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GRUPPO DI RICERCA
•
INNOVAZIONE NELL’IMPIANTISTICA INDUSTRIALE (I3 GROUP)
ATTIVO DAL
•
1990
COMPONENTI 2013_10
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Professori (2): Giovanni MUMMOLO, PO, ING-IND/17 (responsabile scientifico); Raffaello Pio
Iavagnilio, PA, ING-IND/17.
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Ricercatori (4): Ornella Benedettini, PhD, ING-IND/17; Francesco Boenzi, PhD, INGIND/17; Salvatore Digiesi, PhD, ING-IND/17; Giorgio Mossa, PhD, ING-IND/17.
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Dottorandi (4): Giancarlo Caponio, 28°, ING-IND/17; Giuseppe Mascolo, 28°, ING-IND/17;
V. Alessio Romano, 27°, ING-IND/17; Francesco Facchini, 26°, ING-IND/17.
SSD
ING-IND/17, Impianti Industriali Meccanici
SETTORI ERC (European Research Council)
PE7_3 - Simulation engineering and modelling
PE8_11 - Industrial design (product design,
ergonomics, man-machine interfaces...)
PE8_12 - Sustainable design (for recycling, for
environment, eco-design)
PE8_10 - Production technology, process engineering
TEMATICA:
•
Ingegneria dei Sistemi industriali
LINEE DI RICERCA
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Progettazione e gestione sostenibile dei sistemi di produzione
a. Nuovi modelli di operations management in ambienti produttivi ad elevato impiego di risorse
umane (Working Time, Job Rotation e Scheduling Problems / Models).
b. Modelli di valutazione dell’impatto dell’innalzamento dell’età media dei lavoratori sulle
prestazioni dei sistemi di produzione.
•
Gestione sostenibile di servizi di supporto all'industria ed alle reti di una ‘smart city’
c. Configurazione e gestione di servizi di una smart city.
d. Sostenibilità dei servizi (economica, tecnologica, ambientale e sociale). Particolare attenzione
è devoluta ai servizi sanitari (healthcare systems) ed ai servizi logistici (valutazione delle
esternalità nei problemi di inventory management).
e. Servitizzazione (progettazione e sviluppo di servizi e soluzioni - Product-Service Systems integrati all’offerta di prodotti di aziende manifatturiere).
RISULTATI DELLA RICERCA 2013 (co–autori in corsivo)
PRODUZIONE SCIENTIFICA
• Contributi in rivista: 2 – Mummolo, 2; Digiesi, 1; Mossa, 2.
• Contributi in volume:
• Monografie:
• Proceedings: 11 – Mummolo, 9; Benedettini, 2; Boenzi, 3; Digiesi, 7; Mossa, 7; Caponio, 1; Mascolo, 1;
Romano, 2; Facchini, 3.
• Brevetti:
• Curatele:
• Altra tipologia:
PUBBLICAZIONI CON CO–AUTORI STRANIERI: 3 – Mummolo, 1; Benedettini, 2; Boenzi, 1.
MOBILITÀ INTERNAZIONALE: Benedettini (Regno Unito); Mascolo (Austria).
PROGETTI COMPETITIVI:
• Progetto PON04a2_E, Sinergreen - Res Novae - Smart Energy Master per il governo energetico
del territorio finanziato a valere sull’Asse II - Sostegno all’Innovazione - Obiettivi Operativi 2.3.1
e 2.3.2, 2012-2015)
• FRA 2012 "Biometano da biogas: valutazione tecnico-economica dei processi di upgrading in
funzione degli utilizzi finali" – 2° classificato nella valutazione comparativa delle proposte
A DSS to minimize Carbon footprint of Integrated
Waste Management System
Francesco Boenzi 1, Salvatore Digiesi1, Giorgio Mossa1, Giovanni Mummolo1,
Rossella Verriello1
Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Viale Japigia 182,
Bari, 70126, Italy
{giovanni.mummolo,francesco.boenzi,salvatore.digiesi,giorgio.
mossa]@poliba.it, r.verriello@gmail.com
1
Abstract. The concept of ‘Sustainability’ is increasingly used to describe a paradigm
upon which future policies must be based. In the view of a urban sustainable
development, a strategic role in the governance of the smart City is played by waste
integrated management systems (IWMS). The raising complexity of a IWMS relies
on the high number of design and management variables and relationships related to
collection, treatments and disposal phases. Waste management practices can affect
greenhouse gas emission by influencing energy consumption, methane generation,
carbon sequestration and non-energy related manufacturing emission. A decision
support system can be modelled aiming to evaluate and minimize the carbon
footprint of a IWMS. The model proposed goes beyond the existing technical and
organizational solutions outlining the different options in a much broader view
concerning both waste collection and treatments.
