Eco-efficient Prototype of Wastewater Treatment Plant Applying Clean

Development Mechanism Methodologies – Mediterranean Countries

FIRAS FAYSSAL

Doctoral School & Faculty of Engineering

Doctoral School of Science and Technology (EDST), Lebanese University & Higher School of

Engineering in Beirut, Saint Joseph University

B.P. 11-2806 Beirut & B.P. 17-5208 Mar Mikhael

LEBANON

ADEL MOURTADA

Doctoral School

Doctoral School of Science and Technology (EDST), Lebanese University

B.P. 11-2806 Beirut

LEBANON

MAZEN GHANDOUR

Faculty of Engineering

Faculty of Engineering, Lebanese University

B.P. 11-2806 Beirut

LEBANON

REMI DAOU

Higher School of Engineering in Beirut (ESIB), Saint Joseph University

B.P. 17-5208 Mar Mikhael, Beirut 1104 2020

LEBANON

Abstract: Municipal wastewater treatment is committed reducing greenhouse gases emissions in line with

United Nations Framework Convention on Climate Change (UNFCCC) norms in order to preserve Earth's

blanket and lower climate acute changes. Greenhouse gases emissions reduction is the avant-garde of municipal

wastewater treatment technologies; however, the process requires particular segmentation of all phases to

contain the excessive energy required for treatment. Consequently, Energy analysis is endorsed as essential to

sustain a thermodynamic equilibrium of the treatment plant with its environment. Decarbonization,

denitrification & phosphorus removal urge the exploitation of sustainable energy whether recovered or

renewable to engine the treatment facility. This literature values compile the eco-design of wastewater

treatment plant with the avant-garde technologies of greenhouse gases emissions reduction, considering

environmental aspects at all stages of treatment process, targeting the lowest possible environmental impact

throughout the plant life cycle to create a CO₂-free facility prototype. UNFCCC introduced the greenhouse

gases emissions definition in wastewater plants as a project design document for the Clean Development

Mechanism (CDM) project AM00801 activity. The literature adopted this project and submitted it as a friendly

user interface or a Software to model an Eco-efficient management strategy for wastewater treatment plant

aerobic activated sludge type with the offset of environmental footprint measures based on decision making

analysis, Input-output analysis, benchmarking and energy balance, net negative emissions including

environmental declaration assessing the life cycle from costing, management, and sustainability perspectives.

Key-Words: WWTP, Energy consumption, Aerobic Activated sludge, Life Cycle Assessment, Decision-

Making, Data Acquisition, Greenhouse Gas Emissions

Received: September 25, 2021. Revised: October 3, 2022. Accepted: November 12, 2022. Published: December 6, 2022.

1 Introduction

The United Nations (UN) declared in the

emissions gap report the GHG emissions

approximately 58.1 gigatons of CO2 equivalent

(GtCO2e) in 2019 [17], which leads to a significant

indication of surface water runoff and wastewater

discharge pollution specifically. However, the UN

through UNFCCC affirmed environmental policy

shifting in order to decrease waterways pollution.

Under the Kyoto Protocol, emission caps were set

for each Annex-I country [18], amounting in total to

an average reduction of 5.2% below the aggregate

emission level in 1990. Each country has a

predetermined target of emission reduction as

compared to 1990 level. No emission cap is

imposed on Non-Annex I countries [18]. However,

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to encourage the participation of Non-Annex I in the

emission reduction process a mechanism known as

Clean Development Mechanism (CDM) has been

established. The carbon markets are a prominent

part of the response to climate change and have an

opportunity to demonstrate that they can be a

credible and central tool for future climate

mitigation. Therefore, developed countries agreed to

limit their GHG emissions, relative to the levels

emitted in 1990 or to substitute the excess emissions

with carbon trading by investing in emissions

reduction in non-developed countries (such as

Morocco & Lebanon subject matter of literature’s

case study). Municipal wastewater utilities are a

direct and effective target for regulation and control,

the matter that has been reflected in Kyoto Protocol

and all ensuing conventions and agreements such as

GHG emissions reduction process integration. Table

1 shows all emissions that currently are in common

practice for municipal WWTPs

Table 1: The global warming potential of six

major greenhouse gases [19]

Name of the Gas

Global

Warming

Potential

Atmospheric

Life ( years)

Water Vapor (H2O)

0.1 to 0.23

Few days

Carbon dioxide

(CO2)

5 to 200

Methane (CH4)

Nitrous oxide (N2O)

310

114

Hydrofluorocarbons

(HFC)

140 to 11,700

1.4 to 260

Perfluorocarbons

(PFC)

6,500 to 9,200

10,000 to

50,000

Sulfur hexafluoride

(SF6)

23,900

3200

GHG emissions reduction methods primarily

facilitate the decarbonization in all treatment

processes from preliminary to tertiary mainly

aeration at secondary phase and require energy in

form of electricity thus exploited methods should

account to decrease biological oxygen demand for

all point of sources, treatment and GHG emissions

reduction. CDM Mechanisms consider a baseline

project and leakage emissions from electricity

consumption and monitoring of electricity

generation for all influential detected types of

emissions throughout the treatment process (carbon

dioxide (CO2), methane (CH4), nitrous oxide

(N2O)). This literature is concerned mainly to

develop the AM0080 Project (Mitigation of

greenhouse gases emissions with treatment of

wastewater in aerobic wastewater treatment plants)

into an Eco-efficient project where an application of

sustainability assessment tools is translated into a

user-friendly interface (1) resuming the techno-

economic analyses, (2) systematically examining the

sustainability aspects of WWT processes, (3)

assessing all environmental, social and economic

dimensions, (4) recovering energy required for both

treatment functional processes and emissions

reduction processes through management of

resulting biosolids. The methodology adopted to

support informed decisions making via data

acquisition, filtration, benchmarking and decision-

making tools in order to assure the execution of

WWTP eco-design and instantaneous improvement

actions enhancing sustainability, without burden

shifting. The main focus in the literature is to

develop, upgrade, simulate and model the proposed

baseline project into a prototype exploiting most

available technologies of wastewater treatment in

Mediterranean Countries specifically Non-Annex I

countries (or non-developed countries).

