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A multi-objective optimization model to plan city-scale water systems with economic and environmental objectives: a case study in Santiago, Chile

10 Jan 2021-Journal of Cleaner Production (Elsevier)-Vol. 279, pp 123737

AbstractClimate Change and its effects in water scarcity has become an important challenge for cities with water management problems. These problems require an integral planning of the city, which can be supported by optimization. The main goal of the research is to provide a regional optimization model for water networks, including new treatment options. The model is formulated as a multi-objective mixed-integer programming problem, focused on environmental and economic impact of the network, minimizing water extracted from natural sources and total cost. The formulation is developed with the goal-programming methodology. The model covers a complete existing city-scale water network, including 4 different options of water reuse within the city: drinking water, fresh water, irrigation, and discharge in natural courses. The case study is Santiago, capital of Chile, which is the political, economic, and institutional center of Chile. If both objective functions have equal importance to configure the solution, the following ideas characterize the optimal water network: (i) it is more environmentally and economically convenient to reuse water within the network rather than recycling water to the natural source; (ii) the reuse of water is preferred in the form of irrigation and drinking qualities rather than industrial qualities to reduce transport costs, and (iii) the modification of the current treatment plants is preferred, because of the high cost of installation of new plants. An environmental and cost-effective solution for Santiago, Chile, can reduce the source water extraction in 35.7%. The model can be implemented in other contexts, providing orientations to decision-makers so as to plan city-scale water networks with simultaneous environmental and economic considerations.

Topics: Water scarcity (60%), Reuse (54%), Total cost (51%), Multi-objective optimization (50%), Economic impact analysis (50%)

Summary (4 min read)

1. Introduction

  • Climate change has become an important issue in several re gions worldwide since human behavior is conditioned by the ef fects of Global Warming on Earth (Santibanez, 2018).
  • Under uncertainty has been a priority area of research aiming at improving large scale regional water systems (Vucetic and Simonovic, 2011).
  • Optimization techniques can be a valuable tool for water resource management in order to redesign regional water systems.
  • This topic constitutes the main focus of this project.
  • Previous research shows that there are efforts focused on different scales.

2. Literature review and novelty of this paper

  • Lovelady and El Halwagi (2009) developed a model to plan water management among multiple processes in a common EIP facility.
  • McGivney and Kawamura (2008) researched installation and operating costs of various water treat ment technologies, including drinking water and wastewater treatment technologies.
  • The present investigation attempts to minimize both impacts in the same model.
  • The present investi gation uses Goal programming technique to solve the multi objective problem.
  • The following sections present the formulation and solution of a Multi objective optimization model so as to redesign a city scale water network with environmental and economic objectives.

3. Problem structure

  • The methodology used is shown in Fig. 1. First, an investigation of the current structure is carried out, and then possible changes in the network, its characteristics and requirements are studied.
  • Each sub region has its own population center to assign the location of the participants in the network.
  • Each consumption node has its own demand and in the base case all the demands are satisfied with drinking water.
  • Modified plants are positioned in the original location of the existing ones.
  • With these new treatment plants it is possible to reduce water consumption from the natural source and create new possible connections and recycling within the system.

4. Mathematical model

  • The proposed model is based on the superstructure shown in Fig.
  • The model consists of a set of mass balances in the treatment plant nodes, distribution and collection nodes, and consumption nodes.
  • The sets, variables and subscripts used in the model are defined in Nomenclature section.

4.1. Mass balances

  • It is considered that only total mass balances are required.
  • This assumption implies that water quality at the exit of each treatment plant and network consumption satisfy the quality constraints for their respective user.
  • Stationary state is also assumed for each node.
  • Thus, the incoming flow rates will be the same as the outgoing flow rates.
  • Finally, each flow density is assumed constant, then volu metric balances can be made.

4.1.2. Mass balances for each node

  • At any node, the incoming flow rates will be the same as the outgoing flow rates, as a result of the steady state assumption.
  • Fig. 3 represents the flows and sets involved in the mass balance of commercial and residential consumption.
  • Inflows of large existing WWTPs come from collection nodes and the outflows go into natural discharge courses and to the sink.

4.2. Covering the demand

  • As mentioned above, each consumption node has an associated water demand.
  • Depending its consumption type and location, the demand is different.
  • To satisfy the demand of each node, Equation (19) must be respected.
  • X i2ONj Fi/j DM fj;pg;c j2ADj;c p2CT (19) ONj, j being the current node, i.e. where the demand must be satisfied (ADj), which can be a residential, commercial, industrial, agricul tural, and urban park irrigation consumption demand; and p represent each district.

