ISSN (0970-2083)

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**Mahmad Raphiyoddin Shaphiyoddin Malik ^{1}, E. Rasul Mohideen^{1}, Layak Ali^{2*}, Syed Raziuddin^{3}**

^{1}Department of Civil Engineering, B. S. Abdur Rahman University, Chennai, India

^{2}School of Engineering, Central University of Karnataka, Karnataka, India

^{3}Department of Information Technology, Deccan College of Engineering and Technology, Hyderabad, India

- Corresponding Author:
- Layak Ali

School of Engineering, Central University of Karnataka, Karnataka, India[email protected]

E-mail:

**Received date:** May 20, 2016; **Accepted date: ** June 12, 2016

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Journal of Industrial Pollution Control

Due to rapid growth in Industrialization and Urbanization, there is an exponential increase in the air pollution. This increased air pollution has major impact on environment and human health. The growth in industrialization and urbanization has everlasting demand of electricity. Most of the electricity (almost 60%) to meet the said demand comes from Thermal Power Plants (TPP). Almost all the TPPs generate electricity by burning fossil fuels which releases poisonous gas in the air contributing to pollution. The air pollution caused by TPPs may be reduced by using non-conventional energy source like solar and wind, unfortunately they are in their infancy, and thus the tuning and optimizing existing TPPs are of prime importance to reduce the air pollution. To device the alternate strategies, one need to have a mathematical model that depicts the performance of existing TPPs. There are many mathematical ways to model optimization of air pollution in TPPs. Since such models turns out to be nonlinear, complex and multimodal, hence most of the classical optimization methods fail to give appropriate solution. This paper integrates an efficient variant of Nature Inspired (NI) algorithm namely weighted distance grey Wolf Optimizer (WdGWO) for minimizing air pollution caused by Delhi Thermal Power Plants. The results show that the WdGWO has reduced total pollution by 8.2350% as compared to the state of-the-art which has reduced even less than 4% only. Thus results support the application and superiority of WdGWO algorithm for Delhi TPPs.

Thermal power plant, Grey wolf optimizer, Constraints, Air pollution

The Air pollution has hazardous effect on both
environment and human health and is a serious
concern of the 22^{nd} century (IEEE working group,
1995). The poisonous air pollutants are mainly due
to the rapid industrialization and urbanization.
This rapid growth demands sustained power and
hence there is an exponential growth in electricity
consumption and generation and generation. The
major portion of electricity is produced by burning
of fossil fuels in thermal power plants (TPP). The
fossil fuels like coal, petrol, diesel and kerosene etc.
are major source of thermal energy due to their high
calorific value and are available in abundant. A large
amount of fossil fuels are burnt in TPP to produce
electricity which causes huge amount of air pollution
(Basu, 2010). The major harmful gases and pollutants
released in air are Sulphur Dioxide (SO_{2}), Oxides of
Nitrogen (NO_{x}) and Total Suspended Particulate
Matter (TSPM). Though there are many ways to
avoid the release of poisonous gases from TPPs by
making use of non-conventional energy sources
(solar, wind etc.), but they are still in their infancy.
Due to many good reasons, the establishment of new
TPP is not possible and the hence control strategies
for existing TPP is very much in demand. Thus it
is on the radar of government and researchers to
device strategies to reduce air pollution without
effecting electricity production. This can be achieved
by constructing mathematical model of air pollution
reduction strategies. There are many mathematical models available to reduce air pollution from existing
TPP. These equivalent models have nonlinear,
complex and multimodal characteristics. Due to the
complexity involved in optimization most of the
classical optimization methods fail to gives required
solution (Fletcher, 2000; Bonnans JF and Sagastizabal,
2003; Vanderbei, 2008) and hence alternatives like
Nature Inspired (NI) algorithms may be preferred.
The previous works show poor results due to their
inherent capability; they find difficulty in optimizing
the problems that have non separable and strongly
dependent nature among the variable in the problem.

The NI algorithms are the optimizing paradigms developed by mimicking nature's searching behavior and used for solving complex optimization problems (Ali et al., 2012; Rajesh et al., 2015; Civicioglu, 2013; Sabat et al., 2009; Goldberg, 1989; Eberhart and Kennedy, 1995a, b; Karaboga, 2005; Karaboga and Basturk, 2006; Yang and Suash, 2009). Many NI algorithms have proved well on standard benchmark optimization problems and opens the door for their usage to real world applications. This paper uses an efficient NI algorithm called weighted distance grey wolf optimizer (WdGWO) (Malik et al., 2015), a variant of GWO (Seyedali et. al., 2014) for reducing air pollution without affecting the amount of electricity being produced by Delhi TPP. Since NI algorithms including WdGWO, have stochastic behavior, hence can easily solve the complex problem problems. The rest of paper is organized as follows: First the brief explanation about TPP and Air pollution is given. Then the problem formulation and TPPs pollution statistics is presented. The WdGWO algorithm is explained followed by the detailed set used for simulation and algorithms used for comparison. The obtained results are discussed in details followed by conclusions in the last part.

