CHEMICAL ENGINEERING TRANSACTIONS
VOL. 76, 2019
A publication of
The Italian Association
of Chemical Engineering
Online at www.aidic.it/cet
Guest Editors: Petar S. Varbanov, Timothy G. Walmsley, Jiří J. Klemeš, Panos Seferlis
Copyright © 2019, AIDIC Servizi S.r.l.
ISBN 978-88-95608-73-0; ISSN 2283-9216
A Framework for Biogas Exploitation in Italian Waste Water
Treatment Plants
Gbemi Oluleyea,*, Daniel Wigha, Nimil Shaha, Marco Napolib, Adam Hawkesa
aDepartment of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK
bDepartment of Energy, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy
o.oluleye@imperial.ac.uk
Effective utilisation of biogas is an important step in increasing usage of renewable energy, due to the great
flexibility that solar and wind power in particular lacks. Biogas generated through anaerobic digestion (AD) of
sewage sludge addresses environmental concerns together with creating electricity generation potential. There
is currently no optimisation-based decision-support framework to determine the best use of biogas from a Waste
Water Treatment Plant (WWTP), and provide a market outlook for each of the options. This work proposes a
novel multi-period Mixed Integer Linear Program (MILP) model for dispatch and selection of technologies
capable of exploiting biogas produced from sludge. The novelty is also highlighted by extrapolating the optimised
results to a broader analysis of 855 Italian WWTPs with Population Equivalent (P.E.) > 20,000. The use of real
input data provides a unique added value to the work. The modelling framework is applied to several case
studies. Results show that 7–23 % savings in operating costs are possible from integrating three systems to
exploit biogas, and the trade-offs between capital and operating costs affect the optimal system choice.
Furthermore, market driven scenarios are used to analyse how to improve the economic performance.
1. Introduction
Wastewater treatment is one of the most expensive public utilities accounting for more than 1% of Europe’s
electricity consumption (Enerwater, 2010), reduction of the energy use and emissions is essential to achieve
the EU plan for a climate-neutral economy by 2050. Biogas produced from AD is converted using conventional
devices such as Solid Oxide Fuel cells (SOFC) and Internal Combustion Engines (ICE) to heat and power, or
upgraded to produce biomethane. Advanced biogas upgrading is needed to convert biogas into a storable fuel
or for grid injection. Biogas, besides having the distinct advantage in controlling organic waste can produce
carbon dioxide (for the food and beverage industry), fertilizer, and methane which is useful in various industrial
applications (solvents, insecticide industry and plastic). Hence, there is need for an optimal framework to
determine the best use of biogas for any site, since the decision is non-trivial. The high quality residual heat
from energy conversion devices like the SOFC and ICE improves biogas production through thermal pre-
treatment of the substrate for AD (Saadabadi et al., 2019).
Previous works on biogas exploitation focus on modelling the AD process (Anyaoku and Baroutian, 2018),
biogas fed SOFC neglecting other exploitation paths (Saadabadi et al., 2019), techno-economic aspects of
SOFC integration and financial appraisal of a biogas to electricity project (Govender et al., 2019),
thermodynamic analysis of biogas fed SOFC (Prodromidis et al., 2017) and transient numerical modelling of
waste to energy technologies (Montorsi et al., 2018). Some other authors focus on biomethane production
(Paolini et al., 2018), detailed simulation of the biogas upgrading process (Vogtenhuber et al, 2018), and techno-
economics of biomethane production (Aguilera and Ortiz, 2016). Few attempts have been made to compare
biogas exploitation paths, and no work has considered extrapolating from the basis of a detailed optimisation
framework to WWTPs in a country.
Gandiglio et al. (2016) compared three biogas exploitation paths from an energetic and economic point of view.
However, the basis was a simulated energy system. Whilst simulations are useful, they do not capture trade-
offs nor determine the dispatch of technologies taking into account biogas availability throughout the year, and
energy prices. The cost of electricity and natural gas drive the selection of exploitation paths, and if not included
991
DOI: 10.3303/CET1976166
Paper Received: 15/03/2019; Revised: 12/04/2019; Accepted: 12/04/2019
Please cite this article as: Oluleye G., Wigh D., Shah N., Napoli M., Hawkes A., 2019, A Framework for Biogas Exploitation in Italian Waste
Water Treatment Plants, Chemical Engineering Transactions, 76, 991-996 DOI:10.3303/CET1976166
in decision making, outputs from a simulation could be misleading often times resulting in lower revenues. A
similar analysis is done in Wu et al. (2016) but focusing on electricity generation, again the basis is a simulation
of the plant. Hakawati et al. (2017) also compared biogas-to-energy routes using final energy consumption/
energy efficiency based on simulation of the options. No study has been undertaken to quantify the market
share of each exploitation path in a country building on detailed optimisation. This is required to determine how
each technology fares in the market, since whilst a single technology maybe selected for a site, a mix is usually
the case in a national scenario. The choice of technology, dispatch of systems and sizing the market for policy
making is non-trivial. Therefore, a systematic framework is required.