4.3.1. New drinking water treatment plant capacity

  • As mentioned above, there are large and small treatment plants.
  • The problem becomes mixed integer, with continuous and discrete variables.
  • In fact, the plants do not have a fixed ca pacity only for the consumption of its district inhabitants, its ca pacity varies depending on the requirements of the participants of the systems.
  • This flexibility is given by the parameter ‘m’.
  • I corresponds to each new small drinking water treatment plant in each district, j corresponds to all consumers requiring drinking water in the district p’, and p’ corresponds to each district, in particular, where the plant i is located.

4.3.2. New wastewater treatment plant capacity

  • For new big wastewater treatment plants, Equation (22) must be respected.
  • M corresponds to the parameter mentioned above, i corresponds to each new small wastewater treatment plant in each district p’, j corresponds to each sewer user in the district p’, and p’ corresponds to each district, in particular where the plant i is located.
  • This variation does not imply an extension of existing plants.
  • On the other hand, the plant can treat 23% more than the current flow, which is the summer variation.

4.4. Logical relationships of existence

  • If the plant does not exist, then the incoming flowsmust be zero.
  • This can be written mathematically trough Equation (28) applying the BigM method (Song, 2015).
  • This value has to be greater than all the flow rates being treated.

4.5. Cost

  • The costs in the problem are divided in operational costs (OpC) and capital costs (CapC).
  • The OpC are estimated by water transport costs, while CapC are estimated by the cost of installing new plants or of modifying the existing ones.

4.6. Objective functions

  • The problem has two opposing objective functions to be mini mized: water flow used from the water source and the total cost.
  • Thus, these two objective functions FO1 and FO2 can be repre sented by Equations (32) and (33), respectively.
  • Where G0 is the total fresh water consumed, OpC is the operational cost, and CapC is the investment cost.

5. Multi-objective optimization strategy

  • All other constraints are also respected, from equation (1) to equation (31).
  • The indices id and nid correspond to ideal and non ideal solutions respectively, and represent the different ideal and non ideal points outside the Pareto curve in the goal programming methodology.
  • Pa rametersw1 andw2 represent the relativeweights of each objective function.

6. Case study

  • The model presented was applied in the city of Santiago, capital of Chile.
  • Santiago is Chilean political, economic, and institutional center.
  • The system is represented by one large and one small drinking water treatment plant and one large and one small wastewater treatment plant.
  • These 4 plants can treat all the real city flow, since all the plants were added in the 4 that are represented in the problem.
  • Population was distributed geographically according to the districts demographic information (INE, 2018b).

7. Results

  • The MIP model of the case study has 484 constrains, 1075 var iables (including 50 binary variables), andwas executed in an INTEL CORE i7 7700 HQ computer with 16 GB of memory.
  • With the results of Table 2, the multi objective problem is formulated obtaining the Pareto curve shown in Fig. 22, where the values at the extremes of the curve were removed to make other intermediate valuesmore clearly visible.
  • The mass balance for the complete system is shown in Fig. 26.
  • The annualized total cost of the water network grows a 3.2% when compared with the solution at the economic extreme of the Pareto curve, when the importance of the economic objective function is complete.

8. Discussions

  • With respect to the obtained results, the current network is not optimal for water treatment, under the assumption that both ob jectives have the same importance.
  • The optimal result includes the installation of a new small DWTP instead of a new large DWTP, in order to supply drinking water consumers.
  • An interesting observation is that the model tries to reduce the G0 and TC, so the water extracted is mainly used for irrigation and large industrial consumption, since there are no costs associated with transport, nor losses fromwater treatment.
  • It is possible to make the model more complex by adding other costs, such as treatment plant operating costs and pipeline installation costs.

9. Conclusions

  • This paper deals with the management of water resources by integrating new water treatment plants to find the optimal configuration of the water network, applied to the case study of Santiago, Chile.
  • With parameters of demands, consumption, losses, locations, and costs, it is possible to characterize water use of the sets present in the model, which allowed to establish the optimal configuration for the problem.
  • Their respective economic and environ mental indicators are defined.
  • These re sults show that (iv) it is more environmentally and economically convenient to reuse water for irrigation and drinking consumption rather than recycling water to the natural source.
  • Model implementation, analysis, data curation, writing, review, and edition, also known as Daniela Gormaz-Cuevas.

