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CHEMICAL ENGINEERING TRANSACTIONS

VOL. 56, 2017

A publication of

The Italian Association
of Chemical Engineering
Online at www.aidic.it/cet

Guest Editors: Jiří Jaromír Klemeš, Peng Yen Liew, W ai Shin Ho, Jeng Shiun Lim
Copyright © 2017, AIDIC Serv izi S.r.l.,
ISBN 978-88-95608-47-1; ISSN 2283-9216

Centralised Sewage Treatment Plant Assisted by Geographic

Information System for Electricity Generation

Muhammad Saufi Tarmizi, Zarina Ab Muis*, Haslenda Hashim, Jeng Shiun Lim, Wai

Shin Ho

Process Systems Engineeri ng Centre (PROSPECT), Research Institute for Sustai nable Environment, Universiti Teknologi

Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia

zari namuis@cheme.utm.my

Municipal wastewater or sewage management is crucial as it is one of the major contributors for greenhouse

gas (GHG). The biogas yield from biological treatment in sewage treatment plant (STP) contains about 60 %

methane. Existing plant in Malaysia are lacking in capturing this by-product. There is a great potential for

capturing biogas and combust it to harness energy. Centralised sewage treatment plant (CSTP) is suggested

as long term solution for the increasing number of population and environment concern. Optimum location is an

important criterion in building new CSTP in order to keep up with rapid development. The main objective of this

study is to develop multi-period planning of centralised sewage treatment plant (CSTP) for electricity generation

in Iskandar Malaysia. It can be divided into 5 stages; data gathering, problem formulation and superstructure

construction, mathematical modelling, General Algebraic Modeling System (GAMS) coding and result analysis.

In this study, GIS is used as preliminary step to generate and prepare data for mathematical model. It should

plot the sources, substation and new location of CSTP. The software provides the distance between sources of

sewage and new plant plus. The plant is able to produce 8 GWh/y of electricity. The model is capable of

proposing location of new centralised sewage treatment plant, technology selection and its capacity.

1. Introduction

Renewable energy is starting to take place as reliable energy provider throughout the world. Nevertheless, the

demand of fossil fuel is still high although it is known to be main source of greenhouse gas. Apart from wind and

solar energy, biogas yield from anaerobic digestion of sewage is an alternative energy. This abundant supply is

untapped. In conventional approach, sewage sludge is dewatered, yielding sludge cake as final product. It is

later transport by lorry and dump at nearest landfill. As shown in Figure 1, the dot green box represents the

alternative pathway where the biogas is combusted, thus producing electricity. Population growths indirectly

affecting the volume of sewage produce. Due to scattered STP, it is necessary to find location and build new

CSTP as a solution.

Finding the best location for STP is a challenge. Previous study has presented an application of eco-suitability

evaluation, solving the location of STP and outfalls (Zhao et al., 2009). A study in Upper Mahaweli Catchment,

Sri Lanka uses GIS integrated with local factor (Ratnapriya et al., 2009) while in Tamil Nadu, India applied

suitability score based on planning and design constraints (Benujah and Devi, 2013). The latest research

combines GIS and Analytical Hierarchy Process (AHP).The latter is a method used to determine the weight of

parameter (Mansouri et al., 2013). Another study use GIS as data input for MSW management (Tan et al.,

2014). Based on the literature, GIS is vastly used to find the optimum location for new STP. This is acceptable

because the authors are focusing on the geographical and ecological criteria. It lacks the discussion on the

sewage network, the incurred cost to build the STP and the location of new CSTP with respect to existing small

STP. There is a gap in finding the location to build new STP with the minimum cost without jeopardising the

environmental aspect. This is the continuation of preliminary study on multi period planning on CSTP (Tarmizi

et al., 2015).

DOI: 10.3303/CET1756195

Please cite this article as: Tarmizi M.S., Muis Z.A., Hashim H., Lim J.S., Ho W.S., 2017, Centralised sewage treatment plant assisted by
geographic information system for electricity generation, Chemical Engineering Transactions, 56, 1165-1170 DOI:10.3303/CET1756195

1165

Figure 1: A typical schematic of modern mechani sed STP process flow in Malaysia (Palanisamy and

Shamsuddin, 2013)

2. Methodology

Figure 2 shows the process flow of this study. The first step is to collect data from dependable sources, then

problem formulation and superstructure construction. A superstructure is constructed to represent the entire

concept of electricity generation from sewage by connecting sources, location, technologies and end product.