Keywords: Integrated Waste Management System, Sustainability, Carbon footprint,
Smart city, Decision Support System
1
Introduction
Industrial and societal challenges are mutual dependent and provide significant
impacts on the grand challenges of the EU 2020 strategy: reduction of 20% of GHG
emission, the achievement of 20% energy demand by renewable energy together with
an increase of 20% of energy efficiency [1]. The challenge is considerable since cities
are responsible for about 70% of the overall primary energy consumption and for as
much as 80% of global Greenhouse Gas (GHG) emissions [2]. A wide literature is
available on methods and experience to tackle urban greenhouse gas emissions [3], [4].
In the 2020 European strategies, the innovation of the city is identified as the starting
point towards the ambitious path to sustainability at regional level. Making "smart"
cities means undertake coordinated set of interventions that aim to make cities more
sustainable primarily from the point of view of energy and environment, through
choices and technologies that save energy, use of renewable energy in both the public
in the private sector. The Strategic Energy Technology Plan (SET Plan) identifies the
smart cities as one of the seven priorities for investment by allocating a huge sum to
“Smart City Industrial Initiative” [5]. In the view of a urban sustainable development,
a strategic role in the governance of the City is played by integrated waste
management systems. The waste sector has experienced a decrease in emissions of
greenhouse gases rising from 204 million tons in 1990 to 133 million tonnes in 2011
[6]. Due to the rapid population growth and the rapid expanding of urbanized areas
as well as to changes in human activities and lifestyles, nowadays IWMS plays a key
role at city level. Integrated solid waste management reflects the need to approach
solid waste in a comprehensive manner with careful selection and sustained
application of appropriate technology, working conditions, and establishment a
‘social license’ between the community and designated waste management authorities.
The aim of the research project is to develop a Decision Support Systems (DSS) for
more sustainable Integrated Waste Management Systems focusing on the:
- Definition of a reference framework to support public decision-makers in designing
and planning the Integrated Waste Management Systems (IWMS).
- Definition of a Tool for optimizing and monitoring the carbon footprint of IWMS.
2
Study Methodology
A systematic and holistic approach is required to tackle the complexity and ensure
transparency and repeatability of decision making process. The authors propose a
Reference Framework for Planning a Municipal Waste Integrated Management
System to jointly:
design and plan the integrated waste management system;
estimate and minimize the net emission due to the entire cycle.
On the basis of the mathematical model adopted in [7] for optimizing collection
phase, a model including also the treatments phase is proposed in [9]. The dry
fractions collected are sent to the selection facilities that could have a different level
of automation. Innovative technologies are promising high performance in terms of
costs reduction, low environmental impacts and high and constant quality of waste
to be recycled. After an appropriate pretreatment, the organic fraction is usually sent
to an anaerobic digestion to recovery materials and biogas/bio-methane having
different possible utilizations. Mechanical-biological treatment is required by nonrecycled fraction before the thermal recovery. The gasification process is modelled
according to [8]. The logical structure of the problem suggests the formulation of a
Mixed Integer Nonlinear Programming (MINLP) problem to support decision
makers in finding out the integrated waste systems with the lowest carbon-footprint.
Table 1. Notation adopted in the model
Symbol
k
i
j
w
feji
W
Pj,i
Description
Waste fractions k = 1: organic; k = 2: glass; k = 3: paper; k = 4: plastics, metal cans
Grouping Systems i =1: ‘single stream’; i = 2: ‘multi stream’;
Collection Systems j = 1door-to-door’;j = 2‘aggregate’; j = 3‘proximity’;j= 4‘street’;
Stages of IWMS; w=1 sorting; w=2 selection&sorting; w=3 organic pretreatments;
w=4anaerobic digestion; w=5 composting; w=6 Tmb.
Emission factor of the transport mean to collect waste fraction according to
the i-th grouping and the j-th collection system
Number of weeks in the observation period
Number of picking operations of unit loads for i-th stream grouping and the
j-th collection system
Number of work-shifts per day
Average speed of the transport mean adopted by the j-th collection system
Production per capita of urban waste
collecting efficiency of the k-th material collected by the j-th waste collection
system according to the i-th waste stream grouping;
percentage of the k-th type of waste in the total solid waste produced.
average number of members in a family
bin load capacity
Total amount waste produced
Waste produced by commercial users
Efficiency of w-th stage for the k-th waste fraction. Skw=1- ekw
Electricity emission factor measured as tCO2eq/Kwh
Emission factor of w-th stage for the k-th fraction measured as tCO2eq/twaste
Activity data [Kwh/ton]
Energy from recovery of waste (from biogas and syngas)
Avoided emission from recovery and prevented disposal
Organic flow after TMB send to landfill (%)
Maximum annual amount treated by anaerobic digestor and TMB
Emission factor for disposal respectively dry fraction and organic fraction
H
vj
Pc
ɳkji
Pk
c
Bj
Glob.waste
XCom.k.waste
ekw
EFel
EFkw
actw
Ek
Fk
OTMB
XMax
EFl1 -EFl2
The decision variables are
• Ni,k : boolean variable to infer if the j-th grouping system is adopted;
• Nj,I,k,: number of users served by the j-th collection system and the i-th waste
stream grouping for the k-th waste fraction;
• nwj,i,k: weekly collection frequency of the k-th fraction, by the j-th system
according to the i-th waste stream grouping;
with X k =
( p c p nw
c
k
jik
N ik N jik
)
and X ' k = X k + XCom.kwaste .