2 Methods

2.1 Goal and scope definition

The goal of this study is to (1) innovate a user-

friendly interface (2) where embedded all simulated

models for an eco-efficient prototype WWTP (3)

involving all available technologies and

environmental analysis (4) comparing the life cycle

impacts to baseline project referring to CDM

mechanism (5) using applicable mathematical

algorithms and methods.

We shall evaluate four different management

strategies in that context for the purpose of

generating such software: (1) Data Acquisition (2)

Energy Balance (3) Life Cycle Assessment (4) GHG

emissions Definition & Reduction.

life cycle inventories involved both baseline and

project data on process simulation creating a record

of input and output flows for plants in Morocco

(UNFCCC Case study), Lebanon (All country’s

functional plants), Greece (the most advanced AAS

plants) & France (Self-sufficient WWTPs AAS).

Such sampling included inputs of wastewater

parameters, energy consumption, and emissions

releases based on comparable data (Mediterranean

countries) of both industrial, developing and non-

developed countries. This study is innovative in

inaugurating, for further expansion of focused

economic and energy performance, a valid

referential comparative of WWTPs in countries at

Mediterranean coasts using types and technologies

other than the study subject matter (AAS).

In line with UN regulations, Mediterranean

municipalities are mandated to implement GHG

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emissions reductions guidelines that are ultimately

on paper with the goal of protecting Mediterranean

surface water and improving environmental quality

that entails eventually the exploitation of novel

treatment technologies [20]. However, the cost of

implementation is typically the major factor for

decision making. Accordingly, technologists and

legislators shall benefit of the current literature

outcomes either the environmental impacts fallouts

using LCA or the economical results using CDM

project accreditations through CO2e savings or CER

(certified emission reduction or carbon Credit which

is the reduction of 1 ton of CO2 emission from the

baseline project activity) which can be evaluated in

terms of energy saving. This study is based on a set

of equations extracted from CDM mechanisms and

algorithms formulated on JMP Pro14, rendered and

simulated into models on MATLAB.

The outcome is gathered up on a user-friendly

interface which is the resulting Software of the

literature out of flexibility and ease of use

considerations with commercial intents. The target

of the literature defined in the submitted proposal to

the CDM committee embodied in the resulting

software, is to enhance Carbon Trading through

different approaches included and implemented in

the submission. The current submission concerns

are the following: (1) Energy efficiency for new or

makeover projects (existing or proposed baseline

plant) by means of energy recovery and exploitation

of allowable renewable energy (including and not

limited to: Cogeneration, installing an anaerobic

digester, improvements or switching to less carbon

intensive energy sources, solid management,

Reducing the frequency of the transport activity) (2)

Environmental Impact (including and not limited to:

LCA, LCI, LCAI) (3) Resulting reduction of any

category of greenhouse gas emissions.

In this study, the mined for performing data to be

incorporated into a master list establishing the

adopted scenarios, are: (1) the baseline set by

UNFCCC at Fès Morocco, (2) the mean WWTP in

Lebanon (referring to CDR study), (3) Greece self-

sufficient plant parameters [21], (4) France Neutral

plant parameters [22]. All reported data from sample

WWTPs were given equal weight in this study and

reference was always the CDM mechanisms listed

baseline parameters to develop an eco-efficient

prototype with three different levels of energy

efficiencies (listed scenarios). In condensing this

grouping to one prototype WWTP processes for an

Open input-output LCA adjustment, the resulting

linear mean of values was used for the process

inventory. The intention of this method was to

create a process representative of the project set by

CDM program by taking surveyed data of

Mediterranean countries from multiple significant

plants. This assumes that studied and surveyed

plants are representative of plants as a whole and

that the data acquisition method has comparable

results to the set baseline uncertainty analysis for

discussion characterized the potential impacts of the

variability in literature values and addressed

possible alternative assumptions. Values from

adopted scenarios are used to put boundaries and

deduce benchmarks on emissions and energy

consumptions for baseline used technologies and

proposed project submission CDM-PDD. No

alternatives were addressed as all varied operating

conditions and necessary inputs as suggested in

baseline and from modeled acquired parameters and

variables, were included and simulated without

detected flaws. The outcome of modeled values was

proposed for the new inventories and reassessed for

the environmental impact study applied to the

baseline project. Differences between proposed

scenarios were highlighted in models with mention

to potential grounds and odds and adjustment

coefficient was integrated to emissions equations to

unify the study of different means.

2.2 System Boundaries

The current study implemented a combination of

novel technologies in WWT sector with the

intention of meeting GHG emissions reduction goal

set by CDM mechanism and as such uses the best

mechanical, chemical & biological practices to

assure energy self-sufficiency or neutrality of the

project plant; which allows decision-makers to

easily compare the estimated financial &

environmental impacts incurred by meeting the set

goals.

The functional unit for comparison in this study is

kilogram (kg) of CO2 equivalent removed or saved.

Figure 1: System Boundaries – Energy Weight in &

out

Comparisons were performed based on total supply

chain environmental impacts, with highlights on the

local energy consumption and fugitive emissions

due to their high impacts. The approved

methodology included in PDD-CDM submission

assumed high energy consumption and fugitive

emissions that have relatively simple computation

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and expected to have a useful lifetime of 20 years as

per baseline definitions. In order to tender a set of

algorithms adjusting to parameters’ variations to

define an immediate precise carbon footprint

calculator, data acquisition method, energy balance

process and LCA of baseline WWTP, comparable

scenarios should be well investigated accordingly.