The major source of power generation among all other sources is the TPPs. The TPPs usually convert heat energy to electric power. The heat energy generated by burning of fossil fuels used for driving steam turbines and an electrical generator. The process of burning fossil fuels causes the poisonous gases to be liberated in the air and hence cause the pollution. Following section describes both TPPs and air pollution in details.

The TPPs accounts for major part of electricity generation in the world and India in particular. The online literature (Wikipedia, 2016) reveals that, the energy policy of India is largely defined by the country's burgeoning energy deficit and increased focus on developing alternative sources of energy, particularly nuclear, solar and wind energy. About 70% of India's energy generation capacity is from fossil fuels, with coal accounting for 40% of India's total energy consumption followed by crude oil and natural gas at 24% and 6% respectively. India is largely dependent on fossil fuel imports to meet its energy demands; by 2030 India's dependence on energy imports is expected to exceed 53% of the country's total energy consumption. In 2009-10, the country imported 159.26 million tones’ of crude oil which amount to 80% of its domestic crude oil consumption where as 31% of the country's total imports are due to oil. To save primary energy resources i.e. to reduce fuel consumption, and to reduce emissions, maximum power plant efficiency is a crucial parameter.

The TPPs generally consist of three main elements which are boiler, turbine, an alternator, and other complementary accessories such as a fuel handling system, water handling, and emission control system. The construction of a new thermal power plant is relatively reduced. Recently, there have been concerns regarding the efficiency improvement of existing thermal power plants. The efficiency of such type of power plant is very low and great amount of loss in thermal energy may be noticed (Kumar et al.) In order to generate a required electric energy, the turbine needs an equivalent amount of thermal energy in addition to the loss. Minimizing the loss leads to a reduction of pollutants in the environment as well as production cost.

Air pollution is a state of air containing chemicals
like gases, dust, fumes or odor in the atmosphere
that has harmful effects. The substances that cause
air pollution are called pollutants. Air pollution
consists of gaseous, liquids, or solid substances
that, when present in sufficient concentration, for a
sufficient time, and under certain conditions, tend
to interfere with human comfort, health or welfare,
and cause environmental damage. Air pollution
causes acid rain, ozone depletion, photochemical
smog, and other such phenomena. The large amount
of electricity is produced from thermal power
plants, where the coal and other fossil fuels are
burnt extensively to boil water and thus produce
electricity. In addition to electricity, the poisonous
gases like SO_{2}, NO_{x} and TSPM are also produced as a byproduct. The detailed about these poisonous
gases is explained in the following section.

**Sulphur Dioxide (SO _{2})**

It is a poisonous compound and has chemical
formula SO_{2}. In general and standard atmosphere,
it has a pungent and irritating smell. It enters the
atmosphere in two ways, both man-made and natural
phenomena; combustion of fossil fuels, oxidation
of organic material in soils, volcanic eruptions and
biomass burning. Its presence in the atmosphere
in any quantity is injurious and is a qualitative
pollutant.

**Oxides of Nitrogen (NO _{X})**

It is generic term referred for the mono and dioxide of
Nitrogen (NO nitric oxide and NO_{2} nitrogen dioxide).
It is a byproduct liberated from the reaction among
nitrogen, oxygen and even hydrocarbons (during
combustion), especially at high temperatures. These
are quantitative pollutants and harmful to human
health. It reacts with the oxygen in the air resulting
in ground-level ozone. The ground-level ozone has
very harmful effect on human health especially on
the respiratory system. It also reacts to form nitrate
particles, and acid aerosols. It may react with water
can cause acid rain and the deterioration of the
quality of water.

**Total Suspended Particulate Matter (TSPM)**

Particulate matter is the term used for solid or
liquid particles found in the air. Some particles are
large or dark enough to be seen as soot or smoke.
Others are so small they can be detected only with
an electron microscope, their chemical and physical
compositions vary widely. Particulate matter can be
directly emitted or can be formed in the atmosphere
when gaseous pollutants such as SO_{2} and NO_{x} react
to form fine particles. The presence of these in the
atmosphere with the particle sizes ranging from less
than 0:01m to more than 100 m is harmful to human
health (Devi et al.; Wan-Kuen and Joon-Yoeb, 2006;
Rajesh et al., 2015).

