	0.05	0.03	0.06	0.03	0.04	0.05	0.05	0.05	0.03	0.05	0.06	0.03	0.02	0.03	0.04	0.04	0.02	0.06	0.05
F19	0.05	0.07	0.06	0.03	0.07	0.02	0.07	0.03	0.03	0.05	0.04	0.03	0.06	0.06	0.05	0.05	0.03	0.06	0.02	0.05	0.03	0.07	0.02	0.04
F20	0.02	0.02	0.04	0.02	0.07	0.02	0.03	0.03	0.05	0.03	0.06	0.02	0.02	0.03	0.07	0.02	0.07	0.03	0.04	0.02	0.06	0.04	0.06	0.03
F21	0.06	0.02	0.02	0.03	0.03	0.06	0.03	0.04	0.05	0.07	0.06	0.02	0.05	0.05	0.04	0.07	0.04	0.03	0.03	0.05	0.02	0.04	0.02	0.06
F22	0.02	0.06	0.02	0.04	0.07	0.03	0.05	0.05	0.05	0.02	0.02	0.03	0.08	0.02	0.04	0.05	0.04	0.03	0.03	0.06	0.05	0.02	0.05	0.05
F23	0.05	0.02	0.05	0.06	0.02	0.03	0.03	0.04	0.05	0.02	0.02	0.04	0.05	0.05	0.04	0.05	0.02	0.05	0.03	0.06	0.02	0.06	0.02	0.05
F24	0.02	0.05	0.06	0.02	0.03	0.06	0.03	0.05	0.06	0.07	0.03	0.07	0.02	0.02	0.04	0.05	0.04	0.04	0.02	0.06	0.05	0.03	0.03	0.02

of Appendix B) is formed. The normalized matrix is converted in the crisp matrix using Equation (8). Next, using Equation (22), a total-relation matrix $(\widehat{M})$ (

Table A4. Total-relation matrix.