Figure 2: Carbon Footprint Calculation Scheme

ISO14067

2.3 Scenarios Description

The default scenario is a simple AAS WWTP at Fès

Morocco with defined parameters and variants

through the AM0080 program. the bar against which

all other scenarios are compared have been selected

based on variables that should be controlled with

both lower and higher limits.

Lowest

BOD5

Lowest

COD

kWh/m³-

billing

Highest

BOD5

Highest

COD

WWTP1

(France)

137.98

289.07

0.842

294.55

644.4

WWTP2

(France)

238

1.77

449

707

WWTP3

(Greece)

192

521

1.58

491

1375

WWTP4

(Greece)

164

468

0.24

515

1181

WWTP5

(Morocco)

321.5

8.68

241.33

496.5

WWTP6

(Morocco)

273

2.6

310.67

651.8

WWTP7

(Lebanon)

87.25

298.63

1.17

404.5

765.38

WWTP8

(Lebanon)

307.36

784.8

0.8

633

1990.4

Table 1: default scenario of WWTPs AAS

Parameters & Indicators

the approved scenarios by the CDM committee

were restricted to a single mean sample from

each of France, Greece, Morocco & Lebanon.

Knowing that parameters extracted from France

& Greece plants represent factual data

acquisition from the year 2020 along with listed

parameters for Morocco Fès plant (baseline)

and mean parameters from Lebanon plants also

for year 2020

2.4. Process Description

2.4.1. Data Acquisition & Fault Detection

Method

Data validation, online monitoring of difficult-to-

measure variables, predictive maintenance, system

and energy optimization, and targeted water reuse

are the essentials of big data focus to improve

WWTP operations. Therefore, big data integration

will have the greatest influence on process control at

the WWTP to monitor and manage treatment

operations, for both upper and lower limits of

process variables. Data from case study WWTPs is

collected at various times, ranging from continuous

online sensor measurements to quarterly laboratory

results. Due to the difficulty of combining data of

multiple frequencies and formats, traditional data

management separates data by source. Scaling data

to a single time period is a popular mathematical

strategy for dealing with varied data frequencies.

However, because WWTP data are time-dependent,

co-correlated and the relationships among variables

are related to one another, and nonlinearly related,

this method cannot be used for datasets with a

considerable difference in frequencies. Effluent

quality variables can change differently over time

(Table 2), and they frequently change nonlinearly in

connection to other process variables, making it

difficult to pinpoint the reason for change between

sample events. The frequency with which the

treatment process is monitored is highly dependent

on the analysis' purpose and the process' parameters.

Timescale

Feature

Slow (days-weeks)

Solids retention time (SRT)

Hydraulic retention time (HRT)

Transmembrane pressure (TMP)

Fast (seconds-minutes)

Dissolved oxygen (DO)

Nutrient concentrations

Turbidity

Conductivity

Flowrate

Table 2: Time Lap of WWTP AAS Pollutant

Proceeding

Due to all aforesaid, the combination between

MCDM (Multicriteria Decision Making) and

stepwise regression methods has been found the best

method to represent the irregular nonlinearity of

WWTPs case study behaviour and achieve a

correlation that can be read by the indicator model

itself and through the established bridge model

between different correlated parameters on loophole

basis [11]. All models of the study hereinafter

basically are likewise based and all nonlinear trends

reflected thru the models and bridge models. If

nonlinear and nonstationary activity is found,

modelling nonstationary behaviour can be done in

one of two ways: accounting for a known, or

predicted, underlying trend, or limiting the temporal

window over which a model is trained. Given the

difficulty of modelling nonstationary behaviour in

the WWTP (Table 3) small time frames may be the

best option for achieving approximation stationary

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and normal behaviour, which is the case of analysed

prototype.

Feature

Frequency

Structur

Proceeding

Water quality

Daily-

Monthly

Numer

5-day biochemical oxygen demand,

alkalinity, nutrients

Water quality

Second-

Minute

Numer

Temperature, dissolved oxygen, pH,

nutrient concentrations: sensors

Equipment

monitoring

Second-

Minute

Categ

orical

Power status

Equipment

monitoring

Second-

Minute

Numer

Operating speed, flowrate, pressure

Operating

setpoints

Second-

Minute

Categ

orical

Peak or normal operation for flow

Operating

setpoints

Second-

Minute

Numer

Runtime for batch operations

Table 3: Monitoring Proceedings in WWTPs

Sampling with Data Acquisition Frequency and

Model

Model-based control could be used to forecast what

a variable value should be under particular

situations. Model predictive control contrasts

predictions from mechanistic models with real

process observations. Then defects are discovered as

deviations from the model. The model can be

derived from theory or empirical trends and can be

used to approximate new process variables. Instead

of directly monitoring the variable of interest, a

relationship between variables can be discovered,

which exactly was the basis of the current study

models’ connection due to the complexity and

variability of parameters and indicators. Therefore,

it is essential to comprehend the entire strategy of

data acquisition and clustering process, in order to

proceed in a planned, target-oriented method and to

secure the utmost possible conclusion.

