This document summarizes a research article that aims to optimize resource usage in aquaponics facilities through process monitoring and modeling. The researchers used process monitoring data and image processing of crop growth to develop a process network model of an aquaponics system. They applied a graph theoretical approach called P-graph to search for optimal process pathways by integrating additional processes like biogas production. Their optimal solution identified through modeling had 9 times higher annual net income than the existing process. They also achieved additional light energy savings through practical installation of reflective foils. This work demonstrates how process modeling and integration of additional processes can help aquaponics farms improve profitability while reducing environmental impact.

1. Carbon Resources Conversion xxx (xxxx) xxx
Please cite this article as: Vojtěch Ondruška, Carbon Resources Conversion, https://doi.org/10.1016/j.crcon.2022.04.003
Available online 19 April 2022
2588-9133/© 2022 Published by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Resource optimisation in aquaponics facility via process monitoring and
graph-theoretical approach
Vojtěch Ondruška a
, Bing Shen How b
, Michal Netolický c
, Vítězslav Máša a
, Sin Yong Teng a,d,*
a
Brno University of Technology, Institute of Process Engineering & NETME Centre, Technická 2896/2, 616 69 Brno, Czech Republic
b
Biomass Waste-to-Wealth Special Interest Group, Research Centre for Sustainable Technologies, Faculty of Engineering, Computing and Science, Swinburne University of
Technology, Jalan Simpang Tiga, 93350, Kuching, Sarawak, Malaysia
c
Flenexa plus s.r.o., Přáslavice 335, 783 54, Přáslavice, Olomouc, Czech Republic
d
Radboud University, Institute for Molecules and Materials, P.O. Box 9010, 6500 GL Nijmegen, the Netherlands
A R T I C L E I N F O
Keywords:
Aquaponics
Process monitoring
P-graph
Process optimization
Energy savings
A B S T R A C T
Energy efficiency and economic viability are the often-quoted issues in aquaponic farming. This work aims to (i)
identify process technologies and technical measures which would enhance the profitability of aquaponics
business while conserving energy and other resources, and (ii) to validate the determined optimal measures on
the testing aquaponics farm. The process network synthesis technique was used to search for an optimal process
pathway while the image processing technique was applied to automatically monitor the growth rate of produce
since it is the main revenue stream in aquaponics. With the aid of P-graph method, the optimal feasible structure
has 9 times higher annual net income than that of the existing process. This optimal solution includes the
integration of electrical heat pump, biogas system, and utilizes black solider fly (BSF) facility to produce fish
feed. Additional light energy savings were achieved by practical installation of reflective foils which improved
16.88% of Photosynthetic photon flux density (PPFD) on growth beds. These measures can help the aquaponics
farms to become more competitive and to decrease their ecological footprint.
1. Introduction
Aquaponics refers to a system that integrates conventional recircu­
lating aquaculture systems (RAS) [1] with hydroponics [2]. As well as in
hydroponics the plants are grown without soil [3] in the water that is
enriched in nutrients. The most significant difference between hydro­
ponics and aquaponics comes from the source of nutrients. In conven­
tional hydroponics, the nutrients are added into the water in their ionic
forms known as mineral fertilisers (e.g. nitrate, phosphate, potassium,
etc.) [1], whereas, in aquaponics, nearly all the nutrients are available in
the treated water supplied from the fish tanks [4]. Therefore, aquaponics
is a symbiotic, nearly closed system, which supports organic practices
together with environmentally friendly water management while
conserving the high production efficiency [4].
This designed food production system that couples aquaculture with
hydroponics has experienced a big increase in popularity recently. This
boom is attributed to the rising public awareness of climate change and
other environmental problems and the increased food demand pressure
caused by the population growth. In fact, conventional agriculture has
revealed its shortcomings, e.g., arable land degradation, water depletion
and biodiversity loss at an unprecedented rate [5].
Correspondingly, the number of publications related to aquaponics
has increased exponentially in the past years as it is apparent from Fig. 1
(a) which summarises the number of aquaponics related publications in
the Scopus database since 1998. The exponential popularity growth
among academics is also observed by Yep and Zheng [6] who reviews
current trends and challenges in aquaponics. Another statistic worth
mentioning is the distribution of aquaponics related publications per
country. Fig. 1(b) shows this distribution based on publications in Sco­
pus database. The United States is by far the leading contributors, which
indicates a great academic activity in this field and promises future
development possibly into the whole aquaponics industry.
Aquaponics has several key attributes which make this farming
method a hot candidate for the future tier one technology in vegetable
production. In general, extensive water savings in aquaculture and
plants production can be achieved via aquaponics since it is a process
* Corresponding affliation: Brno University of Technology, Institute of Process Engineering & NETME Centre, Technická 2896/2, 616 69 Brno, Czech Republic.
Current Affliation: Radboud University, Institute for Molecules and Materials, P.O. Box 9010, 6500 GL Nijmegen, the Netherlands
E-mail address: sinyong.teng@ru.nl (S.Y. Teng).
Contents lists available at ScienceDirect
Carbon Resources Conversion
journal homepage: www.keaipublishing.com/en/journals/carbon-resources-conversion
https://doi.org/10.1016/j.crcon.2022.04.003
Received 6 January 2022; Received in revised form 28 March 2022; Accepted 14 April 2022

2. Carbon Resources Conversion xxx (xxxx) xxx
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that utilises aquaculture effluent (normally considered as wastewater) to
grow plants [7]. Alternatively, Palm et al. [8] stated that aquaponics is a
closed-loop symbiotic process where most fertilisers is provided by fish
in an organic form to the plants which clean the water for fish below
toxic concentrations supported by nitrification bacteria. Finally, the
indoor aquaponics farm can produce vegetables all year round with no
need for arable land and zero pesticides use [6]. In other words, the
implementation of aquaponics not only can mitigate the negative
environmental impacts associated to land (e.g., affecting biodiversity
[9]) and pesticide usage (e.g., polluting water supply [10]).
However, the feature of having indoor production serves as a two-
edge sword, and become a significant challenges that hinder its com­
mercialisation. Goddek et al. [11] emphasises several issues related to
aquaponics with high energy-intensity in the first place. Indoor aqua­
ponics consumes a considerable amount of electrical energy and energy
for heating. Aside from that, the determination of optimum nutrient
recycling, suitable pathogen control or efficient supply chain manage­
ment are the other key challenges in aquaponics [11].