	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	F12	F13	F14	F15	F16	F17	F18	F19	F20	F21	F22	F23	F24
F1	0.00	0.01	0.04	0.04	0.02	0.04	0.01	0.00	0.02	0.02	0.00	0.02	0.01	0.02	0.01	0.01	0.04	0.00	0.01	0.00	0.04	0.01	0.01	0.04
F2	0.00	0.01	0.00	0.02	0.00	0.01	0.00	0.01	0.02	0.03	0.00	0.03	0.04	0.03	0.03	0.03	0.00	0.03	0.03	0.01	0.02	0.03	0.01	0.02
F3	0.03	0.00	0.00	0.04	0.00	0.01	0.02	0.01	0.00	0.01	0.04	0.00	0.02	0.02	0.01	0.01	0.04	0.01	0.04	0.01	0.00	0.00	0.04	0.04
F4	0.05	0.02	0.00	0.00	0.05	0.01	0.00	0.01	0.01	0.01	0.00	0.01	0.02	0.05	0.01	0.00	0.01	0.05	0.02	0.01	0.04	0.00	0.02	0.05
F5	0.01	0.02	0.03	0.04	0.01	0.04	0.04	0.02	0.05	0.04	0.01	0.03	0.04	0.03	0.03	0.04	0.03	0.02	0.03	0.03	0.02	0.02	0.03	0.01
F6	0.00	0.02	0.03	0.01	0.02	0.01	0.02	0.03	0.01	0.00	0.01	0.03	0.01	0.02	0.02	0.02	0.02	0.01	0.04	0.04	0.01	0.02	0.02	0.02
F7	0.03	0.03	0.01	0.02	0.00	0.02	0.00	0.00	0.03	0.01	0.05	0.01	0.00	0.02	0.02	0.03	0.01	0.04	0.05	0.01	0.04	0.05	0.05	0.01
F8	0.00	0.03	0.03	0.04	0.05	0.04	0.00	0.00	0.04	0.03	0.03	0.05	0.00	0.00	0.00	0.03	0.00	0.02	0.04	0.03	0.04	0.03	0.00	0.02
F9	0.04	0.06	0.03	0.05	0.00	0.03	0.04	0.03	0.00	0.04	0.03	0.05	0.00	0.01	0.02	0.00	0.04	0.01	0.00	0.00	0.02	0.00	0.05	0.04
F10	0.03	0.05	0.02	0.03	0.04	0.02	0.03	0.04	0.03	0.01	0.02	0.04	0.04	0.04	0.03	0.02	0.04	0.02	0.03	0.04	0.02	0.03	0.04	0.02
F11	0.03	0.00	0.03	0.00	0.01	0.04	0.03	0.04	0.00	0.05	0.00	0.02	0.03	0.04	0.05	0.00	0.01	0.02	0.03	0.03	0.01	0.05	0.03	0.02
F12	0.05	0.00	0.00	0.00	0.04	0.04	0.05	0.00	0.00	0.00	0.03	0.00	0.00	0.05	0.01	0.05	0.05	0.04	0.01	0.01	0.04	0.01	0.00	0.00
F13	0.05	0.02	0.01	0.01	0.04	0.03	0.03	0.03	0.03	0.03	0.01	0.03	0.01	0.00	0.01	0.02	0.01	0.01	0.01	0.04	0.01	0.02	0.03	0.01
F14	0.01	0.01	0.01	0.04	0.03	0.01	0.02	0.03	0.04	0.01	0.04	0.05	0.03	0.00	0.02	0.00	0.05	0.01	0.01	0.01	0.03	0.05	0.00	0.01
F15	0.04	0.04	0.04	0.00	0.01	0.01	0.03	0.03	0.04	0.04	0.01	0.01	0.00	0.00	0.00	0.04	0.01	0.01	0.05	0.01	0.01	0.04	0.01	0.01
F16	0.00	0.00	0.05	0.05	0.01	0.01	0.04	0.02	0.01	0.03	0.02	0.01	0.02	0.05	0.00	0.01	0.04	0.04	0.00	0.01	0.01	0.01	0.04	0.01
F17	0.01	0.03	0.04	0.02	0.03	0.01	0.04	0.04	0.02	0.03	0.04	0.02	0.04	0.02	0.04	0.02	0.00	0.04	0.04	0.02	0.03	0.02	0.03	0.02
F18	0.03	0.06	0.00	0.04	0.02	0.03	0.01	0.04	0.01	0.02	0.03	0.03	0.03	0.01	0.04	0.04	0.01	0.00	0.01	0.02	0.02	0.00	0.04	0.03
F19	0.03	0.05	0.04	0.01	0.05	0.00	0.04	0.01	0.01	0.03	0.02	0.01	0.04	0.04	0.03	0.03	0.01	0.04	0.00	0.03	0.00	0.05	0.00	0.02
F20	0.00	0.00	0.02	0.00	0.05	0.00	0.01	0.01	0.03	0.01	0.05	0.00	0.00	0.02	0.05	0.00	0.05	0.01	0.02	0.00	0.05	0.02	0.04	0.01
F21	0.04	0.00	0.01	0.01	0.01	0.04	0.01	0.02	0.03	0.05	0.04	0.00	0.03	0.03	0.02	0.06	0.02	0.01	0.01	0.03	0.00	0.02	0.00	0.04
F22	0.00	0.04	0.00	0.02	0.05	0.01	0.04	0.03	0.02	0.00	0.00	0.01	0.06	0.00	0.02	0.03	0.02	0.01	0.01	0.04	0.03	0.00	0.02	0.03
F23	0.03	0.00	0.03	0.04	0.00	0.01	0.01	0.02	0.03	0.00	0.00	0.02	0.03	0.03	0.03	0.03	0.00	0.03	0.02	0.05	0.00	0.04	0.00	0.03
F24	0.00	0.03	0.05	0.00	0.01	0.04	0.01	0.03	0.04	0.05	0.01	0.05	0.00	0.00	0.02	0.03	0.02	0.02	0.00	0.04	0.03	0.01	0.01	0.00

of Appendix B) is constructed. Using Equations (23) and (24), the sum of the rows $(R)$ and columns $(C)$ is calculated. With this, the dataset for $(R+C)$ and $(R-C)$ is estimated, and a diagraph is graphed (Figure 2). Based on $(R+C)$ and $(R-C)$ values, the factors are grouped into cause and effect categories, as shown in

Table 6. Cause and effect relationship of the factors.