The novel methodology developed in this work expands the optimisation model in Giarola et al. (2018) to include
different exploitation paths (Figure 1a). The method is then applied to the WWTP archetypes identified by Sechi
et al. (2018). Another novelty is the detailed economic analysis of different scenarios, for which the basis is the
optimisation framework and extrapolating to all the WWTP in Italy with secondary treatment (data available from
Water Base, 2014). The methodology presented in relevant for exploiting methane use in industry, biogas to
fertilisation and CO2, biogas reforming to produce hydrogen. This present work aims to provide an overview of
biogas exploitation paths in an Italian context, focusing on WWTP with P.E. greater than 20,000. Such a method
can aid policy makers in the decision process for biogas use.
2. Methodology
2.1 Energy system under consideration
A schematic of the energy system under consideration is in Figure 1a. The site currently uses a biogas boiler
for heat demand (backed-up by a natural gas boiler), and electricity is imported from the grid. Integrating an
SOFC or an ICE means some of the grid electricity and natural gas can be displaced. Upgrading biogas to
biomethane, implies the site energy demand would need to be satisfied from a natural gas boiler and electricity
imported; this is often neglected in studies on biogas upgrade. Each of the three exploitation paths is applied to
the WWTP archetypes (Sechi et al., 2018) as shown in Figure 1b to ensure that extrapolation to an Italian
context accounts for scale.
(a) (b)
Figure 1: Energy system and WWTP archetypes (a) Energy system schematic and (b) Methodology overview
2.2 Mathematical optimisation framework
The optimisation framework is necessary to select the best technology and system to exploit biogas. Hence it
is formulated as a multi-period MILP problem in GAMS. The optimisation forms the basis for the economic
assessment, which in turn forms the basis for the Italian Market analysis. The objective in Eq(1) is formulated
to minimise the Equivalent Annual Cost (EAC) defined as the sum of the Annualised Capital Cost (ACC), the
fuel costs (FC) and maintenance costs (MC) and the cost associated with grid electricity import (CWGrid).
𝑀𝑖𝑛: [𝐴𝐶𝐶 + 𝐹𝐶 + 𝑀𝐶 + 𝐶𝑊 𝐺𝑅𝐼𝐷 ] (1)
The ACC is defined in Eq(2). Where Size is the technology size, Z is binary variable for technology selection,
and IC is the installed capital, and i represents the set of all technologies.
𝐴𝐶𝐶 = 𝐴𝐹 × ∑ ((𝑆𝑖𝑧𝑒𝑖 × 𝑍
𝑖 ) + (𝐼𝐶𝑖 ))
𝑖
(2)
Constraints include the balance around biogas flow (B) in Eq(3), heat (Q) in Eq(4) and electricity (W) in Eq(5).
The biogas can be kept in a holder. Where BGD and BGS are biogas wasted due to shut down and start-up
992
events respectively, BOI is boiler, and t represents the time period. BBOI and NGBOI are biogas and NG boilers,
PSD and PSU are power absorbed during shut down and start-up events.
GasHolder𝑡+1 − 𝐺𝑎𝑠𝐻𝑜𝑙𝑑𝑒𝑟𝑡 + 𝐵𝑓𝑙𝑎𝑟𝑒𝑡 + 𝐵𝐺𝐷𝑡 + 𝐵𝐺𝑆𝑡 + 𝐵𝑡
𝑆𝑂𝐹𝐶 + 𝐵𝑡
𝐼𝐶𝐸 + 𝐵𝑡
𝑈𝑝𝑔𝑟𝑎𝑑𝑒
+ 𝐵𝑡
𝐵𝑂𝐼 = 0 (3)
𝑄𝑡
𝑆𝑂𝐹𝐶 + 𝑄𝑡
𝐼𝐶𝐸 + 𝑄𝑡
𝐵𝐵𝑂𝐼 + 𝑄𝑡
𝑁𝐺𝐵𝑂𝐼 − 𝑄𝑡
𝐷𝐸𝑀𝐴𝑁𝐷 = 0 ∀ t ∈ T (4)
𝑊𝑡
𝑆𝑂𝐹𝐶 + 𝑊𝑡
𝐼𝐶𝐸 + 𝑊𝑡
𝐺𝑅𝐼𝐷 + 𝑃𝑆𝐷𝑡 + 𝑃𝑆𝑈𝑡 − 𝑊𝑡
𝐷𝐸𝑀𝐴𝑁𝐷 = 0 ∀ t ∈ T (5)
Ramping constraints, and biogas and electricity consumption for start-up and shut-down events are provided
below: rup is the ramp rate of the CHP technologies. Where Y is the binary variable for operation, and τ the
maximum number of hours in any time period t.