Based on the designed superstructure, a mathematical model comprises of two important components is

developed: the objective function and constraints. An optimisation software, General Algebraic Modeling System

(GAMS) is used to code the model, producing output data.

2.1 Case Study

In this work, Kulai district in Johor is used as the case study area as shown in Figure 3. Location and flowrate

of 70 sources of existing STP is obtained from local authority (Indah Water Konsortium). Geographical

Information Systems (GIS) is used to plot the locations of the existing STP and substation. Apart from that the

new CSTP location 2 substations and 10 identified new centralised STP locations were studied. In order to have

better result, the distance between existing STP and new centralised STP were estimated along driving road.

In practice, sewage pipe are embedded beneath road. For locations of new CSTP, the potential site identification

was conducted using GIS. It should be 200 m from residential area and located beside river to aid removal of

treated water effluent.

This study just assumed using one standard anaerobic reactor with constant efficiency. Other type of reactor

can be included in future study. According to literature, there were 2 types of biogas engine that are commonly

used; internal combustion engine and gas turbine (Coelho et al., 2011). The time range covers 10 y period from

2016 until 2025. General Algebraic Modelling System (GAMS) version 23.7 is used in this study and CPLEX

solver is applied to solve the model.

Activated

Sludge

Pumping

Station

Grid

Chamber

Primary

Clarifier

Aeration

Tank

Secondary

Clarifier

Sludge

Dewatering Combustor

Gas Engine

Electricity

Outfalls to

river

Measuring

Tank

Digester

Tank

Gas Holder

SCADA

Room

Sewage

Sludge Cake

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Figure 2: Process flow of this study

Figure 3: Case study area plotted using ArcGIS (brown-existing STP; green-new CSTP)

2.2 Mathematical formulation

The model consists of an objective function and several constraints. The objective function is to minimise the

total cost of electricity generation. It consist of capital and O&M cost of building CSTP, anaerobic digester and

biogas engine and cost for pumping, piping and transmission line (Tarmizi et al., 2015).

Mathematical model

GAMS optimisation Literature

Review

GIS Tool

Output Data Optimisation model Input Data

Economic data of

technologies

Electricity demand

Distance from

source to sink

Optimal

technology

Annual electricity

generation

Optimal location

Sewage

availability

Sewage sources

location

Potential new

CSTP location

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(1)

The balance equation is formulated from the mass balance model of STP, and the average daily flow, adfi is

shown in Eq(2).

adfi = ∑ FWi,l
l

× yl(l) ∀i (2)

where FWi,l is flowrate of domestic wastewater from source, I, to location, l (m
3/d). yl(l) represents the binary

variable to select the location, l.

The domestic wastewater undergoes primary treatment to separate solids from the water is written in Eq(3).

FWi,l × treat = FEi,l ∀i , ∀l (3)

where FEi,l is flow rate of water effluent after treatment at location, l, from source, i; and treat is the fraction of

water effluent produce during treatment at location, l. Its value is assumed to be the same at each location. The

flow rate of sewage sludge, FSi,l, as the by-product from source, i, at location, l, as stated in Eq(4).

FSi,l = FWi,l − FEi,l ∀i, ∀l (4)

The total flow rate of sewage sludge FSinl,r from location, l, entering reactor, r, is formulated in Eq(5).

∑ FSi,l
i

= ∑ FSinl,r
r

∀l (5)

The flow rate FPl,r,p at location, l, of product, p, yield from reactor, r is indicated in Eq(6).

FSinl,r × yieldr,p = FPl ,r,p ∀l, ∀r, ∀p (6)

where yieldr,p is yield of product, p, produce from reactor, r. The flow rate FPinl,p,t at location, l, of product, p,

from reactor, r, to technology, t, is stated in Eq(7).

∑ FPl ,r,p
r

= ∑ FPinl ,p,t
t

∀l, ∀p (7)

The flow rate FGl,p,t,g at location, l, of generation, g, from product, p, using technology, t, is shown in Eq(8).

FPinl,p,t × convp,t,g = FGl,p,t,g ∀j, ∀p, ∀t, ∀g (8)

where convp,t,g is conversion of product, p, using technology, t, to generate g. The flow rate FGinl,g,ss from

location, l, of generation, g, to substation, ss, is shown in Eq(9).