Consequently, frameworks for formalizing the

knowledge encounter process have been developed

through experimental feedback and mathematical

applicable processes. These process models explain

the knowledge outcome project's life cycle and give

a roadmap for executing similar projects under any

comparable circumstances. The UNFCCC AM0080

Pilot project depicts the process model used in the

procedures discussed in the submission. The model,

that can be generalized at the scale of

standardization to all WWTPs subject matter, is

based on a combination of an industry-oriented

standard process for DA model and created by a

consortium of significant proven algorithms

reflecting models of all required and necessary

applicable KPI’s for AAS WWTPs type. Hereafter a

detailed elaboration shows the adopted algorithms

and models of all indicators leading to GHGs

emissions arithmetical definition. The process is

engineered of six interconnected, highly

participatory, and iterative phases: (1) A thorough

understanding of the issue defining problems and

setting objectives are the start in this stage based on

strong relativity indicators. (2) Data comprehension,

after all data is collected, verified, and merged, is

essential to acquire prior knowledge of raw data

extracted from plant management. The data's

relevance in relation to the objectives shall be

confirmed at that stage. (3) Preparation of data at

that phase determines which data will be used and in

what format. As a result, this step includes

significance testing, data cleansing, deriving new

attributes, and feature selection and extraction. The

data is now in a format that can be used with the

tools chosen in the first stage. Bugs are being

filtered with a specific regression model as shown

hereinafter. (4) Many approaches could be used to

extract knowledge from the pre-processed DA.

Extracted knowledge can take any shape, such as a

list of rules or a model which is the case witnessed

after generating all KPI’s models. This phase

evaluates accuracy and generality. (5) Assessment

of newly acquired information is the process to

interpret and segregate outcomes between normal

feedback (Déjà vu) and new data requiring further

assessment. If there are any new or intriguing

patterns, they shall be recorded and the model notes

here a looping to rectify according to the corrected

pattern. Within the guidelines noted in CDM

mechanisms, all feedback should be processed as

repetitive knowing all parameters. (6) The

deployment of the system is all about deciding on a

distribution strategy: What should be done with the

newfound information? and where should it be

applied? Upon trials and debugging the answers

shall apply in due course and reflected in generated

models. Briefly, Process abnormalities in WWTPs

case study subject matter can be caused by a variety

of system failures or changes in circumstances. A

change in influent quality, an outbreak of treatment-

inhibiting microorganisms, irregularities or damage

to treatment units, mechanical failures, or sensor

failure are all examples to crucial circumstances that

might undergo the DA and modelling/modelled

process. When creating a fault detection

algorithm/software, it's fundamental to think about

how versatile an analytical technique is, and what

kind and range of errors should be recognized.

Many variables could be changed if a sensor fails,

especially that the sensor's measurements are used

in a control loop which would duplicate the error at

every chain of data processing. A sensor

malfunction, on the other hand, may only influence

the measured sensor variable if the sensor's

measurements are not included in a control loop.

Control charts are the most important tools for

determining whether a process is in control at a

glance. As well as the principal component analysis

method that is a widely used statistical method for

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continuous monitoring of a wide range of variables.

It captures the correlations between linear

combinations of variables rather than the variables

themselves. Beside the main statistical role, the

partial least squares method can detect errors in a

separate linear combination of the measured

variables in the same way as executing the initial

data pool of extrapolated WWTPs [15].

Decision Making is the adaptive or flexible decision

method that can be used in uncertain conditions, as

due to the fact that the future of WWTPs is

uncertain and unpredictable, robust options tend to

be more recommended than optimal options. On the

other hand, the best option is more suitable when the

future can be predicted [14]. The purpose of

decision making is to identify full-bodied strategies,

which can adapt or perform well under uncertain

situations in the future. The method is helpful for

decision makers in long-term consequences. The

fundamental steps for automated decision making

are to: (1) identify the issues and set a goal; (2) find

information, strategies, risks and select a robust

strategy; (3) take an active towards the goals; (4)

determine whether the strategy is effective; and (5)

update and resolve the strategy. The last step is an

essential one because if the strategy is not effective,

decision makers can change strategies until they

meet their goals [12]. Pilot project didn’t adopt any

automated decision-making methodology, therefore

the PDD submission had to assure MCDM as the

decision-making methodology to be followed for all

ascended uncertainties along with stepwise

regression logic where necessary and control charts

logic for fault detection. Finally, the summarization

logic is used to gather all models to extract the

GHGs emissions as a linear trend according to all

variabilities imposed by change of parameters.

Figure 3: Framework of Data Acquisition & Method

Selection

2.4.2 Energy Balance Process

Wastewater utilities consume approximately 2% to

4% of the power at national level as an average at

all Mediterranean case study countries, which is

typically obtained from the grid. WWTP’s

secondary treatment phase is the processes

consuming the largest quantities of power. Reducing

process energy consumption in WWTP starts with a

process energy survey. The mission of this study is

to provide all conceivable aspects that may help in

the transition to energy neutrality in WWTPs with

comparable examples from industrial developed

countries (France in our case study), which will be

expanded further in this literature using arithmetic

models based on UNFCCC methodologies. The

sources of energy in wastewater were explored, as

well as several indicators for expressing energy

consumption, using experimental cases of

operational WWTPs in the Mediterranean countries:

Lebanon (non-developed countries’ sample),

Morocco (baseline case as indicated in AM0080

project), Greece (Developed country with high

potentials of sustainability) and France (Industrial

developed country with energy neutral plants

comparable case study). The operational methods

and technology upgrades of the WWT processes

were assessed, as well as the different lanes for

energy consumption reductions. The methods for

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recovering the potential energy hidden in

wastewater, as well as the use of renewable energies

in WWTPs, were then explained and modelled. The

available assessment methodologies were

introduced, which may support the analysis and

comparison of WWTPs in terms of energy and GHG

emissions. Finally, successful case studies on

WWTP energy self-sufficiency were listed in a

threshold prototype comparable to baseline project

and its potential advancement. The literature’s

innovative project, the Software, was presented after

analysing the results and discussing energy saving

strategies and energy conservation measures in

order to reach the GHG emissions computation

through the models simulating energy requirements

and potential savings for WWTPs. For this purpose,

wastewater industry's energy demand, distribution

and performance were projected to account for an

average of 1.8% of all electricity consumed at the

national level for developed Mediterranean

countries depending always on country’s energy

sustainability approaches with differences in

relativity and data availability. However, WWT and

transportation sectors consume approximately 3% of

total electrical power produced in a non-developed

country according to the annual report of energy

consumption worldwide published by UN Stats, the

statistics division of UN [2]. Nevertheless, all

studies anticipated an increase of at least 20%

within the next decade to the amount of power

required for WWT in non-developed and newly

developed countries, leading to a significant

increase in CO2 emissions and resource

consumption. The amount of energy used to treat

wastewater is influenced by a variety of factors and

determined by the following: the location of the

plant, its size, the type of treatment process and

aeration system used, the effluent quality criteria,

the facility's age and lifetime and the operators'