This paper aims to address the issue of energy intensity by exami­
nation of perspective measures or Process Integration opportunities
which could lead towards more energy-efficient aquaponics systems.
This paper is structured as follows: in Section 2, the current knowledge
in the field of aquaponics is reviewed followed by a review of the inte­
gration opportunities and resource optimisation methods. The research
method is then presented in Section 3, where P-graph method is used as
a process network synthesis tool in the optimisation phase, while image
processing method is applied to monitor and evaluate the growth con­
dition of the plant. It is then followed by a description of the industry
case study in Section 4 and result and discussion in Section 5. Last but
not the least, Section 6 presents the concluding remarks obtained from
this work.
2. Literature review
There has been a pursuit of optimisation in aquaponics since its
emergence in 1970s in the USA [1] and this pursuit escalated recently
with the rising awareness of the climate change impacts. This section
provides a review of the resource-oriented optimisation measures that
can lead to further enhancements in this field.
Aquaponics, as a resource-consumption process, aims to be opti­
mised to satisfy both economic and ecological demands. To achieve this,
there are several – often interdisciplinary – research topics to be tackled.
Among the biggest challenge is the optimisation of growth conditions for
plants and fish resulting in increased nutrient recycling and increased
production efficiency [12]. In this context, resource optimization is
crucial to maintain a sustainable and cost-efficient aquaponics facility.
The process engineering approach is applied to address the resource
conservation issues in hydroponic which related to water and energy
consumptions [13]. Aside from that, the integration of renewable energy
into the system can also significantly enhance the ecological perfor­
mance of the system. From the aforementioned works, it is clear that
aquaponics systems contains many possibilities for the implementation
of efficient and sustainable technology. This also includes integration
opportunities such as biogas and solar power [14]. Energy consumption
improvement is also highlighted by Goddek et al. [15] as an important
measure to be further developed. This paper compares and optimizes the
best process pathway for the integration of such technologies while
introducing practical process improvement measures.
One of the possible enhancements in aquaponics can be achieved by
the integration of the aquaponics process with other relative processes
through water, heat, electricity, or material exchanges. Table 1 sum­
marises the processes whose integration with aquaponics have been
developed or at least described in the respective publications.
The biogas and remineralisation processes are the most well-
researched integration opportunities. Yogev et al. [4] and Goddek
[18] described a configuration that implements remineralisation and
biogas production into the aquaponic cycle. Gigliona [22] even deter­
mined the minimum size of aquaponics so that the biogas system is
feasible. Goddek and Keesman [12] focused on the improvements that
the integration of desalination technology can bring to the aquaponic
system. Close to the desalination integration are the alternative types of
aquaponics called maraponics (i.e., marine aquaponics using seawater
or brackish water on-land) and haloponics (i.e., a system utilising saline
water below oceanic level) [20]. In fact, since these alternatives are
relied on alternative water resources (e.g., seawater), this can, therefore,
lead to a greater saving in freshwater consumption [23]. The sludge
from aquaculture facilities can also be used as feedstock for feeding
black soldier fly [21] within a covered facility. Subsequently, the black
soldier fly larvae can be fed to fishes within the aquaponics facility [24].
On the other hand, Alkhalidi et al. [7] reported a real case which in­
corporates the integration of solar photovoltaics and solar thermal
heating into the system. In the same year, de Graaf and Goddek [19] also
introduced the possibility to use the heat pump in aquaponics with in­
tegrated solar or wind electricity production, as a stabilizing element in
case of electricity oversupply. Note that the heat pump can then convert
the electric energy into thermal energy (e.g., as hot water storage) and
Fig. 1. Number of aquaponics related publications (a) over time (b) per country.
Table 1
Overview of the integration opportunities in the aquaponics systems.
Potential processes to be integrated Reference Year
Wastewater Treatment Sánchez [16] 2014
Biogas systems Yogev, Barnes, and Gross [4] 2016
Algae production Addy et al. [17] 2017
Remineralization Goddek [18] 2017
Desalination Goddek and Keesman [12] 2018
Heat pumps de Graaf and Goddek [19] 2019
Maraponics and Haloponics Kotzen et al. [20] 2019
Black Soilder Fly System Schmitt et al. [21] 2019
Solar Photovoltaics Alkhalidi et al. [7] 2019
Solar Thermal Heating Alkhalidi et al. [7] 2019
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3
reversely, the Combined Heat and Power (CHP) unit or the fuel cell can
convert the thermal storage into electricity. Nevertheless, the cost
effectiveness of such idea in the stand-alone aquaponics system (i.e.,
without integrated into a microgrid system) is yet to be discussed.
Research led by Addy et al. [17] disproved the negative assumptions
about microalgae in aquaponics and had successfully shown the strength
of the integration of algae within aquaponics system. These advantages
include microalgae having fast growth rate, non-competing culture with
food crops, ability to grow under limited nutrients, and good ability to
utilize inorganic nutrients from waste effluent [20]. Furthermore,
microalgae can also be harvested and used as fish food, forming a cir­
cular economy synergy within the aquaponics industry (additional to
the existing one in which the nutrient-rich effluent from the aquaculture
wastewater can be “recovered” through the nutrient uptake of the plant,
while the water can be recycled back to the aquaculture [25]). With
more investment, microalgae can also be processed into bio-oil and bio-
crude, becoming high-value product in the future [26]. This highlights
the sustainability advantages of integrating microalgae with aqua­
ponics. It is worth mentioning, back in 2014, Sánchez [16] had
attempted to enhance the circularity of the system by incorporating
human urine wastewater treatment into the aquaponics system.
3. Methodology
To give a concise overview of the research methodology (see Fig. 2),
this work performs few aspects which includes (i) process monitoring,
(ii) image processing, (iii) process network synthesis via P-graph, (iv)
manual improvement via reflective foils and (v) cost analysis table.
Firstly, process variables (pH, electrical conductivity (EC), dissolved
oxygen, total solutes, salinity and water temperature) and crop images
were monitored using physical sensors and 1 growth monitoring cam­
era. These data forms a basis to construct process network model.
