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
Sustainable Energy Transition toward Renewable Energies in
the New Zealand Dairy Industry: An Environmental Life Cycle
Assessment
Amir Hossein Tarighaleslami*, Sachin Kambadur, James R. Neale, Martin J. Atkins,
Michael R. W. Walmsley
Energy Research Centre, School of Engineering, University of Waikato, Private Bag 3105 Hamilton 3240, New Zealand
amir.tarighaleslami@waikato.ac.nz
The dairy processing industry is considered as energy-intensive processes between New Zealand industries
that use fossil fuels as a primary energy source which results in greenhouse gas emissions as well as raising
other environmental impacts. To mitigate the environmental impacts of fossil fuels that are used in dairy
processing plants, it is essential to design a pathway moving from fossil-based energies towards renewable
energy sources in a transition period. To do so, Life Cycle Assessment (LCA), as a standardised approach, has
been implemented to quantify the environmental profile of different products by evaluating the environmental
impacts of product systems. In this paper, three impact categories are considered adopting accessible inventory
databases that match the regional and local data along with ReCiPe as the life cycle impact assessment method.
These impact categories are 1) human health; 2) ecosystem; and 3) resources. Each category contains
subcategories; seven different-statistically discernible energy mix scenarios (moving from coal through natural
gas towards biomass) assisted by Monte Carlo simulation are defined to assess environmental impacts.
Furthermore, Cumulative Exergy Demand (CExD) as an aggregated criterion has been exploited to indicate the
sustainability of the whole system. Results show that for a cheese production process a biomass based scenario
has the lowest environmental burden impact in 13 impact categories out of 14 studied impacts. Indeed, it has
the lowest CExD with 1.05x10-5 MJ-Eq while surprisingly natural gas based scenario has the highest
environmental burden with CExD of 3.85x10-1 MJ-Eq followed by coal with 4.73x10-4 MJ-Eq.
1. Introduction
The food industry (including dairy and beverage) was the highest energy consumer between New Zealand
industries in 2016 with approximate use of 49 PJ energy (Tarighaleslami, 2018). New Zealand dairy processing
factories as energy-intensive plants use coal and natural gas as the primary energy source that results in
emissions that raise environmental concerns. Greenhouse gasses (GHG), especially CO2, are byproducts of
the fossil fuel combustion that is used to produce the required process heat and electricity of a dairy plant. GHGs
significantly contribute to climate change impacts. Therefore, it is essential to plan a sustainable transition from
fossil fuels towards available renewable energy sources. Besides fossil fuel combustion, the dairy processing
process itself lead to additional environmental impacts.
Life Cycle Assessment (LCA) is a commonly utilised tool to analyse environmental impacts. By definition, LCA
is a holistic approach for quantifying the environmental impacts of products (ISO 1404, 2006). As it is known as
a cradle-to-grave analysis, it can help to assess the environmental impacts associated with all stages of a
product’s life in a cycle from extraction of raw material through to transport to manufacturing factory, processing
and production, distribution, use, disposal and recycling. However, each step can also be studied considering
the gate-to-gate or cradle-to-gate concept (Islam et al., 2015).
LCA has been considered as a reliable tool to evaluate environmental impacts in the dairy industry. Rotz et al.
(2010) studied the carbon footprint of a dairy production system through LCA. Recently, Finnegan et al. (2018)
reviewed environmental LCA studies examining cheese production. They reviewed sixteen LCA studies since
DOI: 10.3303/CET1976017
Paper Received: 15/03/2019; Revised: 05/05/2019; Accepted: 06/05/2019
Please cite this article as: Tarighaleslami A.H., Kambadur S., Neale J.R., Atkins M.J., Walmsley M.R.W., 2019, Sustainable Energy Transition
toward Renewable Energies in the New Zealand Dairy Industry: An Environmental Life Cycle Assessment, Chemical Engineering Transactions,
76, 97-102 DOI:10.3303/CET1976017
97
the year 2000 and the production of raw milk was consistently found to be the most significant contributor to
the environmental impacts followed by processing. However, none of the studies considers energy transition for
cheese processing. González-García et al. (2013) studied environmental LCA of yoghurt processing
implementing Energy Cumulative Demand (CED). Due to the different varieties of yoghurt processing, they
considered the process as a black box to be able to focus on the environmental impacts by the energy
consumption within the plant.