expertise and abilities. Despite the fact that the

average energy consumption per cubic meter of

treated wastewater (kWh/m3) is rather consistent

between countries, the amount of energy required

for operations varies greatly amongst WWTPs and

requires more accurate indicators to reflect the

actual status. For analysed WWTPs, the reported

figures on specific energy consumption range from

0.25 kWh/m3 to 0.50 kWh/m3 [2] . For WWTPs

using modern biological removal systems with

multiple energy-saving techniques, the reported

figures on specific energy consumption can be as

low as 0.25 kWh/m3 [7]. The climate in which the

WWTPs were run proved to have only a slight

impact on energy consumption, with colder

temperature circumstances possibly resulting in

lower energy consumption than hot and humid

environments [15]. Consequently, the provided

results from CDM baseline indicated that the target

average energy consumption of WWTPs was around

0.06 kWh/m3 to 0.20 kWh/m3 without being able to

precise a threshold due to the lack of benchmark,

which was much lower in certain more developed

countries. Regardless of the size of the WWTP, the

biological treatment phase consumes the majority of

energy required for the whole plant, which can

account for up to 75% of total consumption [15].

Figure 4: AAS WWTP Power Consumption

Distribution

Approaches to Energy Balance in AM0080 Pilot

Project:

wastewater contains a quantity of energy that can be

recovered, proven thermal energy content estimated

at 75kWh/P.E./a, proven energy potential from

organic matter estimated at 153kWh/P.E./a [9], and

proven hydraulic potential energy depending on

inflow rate and available hydraulic height.

Moreover, wastewater energy consumption is

relevant to national electric energy consumption

accounting approximately for 30% of total energy

consumption of municipalities. The electric energy

saving potential is high and proven estimated at

around 25% [15]. The proven recovery from biogas

production is around 17kWh/P.E./a of electric

energy and 27kWh/P.E./a of recuperable thermal

energy [2]. A detailed analysis of the applicability

of these facts of energy consumption, saving and

balance to the AM0080 pilot project are well

elaborated in the generated models and simulation.

Figure 5 below shows the typical energy flows

produced by AAS digestion in Fès WWTP, that

convert the chemical energy content of COD to

electricity and thermal energy. Consequently, the

biogas energy recovery can cover up to 62% of total

energy requirements. (Refer to the biogas model

renewable energy section hereafter). the energy

balance of conventional WWTPs shows that the

most important energy consumers are the aeration

system (60%), wastewater pumping (12%) and

anaerobic digestion (11%), which together account

for the 83% of global energy consumption of the

plants [10]. The main purpose of determining the

role of energy efficiency in sustainable WWT

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processes for AM0080 is to reach an optimum

prototype AAS WWTP where all energy recovery

and external renewable resources are ultimately

used and GHGs emissions are diminished to the

maximum possible [8].

Figure 5: GHG emissions during treatment process

at Fes WWTP

This study adopts proven theoretical methods to

produce models on energy efficiency, evaluating

appropriate sustainable WWT technologies, and

applying to the pilot project. Energy efficiency plays

multiple roles such as sustainable growth and

advancement, and economic development through

the application of the set mechanism. It can also

lead to carbon minimization resulting in reducing

climate change however many exclusions for the

sake of simplification were applied by CDM to

avoid an optimum GHG emissions reduction.

A- Design Process Guidance: is a circular process

that aims at continual development over time and

require beside a robust methodology, starting with

predesign operational advanced technologies

(mechanical, biological & chemical), accurate DA,

steady LCA and LCC, some technologies that

provide a fast data input combination and decision

making that would be achieved through IoT.

Relying to a complete structure of control, WWTP

function should follow a complex hierarchy based

on both input data and results at the same time. All

predesign measurements were reported by CDM as

included in Fes’s pilot project AM0080, the current

mechanism according to both tender document,

execution and operational records. Some of the main

design measures audited, surveyed, simulated and

modelled in this literature are: (1) Pumping: pumps

selection, pumping system peak, Continuous

calculation of system energy performance,

Evaluation of cost assessment including energy,

operation and maintenance costs, Equipment

management. All these factors were considered in

AM0080 methodologies and included in related

framed equations and tools [4]. (2) Aeration systems

of the Fès facility in the secondary treatment process

account require up to 30-60% of total energy used at

the conventional AS WWT system [7]. Adding

dissolve oxygen sensors and automatic controller,

fine bubble diffusers, proper blowers, and variable

speed motors, assure constant evaluation of oxygen

need and DO control all over the clock. All these

factors were translated into equations and

algorithms rather than models to be incorporated in

software. (3) Solids Handling: Solid volume

reduction ensures energy requirement and solid

handling. Anaerobic digestion is the method

implemented in this pilot project generating biogas

production and heat. All mentioned factors were

translated into equations and algorithms rather than

models to be incorporated in software. (4)

Ultraviolet system method is partially used for

tertiary treatment so the disinfection system takes

place at WWT facility. The upgrade of this option

further than baseline conditions was disregarded.

Figure 6: Energy self-sufficient WWTP Process,

Theoretical Analysis to Practice

Each technology has different removal efficiency

and can be evaluated on past experience, full-scale,

or pilot studies. Existing pilot project is a baseline

considered as the reference to the additions made.