This section discusses the mathematical model of Delhi TPPs for reducing air pollution, the data related to TPPs and constraints handling methods.

It is difficult to stop the operation of existing TPPs, as the non-conventional energy sources like solar and wind are not efficient enough to provide sufficient amount of electricity for the present demand. The non-conventional energy sources are still in their infancy in developing countries like India. Hence there is a requirement of finding a sustainable solution which may not reduce electricity production but must reduce the increasing pollutants. This paper proposes a single objective constraints model which may be helpful to reduce the rising quantity of pollutants from TPPs as well as help to maintain electricity production. The following section provides the details of TPPs of Delhi (India) and presents an optimizing model for pollutant reduction. There are mainly five TPPs operating in Delhi (India) to provide required electricity demand of the country. This paper considers the five TPPs viz. Rajghat, Indraprasth Gas Turbine (IGT), Indraprasth (IP), Badarpur and Pragati power station. The following equations may be built to minimize the air pollution and maximize the electricity generation (Rajesh et al., 2015).

The f(X) is an objective function that maximizes
the electricity generation. The g_{1}(X), g_{2}(X) and g_{3}(X)
are the constraints that minimizes the amount of
SO_{2}, NO_{x} and TSPM emitting from various TPPs
respectively.

Since the modeled TPP problem involves constraints to be satisfied, hence it is worth mentioning general constraints handling strategies. The generic optimization problem with constraints can be expressed as

Finding

That optimizes

Subject to:

Where

Where “f” is the objective function, “g_{i}” and “h_{i}” is
the inequality and equality constraints respectively.

The values “l_{i}” and “u_{i}” for all “i” belongs to “D” are
the lower and upper bounds of the solution defining
the search space.

The usual way of handling constraints is to convert constrained optimization problem into an unconstrained problem. Then any unconstrained optimization algorithms may be applied without loss of generality. This conversion from constraint to unconstraint is implemented with the introduction of penalty function as

Where “φ ≥ 0 ” is a real valued function that imposes
a penalty. The penalty on each constraint is imposed
by the penalty factor “r_{k}”. Although the above
penalty method works well for certain constrained
optimization problems, selecting the penalty factor
“r_{k}” remains a challenge. If the penalty factor is
chosen to be too small, an infeasible solution may not
be penalized enough (underpenalization), resulting
a final infeasible solution. If the penalty factor is too
large, a feasible solution is very likely to be found
(overpenalization), but could be of poor quality
(Runarsson and Yao, 2000). Thus underpenalization
and overpenalization are not good for handling
constraints.

Despite its simplicity, a penalty function requires the definition of penalty factors to determine the severity of the penalization, and these values depend on the problem being solved (Runarsson and Yao, 2000). Due to this major disadvantage, several alternative constraint-handling algorithms have been proposed. Stochastic ranking technique has been proposed (Runarsson and Yao, 2000) to maintain the required balance between objective function and penalty function. This technique uses stochastic bubble-sort algorithm to rank the individuals for generating offspring’s for the next generation. In order to solve complex constrained problems, WdGWO algorithm is hybridized with stochastic ranking (Runarsson and Yao, 2000). Similarly the stochastic ranking is hybridized with GWO algorithm and PSO algorithm for comparison (Layak et al., 2012).

Delhi is the capital city of India and has major
requirement of electricity. It has many TPPs for
generating electricity to meet the demand of the city
and the country. These TPPs are producing electricity
in different amounts and consuming various fossil
fuels in different amount, thus producing pollutants
in different amounts. The detailed pollution statistics
of prominent five TPPs in Delhi (Rajesh et. al., 2015)
is tabulated in **Table 1**.

TPP | Power (MW) | SO_{2} (mg/m^{3}) |
NOx (mg/m^{3}) |
TSPM (mg/m^{3}) |
---|---|---|---|---|

Rajghat | 135 | 189.73 | 55.73 | 57.80 |

IGT | 1500 | 0.26 | 32.66 | 0.93 |

IP | 135 | 116.90 | 74.65 | 37.58 |

Badarpur | 705 | 319.60 | 1050.79 | 616.64 |

Pragati | 350 | 0.0037 | 61.69 | 1.81 |

TOTAL | 2825 | 626.4937 | 1275.52 | 714.76 |

**Table 1:** Delhi Thermal Power Plant Specification

Nature Inspired (NI) algorithms, are the iterative search algorithms that has origin from nature. The nature has the efficient and robust searching strategy; the NI algorithms are the mimicking computer program for the same.