An LCA approach considering Cumulative Exergy Demand (CExD) supported by Monte Carlo simulation (MCS)
for statistically discernible energy mix scenarios was proposed by Ghannadzadeh (2018a). The method was
implemented on the ethylene dichloride–vinyl chloride production process as well as polyol ether production
process (Ghannadzadeh, 2018b) during the transition period from residual fuel oil, as a fossil fuel, to biomass,
as a renewable energy source, through several energy mix scenarios. Later, the method has been successfully
examined for the sustainable energy transition of a chlorine production process (Ghannadzadeh and
Tarighaleslami, 2019) and a petroleum refinery (Ghannadzadeh and Park, 2018).
To fill the gap in the literature regarding the energy transition period in the dairy processing industry, this paper
deals with environmental LCA study of a cheese processing process to evaluate as a representative of the dairy
processing industry. Natural gas is used as a second major fuel in the New Zealand dairy industry as well as in
power plants where in public perspective natural gas is known as a clean fossil fuel. Therefore, in this paper, a
transition stage from coal to biomass through natural will be studied.
This paper aims to analyse an LCA approach for the environmental impacts and energy balance derived from
a cheese process. To do so, an exergy-aided LCA approach (Ghannadzadeh, 2018) will be applied to a dairy
processing factory. To enhance the sustainability of a dairy processing factory in an energy transition period,
assessment of power generation from alternative renewable energy sources such as biomass will be presented
through step-wise scenarios supported by MCS. The standard framework of LCA will be followed an inventory
data on databases applying into openLCA software (openLCA, 2018), and completed using the literature and
the available databases.
2. Materials and methods
Prior to the assessment, the definition of the scope of the work including process constraints and technical
conventions is required. This study deals with the energy transition pathway in New Zealand’s dairy processing
industry starting from fossil fuels, moving through so-called green fossil fuels (i.e. natural gas) toward renewable
energy.
2.1 Inventory databases
LCA is used to estimate the environmental impacts of a cheese processing process as a representative of
different processes in New Zealand’s dairy processing industry. The inventory data are taken from an accessible
database of United States Department of Agriculture, USDA, that contains worldwide dairy processing inventory
data including the Pacific region (USDA, 2018) in line with the United States Life Cycle Inventory (US LCI)
database (NREL, 2014). ReCiPe impact assessment method is also added to connect USDA and NREL flows
to the impact assessment (ReCiPe, 2014). To estimate the environmental impacts of the case study openLCA
1.7.4 (openLCA, 2018) is used.
The functional unit is specified as 1 kg cheese processing. It means the plant aims to process 1 kg cheese using
0.123 MJ electricity from the grid, 0.025689 kg coal and 9.74598 kg coal boiler, as well as 7.309x10-6 m3 natural
gas proceed to plant. Moreover, the environmental impacts are evaluated with the impact method specified in
ReCiPe 2014 endpoint method for the “Hierarchist” perspective by means of the physical allocation method and
“World ReCiPe 2014 H/H [person/year]” normalization and weighting set (Ghannadzadeh, 2018a).
2.2 Scenario definition
A wide range of energy resources can be considered to mix in order to meet the final energy demand of the
process. However, as the transition period is considered in this research, three fuels are chosen to define the
energy transition pathway. In most New Zealand dairy processing plants coal and natural gas are the main fuel
while in power plants coal, natural gas is used along with biomass and hydro-energy as the most available
renewable energy sources. Geothermal, wind, and wave energies are the other available renewable energy
sources. Therefore, in this research, to generate power an ideal energy mix can be considered moving from
coal through natural gas to biomass. Three steps are chosen to introduce each alternative energy source into
fuel mix that in each step one-third of the total energy mix is deduced from the base case and is added to the
alternative energy source. Therefore, seven different scenarios can be defined as it is shown in Table 1. As it
can be seen in the table, coal, natural gas, and biomass alone provide sufficient energy for the power in
scenarios 1, 4, and 7 respectively.
98
Table 1: Energy mix scenario specification.
Fuel Type Energy Mix (%)
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7
Coal 100 67 33 0 0 0 0
Natural Gas 0 33 67 100 67 33 0
Biomass 0 0 0 0 33 67 100
2.3 Cumulative exergy demand
Working with a number of impacts can lead scenarios to different routes, as it is the case in ReCiPe. Cumulative
Exergy Demand is an aggregated criterion that indicates the sustainability of the whole system into only one
figure. CExD is based on exergy as a global term in the system which is independent of the time and place.
Exergy is known as a parameter to measure the quality of energy. It means exergy analysis is an effective tool
that presents the maximum quantity of a resource that can be converted to work. Therefore, it is more suitable
for the regions that environmental parameters are different from the parameters assumed in the ReCiPe
characterization model. Exergetic methods can present the life cycle point of view of the cumulative exergy
consumption for a product or a process from cradle-to-grave which means exergy can be part of an LCA by
representing a method for life cycle impact analysis of resource consumption; this can be done by CExD.