Process

Recommendations

Energy

Savings

(%)

Influent

Pumping

Pump Control based on flow

10%

Conducting Inflow & Infiltration

long term analysis

35%

Rehabilitation of Pumps

20%

Aeration

Short Term strategy: Mixers off

with Aerator On

90%

Short Term strategy: Replace DO

meter

10%

Long Term strategy: integrate DO

meter to control

40%

disinfectio

Short Term strategy: Replacing

Bulbs with low emissions

50%

Solid

Handling

Retrofitting sludge feeding

network from thickener

100%

Table 4: Energy Saving Recommendations on

Mechanical Process

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Figure 7: Initial Model Scope of Work

B- Resource Recovery: Two nutrients in wastewater

are phosphorus and nitrogen can be used to produce

fertilizer and reduce GHGs emissions. Furthermore,

landfill gas technologies can capture methane or

biogas using anaerobic digesters. These

technologies should be applied for resource

recovery in WWTPs [15]. The pilot project

disregarded these two essentials energy recovery

factors that have been added to the PDD submission

after being simulated and modelled.

Figure 8: Recovery Diagram in Fès WWTP

Figure 9: Energy utilization diagram of Fes WWTP

C- Renewable Energy Exploitation: The WWTSs

should save energy consumption by reducing energy

used and implementing technologies that can

produce renewable energy [16]. Local governments

should consider applying energy-saving measures in

WWT facilities [11]. The goal is to improve energy

efficiency in the plant. WWTP at Fès considered

developing less energy consuming equipment

however no renewable energy has been exploited to

make Fès zero energy plant. PDD considered this

criterion as an essential factor of developing

additions whether solar or wind that are both

simulated and modelled.

2.4.3. Life Cycle Assessment & Life Cycle

Costing

Technological advancements highlighted in CDM

mechanisms are paving the way for new behaviours

to support the circular economy by recovering

resources from wastewater. The use of LCA and

LCC in the development of technical solutions for

treating wastewater and recovering by-products is a

prerequisite by UNFCCC pilot project submission

which is evaluated based on employing unique

novel technology configuration to process and

recover wastewater. Therefore, the current

assessment focuses on finding hotspots and potential

design improvements. Furthermore, the construction

of a unified strategy for projected LCA and LCC is

demonstrated in order to improve the robustness and

consistency of the project pilot systems' analysis.

The impact and cost of treatment are significantly

depending on the effluent composition, according to

the investigation specifically, whether the recovery

of biogas and treatment of wastewater can offset the

treatment systems' impact and expense. preliminary

analysis indicated that this is achievable. For the

baseline study, estimates of GHG emissions were

acceptable with many exclusions considered.

However, adding all excluded sources of GHG

emissions to the baseline, it is predicted to reduce at

full scale the energy consumption, as well as

operational costs and increase revenues at the level

of emissions CERs and expenses saving.

The LCA technique adopted follows ISO 14040 and

14044 as recommended by AM0080 program

guidelines [6]. As a result, it is believed that

comparable available resources are much cheaper at

the level of emissions yet the energy neutrality of

WWTP necessitates the neutrality of environmental

impact, accordingly a project-wide methodology has

been established out of the existing pilot project.

The initial stage's goals are to compare each

baseline set by CDM to the reference case or

simulated baseline formulated in that study, while

the project's ultimate and final goal is to compare

each of the baseline and monitoring for both DA

(with and without exclusions). Background data was

gathered from the CDM methodologies and tools.

Additional data is based on bench scale

measurements and simulations with the addition the

study proposed to the UNFCCC program. The

simulation achieved in the current analysis can

calculate a variety of geochemical processes

involving wastewater and biogas emissions, beside

sludge management. the technology units employed

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in the pilot project from which data was obtained

are well defined, referring to ISO 14040 and 14044

[6]. The scope of this study is limited to the major

actor of financial and environmental LCC, which

comprised listed purchase costs (CAPEX) by CDM

in tender project, operational costs such as energy

and other resource consumption, maintenance and

repair expenses (OPEX), and end-of-life costs such

as collection and recycling costs. Revenues from

recovered by-products are also accounted for as

negative costs (except recovered energy) [1]. The

pilot project is indicated to have a 20 years life span

as a functional plant (15 years is the standard

lifetime set for WWTP). Nonetheless, the project's

next steps (UNFCCC submission) are to be

conducted with a comprehensive environmental

LCC, which will include both internal and

monetized external environmental costs, such as

those related to global warming once defined by the

Software subject matter. The chain of process below

represents the integrated Method approaching LCA

& LCC with the goals of aligning the overall

approach and main LCA components; develop an

understanding of the project pilot, technologies,

data, and simulation challenges; provide an initial

LCA and LCC and identify the main hotspots; and

identify preliminary design implications and where

additional efforts to improve data quality may be

required.

Figure 10: Flowchart of LCA & LCC Approach

Phases

The AS treatment system has substantially higher

energy requirements than any other treatment type

due to the aeration machinery operating almost all

day [2].

Equipment

Power

Operation

Time

Control

Aerator

Continuous

VFD fixed

speed

Centrifuges

10 to 20

hrs./week

VFD flow

speed

Influent

Pumps

Continuous

VFD flow

speed

Blowers

Intermittent

Fixed Speed

Mixers

Continuous

Fixed Speed

UV System

Continuous

Fixed Bank

Table 5: Major Energy Consumers for WWTP AAS

These findings are consistent with pilot project

operations factsheets. The biggest contributor to the

effect categories of abiotic depletion and global

warming is energy consumption by fossil fuel and

electricity, which explains why AS has a higher

impact among all categories [13]. The objective of

the study beside the software, is the background

engine where all indicators the most important

amongst energy consumption, benchmarking,

recovery and renewables, to be well reflected

through numerical trends and algorithmic models

screening the evolution and interaction of each

factor and parameter. The objective of establishing a

set of key performance indicators computed and

modelled is to gather all models with a convenient

correlation algorithm that defines bottom line an

accurate amount of resulting GHGs emissions from

treatment process referring to CDM mechanism,

AM0080 project and tools specifically [3].