Nevertheless, the images of the crops has to be converted to a piece-wise
linear form to be implemented in P-graph. In step 2 (Fig. 2), the images
are analysed using image pixel filtering and linearly interpolated to the
crop mass. Using the process information and growth curve, the process
network model can be initialized in P-graph for process network syn­
thesis (see Section 3.2). Other possible process alternatives (see Table 1)
are also included within the P-graph superstructure (see Section 4.2) for
process integration analysis. Alternatively, manual process improve­
ment strategies via reflective foil installation (Step 4, Fig. 2) are also
studied by measuring the photosynthetic photon flux density (PPFD) on
the aquaponics growth bed. Lastly, cost analysis is performed and
compared for both the original situation and after the process
improvement to check for the practical feasibility of the solution from a
commercial viewpoint.
The subsequent sections will discuss in detail about process moni­
toring and image processing (Section 3.1), process network synthesis via
P-graph approach (Section 3.2), the industrial case study at Flenexa plus
s.r.o. (Section 4), process structure of case study (Section 4.1) and pro­
cess superstructure with alternative process pathway (Section 4.2).
3.1. Process monitoring and image processing
Process monitoring is an essential procedure for the optimization of a
process. It provides input data for further analysis and feedback after
application of optimization measures. With process monitoring, the
growth rate of the crop can be determined and further used for process
optimization. For this purpose, this work proposes a non-destructive
crop monitoring method based on image processing to evaluate the
growth rate of the crop. This method is less time consuming, and less
labor intensive as compared to the direct measurement methods [27].
Saputra et al. [28] and Lin et al. [27] presented the application of such a
method where both works used multiple cameras and image processing
algorithms. For instance, Saputra et al. [28] implemented artificial
neural network (ANN) algorithms to predict the age and weight of the
plants, where Lin et al. [27] used a more conventional algorithms which
is based on stereo vision techniques to calculate the projected leaf area,
plant height, plant volume, and equivalent diameters including the
automatic measurement platform.
In general, the algorithm evaluate the crop images by analyzing the
RGB pixel filtering which was calibrated using a linear interpolation.
The main part of this algorithm was based on color detection. The spe­
cific color range was determined to differentiate the pixels with pre­
vailing green color from the rest and all the pixels were examined in a
loop to find the pixels that belong to the lettuce. The decision condition
to differentiate the lettuce pixels from the surroundings was based on the
difference between the 8-bit code for green color and the 8-bit code for
red and blue colors. The acceptance tolerance for pixel filtering was set
to 10. In this phase, the remaining pixels was set to pure white color
which means the RGB code of (255, 255, 255). The initial weight of the
crop is measured, and linearly calibrated to the number of filtered green
pixels as described in Eq. (1).
ml
i =
pg
i
pg
0
• ml
0 (1)
where the number of green pixels in image number (i) is denoted as p
g
i ,
the weight of lettuce in the image is denoted as ml
i, the measured initial
weight of lettuce is denoted as ml
0 and the initial number of green pixels
in the first image is denoted as p
g
0. If this formula is applied to every
image captured during the growth cycle that has the capture time
Fig. 2. Flow diagram for overall research methodology.
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assigned in its metadata, the growth rate curve can be effectively
monitored as a linear interpolation of the filtered pixels points.
3.2. Process network synthesis via P-graph
Process network synthesis (PNS) is a method mostly used in the
chemical industry which aims to find the best process routes to gain the
required product with specific raw materials [29]. In this work, P-graph
method is used to identify the best feasible unit configurations and
process connections.
P-graph method for PNS was firstly introduced by Friedler et al. [30]
in the early 1990s. It is known as a powerful graph-theoretic approach
for combinatorial optimization of PNS which capable of determining all
feasible solutions simultaneously and ranked subsequently according to
the objective function. It is a bipartite graph that constitutes a process
structure composed of nodes and edges [31]. Fig. 3 shows the basic
components of P-graph and the representation of different nodes and
edges arrangements, where the horizontal bars represent the operating
units, while the solids circles represent the materials which flow in the
specified direction by arrowed lines. Each material has its price per unit
and required and maximum flow, while operating unit is defined by
investment and operating costs, working hours per year and capacity
multiplier range. Note that the first and the third layouts in Fig. 3 rep­
resents the OR function (with single and multiple raw materials options
respectively), while the second layout represents the AND function [31].
In both OR distributions the operating units can be mutually excluded in
case these units cannot be in operation simultaneously.
Three algorithms have been developed and embedded into P-Graph
Studio software within the PNS problem solver (i.e., maximal structure
generator (MSG), solution structure generator (SSG) and accelerated
branch-and-bound (ABB) algorithms). Generally, MSG defines the
maximal structure of the model which represents the full collection of
combinatorically feasible process structure of the problem [32], while
SSG then exhaustively identify the feasible process structure that is
Fig. 3. P-graph components representation and the logic interpretation of their distribution.
Fig. 4. Piping and Instrumentation Diagram (P&ID) of the studied aquaponics system.
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5. Carbon Resources Conversion xxx (xxxx) xxx
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capable of yielding the desired products by decomposing the maximal
structure [33]. Thereafter, ABB algorithm will search the possible
structures generated, bound the search space, and identify the n-best
optimal structure [34].
Initially, it was designed to solve PNS problems particularly chemical
industry (e.g., azeotropic distillation [35], ammonia synthesis [36],
methanation of CO2 with H2 [37]. Recently, its utility has been widened
to numerous PNS problems, include but not limited to supply chain
management [38], integrated biorefinery [39], water regeneration
network [40], municipal solid waste [41], circular economy [42], car­
bon management network [43], hydrogen network [44], biorefineries
[45], and generic problems with flexible input ratios [46]. However, to
the best of the authors’ knowledge, none of works has attempted to
apply P-graph to solve the aquaponics optimization problem till date. It
enables to identify the best aquaponics setup including not only the
technology commonly used in aquaponics but also the right combination
of fish species and vegetable varieties. Moreover, it can show the
viability of some of the process integration opportunities for this work.
4. Industrial case study
To compare the theoretical conclusions with the real practice an
industrial case study was elaborated in cooperation with Flenexa plus s.
r.o. who runs the aquaponics test farm near Olomouc in the Czech Re­
public. The farm is situated underground in old military premises with
thick concrete walls. The process can be studied from the piping and
instrumentation diagram in Fig. 4. The main goals of this case study
were to determine the optimal process structure for the aquaponics
system.
4.1. Process structure of the existing aquaponics facility
The farm can be divided into four main sections: the fish section, the
filtration section, the grow section, and the stabilization section where
the overall layout can be seen in Fig. 5. In the fish section, the fish tank
filled with water (Fig. 5, Label 4) serves as an environment for fish to
grow, while the water can also be used as fertilized water for the
vegetable production. Note that the water must be thoroughly oxygen­
ated by the air pump to ensure sufficient O2 for the healthy aquaculture.