Most of the NI algorithms composed of artificial and or natural individuals that coordinate using decentralized control and self-organization for searching target or food efficiently. Almost all the NI algorithms have shown promising results on standard optimization benchmark problems compared to classical optimization. Few among the NI algorithms are Particle Swarm Optimization (Eberhart and Kennedy, 1995a,b), artificial bee colony (ABC) (Karaboga, 2005; Karaboga and Basturk, 2006), Firefly Algorithm (FFA) (Xin-She Yang, 2009) and Cuckoo search algorithms (CSA) (Yang and Suash, 2009).

The Weighted distance Grey Wolf Optimizer (Malik et. al., 2015) is a variant of Grey Wolf Optimizer (Seyedali et al., 2014). The grey wolf optimizer (GWO) (Seyedali et al., 2014) is one of the NI algorithm developed by Seyedali et al., in 2014, that mimics the prey hunting mechanism of grey wolves.

The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Using the hierarchy of wolves, GWO implement four main steps of hunting, searching, encircling, and attacking the prey (Seyedali et al., 2014). The GWO defines mainly four types of grey wolves such as alpha, beta, delta, and omega to simulate the leadership hierarchy for hunting the prey as they have the ability to identify location of prey. The movement of the whole pack of wolves will be guided by the above wolves. The location update of all the wolves in pack is done by simple average of three best location of the pack and whole pack follows it.

This paper uses a weighted distance method for updating location vector of the pack, hence called WdGWO (Malik et al., 2015). The location of the wolves in the pack is influenced by the weighted best locations of the leaders in the pack. The weights are calculated in every iteration based on the coefficient vectors.

In WdGWO algorithm, the position update equation is weighted in every iteration as shown in following equations. The weights “wi” are calculated based on coefficient vectors “Ai” and “Ci” as per equation (2) and equation (1) respectively. The location update equation is modified as per the calculated weights, shown in equation (3). This strategy particularly very helpful in optimizing complex problems.

(1)

(2)

(3)

The WdGWO (Malik et al., 2015) is explained in the Algorithm 1.

Algorithm 1 Pseudo code for proposed SRWdGWO

1: Initialize iteration count (MaxIter)

2: Initialize size of the pack (NG).

3: Initialize grey wolf population X

4: Initialize a, A and C

5: Evaluate fitness of each grey wolf f(X)

6: Compute X_{α}=the first best grey wolf

7: Compute X_{β}=the second best grey wolf

8: Compute X_{δ}=the third best grey wolf

9: While t <=MaxIter do

10: While i <=NG do

11: Update the position of the current grey wolf

12: End while (for i)

13: Update a, A and C

14: Update X_{α}, X_{β} and X_{δ}

15: Calculate weights as per equation ( 2)

16: Update position vector as per equation ( 3)

17: Evaluate fitness of each grey wolf f(X)

18: Evaluate constraints of each grey wolf g(X)

19: Check for feasible solution

20: Update penalty factor (Stochastic Ranking)

21: End while

22: Report Results

The algorithm first starts with all initializations like maximum iterations (1000), size of the pack (20) and the whole pack location is randomly initialized with Gaussian distribution random strategy. Immediately the best three wolves are recorded and then searching starts. During search process, the fitness of each wolf will be calculated; from this the best locations of three wolves are updated and recorded. The updated best grey wolves further enhance the search process and final results are recorded.

The code for presented algorithms are written in Matlab 7.2 installed on computer with Core 2 Duo processor and 2GB RAM on a Windows-XP platform. The NI algorithms used for comprehensive performance analysis are Stochastic Ranking Particle Swarm Optimization (SRPSO) (Layak et al., 2012), Goal Programming (GP) (Rajesh et al., 2015), GWO (Seyedali et al., 2014) and WdGWO (Malik et al., 2015) hybridized with Stochastic Ranking.

The SRPSO (Layak et al., 2012) is a variant of PSO (Eberhart and Kennedy, 1995a, b) integrated with Stochastic Ranking (Runarsson and Yao, 2000) for handling standard constraints benchmark optimization problems. The PSO is a NI algorithm developed in 1995 by Kennedy and Eberhart that mimics the food searching behavior of flock of birds or fish school. It has proved to be one of the good algorithms to solve complex optimization problems.

The Goal Programming (GP) is an optimization technique which treats the constraints of linear programming problem as their goal. It was first in 1955 by Charnes and Cooper (Charnes et al., 1955). It has played a vital role for many years to solve many real world problems.

The GWO is also a nature NI algorithm developed in 2014 by (Seyedali et. al., 2014). The GWO mimics the prey hunting mechanism of Grey wolves. The grey wolves first locate the prey, they encircle and exploit, and then they attack. This strategy was extracted and implemented as computer algorithm for solving global optimization problems (Seyedali et al., 2014; Malik et al., 2015).