2.4 Monte Carlo simulation
Monte Carlo simulation is a valuable tool to prove that the scenarios under consideration are statistically
discernible (Ghannadzadeh, 2018a). In this study, the definition of scenarios is not by means of MCS; however,
the analysis of scenarios through MCSs can be helpful when judging the significance of the variations in the
comparison of scenarios. It means MCS is used to evaluate how much the entire life cycle of the production
process is responsive to one-third change steps of energy mix. In this research, MCS can therefore be
implemented to correlate the power input with the output parameters, e.g. CExD.
3. Results and discussions
The environmental impacts are reported in three categories based on the ReCiPe. These categories, which
each have a number of subcategories are: Human Health with six subcategories; Ecosystem with six
subcategories; and Resources with only one subcategory.
3.1 Human health category
Six subcategories present environmental impacts under human health. Comparison of these impacts for each
category is shown in Figure 1. Climate change impacts represent GHGs (carbon dioxide emission equivalent)
emissions (Figure 1a). In this impact, the worst scenario is Scenario 1 which represents coal with 5.98x10-7
DALY followed by Scenario 2 and Scenario 3 combinations of coal and natural gas. The lowest environmental
impact in this category is for Scenario 7 which represents 100% biomass. Particulate matter formation, which is
due to discharge of the formed nitrogen and sulfur oxides (sulfur dioxide), and particulates between 2.5 to 10
μm, results (Figure 1d) has a similar trend as climate change where the worst fuel chose is coal with 2.54x10-7
DALY while biomass is the best choice with 2.36x10-7 DALY.
Human toxicity which is caused by the emission of barium, mercury, and arsenic ions is presented in Figure 1b.
Surprisingly for this impact, Scenario 4 (100 % natural gas) has the worst impact with 2.96x10-8 DALY and
biomass shows the lowest impact of 1.28x10-8 DALY. Figure 1c presents ozone depletion where for natural gas
and biomass there is an identical number of 8.49x10-14 DALY while for the scenarios with a higher percentage
of coal the situation is worst, i.e. Scenario 3, 2, and 1. The reason for ozone depletion is due to the release of
methane and Freon gases.
Photochemical oxidant formation impact is presented in Figure 1e. Biomass with 5.18x10-10 DALY causes the
highest environmental damage whereas Scenario 4 shows the lowest environmental damage with only
1.44x10-11 DALY. It should be noted that photochemical oxidants are secondary air pollutants that are formed
by sunlight action on nitrogen oxides and some reactive hydrocarbons. The most important phytotoxic
components are ozone, and nitrogen and sulphur oxides. However, the summation of all human health impacts
shows that scenarios with a combination of biomass, specifically Scenario 7, seem to be a better choice as
opposed to the other scenarios (Figure 1f).
99
3.2 Ecosystem category
Six different subcategories represent environmental impacts under the ecosystem are presented in Figure 2.
With respect to climate change, Scenario 7 has the lowest impact with 3.76x10-10 species.yr as opposed to
Scenario 4 and Scenario 1 with 2.13x10-9 and 3.39x10-9 species.yr respectively (Figure 2a). Both biomass and
coal have significantly lower freshwater ecotoxicity compared with natural gas with 4.86x10-13 species.yr as
shown in Figure 2b. However, Scenario 7 (biomass) with 3.77x10-17 species.yr has a lower impact than
Scenario 1. Freshwater ecotoxicity is caused by discharges of copper, barium, silver, nickel.
Figure 1: Impact categories of Human Health (HH) based on ReCiPe results.
Figure 2: Impact categories of Ecosystem (ES) based on ReCiPe results
Marine ecotoxicity impact is presented in Figure 2c. Marine ecotoxicity has a similar trend to freshwater
ecotoxicity where biomass has the lowest impact of 2.79x10-17 species.yr. The main reason for this impact is
discharges of nickel, barium, silver, and mercury. Figure 2d presents terrestrial acidification impact where the
worst impact is due to using coal as a fuel (Scenario 1). Biomass with 1.57x10-12 species.yr shows the lowest
environmental damage where it has lowest release of sulphur and nitrogen oxides to the environment. As it can
be seen in Figure 2e for terrestrial ecotoxicity impact Scenario 4 onward cause significantly lower environmental
burden compared to the scenarios containing a portion of coal as fuel (Scenarios 1, 2, and 3). The urban land
occupation impact is identical for all scenarios as it has been assumed all fuel types require the similar size of
infrastructure (Figure 2f). However, in many cases natural gas supplied equipment have smaller sizes compared
to coal and/or biomass supplied equipment, e.g. boiler. Considering the summation of the category, as expected,
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Scenario 7 with 3.90x10-10 species.yr has the lowest impact on ecosystem compared to fossil fuels such as
natural gas 2.16x10-9 and coal with 3.43x10-9 species.yr.