Fish feed, on the other hand, is delivered automatically at a given period
and amount by an automatic feed dispenser. The fertilized water is then
continuously pumped to the filtration section by a sump pump. This set
Fig. 5. Overall layout of the test aquaponics farm with major parts of the whole
set up including grow bed (1), floating raft (2), LED lights (3), fish tank (4),
ventilator (5), filtration section (6) and bulb (7).
Fig. 6. Superstructure which incorporates alternative process pathways.
V. Ondruška et al.

6. Carbon Resources Conversion xxx (xxxx) xxx
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up also has a reverse stream which helps to regulate the pump perfor­
mance with a butterfly valve.
There are two main components in the filtration section, i.e., (i)
mechanical filter (which is used to remove most of the undissolved solid
particles), and (ii) biological filter (used to improve the nitrification
process). At this point, the growth and stabilization section can be
bridged back to the fish tank after the filtration phase in case of main­
tenance, accident, or contamination. In this case, the mechanical filter is
important to remove physical impurities and undissolved particles, and
prevent recirculation of impurities within the system. Given that the
nitrification reactions are very oxygen intensive, the biological filter is
constantly aerated with an air pump to ensure sufficient O2 supply is
available. After the nitrification process, the water rich in nitrates flows
naturally into the grow section, which consists of grow bed with a
floating raft and LED lights (Fig. 2, label 2). The grow beds are filled with
nutrient-enriched water which serves to the plants as a growing me­
dium. This section is generally energy-intensive due to the consistent use
of electrical energy by LED lights which is essential for the vegetable
growth. Furthermore, the resulting P-graph represented process struc­
ture can be found in Fig. 11. More illustration of the aquaponics facility
can be found in Figs. B15-B20.
4.2. Superstructure with alternative process pathways
All the considered of integrable units and alternative process
pathways within the superstructure is demonstrated in Fig. 6. The pro­
cess superstructure considers the integration of biogas anaerobic
digestion, solar system, combined heat and power system, fertilizer
additives, black soldier fly facility, recycling of high-nutrient water, and
lighting (see Table 1). This process superstructure also considers 3
different fish species, including sturgeon, trout and catfish. The crops
that were considered is Cousteau lettuce and conventional lettuce that
are sold as crop products from the aquaponics system. The use of fer­
tilizers is also studied for two different brands of fertilizers with their
brand name anonymized and represented as fertilizer additive 1 and
fertilizer additive 2. In this superstructure, the purpose is to maximize
the net profit within the aquaponics process.
5. Results and discussion
Electrical energy and fresh water are two main resources consumed
in every aquaponics farm. Fig. 7 shows the energy distribution of the
studied aquaponics farm. The electricity consumption was measured on
every electrical appliance independently with a standard electricity
meter. Note that all the percentages shown are in the basis of a total
daily electricity consumption of 45.736 kWh/d.
The findings reveals that the most significant electricity consumption
is held for the lighting, which accounted for more than 80% of the total
consumption (equivalent to 36.95 kWh/d). A similar distribution of
electricity consumption is anticipated also on other aquaponics farms
given the in-door nature of the system. This indicates the need of opti­
mization to reduce the energy consumption. In fact, the ecological
footprint of the aquaponics process can be improved either by imple­
menting energy savings strategies or by shifting the energy sources to­
wards renewable energy.
Small improvement in the efficiency of the other appliances is useful,
but the associated benefits is less significant as compared to that of
making improvement in lighting efficiency. For instance, by reducing
the number of pumps in the system to the minimum (i.e., installing only
a single pump in the system) would merely lead to a 1 kWh reduction of
electricity consumption. Another important process parameter is the
energy intensity per unit of the product. The output unit of this process is
one piece of lettuce which daily consumes 0.114 kWh of electricity. With
20 days long growing period this makes a cost of €0.20 at the prices of
electricity.
A big advantage of the studied aquaponics farm is the use of thick
concrete insulation, which helps to stabilize the internal temperature
without the need for external heating source apart from the heat sup­
plied from the installed LED lights. It is worth noting that, greater energy
consumption is expected in winter season given to the significant heat
lost to the surrounding. To ensure the reliability of the final results, the
heat losses are considered in the P-graph model (see Section 5.2).
In terms of water consumption, it offers less impact as compared to
Fig. 7. Total daily electricity consumption on the aquaponics farm and electricity distribution among all electrical appliances.
Fig. 8. Water distribution on the aquaponics farm. DWC: Deep Water Culture.
V. Ondruška et al.

7. Carbon Resources Conversion xxx (xxxx) xxx
7
energy consumption given to the water-saving feature of the aquaponics
systems. Fig. 8 shows the total amount of water in the system and its
respective distribution among units including fish tanks, filter, sump
tanks, and the deep water culture (DWC) tank of the aquaponics farm.
The main consumers of water is the DWC tank and the fish tanks.
Out of total 5900 l of water, the fish tank consumed a total volume of
2200.7 l (equivalent to 37.3% of the total water consumption) with the
maximum capacity of 120 kg of fish. Based on previous experience, the
fish stay in the tank for 0.5 years which makes the capacity of 240 kg/y.
There are four DWC tanks (grow beds) on the farm with 800 l of water
each. Given the capacity of 400 plants per cycle which usually lasts
twenty days, the annual capacity of the grow beds is estimated to be
7300 pieces of vegetables. The remaining water consumption is attrib­
uted to either filtering section or the sump tanks. All the results in this
work are related to these sizing parameters.
The small water losses that occur in the aquaponics system may be
caused by three factors, i.e., the evaporation from the water surface,
plant transpiration and sludge effluent from the mechanical filter and
the fish tank. Overall, the evaporation and transpiration are negligible
compared to the total amount of water. The purpose of the regular
sludge effluent is to remove the solid sediments and ensure the proper
functioning of the filters and cleaner environment in the fish tank. The
results from electricity consumption measurements and volumetric flow
rate measurements are further used as inputs for the P-graph model (see
Section 5.2).
5.1. Process monitoring
To monitor the improvements in process efficiency after any opti­
mization procedure, the proper process monitoring must be developed.