Factors	$(\mathit{R}+\mathit{C})$	$(\mathit{R}-\mathit{C})$	Cause/Effect	Rank
F1	0.9133	0.1184	EFFECT	24
F2	0.9312	0.1019	EFFECT	22
F3	0.9467	0.0857	EFFECT	21
F4	0.9804	0.0500	EFFECT	19
F5	1.1639	0.1314	CAUSE	2
F6	0.9317	0.1022	EFFECT	23
F7	1.0429	0.0105	CAUSE	8
F8	1.0332	0.0003	CAUSE	11
F9	1.0812	0.0480	CAUSE	6
F10	1.2148	0.1813	CAUSE	1
F11	1.0550	0.0237	CAUSE	7
F12	1.0134	0.0194	EFFECT	15
F13	1.0054	0.0256	EFFECT	17
F14	1.0306	0.0014	CAUSE	10
F15	1.0294	0.0032	EFFECT	12
F16	1.0092	0.0236	EFFECT	16
F17	1.1531	0.1227	CAUSE	3
F18	1.0829	0.0516	CAUSE	5
F19	1.0927	0.0573	CAUSE	4
F20	0.9684	0.0652	EFFECT	20
F21	1.0411	0.0085	CAUSE	9
F22	1.0239	0.0082	EFFECT	14
F23	1.0076	0.0269	EFFECT	18
F24	1.0231	0.0063	EFFECT	13

.

5. Result and Discussion

In this section, the results obtained using FAHP and FDEMATEL are discussed. In FAHP, the factors are ranked based on the weight of the importance of the factors. In FDEMATEL, the factors are categorized into cause and effect groups, based on the $(R-C)$ values. According to weight of the importance, the factors are ranked, as shown in Table. Based on the $(R-C)$ values given in the table, the factors affecting the air preheater performance are categorized under two groups: cause and effect. Here, eleven factors are coming under the cause group, while thirteen factors are coming under the effect group. The factors coming under the cause group deserves more attention, as the influential power $(R)$ of these factors is higher than the influenced power $(C)$. When the $(R-C)$ value is higher, the higher the impact [57]. Since the $(R-C)$ value of the effect factors is negative, there is a chance of being highly influenced by the cause factors.

5.1. Cause Factors

According to $(R-C)$ value, the factors under the cause group are ranked as follows: F10 > F5 > F17 > F19 > F18 > F9 > F11 > F7 > F21 > F14 > F8. Among all the cause factors, air preheater leakage (F10) has the highest $(R-C)$ value, and it denotes its influential power. This factor also secured the top position in the FAHP results. In tri-sector air preheaters, the primary and secondary air path pressure is much higher than the flue gas flow path. Due to the improper seal setting or heavy wear in axial, radial, and circumferential seals, the large quantities of high-pressure air enter the low-pressure flue gas path and dilute the high-temperature flue gas coming from the boiler house [58]. Due to the reduced differential temperature, the heat transfer will be affected and, thus, reduce the air preheater efficiency. In addition to this, due to the leakages, a huge volume of air is added into the flue gas, increasing the normal flue gas flow rate. So, it increases the primary, secondary, and induced draft air fan load, increasing the plant auxiliaries’ consumption. This finding was supported by the study of Chen et al. [55], which indicates that air preheater leakage affects heat transfer efficiency on energy conversion.

The next important factor is high air ingress level (F5) in both the FAHP and FDEMATEL results. An increase in the level of air ingress happens due to heavy erosion of boiler walls and flue gas ducts of the boiler. This ingress air dilutes the flue gas and reduces the flue gas inlet temperature at air preheater inlet. Due to the reduced differential temperature between the hot and cold medium, the rate of heat transfer gets affected and reduces the performance of the air preheater. Akpan and Fuls [51] embarked that the air ingress has close link with the power plants operating efficiency. The third important factor in FAHP and FDEMATEL is plugging by ash (F17). A study by Wang et al. [52] opinioned that plugging by ash largely influences the boiler performance. Now-a-days 100% fly ash collections are carried out, due to stringent environmental laws enforcements. All ash coming out from the boiler is collected through upstream flue gas and passes through the narrow passages of the air preheater to the heating elements. If the flue gas temperature is reduced, high moisture present in ash will be plugged into the narrow passages. It restricts the flue gas and air flow and affects the air preheater performance. If it is not cleared then and there, it permanently pluggs and increases the differential pressure across air preheater, and no heat transfer will take place in that plugged portion.