Figure 11: GHG Emissions Sources & Category

3.GHG Emissions Reductions

The approach for calculating and analysing the

carbon footprint of baseline WWTP is extended

from the CDM booklet by UNFCCC [3].

Accordingly, CF minimization has been prioritized

to include in plants: Electricity, heat, chemicals,

fossil fuels, transportation, and more with the code’s

advancement, however, GHG emissions considered

CO2, CH4, and N2O main emissions to be assessed

as a part of Kyoto Protocol. The assessment may be

used at two stages of the CF reduction process: (1)

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while deciding on a plan to minimize GHG

emissions, the CF identifies the elements that have

the largest environmental effect; (2) CF evaluates

the efficacy of the actions taken to improve the

energy balance. Furthermore, calculating the CF

allows WWTPs management to control GHG

emissions contributions [5]. Through the AM0080

project, the UNFCCC encouraged the evaluation of

the CF of WWTPs from a life cycle perspective and

offered a pilot project to standardize a set of

equations to define the CF. The goal of this study is

to turn this pilot project into a generic software

system based on proven models and algorithms. CF

calculation approach throughout the course of a

plant's complete life cycle, splits the required LCCI

data into two categories: direct & indirect emissions.

However, plants are considered mixing both

categories. Direct emissions are those controlled by

the plant management system having no previous or

subsequent technical history data inputs and outputs

of plant’s items causing GHG emissions. The

complex model simulated in that study as the final

result succeeded to define both emissions types and

assure a better understanding of the result of energy

efficiency and balance proposed at the phase of

predesign or rehabilitation. Several different steps

may be taken to achieve complete energy neutrality

in WWTPs. The first major step would be reducing

the current energy consumption of WWTPs which

ranged between 0.25 to 1 kWh/m3 based on the

mathematical calculation following the guidelines of

AM0080 methodology. The most promising

reviewed operational measures to reduce the energy

consumption comprised aeration control strategies

since aeration held the biggest share of the total

energy consumption in Fès AAS WWTPs.

However, innovating a LCA & LCC model (with

the assistance of RETSCREEN as a feasibility

portal for technical projects) helped to achieve

further energy savings. Novel control systems,

presuming IoT in our case, proved the possibility of

significant reduction of energy for aeration,

pumping, agitators, blowers reaching off more than

25% of the time while maintaining the same

wastewater effluent standards. Furthermore,

chemical and biological applications such as

upgrades to nitrogen removal were able to reduce

the required aeration energy by more than 60%

using these new technological pathways of

treatment. The second step towards energy

neutrality was the increasing of on-site energy

production by energy recovery through biogas

sludge outcome production coupled with CHP

engines and emphasis on renewable resources such

as solar. The remaining electricity demand was

managed to be recovered mainly by organic waste

co-digestion to assure energy neutrality yet positive

energy production. Reviewing all these successful

methodologies in terms of energy self-sufficiency,

linked to an executed monitored plant (Fès) proved

the point that the predesigned or existing inefficient

WWTPs should take a series of actions as reviewed

in this literature to be turned into energy positive

plants. Analysing the priorities of the actions

separately, the literature proposed a CDM-PDD

submission according to the resulting calculations

and established a set of algorithms and models on

MATLAB and histograms on JMP Pro 14 for each

case study abovementioned depending on several

operational environmental economic parameters.

The advanced and complex analysis procedures,

techniques and simulation tools (plant wide models)

supported perfectly the decision-making to meet a

sustainable self-efficient WWTP prototype. The

adopted algorithms, and calculation methods used to

generate models were all gathered in one user-

friendly interface or Software and detailed in PDD

submission. Different scenarios and treatment

configurations have been simulated and documented

by the CDM committee to illustrate the difficulty in

accounting for all constraints imposed on the

system. Therefore, the resulting model will serve as

a basis to target challenges that will set the scene for

determining directions of further developments

within the UNFCCC project delimitations. There are

also some tools within CDM methodology that

divide the carbon emission system of Fès sewage

treatment system into five aspects: material, energy,

material consumption, carbon sink and resource.

The focus of the CDM committee has been mainly

on CO2, CH4, and N2O GHG emissions factors.

The basic input referred to the baseline project on

which the additional improvement PDD submitted

to the CDM committee yet with further extension to

the data pool, energy balance components, energy

efficiency aspects and emissions factors. Using the

operation control method to determine the scope of

GHG assessment of the WWTP, the raw GHG

emissions must be 100% identified, and the

emissions related to sewage treatment must be

classified, as shown in Figure 12.

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Figure 12: Assessment Scope Boundaries

The CO2, CH4, N2O and other GHG emitted by the

sewage treatment plant are uniformly measured by

the amount of CO2 produced. According to the

GWP, the potential value of CO2 is 1, and the

potential values of CH4 and N2O are 23 and 296

respectively, CH4 and N2O can be converted into

carbon emission equivalent according to the

corresponding potential values [6].