Since the monitoring of the key process variables and energy and water
consumption are already satisfyingly covered by the farm operators, this
paper focuses on monitoring the main revenue stream of the farm,
namely the growth rate of vegetables. In particular, the lettuce variety
called Cousteau was monitored during its growth cycle on the aqua­
ponics farm.
As described in Section 3.2, the non-destructive monitoring method
based on image processing was used. As an image capture device, a
camera was used to capture the images on a regular basis. A camera
timer app fulfilled this purpose. The camera was mounted on the LED
block, and it took a picture in flash mode every four hours within 16
days period and upload the images on the data storage cloud. The first
step in image processing was preparing the right dataset from down­
loaded images. Since the vegetables growing process takes place only at
night and the images from the day phase were strongly influenced by
LED lights color, only the night images were used for this analysis. For a
16-day long monitoring period, 3 images per day were used as the inputs
to the algorithm (see Fig. 9). The main goal of the algorithm was to build
the growth rate curve based on the image dataset, which would offer
insightful information to the farm operator regarding the growth rate of
lettuce (see Fig. 10).
Fig. 9. Image sequence during the 16 days long growth period with 1 image representative per day.
V. Ondruška et al.

8. Carbon Resources Conversion xxx (xxxx) xxx
8
As the final weight of lettuce after 16 days appears to be 176 g ac­
cording to the pixel method, it is more probable that the growth rate of
lettuce does not decrease with an increase of its size and therefore only
the linear part is considered valid. According to Tessmer et al. [47], data
points from plant growth measurements are interpolated with linear or
exponential equations. Therefore, this work uses a parsimonious linear
Fig. 10. Growth rate of lettuce expressed as weight increments of lettuce in time. The first 10 days is identified as the acceptable linear dynamic region (MAE: Mean
Absolute Error, MBE: Mean Bias Error, RMSE (LOO): Leave-One-Out Cross-Validated Root Mean Squared Error).
Fig. 11. Existing process structure of the studied aquaponics farm.
V. Ondruška et al.

9. Carbon Resources Conversion xxx (xxxx) xxx
9
interpolation for the first 10 day and consequently the results after this
day should not be considered.
This work describes a conceptual design of a monitoring tool, further
improvement and verification with more data are necessary to make this
tool reliable and versatile. However, the results from the first 10 days
show good interpolative power (R2
= 0.9820) and provide the farm
operator first continuous data related to the lettuce growth rate without
any need of destructive methods such as cutting and weighing the let­
tuces. In general, the pixel filtering method can provide stable estima­
tion within its linear dynamic range for the first 10 days. This estimation
is sufficient as input for process optimization studies.
5.2. Process network optimization via P-graph
The P-graph network is based on process structure of the existing
aquaponics farm (see Fig. 11) where all the technological units are
displayed as operating units in the P-graph network. It represents the
default state and corresponds to the status of the farm as described in
Section 4. This structure is used as the benchmarking case to compare
the optimized structure that is obtained from the extended model which
include the other integrable units (see Fig. 12).
Note that the maximal structure includes a total number of 36
operating units, 15 raw materials, 10 products and 23 intermediate
materials combined into a single interconnected network. The total
summary of all operating units, raw materials and products is included
in Tables A1 and A2 summarizes the respective prices, costs, and flows.
The key inputs into the operating units are investment and operating
costs including working hours per year. All these input data were pro­
vided by Flenexa plus s.r.o. based on their actual procurement (con­
cerning existing units) and their researched prices (concerning units to
buy eventually).
Additionally, important input parameters in the P-graph model are
the prices of products. In order to optimize the process network not only
in terms of technological units but also in terms of vegetable varieties
and fish species, multiple options have been implemented. As a repre­
sentative of the aquaponics vegetables, the lettuce variety Cousteau was
selected and as a representative of common vegetables the classic lettuce
was selected, both in conventional quality at a lower price and in organic
quality at a respectively higher price. Both of these varieties are grown
on the test aquaponics farm. The quality distinguishing parameter was
the presence of synthetic fertilizer additives in the growing solution.
Regarding fish products, the selection was based on fish species which
are raised in the aquaponics farm near Olomouc, i.e. trout and sturgeon.
To have a wider portfolio of fish species, African catfish was also added.
All these prices were acquired as current wholesale prices and converted
to euros based on the above-mentioned exchange rate.
Other possible products are the electricity from renewable resources
(solar photovoltaics and biogas plant) and the digestate as a byproduct
from the anaerobic digestion. The renewable electric power generated
from photovoltaic and biogas exported to the grid via feed-in-tariff
policies. In general, these resources and power can be self-consumed
within the farm (i.e., closing the production loop) or be sold to the
market. On the farm, there have been already successful experiments to
use the sludge generated from the fish tanks and mechanical filter in the
hydroponic seedlings grow room to support the growth of seedlings. The
digestate could also be served as a good fertilizer for seedlings given to
its nutritious-content [48]. In terms of the produced renewable energy
from photovoltaics, the sale price is higher than conventional electricity
[49], although the prices significantly depend on the particular contract
with the transmission system operator. Poncarová [50] stated the
approximate value of around 4 CZK/kWh, which is also used in the P-
graph analysis. Since the P-graph solver evaluated solar photovoltaics as
unprofitable even at this (higher) sale price, the consumption on-site
was not further examined.
Even though the examined aquaponics test farm does not need any
external heat supply, since it is situated underground in old military
premises with a thick concrete insulation layer, it is very desirable to
include the heat loss problematic into the P-graph model to extend its
scope of applicability on other aquaponics farms. The whole farm was
analyzed as a virtual PVC greenhouse with the same size and shape for
virtual, approximate heat loss calculation.
The farm has a rectangular shape with the area of 80.95 m2
with the
Fig. 12. Maximal structure of the extended process network of the aquaponics farm including integrable units.
V. Ondruška et al.

10. Carbon Resources Conversion xxx (xxxx) xxx
10
floor area excluded. The overall heat transfer coefficient of 2.5 W/(m2
K)
was taken from Rasheed et al. [51] for double-layer PVC greenhouses in
absence of night sky radiation. Using the general heat loss equation, the
overall heat loss is 4 kW for the P-graph model.
After running the optimization via accelerated branch and bound
(ABB) solver, the best feasible structure was found. This structure gen­
erates the greatest net income among the 1154 economic-feasible
structures determined by P-graph. This structure is shown in Fig. 13
and it generates the total annual net income of €9650.34.