The high level of ash fouling (F16) is the important cause factor. This finding was supported by the Anitha Kumari and Srinivasan [54] study, which insists that ash fouling is an important challenge to be encountered in enhancing boiler performance. It causes the formation of fine ash layer deposition over the surfaces of heating elements. It purely depends upon the material surface porosities and fineness of ash. Since the ash is a poor thermal conductor, it reduces the heat transfer. Periodical suit blowing will help to remove the fouling. Next, high moisture content in coal (F4) is the important cause factor. It needs more tempering air to control the mill hot air temperature. The air preheater bypasses the air. Due to air bypass, the flue gas outlet temperature of the air preheater will increase. The increased flue gas outlet temperature is the mark of poor heat transfer. A large quantity of heat flow through flue gas is sent to the atmosphere without full use. This leads to a reduction the boiler efficiency. In addition, it increases auxiliary’s consumption, due to the increased volume of flue gas. Soot blowing is highly recommended for ensuring the free flow of mediums, such as air and flue gas, through the air preheater. Need-based soot blowing is better than periodical soot blowing. Poor soot blowing even sometimes leads to tripping of the whole unit.

Other factors, such as profile of heating elements (F11), increase the retention time of flue gas in the heating element, as well as the area of conduct, but the very narrow, complicated profile leads to air preheater chocking. Poor maintenance practice (F9) creates leakages, chocking, and air ingress. The factor air preheater secondary fire (F19) is due to the burning of unburned heavy oil, deposited in air preheater heating elements. It produces a hot zone, uneven expansion of the rotor, and damages the air preheater heating elements. Correct firing viscosity is 15 to 20 cst at the burner tip and correct oil temperature, corresponding to this viscosity, needs to be maintained. High level of corrosion (F18) can be avoided by best operating process. More number of mills in service (F8) increases the mass flow rate of flue gas and develops ash chocking inside the heating elements, as well as more erosion. Increase in tempering air (F14) bypasses the air preheaters in boiler, thus reducing the heat recovery from the flue gases leaving the boiler. This increases the boiler exit gas temperature, which, in turn, increases the dry gas loss in the boiler. APH motor failures for only the rotary type leads to uneven expansion and jamming of the rotor, as well as air preheater firing, which are all very important factors.

5.2. Effect Factors

According to $(R-C)$ value, the factors coming under effect category are ranked as follows: F15 > F24 > F22 > F12 > F16 > F13 > F23 > F4 > F20 > F3 > F2 > F6 > F1. Air preheater element erosion (F15) is important factor in the effect group. The erosion of the air preheater elements results in a loss of boiler efficiency [50]. The flue gas contains coal ash and abrasive-like sands. This will erode the air preheater heating element basket, rotor structure, and diaphragm plate. Erosion of the heating element basket reduces the heat-storing material volume and heat transfer. Damage to the rotor structure leads to rotor stoppage and unit outage. Erosion of the diaphragm plate seals each sector and avoids inter-sector passing, leading to mixing primary, secondary, and flue gas. It greatly affects the draft system and reduces the ID fan margin; the unit cannot be loaded to full capacity. Next, low feed water temperature at boiler inlet (F24), the second critical effect factor, needs more sensible heat in the boiler, so the flue gas inlet temperature to the air preheater will reduce. Hence, the primary and secondary air temperatures are reduced, thus increasing the coal flow rate. It directly increases the working capital. The causes low feedwater temperature at the boiler inlet may be due to regenerative heaters bypassing.

The third important factor is improper water washing (F22). The purpose of water washing is to clean the air preheater, in order to remove the permanent chocks that are not cleared in online suite blowing. This also cleans the fouling of the ash layer upon the heating element surface, thus improving heat transfer. Whenever the opportunity arises, water washing of air preheater internals will improve the performance. It ensures the original full quantity of heat transfer and improves the boiler efficiency. Heating element materials selection (F12), the fourth important effect factor, plays an important role in air preheater performance and decides the quantity of heat energy stored by its property-specific heat capacity. A study by Hasnain et al. [53] emphasizes that the material selection largely influences boiler efficiency. Mostly carbon steel is used for the hot-end and intermediate heating elements. Corten steel is used for making cold end heating elements. Advances in metrological engineering, with the view of increasing the specific heat capacity of the material, will help us improve the performance further.

Similarly, boiler operation with high overload (F23) increases the mass flow rates and reduces flue gas inlet temperature. PA/SA ratio (F2) is the ratio between the primary air and secondary air. These increased primary and secondary air ratios require lower mill inlet temperature and, thus, more tempering air. Malfunctioning of oil burners (F20) leads to carrying unburnt oil deposits in heating elements and increasing risk for air preheater firing. Pressure drop across air preheater (F13) is the indicator for heating element chocking, and the optimized on line suite blower helps to reduce the pressure across the air preheater. Upstream ash evacuation (F6) is hundred percent ash evacuations through flue gas, leads to more ash flow through the air preheater. It will creates chocking, more chances for heating element corrosion and air ingress in flue gas path.