As per the IPCC agreement, it is the amount of CO2

directly emitted during sewage treatment. The CO2

emissions of biogenic wastewater are not included

in the total GHG emissions, according to the "GHG

Inventory Protocol-Corporate Accounting and

Reporting Standards" [6], the total GHG emissions

must be included. investigated and studied, the

amount of CO2 produced during the operation of the

actual sewage treatment plant, clarified to have the

following factors affecting CO2 emissions as per the

calculation formula of CO2 production:

MCO2 =Q*EFCO2 (1)

MCO2 - Biological treatment process emissions (g)

Q - Amount of sewage treated during calculation

(m3)

EFCO2 - emission factor to the CO2 emission of

the A2O process.

the calculation formula for CH4 generation is as

follows:

MCH4=(TOW*EFCH4)-R0 (2)

MCH4 - CH4 emissions from biological treatment

process (kg)

TOW - The organic matter content in sewage during

the calculation period (kg)

EFCH4 - CH4 emission factor to the methane

emission factor of the aerobic treatment process

R0 - The amount of CH4 recovered during the

calculation period (kg) if no sludge digestion R0 =0

The formula for calculating the amount of N2O

produced is as follows:

MN2O=TN*EFN2O (3)

MN2O - N2O emissions from biological treatment

process (kg)

TN - Total nitrogen removal from sewage during

calculation (kg)

EFN2O - N2O emission factor

During the operation of the sewage treatment plant,

blowers, pumps, aeration equipment and other

equipment consume a large amount of electricity,

the carbon emissions of the purchased electricity

during the production process are the indirect

emissions of the sewage treatment plant, the

calculation formula:

MCO2ꞏE=E*EFCO2ꞏE (4)

MCO2ꞏE - Indirect CO2 emissions from power

consumption (kg)

E - Power consumption (kWh)

EFCO2ꞏE - The emission factor of electric energy

consumption (kgCO2/kWh) Ref: Regional Grid

Baseline Emission Factor

Some chemicals are used in the sewage treatment

process, such as disinfectants, flocculants, etc., the

formula for calculating carbon emissions of

purchased chemicals:

MCO2ꞏY=ΣYi*EFCO2ꞏYi (5)

MCO2ꞏY - Indirect CO2 emissions from chemicals

consumption (kg)

Yi - Consumption of chemicals i (kg)

EFCO2ꞏYi - The emission factor of CO2

consumed by chemicals (kgCO2/kg)

Each used chemical should calculate its CO2

emissions with corresponding emission coefficients.

There are some other aspects of emissions, because

it is difficult to obtain GHG emission factors for

calculation, but should be estimated according to the

actual situation of the sewage treatment plant,

determine an appropriate ratio, and include it in the

total GHG emissions. After a comprehensive

analysis, it is determined that other emissions are

10% of the calculable emissions, which are included

in the total GHG emissions of the sewage treatment

plant.

A set of algorithms were formulated ruling GHGs

emissions during all phases and translated into

models to generate a user-friendly interface thesis

subject matter. Models generated present GHGs

emissions controlled and translated into the main

generic model to generate Software interface

literature subject matter.

4. Results and Further Improvements

In this literature, a cooperative decision support

system for energy saving and production in WWTPs

has been presented. The characteristics of this

decision support system are aligned with the

original research question and with the specific

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objectives presented in the UNFCCC program,

CDM methodology, AM0080 project. The

comparison between the baseline project objectives

set by CDM and the obtained results using all

necessary simulations and modelling, was shared

step-by-step with the CDM committee and

preapproved before publication. The proposed

submission has been approved as a consistent model

and presented to committee with a coherent

structure enabling the integration of information,

data gathered online, static data, and expert

knowledge to provide decision making support

along with the advancement and technologies

adopted day by day in this field. Reaching the pre-

validation phase is sufficient to positively answer

the original and developed research questions.

4.2. Further Improvements

The set of Algorithms ruling GHGs emissions

during all treatment phases, translated into Bridge

Models to generate Final Model. The Final Model

gathered all bridge models into Loophole Basis

Control to assess all relevant projects via Software

Interface. Amount of CO2 directly emitted during

WWT according to the "GHG Inventory Protocol”

refers to equations (1), (2), (3), (4), & (5). GHGs

Emissions Reduction Final Equation collecting all

Variables & Parameters: 〖PE〗_(EC,y)=

∑_j▒〖(〖EC〗_(PJ,j,y) × 〖EF〗_(EF,j,y) ×(1+

〖TDL〗_(j,y))〗)

The data normalization of WWTP case study

consists of the new perspective for this research

where the application of a uniform set of

measurement units and the calculation of

comparable key performance indicators are the next

level of automation in WWTP management and

control strategy. This improvement, if ever

achieved, would make the calculations faster, more

stable and reliable, ultimately enabling the

connection of managing a larger number of WWTPs

with the same scale and conditions due course. An

original approach based on the random forest

algorithm was developed yet to be verified before

submission and integration. it is useful to mention

some of the forthcoming foreseen flaws and aims at

a time to stimulate a discussion within the

framework of this thesis knowing that these

shortages were the stimulus of Software further

development through continuous publications under

CDM mechanisms and sponsor upon publication:

application of software at larger scale, database

upscaling, IT & WWT mix of knowledge challenge

integrating core algorithms with fastest response,

central computed connectivity to electrical grid,

sewer mains database incorporation, widening the

application to more WWT typologies, and

commercial enhancement to interface.

5. Conclusions

This literature is the output of close follow up of

baseline plant despite the redundancy and time

salvage waiting committee response to proceed to

the next level along with potential failure in equity

with potential future developments identified and

approved. This study shows the possibility to build a

plant generic decision support system specifically

oriented to optimize the energy balance in WWTPs

and define GHGs emissions with remarkable

reduction on climate change grounds. The two main

lanes consist of ’market-oriented’ and ’research

oriented’ projects. The first lane is the development

of existing methodologies with the aim to deliver a

service to the market. The second lane consists of

the development of new methodologies for decision

making support. at the moment, it is possible to

report a strong interest from the UNFCCC and the

author to continue exploring the topic with new

projects and collaborations to assure the resulting

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interface will be further extended to the flaws noted

hereabove, knowing that the current version of the

generated software delivers the required complete

report for an AAS WWTP energy balance, LCA,

LCC & GHGs emissions with suggestions and

recommendations for predesign or rehabilitation

enhancement based on solid decision making

support tools.

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