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This is an author’s version published in: https://oatao.univ-toulouse.fr/ 2 7119
To cite this version:
Gormaz-Cuevas, Daniela and Riffo-Rivas, Javiera and Montastruc, Ludovic
and Brüning-González, Mariana and Díaz-Alvarado, Felipe A. A multi-objective
optimization model to plan city-scale water systems with economic and
environmental objectives: a case study in Santiago, Chile. (2021) Journal of
Cleaner Production, 279. 123737. ISSN 0959-6526 .
Official URL:
https://doi.org/10.1016/j.jclepro.2020.123737
O
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A multi-objective optimization model to plan city-scale water systems
with economic and environmental objectives: A case study in
santiago, Chile
Daniela Gormaz-Cuevas
a
, Javiera Riffo-Rivas
a
, Ludovic Montastruc
b
,
Mariana Brüning-Gonz
alez
a
, Felipe A. Díaz-Alvarado
a
,
*
a
Department of Chemical Engineering, Biotechnology, and Materials, Faculty of Physical and Mathematical Sciences, Universidad de Chile, Av. Beauchef 851,
Piso 6-poniente, Group of Sustainable Design and Process Systems Engineering, 8370456, Santiago, Chile
b
Universit
e de Toulouse, Laboratoire de G
enie Chimique U.M.R. 5503 CNRS/INP/UPS. 4 All
ee Emile Monso, 31432, Toulouse Cedex 4, France
K
eywords:
Water network
s
Regional integration
Multi-objective optimization
abstract
Climate Change and its effects in water scarcity has become an important challenge for cities with water
management problems. These problems require an integral planning of the city, which can be supported
by optimization. The main goal of the research is to provide a regional optimization model for water
networks, including new treatment options. The model is formulated as a multi objective mixed integer
programming problem, focused on environmental and economic impact of the network, minimizing
water extracted from natural sources and total cost. The formulation is developed with the goal
programming methodology. The model covers a complete existing city scale water network, including
4 different options of water reuse within the city: drinking water, fresh water, irrigation, and discharge in
natural courses. The case study is Santiago, capital of Chile, which is the political, economic, and insti
tutional center of Chile. If both objective functions have equal importance to congure the solution, the
following ideas characterize the optimal water network: (i) it is more environmentally and economically
convenient to reuse water within the network rather than recycling water to the natural source; (ii) the
reuse of water is preferred in the form of irrigation and drinking qualities rather than industrial qualities
to reduce transport costs, and (iii) the modication of the current treatment plants is preferred, because
of the high cost of installation of new plants. An environmental and cost effective solution for Santiago,
Chile, can reduce the source water extraction in 35.7%. The model can be implemented in other contexts,
providing orientations to decision makers so as to plan city scale water networks with simultaneous
environmental and economic considerations.
1. Introduction
Climate change has become an important issue in several re
gions worldwide since human behavior is conditioned by the ef
fects of Global Warming on Earth (Santibanez, 2018). A direct effect
is water scarcity (Gosling and Arnell, 2013), considered a global risk
by the World Economic Forum affecting two thirds of the worlds
population (Van Der Heijden, K and Stinson, C, 2019). To address
this issue, long term planning of city scale water systems has
become a key matter. Therefore, water management and planning
under uncertainty has been a priority area of research aiming at
improving large scale regional water systems (Vucetic and
Simonovic, 2011). In this context, optimization techniques can be
a valuable tool for water resource management in order to redesign
regional water systems. This topic constitutes the main focus of this
project.
Some efforts have previously been made to model and optimize
both Industrial Water Networks (IWN) and water networks within
Eco Industrial Parks (EIP), having environmental and economic
benets.
Campos de Faria et al. (2009) looked for the minimum opera
tional cost and fresh water consumption of an IWN. The results
showed that it is useful to identify reuse opportunities. Boix et al.
(2012) developed a multi objective optimization model of an EIP
* corresponding author.
E-mail address: felidiaz@ing.uchile.cl (F.A. Díaz-Alvarado).