All the algorithms are initialized with population
size of 20 in the search range. The stopping criteria
for all the algorithms are set to maximum number
of iterations (1000). The results obtained are the
average of 25 trials and each trial is of 1000 iterations.
The algorithms are run for several times to find the
good configuration and results are documented in
**Table 2**. From the **Table 2** it is seen that the WdGWO
shows good results compared to other algorithms.
The amount of SO_{2} liberated by WdGWO strategy
is 514.6601 mg/m^{3} which is far less than SRPSO
(Layak et al., 2012) (578.9892 mg/m^{3}), GP (Rajesh
et al., 2015) (625.9737 mg/m^{3}) and GWO (620:0220
mg/m^{3}). The reduction in NO_{x} shown by WdGWO
is 1209.3813 mg/m^{3}, compared to other algorithms
like SRPSO (1245.5730 mg/m^{3}), GP (1210.2 mg/m^{3})
and GWO (1271.7679 mg/m^{3}). Similarly WdGWO
shows very good reduction in TSPM 677.2412 mg/
m^{3} as compared to SRPSO (694.9890 mg/m^{3}), GP
(712.9 mg/m^{3}) and GWO (712.6989 mg/m^{3}). The
percentage reduction in pollutants by algorithms is
calculated as

Original | GP | SRPSO | GWO | WdGWO | |
---|---|---|---|---|---|

Amount of SO2 (mg/m^{3}) |
626.4937 | 625.9737 | 578.9892 | 620.0220 | 514.6601 |

Amount of NOx (mg/m^{3}) |
1275.52 | 1210.2 | 1245.5730 | 1271.7679 | 1209.3813 |

Amount of TSPM (mg/m^{3}) |
714.76 | 712.9 | 694.9890 | 712.6989 | 677.2412 |

Total Pollutants (mg/m^{3}) |
2616.7737 | 2549.0737 | 2519.5512 | 2604.4888 | 2401.2826 |

**Table 2: ** Delhi Thermal Power Plant Results

Using above equation, the % reduction in SO_{2} by
WdGWO is calculated as (see the **Table 2** for obtained
results)

Thus the amount of SO_{2} is greatly reduced to 17.8507%
by WdGWO compared to SRPSO (7.5826%), GWO
(1.0330%) and GP (0.0830%). The reduction in NO_{x}
is achieved to be 5.1852% by WdGWO compared to
GP (5.1210%), SRPSO (2.3478%) and GWO (0.2942%).
Similarly the amount of TSPM is reduced to 5.2491
% by WdGWO compared to SRPSO (2.7661%), GWO (0.2884%) and GP (0.2602%).

The last row in **Table 2** shows the total pollutants.
This row shows the combined reduction by different
algorithms. From the table it is clear that WdGWO
shows more reduction (2401.2826 mg/m^{3}). The
amount of combined pollutant reduction is 8.2350%
by WdGWO compared to SRPSO (3.7154%), GP
(2.5872%) and GWO (0.4695%).

The extra ordinary result of WdGWO is seen on reduction of pollutants compared to other counterparts. These results are mainly due to the weighted distances that are incorporated in the basic GWO. In basic GWO, the distances among the wolves are calculated as simple and linear average, that may be misguiding and the whole pack may get trapped in local minima. Thus the proposed WdGWO which is a variant of GWO has shown remarkably enhanced results compared to GWO.

This paper presents an application of Weighted distance Grey Wolf Optimizer WdGWO on Delhi Thermal Power Plant TPP. The WdGWO is one of the Nature Inspired (NI) algorithms that has well proved on standard benchmark optimization problems. The rapidly growing society needs the sustained generation of electricity. The major sources of electricity are the TPPs, which generates electricity by burning fossil fuels. The burning of fossil fuels causes air pollution and has major environmental and human health hazard. The poisonous pollutant may be brought down by making use of non-conventional energy sources and by installation of new efficient TPPs; unfortunately the former is in its infancy and later is non-feasible. Thus finding appropriate strategy for existing TPP is only left out option. The major concern in today's world is, to minimize air pollutants and increase the power generation. There are many mathematical techniques to model optimization of air pollution. Since such models turns out to be nonlinear, complex and multimodal, hence most of the classical optimization methods fail to give appropriate solution. This paper first proposes a constraints based mathematical model of Delhi TPPs, then integrate a well-known variant of NI algorithm namely WdGWO. The proposed model and the integration turn out to be fruitful in reducing the amount of pollutants generated by TPPs. The applied WdGWO algorithm brings down the air pollution to a considerably larger extent.

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