Table 2 compares the main revenues and expenses of the current
process configuration (before optimization) shown in Fig. 11 and the
optimized network solution (after optimization) shown in Fig. 13. The
differences in incomes and costs are not only caused by the inclusion of
fish species and lettuce varieties, but there is also a substantial decrease
in the cost of raw materials, which indicates that a more efficient
Fig. 13. The best feasible structure optimized by P-graph with the total annual net income of €9650.34.
Table 2
Comparison of the revenues and expenses of the current process structure of the
aquaponics farm and the best feasible process structure after optimal selection
and integration.
Costing Aspect Before
Optimization
After
Optimization
Difference
Annual Revenues
Fish € 2,160.00 € 2,520.00 € 360.00
Lettuce € 4,905.60 € 10,950.00 € 6,044.40
Total Revenues € 7,065.60 € 13,470.00 € 6,404.40
Annual expenses € 2,160.00 € 2,520.00 € 360.00
Total cost of raw
materials
€ 4,790.21 € 1,964.50 -€
2,825.71
Total cost of operating
units
€ 1,275.31 € 1,855.16 € 579.85
Total expenses € 6,065.52 € 3,819.66 -€
2,245.86
Annual net income € 1,000.08 € 9,650.34 € 8,650.26
Table 3
Overview of the maximum investment costs of selected operating units and
minimum multipliers of selected material streams.
Operating Unit Best
Structurea
Current
CAPEX
Maximum
CAPEX
Annual Net
Income
Biogas electricity
generator
No € 268 € 130 € 9,650.39
Black Soldier Fly
treatment
facility
Yes € 175 € 4,795 € 9,170.34
Electric heater No € 110 € − 10,540 € 9,650.89
Heat pump Yes € 580 € 6,535 € 9,144.84
Hydroponic
seedlings
platform 1
No € 500 € 1,200 € 5,932.38
Hydroponic
seedlings
platform 2
Yes € 500 € 1,200 € 9,580.08
LED lighting
panels 1
No € 321 € − 1,040 € 9,650.77
LED lighting
panels 2
Yes € 500 € 1,850 € 9,515.34
Solar
photovoltaics
No € 44,325 € 26,330 € 9,650.57
Solar thermal
heating
No € 10,000 € 4,940 € 9,650.66
Raw material Best
Structure
Growth
multiplier
Min. growth
multiplier
Annual
net
income
Fertilizer additive
1
No 1.20 1.90 € 6,009.48
Fertilizer additive
2
No 1.20 1.57 € 9,664.64
a
Yes: selected in the optimal structure shown in Fig. 12; No: not selected in
the optimal structure shown in Fig. 13.
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11. Carbon Resources Conversion xxx (xxxx) xxx
11
resource management system has been achieved. Nevertheless, given
the incorporation of the new integrable units (e.g., black soldier fly
(BSF) facilities), the costs associated with the operating units (particu­
larly the capital costs) has been increased by 45%. This solution rec­
ommends the integration of electrical heat pump, biogas anaerobic
digestion system, and black soldier fly (BSF) fish feed production system
to co-produce Cousteau lettuce and Sturgeon fish.
To have a better understanding of the process alternatives, sensitivity
analysis was conducted to identify the limiting investment costs of the
technologies and the minimum growth multipliers of the two fertilizer
additives (note both the fertilizer additives are not selected in the top
787 solutions). The main ground for this analysis is a rapid change in
technology prices where the price of technology is lower when it became
more well-established. Another ground is the uncertainty about the
actual prices of the technology used in this model, which strongly
depend on the individual conditions offered by the suppliers. The
analysis outcomes show the limiting investment costs of the respective
technology and thus, provide a threshold price of the equipment as
valuable information in the investment phase of the project.
Table 3 describes and summarizes the outcomes of this analysis. The
results are obtained by constantly reducing the investment costs of the
selected units (generally focusing on those which are not selected in the
optimal structure shown in Fig. 13) until the respective technology is
selected in the optimal structure. Whereas for those technologies which
have already been considered in Fig. 13, the limiting investment cost is
determined by gradually increase the associated investment cost.
The same logic was applied to the effectiveness analysis of the
fertilizer additive application. The minimum growth multipliers factor
in which the lettuce grows with the application of fertilizer additive (to
compete economically) with respective to the organically grown lettuce.
Every scenario result in a total annual net income of the process struc­
ture. This is also summarized in Table 3. The maximum investment cost
(Maximum CAPEX) should not be exceeded to achieve maximum profit.
The conclusions of this analysis are that expensive-efficient tech­
nology has a much higher return on investment long-term than cheap-
Fig. 14. (a) Installation of the reflective foil on sides of the grow bed. (b) Photosynthetic photon flux density (PPFD) increments caused by installation of the
reflective foil.
Table 4
Overview of the maximum investment costs of selected operating units and
minimum multipliers of selected material streams.
Before
Installation
After
Installation
Difference
Investment Costs
Reflective foil € - € 150.00 € 150.00
Duct tape € - € 6.00 € 6.00
Total investment costs € - € 156.00 € 156.00
Annual expenses
Electricity for lighting
panels
€ 1,117.33 € 928.72 € − 188.61
Reflective foils € - € 15.00 € 15.00
Duct tape € - € 0.60 € 0.60
Total costs € 1,117.33 € 944.32 Saving = €
173.01
Table A5
Operating, investment and overall costs of operating units.