in order to minimize (i) fresh water ow rate at the network
entrance; (ii) water ow rate at inlets of regeneration units, and (iii)
the number of connections into the network, obtaining that the use
of regeneration units yields signicant gains. These gains can be
increased again by a direct integration into an EIP. These works
have motivated the development of similar approaches for syn
thesizing water networks at the regional level. Liu et al. (2011)
developed an optimization model for water resources manage
ment for insular areas in Greece. Rojas Torres et al. (2015) proposed
a multi objective optimization model to design a water system in a
city scale, using water reuse for agricultural purpose. P
erez et al.
(2017) also proposed a model to design a water system in a city
in Mexico, but using rainwater reservoirs.
Previous research shows that there are efforts focused on
different scales. The present paper is focused in city scales.
The objective when redesigning water systems is to improve the
use of the resource towards sustainability. With this purpose,
incorporating non conventional water sources and the reusing/
recycling waste water have an important potential. Many efforts
have been made to reuse and recycle water in the industrial sector.
The following section presents a Literature review and the novelty
of this paper.
2. Literature review and novelty of this paper
Lovelady and El Halwagi (2009) developed a model to plan
water management among multiple processes in a common EIP
facility. Campos de Faria et al. (2009) proposed alternatives to
optimize IWN using different regeneration units. Sadegh et al.
(2011) presented a model to minimize the energy of an inter
plant water network in an EIP. These works include reuse and/or
recycle of water within the system.
Some studies refer to the incorporation of new sources and
reclaimed water in a city scale optimization model. Liu et al. (2011)
presented an optimization approach for water management of a
city including desalinated seawater and reclaimed water as water
sources. Rojas Torres et al. (2015) incorporated rainwater harvest
ing and reclaimed water.
The present paper presents a model that allows the recycling of
water in the network with 4 different qualities, allowing the supply
of different consumers.
It is important to note that most of the studies have been
focused on the synthesis problem of different water networks.
Other option is to retrot existing water networks in order to
redesign them. This is particularly useful for regional water net
works because problem solution must be adapted for existing
treatment units and distribution/collection networks.
Campos de Faria et al. (2009) presented a methodology for
retrotting an Industrial Water Network (IWN). Sotelo Pichardo
et al. (2011) proposed a mathematical programming model for
the optimal retrotting of an IWN. Rubio Castro et al. (2012)
developed a model to design an Eco Industrial Park by retrot
ting existing water networks.
As cited above, research with plant modication is mainly
applied in IWN. The present paper includes the possibility of
installing new plants or retrotting existing ones, both on a city
scale.
To achieve a reduction in pollutants in the water and to achieve
the quality required by each user, the use of different treatment
technologies is necessary, so studying characteristics such as costs
and treatment ows is required.
Rodríguez Miranda (2015) investigated the costs of installation
of various technologies of wastewater treatment plants in Cundi
namarca, characterizing each one of them and obtaining invest
ment cost functions for each one, when doing an investigation of
the
existing
projects in the area. McGivney and Kawamura (2008)
researched installation and operating costs of various water treat
ment technologies, including drinking water and wastewater
treatment technologies. It also incorporates recycling technologies.
Guo et al. (2014) studied the costs and characteristics of water
treatment technologies, especially the reuse of water without
environmental buffers. The research presents a detailed analysis of
the treatment ows and costs of technologies such as activated
sludge, membrane bioreactor, coagulation/occulation, reverse
osmosis, among others.
In the present paper, the costs of preliminary studies to establish
the objective economic function are incorporated.
Water system modication has different impacts. These impacts
have been studied considering mainly economic or environmental
dimensions, developing a single objective function. Some authors
focused on reducing the associated cost.
Bagajewicz et al. (2000), focused on minimizing the operational
and investment cost of a water network problem by using a tree
search algorithm; Liu et al. (2011) proposed a MILP problem by
minimizing capital and operating costs applied to a city. The capital
cost includes the installation of pipelines, pumping stations, stor
age tanks and treatment plants; while the operating cost includes
the cost of pumping water and the cost of operating the treatment
plants. Finally, Rubio Castro et al. (2012), proposed a MINLP prob
lem minimizing plant capital and piping operation costs, applied to
an eco industrial park, including several application scenarios.
These authors demonstrate the value of considering different types
of costs in a water network system.
On the other hand, other authors focus on the importance of the
environmental impact generated by the water network, from
different perspectives. Boix et al. (2011) presents a MINLP problem
solved by means of a lexicographic strategy, applied to a industrial
water network, where it seeks to minimize the ow of water
extraction from natural sources, the ow at the entrance of
regeneration units and the number of interconnections within the
network. Mughees et al. (2013) seeks to increase the water ef
ciency of a petrochemical plant by minimizing its water con
sumption and reusing wastewater through a MINLP problem
formulation. Finally, Hansen et al. (2018) also seeks to minimize the
water consumption of a petrochemical plant by formulating an NLP
problem.
The above mentioned research, shows that there are efforts to
minimize both economic and environmental effects. The present
investigation attempts to minimize both impacts in the same
model.
Simultaneous minimization of environmental and economic
impacts is less common, composing a multi objective optimization
problem. In particular, Kantor et al. (2015) developed a model to
reduce network life cycle emissions while seeking to reduce their
cost in an EIP water network. Rojas Torres et al. (2015) proposed a
model to solve planning and scheduling water storage and distri
bution for a city, maximizing overall prot and minimizing fresh
water consumption and land use. P
erez et al. (2019) proposed to
design an optimal water distribution network maximizing reve
nues and minimizing both groundwater usage and investment cost.
The present research seeks to reduce the costs of installing
water treatment plants and operating the network, and to reduce
the ow rate of extraction from natural sources, thus incorporating
the economic and environmental impact.
The primary contribution of the present Multi objective opti
mization model is to decide the installation of new treatment
plants and connections within a city scale water network, or the
modi
cation of existing plants, by assessing environmental and
economic
objectives
at the same time. Besides previous efforts have
been done in the eld of Multi objective optimization applied to