𝑊𝑡
𝑖 + 𝑊𝑡−1
𝑖 ≤ 𝑟𝑢𝑝
𝑖 × τ (6)
𝑃𝑆𝐷𝑡
𝑖 ≥ 𝑃𝑆𝐷𝑎𝑏𝑠
𝑖 × τ × 𝑌𝑡
𝑖 (7)
𝐵𝐺𝐷𝑡
𝑖 ≥ 𝐵𝐺𝐷𝑎𝑏𝑠
𝑖 × τ × 𝑌𝑡
𝑖 (8)
Eq(7) and Eq(8) can be replicated for power and biogas absorbed during start-up events. Eq(9) is formulated to
choose the technology and determine the electricity produced. Eq(10) states that technology can be selected
but may not operate in a time period.
𝑊𝑡
𝑖 − 𝑠𝑖𝑧𝑒𝑖 × 𝑌𝑡
𝑖 ≤ 0 (9)
𝑍𝑖 − 𝑌𝑡
𝑖 ≥ 0 (10)
The economic assessment measures the EAC, the operating costs, and the savings from selecting the
technologies. This is calculated by subtracting the operating costs for the business as usual system from the
selected system. An income can be generated by injecting biomethane into the grid. Extrapolating to the national
context includes designing for each archetypes (Figure 1b) and accounting for the number of plants in each of
the archetypes as shown in Table 1. The system with the lowest EAC dominates the market, and this is applied
to the Italian context under different scenarios based on sensitivity to energy prices and capital cost. The five
scenarios in addition to the Base scenario (with existing market conditions) are: (1) a higher electricity price, (2)
a lower electricity price, (3) lower SOFC capital, (4) combination of (1) and (3), and (5) Biogas injection price.
The computational time is 1.2 s using Cplex solver in GAMS on an Intel(R) core(TM) i7-6700 CPU.
3. Industrial case study
The case study under consideration is the WWTP sector in Italy. Two real sewage treatment plants with domain
in the production of biogas forms the basis for the archetypes analysis in Sechi et al. (2018). There are 855
WWTP’s in Italy with P.E. greater than 20,000 (Table 1). The biogas produced, heat and electricity demand for
the plants are provided in Table 1. Costs assumptions for the SOFC and ICE are in Giarola et al. (2018). The
CAPEX for the upgrade is 799 Euro/ kW, and its maintenance is 0.5 Eurocents/ kWh. The technology lifetime is
20 years, and a discount rate of 5 % is applied. The objective is to determine the best technology for biogas
exploitation and measure it’s penetration in the Italian Market under different scenarios.
Table 1: P.E., number of plants, biogas produced and energy demand in WWTPs
WWTP
Archetype
Population Equivalent
(P.E.)
Number
of plants
Total biogas
(GWh/y)
Total heat demand
(GWh/y)
Total electricity
demand
(GWh/y)
XS 20,000-60,000 554 282 69 152
S 60,001-150,000 202 309 83 164
M 150,001-350,000 64 229 47 92
L 350,001-750,000 25 214 58 114
XL 750,000-1,100,000 10 209 55 109
993
4. Results and discussion of results
4.1 Energy system dispatch
The novel optimisation framework is able to determine the dispatch strategy for the technologies considered.
The heat and electricity demand for the conventional system (Figure 2a) are met with a biogas boiler and grid
electricity import. A NG boiler is used to provide back-up heating for all systems (Figure 2a – 2d). Integrating an
SOFC reduces the grid electricity import (Figure 2b). Upgrading biogas to biomethane implies the heating
demand needs to be satisfied by a NG boiler, and all electricity imported from the grid (Figure 2d). On average
heat and electricity produced from the SOFC can satisfy 24-27.4 % of the energy demand.