∑ FGl,p,t,g
p,t

= ∑ FGinl,g,ss
ss

∀l, ∀g (9)

3. Results and discussions

The result obtained from the preliminary study shows that location 3 (marked with black triangle) is optimal

economically as shown in Figure 3. It depends mostly on the distance towards location of the new CSTP and

headed to substation. The total cost for building CSTP is RM 308,416,734 which will cater around 400,000 PE.

By breaking down the cost, the fraction for piping and pumping is around 25 % of total cost. This is usually not

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considered in normal plant calculation. This gives overview of retrofitting sewage pipe cost compared to

conventional method. The list of parameters and results are summarised in Table 1 and 2.

The biogas yield is 9,388 m3/d producing 22,533 kWh/d of electricity approximately 0.94 MW/y. This is assuming

that the plant run 24/7 continuously for a year with maximum sludge recovered, 3 %. In normal condition, the

fraction of sludge is only around 1 % and the rest are water. Most of the energy is loss in form of heat because

of technology modest efficiency. Any way necessary to recover it such as co-generation system is beneficial.

This is highly favourable in cold country as it can be supplied to nearest residential area in winter. The electricity

produce can also be utilised for in-house purpose such running plant equipment and lightning (Malik and Bharti,

2009). The model selected internal combustion engine (ICE) which is common biogas engine over gas turbine.

This is influenced by cost and in term of environmental friendly, the gas turbine is preferable as stated in

literature. A model that includes pollutant release should be developed in future.

Table 1: List of sets, variables and parameters

Sets

I Source of sewage

T Type of technology

L Location to build new CSTP

Ss Substation

Type of Anaerobic Digester reactor

Time period

Decision Variables

FWi,l flowrate of raw domestic wastewater from source i to location l (m
3/day)

FEi,l flowrate of water effluent after treatment at location l from source i

FSi,l flowrate of sewage sludge as by-product from source i at location l

FSinl,r flowrate of sewage sludge from location l entering reactor r

FPl ,r,p flowrate at location l of product p yield from reactor r

FPinl,p,t flowrate at location l of product p from reactor r to technology t

FGl,p,t,g flowrate at location l of generation g from product p using technology t

FGinl,g,ss flowrate from location l of generation g to substation ss

Parameters

cost l Capital cost for plant (RM/m
3)

cost r Capital cost for reactor (RM/m
3)

cost t Capital cost for biogas engine (RM/kWh)

O&Ml Operation and maintenance cost for plant (RM/m
3)

O&Mr Operation and maintenance cost for reactor (RM/m
3)

O&Mt Operation and maintenance cost for biogas engine (RM/kWh)

cost1 cost for piping and pumping from source i to location l (RM/km)

cost2 cost for electricity transmission from location l to substation ss (RM/km)

dist1i,l Distance from existing STP to new CSTP (km)

dist2l,ss Distance from location of CSTP to substation location (km)

avaii Population Equivalent availability

Binary variable

yl(l) Choosing location

ylss(l, ss) Choosing substation

yt(t) Choosing technology

Table 2: Result summary

Location
Type of

engine

Year to

construct new

biomass

power plant

Capacity to

be build

(MW)

Annual operating capacity in MW

2016 2017 2018 2019 2020 2025

3 ICE 2016 0.94 0 0.24 0.24 0.24 0.94 0.94

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4. Conclusion

A model for the multi-period planning on electricity generation from centralised sewage treatment plant is

developed. It is able to select the plant capacity, location technology and propose future planning. The plant is

able to produce 8 GWh/y of electricity. The selected location and substation is influenced by lowest distance.

Selected technology is based on cost and its efficiency. Different scenario results in different result, such as the

selection of gas turbine over ICE due to the environmental constraint. The electricity potential is quite low

compared to the huge investment. It is significant in the next few years when the price of fossil fuel rise and

economically viable technology. Continuous research is required to meet the energy demand. In the future,

having real life data, the model is able to propose more realistic model.

Acknowledgments

The authors gratefully acknowledge the funding support for this work provided by the Ministry of Higher

Education, Malaysia and Universiti Teknologi Malaysia (UTM) under PAS Grant of Vot number

Q.J130000.2709.01K08, Others Grant of Vot number R.J1300000.7301.4B145, R.J130000.7844.4F544 and

Japan International Cooperation Agency (JICA) under the scheme of SATREPS Program (Science and

Technology Research Partnership for Sustainable Development) for the project Development of Low Carbon

Scenario for Asian Region.

References

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https://pure.utm.my/en/persons/haslenda-hashim
https://pure.utm.my/en/persons/zarina-ab-muis