water systems, these efforts are concentrated in subparts of the
whole system as the distribution network. The present paper
include a representation of the distribution network, different
sources, and a variety of consumption types and water qualities, so
as to model a city scale water system.
There are different methodologies to solve mono and multi
objective optimization problems. According to Rangaiah (2017),
resolution of multi objective problems can be classied in two
types: Generating Methods and Preference Based Methods. The
rst type generates one or more Pareto optimal solutions without
any inputs from the decision maker. On the other hand, preference
based methods utilize the preferences specied by the decision
maker at some problem solution stage. De Le
on et al. (2016)
applied the ε constraint procedure, included within the method
generation, to minimize global economic cost and freshwater
consumption. Xevi and Khan (2006) maximized net revenues and
minimized costs using Goal programming technique, included
within the method preference based Methods. Ghosh et al. (2016)
also uses Goal programming to design a cost effective biological
treatment process for industrial wastewater. The present investi
gation uses Goal programming technique to solve the multi
objective problem.
None of the previously mentioned investigations present a
multi objective optimization problem on a city scale, considering
the reuse of 4 different water qualities to supply different con
sumers. This way of approaching the problem, not considered
before, is presented in this study. A novel superstructure is created
for modeling a city scale water system in order to plan new
treatment plants and connections among stakeholders, taking into
account simultaneous economic and environmental objectives.
The problem is formulated as a Multi Objective Mixed Integer
Programming to decide the optimal conguration of a regional
water system including environmental and economic consider
ations. The model is formulated to decide (i) the installation of new
treatment plants, (ii) the actualization of the existing ones, and (iii)
the connections within the new integration network. These
changes on the water network allow to recycle and reuse water. The
objective functions to minimize are the water usage from the
source and the total cost of the water system. The problem is solved
using the goal programming technique. The main novelties of this
work are the large scale orientation of the formulation and the
integration of economic and environmental objectives in the
planning of a city scale water system.
The following sections present the formulation and solution of a
Multi objective optimization model so as to redesign a city scale
water network with environmental and economic objectives. The
Problem Structure section introduces the water network and its
representation as a superstructure with new possible treatment
plants and connections. The Mathematical Model section presents
the decision variables and equations to model the water network
and decide the best conguration under environmental and eco
nomic objectives. The Multi Objective Optimization Strategy section
presents the goal programming method to optimize simultaneous
environmental and economic objective functions. This model is
applied to Santiago, Chile, in the Case Study section. This section
shows the main results of the optimization model. The Discussions
section analyzes the results and the proposed formulation, to
extract the main ideas in the Conclusions section.
3. Problem structure
The methodology used is shown in Fig. 1. First, an investigation
of the current structure is carried out, and then possible changes in
the network, its characteristics and requirements are studied. With
this, the superstructure is modeled. The objective functions are
dened in order to formulate the problem mathematically, solving
rst the mono objective problems, and then solving the multi
objective problem through the goal programming methodology.
In this problem, a city water system is modeled. This section
describes the superstructure with all possible connections. The
whole surface of the city is divided into sub regions, based on
population
distribution. Each
sub region has its own population
center to assign the location of the participants in the network.
Participants are classied into (i) consumers; (ii) sources; (iii) dis
tribution and collection nodes; (iv) treatment plants; and (v) nal
disposal sinks. These participants are connected through a
network, which is represented by nodes and arcs, composing a
graph. Fig. 2 shows the simplied graph.
There are two subsets of water sources and disposal sinks:
surface and underground water. Consumption nodes represent a
variety of uses: (i) domestic, (ii) commercial, (iii) industrial, (iv)
agricultural, and (v) irrigation of urban areas. Each consumption
node has its own demand and in the base case all the demands are
satised with drinking water. Although some consumers require
less restrictive qualities than drinking water. These demands could
be covered by new treatment plants. It is also considered that
consumption nodes location is in the population center.
In particular, industrial consumption is subdivided in two sub
sets, depending on consumption magnitude. The 20 companies
with the highest water consumption were selected to form the rst
subset: large industrial consumers. The second subset group, other
companies represented as a cluster within each sub region.
Concerning treatment nodes, there are Drinking Water Treat
ment Plants (DWTP), Wastewater Treatment Plants (WWTP), and
Industrial Wastewater Treatment Plants (IWTP). Different sets are
created for existing and new treatment plants and their different
characteristics are explained below. Regarding existing plants, they
are located in their original location. Also, all existing wastewater
treatment plants have discharge quality water. Therefore, there is
no recycling or reuse of water in the present system.
Four new sets of new wastewater treatment plants with
different technological congurations were created. These can
achieve four water qualities: (i) freshwater; (ii) drinking water; (iii)
irrigation water; and (iv) discharge water. In addition, three new
sets of modied treatment plants are created. Modied plants are
existing plants modied to achieve a different output quality. These
plants can achieve irrigation, water source, and drinking water
quality. New treatment plants are positioned at each sub region
boundary, because their installation would be more feasible in
those places. Modied plants are positioned in the original location
of the existing ones. With these new treatment plants it is possible
Fig. 1. Diagram representing the methodology followed in the research.