3.3 Resources category
Figure 3a compares fossil depletion for seven different scenarios. As expected, the contribution of renewable
energy sources can increase the sustainability of dairy product processing, e.g. cheese processing in this case
study, especially in term of fossil resources depletion. Scenario 1 has the highest fossil depletion with
$2.5x10-2 which means coal has 30 % more fossil resource depletion impact than natural gas (Scenario 4) with
$1.47x10-2. By comparing the order of magnitude of biomass fossil depletion with scenarios containing a portion
of fossil fuel it can be said that fossil depletion for biomass is nil.
3.4 Cumulative exergy demand results
Figure 3b presents CExD results of cheese production, including power generation. CExD has important
implication in this study considering exergy destruction as a measure for production sustainability. Results show
that using biomass for power generation is preferred as to oppose to the scenarios containing a portion of fossil
fuel. However, by comparing the order of magnitude of each scenario, it can be seen that gas supplied electricity
with CExD of 0.04 MJ-Eq is worse than coal supplied electricity with 5x10-5 MJ-Eq and biomass with10-6 MJ-Eq.
In other words, considering CExD, the biomass based power is much more sustainable compared to natural
gas and coal based power for New Zealand.
Figure 3: a) Impact category of Resources based on ReCiPe results; and b) CExD results for scenarios
equivalent of non-renewable energy resources.
To enhance the robustness of results, MCS is used for further assessment of the scenarios limited to CExD
where the CExD results are approximated and formed with uniform distribution. The probability distribution of
MSC for 1 kW power demand is followed by the method presented in (Ghannadzadeh, 2018a). Figure 4 depicts
an example of MCS uniform distribution results for each interval in Scenario 1 created by openLCA. Figure 3b
presents the outcome of interval changes for results as the total prediction mean value range of CExD for each
scenario. It exhibits that the complete trials do not possess identical value, revealing their relative independence,
which basically paves the way for the definition of statistically-discernible scenarios. The carried-out MCSs
suggest the defining scenario centred on the reported model is quite responsive to the 33.33% variation in the
power generation source. In this case, the application of MCS is meaningful to prevent misleading conclusions
in view of overestimations or underestimations.
Based on LCA that considers whole dairy processing at the production plant, CO2-e emission has the highest
adverse impact on both human health and ecosystem. This means special attention to power and process heat
generation of the dairy processing plant is worthy to be taken to avoid the burdens associated with the fossil
fuels and quantify the impact of utilities. Although power generation system analysis can be performed on a
standalone basis without considering the cheese production process, analysing both systems in a single study
is helpful. This will allow the engineer to select the best option that meets minimum environmental requirements
based on environmental regulations. For example, by increasing one-third of biomass as the contribution of
renewable energies to the energy mix, the unsustainability of the cheese processing process is significantly
reduced for Scenarios 5-7. It is especially interesting that the introduction of 33.33 % biomass to the energy mix
in Scenario 5 can significantly reduce environmental impacts compared to Scenario 4.
LCA quantifies the environmental impacts of each energy mix scenario to allow the engineer to plan energy
transition roadmap towards sustainable dairy processing production to help achieve New Zealand’s low
emission targets by 2030. Considering LCA results that Scenario 7 (biomass) is the most environmentally
sustainable option, it is recommended that the dairy industry should move towards utilising biomass as the main
source of renewable energy. Finally, it should be noted that sustainability enhancement only deals with climate
change (both human health and ecosystem), ozone depletion, and terrestrial ecotoxicity.
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Figure 4: Monte Carlo simulation result for Scenario 1.
4. Conclusions
An environmentally sustainable pathway from fossil fuel sources towards renewable energy sources for cheese
production in a dairy processing plant was studied in this paper. The study was performed using exergy assisted
Life Cycle Assessment (LCA) supported by Monte Carlo simulation. LCA indicates that the proper choice of
energy mix can increase the sustainability of the dairy production process. Introduction of one-third biomass as
renewable energy to energy mix scenarios can significantly decrease the environmental burdens especially the
impacts of CO2 on the ecosystem and human health categories. The impact of the other renewable energy
sources such as wind and geothermal energy in dairy processing plants will be studied in future work.
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