The objective function is:
Min EmColl + EmTreat + EmTMB + EmLand + EmEn.Re covery + EmAvoidedLand
(
where:
(
EmCol = ∑ ∑ ∑ H ⋅ v jW feji nw jik N ik N jik / B j ⋅ Pji
k
i
2
j
4
),
)
4
w−1
EmTreat = ∑ ∑ (X k' ∏ ek,w−1EFk,w ) + ∑ (X1'EF1,w e1,w )
w=1 k=2
w=1
w=3
EmTMB = ( Globwaste − ∑ X ) act w EFel
'
k
k
2
4
5
EmLand = (∑ ∑ (X k' Sk,w EFl1 ) + ( Globwaste − ∑ X k' ) OTMB EFl 2 + ∑ (X1'S1,w )EFl 2
w=1 k=2
k
EmEn.recovery = Ek EFel
4
2
k=2
w=1
EmAvoidedLand = ∑ X k' (1- ∑ Sk,w )EFk
The function is subjected to the following constraints:
w=3
∑ Nik = 1
, ∀k
i
N 2,1 = 0 ∧ N 2,2 = 0 ; N i,3 = N i,4
∑ ∑ Nik N jik = N
i
TOT
,∀k
j
N jik ≤ N jMAX ∧ N jik ≥ N jMIN ,∀j,i,k
N1,2,1 = N 2,1,2 ∧ N 3,1,2 ≥ 0 ∧ N 4,1,2 ≥ 0
N j,2.3 = N j,2.3 ,∀j
nw jik ≥ nw jikMIN ∧ nw jik ≤ nw jikMAX ,∀j,i,k
∑ ∑ ∑ (p
c
i
j
⋅ c ⋅ pk nw jik N ik N jikη jik ) ≥ SC MIN
k
X 'k ≤ X MAX
Readers could refer to [3] and to [5] for a more detailed description of the model.
3
Preliminary results and future developments
The developed framework is applied to a full case study referring to the municipality
of Bari. The model suggests the adoption of a multi stream grouping system for the
dry recycled fraction and single stream grouping system for organic and glass fraction.
Fig.1: Percentage of users served by the j-th collection system in case of optimization of (i)
only collection phase and (ii) of the whole IWMS.
As one can see in Fig.1, in case of optimization of IWMS(ii) solution obtained
privileges the DTD and Aggregate collection system for organic and dry recycled
fraction, keeping as much low as possible (see upper and lower limits in [7], [9]).For
the glass fraction the model solution suggests, in both case, the collection to be
carried out by the Proximity system meanwhile the Street system is limited at the
minimum values. Details of emission are listed in Table 2.
Table 2: Emission of the Integrated waste Management System
Source Emission
Collection
Sorting facilities
Selection facilities
Anaerobic digestion
Mechanical biological Treatment
tCO2eq/y
1,010
822.0
167.0
714.0
433.0
Landfill
Avoided by recovery materials
Avoided by energy recovery of biogas
Avoided by energy recovery of syngas
Total [tCO2eq/y]
4
24,303.0
-50044.0
-1614.0
-1742.0
-­‐25,952.0
Conclusions
Results obtained outline that the single-stream grouping system has to be adopted for
organic and glass fraction. Differently, the model suggests the adoption of a multistream grouping system for dry recycled fraction; such a grouping system allows for a
reduction in the number of collection cycles resulting in a reduction of GHG
emissions. Collection system with higher efficiency values even though responsible of
higher collection emission reveals to be optimal choices. The reason for this lies in
the right valorization of the waste. The benefits of energy recovery and the avoided
disposal of waste fraction negative emissions) offset both the collection and
treatments emissions (positive emissions). Further research will consider the
economics aspects of the IWMS, as well as the jointly impacts of the environmental
and economic issues on the model. Furthermore, the research project will lead to a
dynamic design and plan of all the phases of the IWMS.
Acknowledgements. The paper has been written within the framework of the project PON04a2_E
“Smart Energy Master per il governo energetico del territorio - SINERGREEN - RES NOVAE”. The
project is supported by the Italian University and Research National Ministry research and competitiveness
program that Italy is developing to promote “Smart Cities Communities and Social Innovation”.
References
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