to reduce water consumption from the natural source and create
new possible connections and recycling within the system.
Both DWTP and WWTP are subdivided into two subsets
depending on their treatment capacity: large and small plants. This
division is useful for the calculation of economic and environ
mental indicators, and to differentiate distribution and collection
networks. In particular, small plants supply/collect directly to their
consumers, while large plants should make use of distribution/
collection nodes to get to their consumers.
To achieve these recycling qualities, the use of certain water
treatment technologies is necessary. The technologies used in each
type of plant are presented in Table 1, which ensure the quality
required for each consumer.
Distribution and collection nodes aim at representing water
distribution and collection networks of the current system. In
particular, this simplication allows to estimate efciently water
losses in the pipelines. There is also a sink node to collect lost water
from the nodes within the network.
The proposal model is a Mixed Integer Programming problem
(MIP) which can be solved by some commercial solvers whose
performance depends on initial values or possible limitation in the
variables. The BARON solver was used because it is robust enough
to solve this type of problem. Finally, the model was coded in the
software GAMS.
4. Mathematical model
The proposed model is based on the superstructure shown in
Fig. 2. The model consists of a set of mass balances in the treatment
plant nodes, distribution and collection nodes, and consumption
nodes. It also includes, a set of constraints that allow the con
sumption demand of each node to be satised (fulllment of the
Fig. 2. Graph of the problem. Current and new plants are included as treatment nodes, and the respective consumption within a city.
Table 1
Technologies used in different types of recycling plants, depending on the quality required. The references used are given by the technologies as follows: i) standard treatment:
Rodríguez-Miranda (2015); ii) activated sludge: Guo et al. (2014); iii) micro-ltration: McGivney and Kawamura (2008); iv) nano-ltration: Adham et al. (1996); v) chlori-
nation: McGivney and Kawamura (20 08); vi) trickling lter: McGivney and Kawamura (2008); vi) ozonization: McGivney and Kawamura (2008); vii) reverse osmosis: Guo
et al. (2014); vii) ion exchange: McGivney and Kawamura (2008); ix) ltration: McGivney and Kawamura (2008).
Quality
required
Technology in big treatment plant Technology in small treatment plant
Freshwater Standard treatment, activated sludge, micro-ltration, nano-ltration and
chlorination.
Standard treatment, trickling lter, micro-ltration, nano-ltration
and ozonization.
Drinking
water
Standard treatment, activated sludge, micro-ltration, reverse osmosis, chlorination
and ion exchange.
Standard treatment, trickling lter, micro-ltration, reverse osmosis,
ozonization and ion exchange.
Irrigation
water
Standard treatment, activated sludge, ltration and chlorination. Standard treatment, trickling lter, ltration and ozonization.
Discharge
water
Standard treatment, activated sludge and chlorination. Standard treatment, trickling lter and chlorination.

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Journal ArticleDOI
Abstract: The concept of circular economy has received much attention due to growing global concern on resource depletion and environmental protection. There are studies to minimize freshwater reduction via mathematical modelling methods. However, study to explore possibilities of combining both domestic and industrial wastewater regeneration, reuse, and resource recovery in a centralized facility is yet to be made. This study develops a non-linear programming (NLP) model that could optimize water regeneration and reuse network, as well as biogas generation from the selected wastewater streams. The main objective is to maximize profit from the network established. A superstructure that consists of sources, regeneration units, outsourced water, freshwater, mixers-demands, and biogas systems is developed. The sources are from domestic and industrial wastewaters. A combination of the sources, regenerated sources, outsource water, and freshwater is performed in the mixers, subject to the demands’ flowrate and contaminant properties, namely Chemical Oxygen Demand (COD), Total Dissolved Solids (TDS), Total Suspended Solid (TSS), Nitrogen (N), and Phosphorus (P). The formulations also incorporate the techno-economic elements such as mass balance and equipment cost. The processing fee and selling price items are also introduced in the model to ensure that the participants (the sources providers, the centralized water utility facility provider, and the demands) obtain benefits from the integration works. The case study results show that the reused water can be mixed with the freshwater for the boiler feed water and cooling water application with a total supply of 656 m3/h. Connection cost and nanofiltration (NF) cost contribute in a relatively large portion of the annual cost. The selling price of the supplied water is the most important factor that determines the overall systems’ economics compared to other items. The annual profit obtained is USD 1,015,784 and the payback period obtained is 3.13 years. Total freshwater consumption is reduced by 34%. This model provides insights on how both domestic and industrial wastewaters can be symbiotically integrated in a centralized facility.