Operating units
Unit name Annual Investment Annual
operating
cost
cost overall cost
Air_pump_biofilter € 5.00 € 120.00 € 17.00
Air_pump_DWC € 5.00 € 60.00 € 11.00
Air_pump_fish € 5.00 € 110.00 € 16.00
Biofilter € 5.00 € 798.00 € 84.80
Biogas_electricity_generator € 50.00 € 268.00 € 76.80
Biogas_plant € 50.00 € 520.00 € 102.00
Biogas_water_heater € 5.00 € 54.00 € 10.40
BSF_treatment_facility € 50.00 € 175.00 € 67.50
Central_ventilation € 10.00 € 380.00 € 48.00
Electric_heater € 10.00 € 110.00 € 21.00
Fans € - € 80.00 € 8.00
Feed_dispenser_1 € - € 50.00 € 5.00
Feed_dispenser_2 € - € 50.00 € 5.00
Fertilising_unit_1 € - € - € -
Fertilising_unit_2 € - € - € -
Fictitious_unit_1 € - € - € -
Fictitious_unit_2 € - € - € -
Fish_lighting € 5.00 € 40.00 € 9.00
Fish_tank_1 € 160.00 € 771.40 € 237.14
Fish_tank_2 € 160.00 € 771.40 € 237.14
Fish_tank_3 € 160.00 € 771.40 € 237.14
Fish_tank_pump € 10.00 € 80.00 € 18.00
Grow_bed_1 € 160.00 € 345.80 € 194.58
Grow_bed_2 € 160.00 € 345.80 € 194.58
Heat_pump € 10.00 € 580.00 € 68.00
Hydroponic_seedlings_grow_room_1 € 300.00 € 500.00 € 350.00
Hydroponic_seedlings_grow_room_2 € 300.00 € 500.00 € 350.00
LED_lighting_panels_1 € 10.00 € 320.50 € 42.05
LED_lighting_panels_2 € 5.00 € 500.00 € 55.00
Mechanical_filter € 500.00 € 212.80 € 521.28
Seedlings_tray_1 € - € 100.00 € 10.00
Seedlings_tray_2 € - € 100.00 € 10.00
Solar_photovoltaics € 100.00 € 44,325.00 € 4,532.50
Solar_thermal_heating € - € 10,000.00 € 1,000.00
Sump_pump € 10.00 € 65.00 € 16.50
Sump_tank € - € 159.60 € 15.96
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12. Carbon Resources Conversion xxx (xxxx) xxx
12
Table A6
Materials. Prices per units, maximum flow, and annual costs.
Materials
Material name Type Unit Price per unit Max. flow Flow Annual cost
Biomas Raw Material u € - unspecified u/y 365.00 u/y € -
Cousteau_seedlings_bought Raw Material u € 0.075 unspecified u/y 0.00 u/y € -
Cousteau_seeds Raw Material u € 0.019 unspecified u/y 7300.00 u/y € 137.24
Electricity_for_aquaponics Raw Material kWh € 0.089 16693.64 kWh/y 14638.30 kWh/y € 1,296.60
Electricity_for_heating Raw Material kWh € 0.089 22338.00 kWh/y 4812.99 kWh/y € 426.32
Fertiliser_additive_1 Raw Material dm3
€ 60.000 unspecified dm3
/y 0.00 dm3
/y € -
Fertiliser_additive_2 Raw Material dm3
€ 60.000 unspecified dm3
/y 0.00 dm3
/y € -
Fish_feed Raw Material kg € 1.500 365.00 kg/y 0.00 kg/y € -
Fresh_water Raw Material m3
€ 1.500 8.76 m3
/y 8.76 m3
/y € 13.14
Juvenile_catfish Raw Material kg € 4.500 24.00 kg/y 0.00 kg/y € -
Juvenile_sturgeon Raw Material kg € 3.800 24.00 kg/y 24.00 kg/y € 91.20
Juvenile_trout Raw Material kg € 4.900 24.00 kg/y 0.00 kg/y € -
Lettuce_seedlings_bought Raw Material u € 0.075 unspecified u/y 0.00 u/y € -
Lettuce_seeds Raw Material u € 0.019 unspecified u/y 0.00 u/y € -
Solar_energy Raw Material u € - unspecified u/y 0.00 u/y € -
Catfish Product Material kg € 10.300 240.00 kg/y 0.00 kg/y € -
Conventional_lettuce Product Material u € 0.560 8760.00 u/y 0.00 u/y € -
Conventional_lettuce_cousteau Product Material u € 1.000 8760.00 u/y 0.00 u/y € -
Digestate Product Material m3
€ - unspecified m3
/y 8.76 m3
/y € -
Organic_lettuce Product Material u € 1.000 7300.00 u/y 0.00 u/y € -
Organic_lettuce_cousteau Product Material u € 1.500 7300.00 u/y 7300.00 u/y € − 10,950.00
Produced_electricity_BG Product Material kWh € 0.150 284.70 kWh/y 0.00 kWh/y € -
Produced_electricity_PV Product Material kWh € 0.150 18221.49 kWh/y 0.00 kWh/y € -
Sturgeon Product Material kg € 10.500 240.00 kg/y 240.00 kg/y € − 2,520.00
Trout Product Material kg € 9.000 240.00 kg/y 0.00 kg/y € -
Fig. B15. Fish tank and its equipment including (1) sump pump, (2) air pump,
(3) automatic fish feeder and (4) reverse stream.
Fig. B16. Filtration section consisting of (1) a mechanical filter and (2) a
biological filter and (3) the stabilization section.
Fig. B17. Details of the aerated biological filter with Pall rings.
Fig. B18. Grow section consisting of (1) a grow bed, (2) a floating raft and (3)
LED lighting panels.
V. Ondruška et al.

13. Carbon Resources Conversion xxx (xxxx) xxx
13
inefficient technology. Furthermore, the effectiveness of the fertilizer
additives and other synthetic supplements should be considered in the
context of local demand for organic products, since local selling price
may surpass high costs of additives. A representative example is the
negative maximum investment cost of the electric heater (see Table 3).
The consumption of electricity is nearly four times higher than with the
heat pump at the same heat power output and thus the investment cost
would have to be negative in order to make it more favorable as
compared to heat pump. Similarly, LED lighting panels 1 shows a un­
favorable outcome, where the more efficient LED lighting panels 2 still
outperformed LED lighting panels 1 (even if it costs three times greater
than that of the latter). On the other hand, some of the new technologies
like solar photovoltaics or solar thermal heating must be discounted
(40–60% reduction) to become favorable.
5.3. Manual energy savings via reflective foils
In addition to the incorporation of new technologies, the installation
of the reflective foils can also contribute to energy saving. This strategy
was tested on the indoor aquaponics farm near Olomouc where the
reflective foils with a diamond pattern were installed on both sides of the
grow beds. This foil can reflect up to 99.9% of the light that would
otherwise be scattered, therefore, this measure substantially decreases
light energy losses by improving the reflectivity of the surfaces in the
growing part of the system. For the experiment purposes, the foil was
installed only on one side of the grow bed (see Fig. 14(a)) and
subsequently the increase in photosynthetic photon flux density (PPFD)
was compared to the initial values without reflective foil.
Installation of the reflective foil not only enables more emitted light
to be absorbed by plant leaves and thus supporting the photosynthetic
reaction, but the reflective foils on both sides of the grow beds also
create a tunnel where air can flow faster which leads to a higher un­
wanted humidity removal and thus a reduced tendency for mold to form.