(a) (b) (c) (d)
Figure 2: Operating schedule for (a) conventional system, (b) SOFC integration, (c) ICE integration and (d)
biogas upgrade. Top four figures are the heating profiles and bottom four are the electricity profiles for an XS
WWTP archetype.
4.2 Economic assessment
Upgrading biogas to biomethane has the lowest annualized capital investment for the XS (Table 2), and the
highest operating costs (for all archetypes) since the energy demand needs to be met using heat from a NG
boiler and grid electricity (Figure 2d). The SOFC has the lowest operating cost due to its higher electrical
efficiency compared to the ICE, resulting in more grid electricity displacement. However, the SOFC high capital
investment in the current term reduces its economic attractiveness. Without incentives for injection of
biomethane to the grid income from upgrading biogas is zero. Scenario 5 considers biomethane injection tariff.
Table 2: System Economic Output
XS S M L XL
Annualised Capital
Investment (Euro/y)
SOFC 97,726 195,452 293,178 1,074,985 2,540,873
ICE 17,682 36,547 57,726 201,265 476,656
Upgrade 15,382 39,472 81,545 201,446 487,258
Operating costs
(Euro/y)
SOFC 213,673 464,646 927,164 1,869,394 3,644,678
ICE 224,986 485,818 960,326 2,050,207 3,914,193
Upgrade 269,677 606,513 1,193,650 2,611,419 5,096,165
Savings/ income
(Euro/y)
SOFC 27,826 57,515 143,559 430,508 690,932
ICE 16,513 36,343 166,620 249,695 421,417
994
4.3 Sensitivity analysis
The scenarios definition is provided in the methodology section. For all WWTP archetypes, a combination of
lower SOFC capital and higher electricity price gives the SOFC the lowest EAC (Figure 3). The EAC for biogas
upgrade is lowest in scenario 5 (with biogas injection tariff) especially for the L and XL plants where the
biomethane produced is highest (Figure 3). Exploiting biogas using the ICE (an established technology) is
attractive due to its low capital investment. However, it is expected that the SOFC will be competitive from 2020.
Figure 3: Economic assessment for all WWTP archetypes
4.4 Italian outlook
This paper builds on the optimized results for each WWTP archetype to provide an Italian market outlook for
biogas exploitation. Such an analysis has never been done before. A technology will dominate the market if its
EAC is the lowest. Based on this, the ICE dominates the market in most scenarios except for a future SOFC
target CAPEX and a higher electricity price i.e. Scenario 4 in Figure 4. The SOFC occupies 28% of the market
in scenario 1. With biogas injection price, the upgrade occupies the L and XL WWTP archetypes market and
occupies 4.1% of the market overall (Figure 4). Market outlook analysis is necessary to inform policy and
manufacturers on conditions required to increase biogas use in WWTP. Conversion of biogas to biomethane is
already a strategic target in many countries, hence more incentives may become available.
Figure 4: Italian market size using all 855 WWTP with P.E. > 20,000
995
5. Conclusions
Biogas exploitation reduces the need for carbon laden energy sources like NG and grid electricity in WWTPs.
Most importantly by producing biogas from sludge, more value is added to liquid waste. The challenge is
deciding the right combination of technology and systems to exploit the biogas. This work presented a novel
optimisation-based decision-support framework, which is also capable of providing a country outlook on biogas
exploitation. Results from the detailed economic assessment show that the highest operating costs and lowest
capital are associated with the upgrade option especially for an XS archetype, and the lowest operating cost
and highest capital associated with the SOFC (for all WWTP archetypes). Such trade-offs make the choice of
the best use of biogas non-trivial justifying the need to build comparisons on an optimisation basis. An analysis
considering the Italian WWTP sector shows that the ICE dominates using existing energy prices; however, the
SOFC share increases to 2.9% for a lower electricity price, 27.7% for a higher electricity price, and 100% for a
lower capital and higher electricity price. Upgrade occupies all the market from a P.E. greater than 750,000,
when the incentive for biogas is included. The quantified market share is relevant for assessing cost reduction
based on manufacturing volumes. Future work will include more options for biogas exploitation and expand the
analysis to all WWTP in Europe. The financial viability of biogas projects can be improved if policy frameworks
are amended to increase the market share of exploitation paths as part of the renewable programme.
Acknowledgments
The authors acknowledge the H2020 grant agreement number 671470- DEMOSOFC.
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