2 citations


Journal Article
TL;DR: This paper determines a constant M0 and proves that the big M method is convergent to an optimal solution of the primal problem when MM0.
Abstract: In the simplex method of linear programming,there is a big M method(the penalty factor method) for finding an initial feasible basis.The current textbooks of operations research only explain that the big M method is efficient when M is large enough,and never give precise evaluation to the parameter M.This paper determines a constant M0 and proves that the big M method is convergent to an optimal solution of the primal problem when MM0.

1 citations


Journal ArticleDOI
Abstract: Estimating and forecasting suspended sediments concentrations in streams constitutes a valuable asset for sustainable land management. This research presents the development of a non-linear autoregressive exogenous neural network (NARX) for forecasting sediment concentrations at the exit of Francia Creek watershed (Valparaiso, Chile). Details are presented on input data selection, data splitting, selection of model architecture, determination of model structure, NARX training (optimization of model parameters), and model validation (hindcasting and forecasting). The study explored if the developed artificial neural network model is valid for forecasting daily suspended sediment concentrations for a complete year, capturing seasonal trends, and maximum and baseflow concentrations. Francia Creek watershed covers approximately 3.24 km2. Land cover within the catchment consists mainly of native and exotic vegetation, eroded soil, and urban areas. Input data consisting of precipitation and stream flow time-series were fed to a NARX network for forecasting daily suspended sediments (SST) concentrations for years 2013–2014, and hindcasting for years 2008–2010. Training of the network was performed with daily SST, precipitation, and flow data from years 2012 and 2013. The resulting NARX net consisted of an open-loop, 12-node hidden layer, 100 iterations, using Bayesian regularization backpropagation. Hindcasting of daily and monthly SST concentrations for years 2008 through 2010 was successful. Daily SST concentrations for years 2013 and 2014 were forecasted successfully for baseflow conditions (R2 = 0.73, NS = 0.71, and Kling-Gupta efficiency index (K-G) = 0.84). Forecasting daily SST concentrations for year 2014 was within acceptable statistical fit and error margins (R2 = 0.53, NS = 0.47, K-G = 0.60, d = 0.82). Forecasting of monthly maximum SST concentrations for the two-year period (2013 and 2014) was also successful (R2 = 0.69, NS = 0.60, K-G = 0.54, d = 0.84).

1 citations


Journal ArticleDOI
08 Jun 2021
Abstract: Conventional agriculture is the greatest enemy of healthy soil; it wasn’t designed for the betterment of the soil, but rather for the rapid economic growth. If we want to improve soil quality and with that our life quality,

References
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Book ChapterDOI
01 Jan 2014
TL;DR: This chapter discusses the fundamental principles of multi-objective optimization, the differences between multi-Objective optimization and single-objectives optimization, and describes a few well-known classical and evolutionary algorithms for multi- objective optimization.
Abstract: Multi-objective optimization is an integral part of optimization activities and has a tremendous practical importance, since almost all real-world optimization problems are ideally suited to be modeled using multiple conflicting objectives. The classical means of solving such problems were primarily focused on scalarizing multiple objectives into a single objective, whereas the evolutionary means have been to solve a multi-objective optimization problem as it is. In this chapter, we discuss the fundamental principles of multi-objective optimization, the differences between multi-objective optimization and single-objective optimization, and describe a few well-known classical and evolutionary algorithms for multi-objective optimization. Two application case studies reveal the importance of multi-objective optimization in practice. A number of research challenges are then highlighted. The chapter concludes by suggesting a few tricks of the trade and mentioning some key resources to the field of multi-objective optimization.

952 citations


Journal ArticleDOI
Abstract: This paper presents a global scale assessment of the impact of climate change on water scarcity. Patterns of climate change from 21 Global Climate Models (GCMs) under four SRES scenarios are applied to a global hydrological model to estimate water resources across 1339 watersheds. The Water Crowding Index (WCI) and the Water Stress Index (WSI) are used to calculate exposure to increases and decreases in global water scarcity due to climate change. 1.6 (WCI) and 2.4 (WSI) billion people are estimated to be currently living within watersheds exposed to water scarcity. Using the WCI, by 2050 under the A1B scenario, 0.5 to 3.1 billion people are exposed to an increase in water scarcity due to climate change (range across 21 GCMs). This represents a higher upper-estimate than previous assessments because scenarios are constructed from a wider range of GCMs. A substantial proportion of the uncertainty in the global-scale effect of climate change on water scarcity is due to uncertainty in the estimates for South Asia and East Asia. Sensitivity to the WCI and WSI thresholds that define water scarcity can be comparable to the sensitivity to climate change pattern. More of the world will see an increase in exposure to water scarcity than a decrease due to climate change but this is not consistent across all climate change patterns. Additionally, investigation of the effects of a set of prescribed global mean temperature change scenarios show rapid increases in water scarcity due to climate change across many regions of the globe, up to 2 °C, followed by stabilisation to 4 °C.