6. Conclusions and Policy Implications

Obviously, the overdependence on non-renewable resources like coal for energy production has led to resource scarcity and negative environmental impact. Such consequences have necessitated the need to search for renewable resources. However, with the present technological capability and level of energy produced using renewable energy resources, it is difficult to meet the global energy demand. Hence, it is advisable to use the blend of energy obtained from renewable and non-renewable energy resources to meet the energy demand. Regarding this, there is a need to optimize the existing energy generation process that rely on non-renewable energy resources. To meet this need, this study aims to identify and evaluate the factors that affect the performance of CTPPs. Since it is a real-world industrial problem, meeting with experts was also carried out for collecting factors and literature review. Then, an integrated approach of FAHP and FDEMATEL is used to evaluate the factors. Outcomes of FAHP indicate air preheater leakage, high air ingress level, plugging by ash, air preheater secondary fire, and high level of corrosion as the top five critical factors that affect the performance of CTPP. FDEMATEL results categorize eleven factors under the cause category and thirteen factors under the effect category. The following are the important factors coming under the cause category (air preheater leakage, high air ingress level, and plugging by ash) and are of significant importance.

Based on the outcomes, this study offers some managerial implications that could help CTPP management improve performance of thermal power plant. Ash plugging blocks the heating elements and forces the flue gas flow. Hence, water washing the heating elements, during boiler shutdown, when the gas inlet side temperature is around 100 °C, and proper drying of heating elements after water washing has to be done. Additionally, there is a need to ensure complete cleaning of heating elements, whenever water washing is done. Otherwise, the left-out ash would form into hardened cement and become difficult to remove. The small stones present in coal are crushed and travel through the pulverized coal. These high-velocity sand particles act as sandblasting and erode the internal surface of the boiler. These particles hit the heating elements and damage the hot-end heating elements. This erosion can be solved by removing the stones before the crusher house, using trash screen arrangements or manual stone picking. Well-planned scheduled maintenance practices reduce the breakdowns. The different works to be carried out and all necessary spares required for the maintenance work must be kept ready before overhauling. With proper maintenance practice, the quality of work can be ensured. All the works, and their duration, are first listed out and a maintenance schedule has to be prepared and executed as per the plan. Mostly, the maintenance schedule can be prepared based on the recommendations of the maintenance manual. The deviation in this schedule will cause the performance deterioration of equipment and continues deviation, leading to major replacement of the equipment.

This study offers some notable contributions to the literature. First, this study provides more insight, regarding the need for optimizing the existing energy sources. Studies by Bertheau [35] and Swain and Karimu [36] investigated the impact of renewable energy resources in local development and SDGs. Another study by Büyüközkan et al. [59] examined the possibility of selecting the optimal renewable energy model in attaining SDGs. With renewable energy resources, it is possible to lower the adverse environmental impacts caused by conventional energy resources. However, it will not suffice the global energy demand. So, energy production, using conventional energy resources, need to be optimized by minimizing carbon emission. Therefore, only a combination of conventional and renewable energy resources will be beneficial in meeting SDGs and lowering the adverse environmental impact. Second, in this study, a list of factors affecting the performance of CTPPs is provided. This list of factors acts as a guide or database for critical factors that need to be considered in CTPPs. Third, the proposed integrated FAHP and FDEMATEL method help in identifying the most critical factors that need to be addressed sequentially and uncovers the relationship between different factors. By understanding the relationship between the factors and addressing them, it is possible to avert or eliminate other factors. To be precise, when one factor is addressed, it eliminates the emergence of other factors. By providing such crucial information, this study contributes significantly to the literature on challenges in CTPPs. Finally, this study proposes some managerial implications to help CTPPs management enhance performance.

This study has some limitations that may be considered as scope for future works. For example, in this study, only the factors affecting the performance of CTPPs are considered. In future studies, factors affecting the performance of diesel-based thermal power plants could be investigated. Additionally, in this study, only the CTPPs located in India are considered. A similar study in other country contexts may reveal different challenges and results. Additionally, processes like Failure Modes and Effects Analysis (FMEA), system theoretic process analysis (STPA), and Hazard and Operability Analysis (HAZOP) could be integrated with the MCDM methods in future studies regarding this work.