Fig. 14(b) shows the comparison of piecewise linear interpolated PPFD
values before installation (blue lines) and after installation (red lines) of
the reflective foil.
It also shows that the photosynthetic photon flux density (PPFD) has
been improved only at the closest measuring point to the foil and all the
other measuring points remain the same. However, the improvement
was significant. There was a mean increase in PPFD value by 16.88%
considering the edge of the foil. Based on the observed dependency
between LED lights power consumption and emitted PPFD of 1 W of
electricity corresponding to 1 μmol/(m2
s) it means 16.88% savings in
electricity consumption on the edges of the grow beds. In the middle part
of the grow beds there was no observation of improvement in PPFD
values. The rear part of the grow bed cannot be compared in the same
way as the front part since there was a white wall (see Fig. 14(a)) and the
foil could not be installed there.
If all these assumptions are considered, the average electricity con­
sumption for the entire grow bed can be potentially reduced by 16.88%
as compared to the original setting (i.e., without reflective foil). These
energy savings, within the most energy-intensive section of the aqua­
ponics farm, have a corresponding impact from an economic point of
view. Considering the energy consumption and electricity prices, the
whole aquaponics farm can achieve an annual savings on electricity of
€188.61. If the investment cost of the reflective foil is included and the
cheapest installation possibility is considered, it makes total annual
savings of €173.01. The results are summarized in Table 4.
Although the reflective foil strategy can provide significant savings
in energy consumption, the main disadvantage of the strategy is the
poorer accessibility to the crops during planting, maintenance, and
harvest. In such cases, the farm operator may spend some extra time
removing the reflective foils, but in comparison with the energy savings
that the reflective foils bring, this is feasible. Furthermore, this draw­
back can also be prevented by installing reflective foils with a roller
mechanism. In which the farm operator can roll-up the reflective foil
with ease during maintenance and harvest.
6. Conclusion
This work tackles the resource efficiency issues that are associated
with indoor aquaponics farming. Current knowledge and fundamental
principles of aquaponics, based on the review of contemporary litera­
ture, are summarized in the first part of this part constituting a process
background. Subsequently, several integration opportunities were
reviewed to be further examined, the theoretical background for the
process network synthesis was established and the process monitoring
method was introduced to improve the feedback from the optimization
procedures tested on the aquaponics farm.
The daily consumption of electricity on this farm is 45.736 kWh of
which 80.8% is only for lighting. Such a big proportion of electricity
consumption determines the main point of interest for further optimi­
zation measures. In terms of water consumption, the daily drinking
water input is 24 l which corresponds to the effluent from the me­
chanical filter and the fish tank in the form of sediments. Compared to
electricity consumption, water consumption is considered as having a
less environmental impact.
The crucial parameter for energy savings is the PPFD value which is
unevenly distributed across the LED blocks. Even distribution of PPFD is
the goal which has been approached by the installation of the reflective
foil as an energy-saving measure. The PPFD value is directly propor­
tional to the electricity consumption of the LED lights.
Fig. B19. Plant roots in the grow section of the aquaponics farm.
Fig. B20. Stabilization section consisting of (1) a sump tank, (2) a sump pump
and (4) level sensors with (5) controller. This section follows (3) the grow beds
and precedes (2) the fish tank.
V. Ondruška et al.

14. Carbon Resources Conversion xxx (xxxx) xxx
14
This work also developed a simple and effective aquaponics crop
monitoring method based on image processing of the image sequence
taken by a single camera during the growth period of lettuce. By
counting the number of green pixels in the image, the algorithm can
differentiate lettuce from the surroundings. Using a linear interpolation
between the green pixels and the weight difference of lettuce, the al­
gorithm composes the growth rate curve which helps the farm operator
to monitor the aquaponics process (R2
= 0.9820 for first 10 days of
growth).
Next, the P-graph optimization study was conducted based on the
extended process structure. The structure was based on the current state
of the aquaponics farm and extended with the integration opportunities
and process alternatives, forming together a maximal structure. Out of
1154 of suggested profitable feasible structures, only the most profitable
one was selected. The annual net income of the whole aquaponics farm
with the best feasible structure is €9650.34 which is €8650.26 more than
the current configuration of the aquaponics farm. This large improve­
ment is a combined improvement of technological measures, fish species
and vegetable varieties to achieve higher quality. This optimal solution
integrates electrical heat pump, biogas anaerobic digestion system, and
black soldier fly (BSF) fish feed production system to co-produce Cous­
teau lettuce and Sturgeon fish.
Manual energy saving measures were also carried out by the instal­
lation of the reflective foils. This measure resulted in annual savings of
€173.01 and average energy savings of 16.88% by light energy saving.
This measure has a substantial economic and ecological impact espe­
cially if the energy source is not renewable.
To conclude this work brings a combined approach to both monitor
crops and optimize costs in a sustainable aquaponics form. The findings
in using image monitoring, P-graph network optimization and reflective
foil installation are applicable in other aquaponics facilities to simulta­
neous improve profit and incorporate sustainable technology integra­
tion. The work can be extended to incorporate various process
uncertainties into the evaluation model, e.g., applying stochastic
techno-economic analysis to determine the risk profile of the proposed
integrated process structure [52]. Further environmental impacts can
also be studied within such systems via life-cycle assessment [53]. Be­
sides, the optimization of the operational decisions for the integrated
structure via data-driven optimization method [54] is also another po­
tential extension of the work.
CRediT authorship contribution statement
Vojtěch Ondruška: Data curation, Software, Validation, Formal
analysis, Investigation, Writing – original draft. Bing Shen How:
Writing – review & editing, Visualization, Validation. Michal Neto­
lický: Resources. Vítězslav Máša: Methodology, Writing – review &
editing, Resources, Project administration. Sin Yong Teng: Supervision,
Conceptualization, Writing – original draft, Writing – review & editing,
Project administration.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements
The research leading to these results has received funding from the
Ministry of Education, Youth and Sports, Czech Republic under OP RDE
grant number CZ.02.1.01/0.0/0.0/16_026/0008413 “Strategic Part­
nership for Environmental Technologies and Energy Production”. How
BS would like to acknowledge the financial support from Swinburne
University of Technology Sarawak via Research Success Award (grant
number: 2-5747)
Appendix A. Overview of all operating units
Appendix B. Additional Illustration of Case Study
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