381 citations


"A multi-objective optimization mode..." refers background in this paper

  • ...A direct effect is water scarcity ([2]), considered a global risk by the World Economic Forum ([3])....

    [...]


BookDOI
01 Feb 2017
TL;DR: This paper presents a meta-anatomical architecture for multi-Objective Optimization of multi-Product Microbial Cell Factory for Multiple Objectives and some of the principles used in this architecture were previously described in the book “Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives.”
Abstract: Introduction (G P Rangaiah) Multi-Objective Optimization Applications in Chemical Engineering (Masuduzzaman & G P Rangaiah) Techniques: Multi-Objective Evolutionary Algorithms: A Review of the State of the Art and Some of Their Applications in Chemical Engineering (A L Jaimes & C A Coello Coello) The Jumping Gene Adaptations of Multi-Objective Genetic Algorithm and Simulated Annealing (M Ramteke & S K Gupta) Multi-Objective Optimization Using Surrogate-Assisted Evolutionary Algorithm (T Ray) Why Use Interactive Multi-Objective Optimization in Chemical Process Design? (K Miettinen & J Hakanen) Net Flow and Rough Set: Two Methods for Ranking the Pareto Domain (J Thibault) Applications: Multi-Objective Optimization of Gas-Phase Refrigeration Systems for LNG (N Shah et al.) A Multi-Objective Evolutionary Algorithm for Practical Residue Catalytic Cracking Feed Optimization (K C Tan et al.) Optimal Design of Chemical Processes for Multiple Economic and Environmental Objectives (E S Q Lee et al.) Multi-Objective Emergency Response Optimization around Chemical Plants (P S Georgiadou et al.) Array Informatics Using Multi-Objective Genetic Algorithms: From Gene Expressions to Gene Networks (S Garg) Multi-Objective Optimization of a Multi-Product Microbial Cell Factory for Multiple Objectives - A Paradigm for Metabolic Pathway Recipe (F C Lee et al.).

176 citations


Journal ArticleDOI
Abstract: This work is aimed at developing an optimization-based approach to the design and integration of eco-industrial parks (EIPs). Focus is given to the management of water among multiple processes in a common EIP facility. Recycle, reuse, and separation using interception devices are considered as possible strategies for managing wastewater. A source-interception-sink structural representation is used to embed potential configurations of interest. The representation accounts for the possibilities of direct recycle, material (waste) exchange, mixing and segregation of different streams, separation and treatment in interception units, and allocation to process users (sinks). Then, the EIP design problem is formulated as an optimization program whose objective is to minimize cost of the EIP while determining optimal recycle and separation strategies. A case study is solved to illustrate the applicability of the devised approach.

135 citations


"A multi-objective optimization mode..." refers background in this paper

  • ...Lovelady and El-Halwagi (2009) developed a model to plan water management among multiple processes in a EIP facility ([9])....

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Journal ArticleDOI
01 Dec 2005
TL;DR: This approach is described by analyzing the conflicts that may arise between profitability, variable costs of production and pumping of groundwater for a hypothetical irrigation area by linking bio-economic objectives with the optimum use of all water resources under conflicting demands.
Abstract: The management of river basins is complex especially when decisions about environmental flows are considered in addition to those concerning urban and agricultural water demand. The solution to these complex decision problems requires the use of mathematical techniques that are formulated to take into account conflicting objectives. Many optimization models exist for water management systems but there is a knowledge gap in linking bio-economic objectives with the optimum use of all water resources under conflicting demands. The efficient operation and management of a network of nodes comprising storages, canals, river reaches and irrigation districts under environmental flow constraints is challenging. Minimization of risks associated with agricultural production requires accounting for uncertainty involved with climate, environmental policy and markets. Markets and economic criteria determine what crops farmers would like to grow with subsequent effect on water resources and the environment. Due to conflicts between multiple goal requirements and the competing water demands of different sectors, a multi-criteria decision-making (MCDM) framework was developed to analyze production targets under physical, biological, economic and environmental constraints. This approach is described by analyzing the conflicts that may arise between profitability, variable costs of production and pumping of groundwater for a hypothetical irrigation area.

118 citations