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Proceedings Venice2016, Sixth International Symposium on Energy from Biomass and Waste,
14 - 17 November 2016
Great School of St. John the Evangelist, Venice, Italy
©
2016 by CISA Publisher, Italy
THE POWER OF LOGISTICS:
A REGIONAL OPTIMIZATION MODEL FOR
WASTE-TO-ENERGY GENERATION USING
AGRICULTURAL VEGETATIVE
RESIDUALS
Orna Raviva, Dani Broitmana,b, Ofira Ayalona, Iddo Kanb
a Department of Natural Resources & Environmental Management, The
University of Haifa, Israel
b Department of Environmental Economics and Management, The Robert H.
Smith Faculty of Agriculture, Food and Environment; The Hebrew University
of Jerusalem, Israel
ABSTRACT: The spatial distribution of vegetative agricultural residuals makes the
economic feasibility of waste treatment solution particularly sensitive to transportation
costs. The research evaluates the effect of feedstock transport costs on the economic
feasibility of vegetative agricultural residuals treatment facilities for a few districts in the
area of 20,000 square kilometers. The research methodology is based on an economic
optimization model aimed to maximize social welfare, which is represented by the
profitability of a waste treatment system. The waste treatment system profitability is based
on the assessment of the residuals' feedstock availability, the feedstock transport costs,
the available technology to treat the different residuals, the operational costs of the
treatment facility, and the market price of their products. The research results define the
required treatment facilities, capacity and siting within the waste treatment system, in order
to benefit from the available agricultural residuals and maximize social welfare. The results
are supported by a sensitivity analysis of the potential changes in transportation costs for
different feedstock-to-facility distances and with different transportation means, and also
with the analysis of the potential changes in the market price of the facilities' products. The
results show that transportation costs affect directly the system profitability, and modify the
distribution of waste input among treatment facilities. However, the most important
parameter influencing the system configuration is the development stage of the biochar
market. If biochar price reaches the level forecasted by experts, its production
requirements will dominate the whole waste management system, while transport costs
have a lower effect on the system's design. The research methodology and results are
crucial to support policy makers with the required data to plan the waste treatment system,
which will reuse agricultural residuals while growing social welfare.
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KEYWORDS: agriculture waste, economic-feasibility, energy, biochar, RDF, transportation, modeling
1. INTRODUCTION
The agricultural sector produces large amounts of vegetative residuals and waste with high
treatment and disposal costs. The spatial distribution of the vegetative wastes makes the
economic feasibility level of the waste management system (WMS) particularly sensitive to the
transportation costs of the residuals. Furthermore, the economic feasibility of each treatment
facility is heavily dependent on the market price of the waste treatment products, like biochar,
charcoal, RDF (Refused Derived Fuel), heat, steam and electricity.
The academic articles and policy reports dealing with the economic analysis of a WMS are
mainly focusing on municipal solid waste, rarely also taking into account the agricultural
residuals as part of the potential WMS's feedstock (for example: Rentizelas et al, 2014; Madar,
2015). The studies which analyze the economic feasibility of WMS, which is purely based on
the reuse of vegetative agricultural residuals, are mainly focusing on a few types of
assessments: (a) a single technology using different feedstock types or different levels of
feedstock capacity, while analyzing the related investment and operational costs of the waste-
to-energy solution (for example: Baruya, 2015; Tidaker et al, 2014; Srivastava et al, 2014;
Brown et al, 2013); (b) a review of several waste-to-energy technologies, while assessing the
potential different feedstock types and production costs (for example: IRENA, 2012; IRENA,
2014; UK, 2014; UNEP, 2009); (c) the environmental costs of a specific treatment facility, or
one of the treatment stages, like transportation (for example: Delivand et al, 2015; Favero and
Massetti, 2013); and (d) the generation of a bio-fuel (for example: Ayalon et al, 2013;
Petrakopoulou, 2015); The economic studies which include both, energy and non-energy
treatment technologies are scarce. Several recent local studies (within this research's districts
area) suggested the deployment of a gating fee for the potential treatment facilities, to prevent
landfilling and enable the profitability of each waste-treatment facility, while using all kinds of
agricultural residuals for feedstock, including vegetative, animal and plastic waste, and
assessing energy and non-energy related treatment options (Goldfarb et al, 2015; Greenhot et
al, 2015; Hadas et al, 2013).
This research provides a holistic WMS analysis approach, focusing on the reuse of the
vegetative agricultural residuals in a predefined area, while taking into account all potential
treatment technologies, energy and non energy related, in order to design a profitable WMS
(facilities and siting) which do not require any gating fee. In order to mitigate future risks for the
proposed WMS, the research also includes a sensitivity analysis, which takes into account
potential changes in the market price of the WMS products.
The research analyzes the effect of transportation costs and the market price of the waste-
treatment products on the profitability of the WMS, while using agricultural vegetative residuals
as the main feedstock. Assuming different transportation costs (due to different distances, and
different vehicles, like trucks or train), the optimal distribution of waste treatment facilities,
optimal technology and treatment capacity are being evaluated, for the different districts in the
area of 20,000 square kilometers, with dispersed agricultural waste sources.
The research methodology is based on the development of an economic optimization model
aimed to maximize social welfare, while deploying a single WMS design with spatial
distribution of waste treatment facilities, focused on waste-to-energy generation. The criteria
defining which treatment facilities should be deployed as part of the WMS, is based on (1) the
technology selection as a function of the type and amount of residuals (sub-section 2.2.1), (2)
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the economic feasibility of the treatment facility as a function of the investment and operational
costs in compare to the market prices of the products (sub-section 2.2.2.), (4) the treatment
facility's siting as a function of the residuals capacity availability and the potential market for
the products (chapter 3), and (4) the profitability of the WMS as a function of logistics and the
unknown market prices of the treatment facilities products.
The unknown cost of logistics depends on the transport means, the residuals availability
and the distance between the residuals and the location of the treatment facility, while the
unknown market price of the facility's product depends on the product's application and their
market availability. These two uncertainties affect the economic feasibility of the WMS, and
therefore will be part of the sensitivity analysis of the defined scenarios (chapter 4).
The sensitivity analysis focuses on the impact of two different parameters on the distribution
of input waste among the assumed existing facilities and the overall profitability of the WMS:
First, the impact of transportation costs. Second, the impact of the level of demand and the
market price for waste treatment final products. Biochar is one of the most interesting waste
treatment final products, since on one hand it has several potential applications and high
market price, and on the other hand the uncertainty regarding its market price may
dramatically affect the economic feasibility of the waste system. This effect will be analyzed in
the results and discussion chapter (chapter 4).
2. WASTE TYPES AND TREATMENTS
2.1 Waste types and spatial diversity
The waste or residuals types examined in this research are the product of agricultural crops,
and public and private forests in the area of fifteen different districts (Table 1).
Table 1: The types of agricultural residuals in all districts.
District
Foliage
[ton/year]
Woody
[ton/year]
F&V
[ton/year]
North-East (1)
42,676
12,181
9,223
North East (2)
67,012
18,680
15,821
North East (3)
13,787
883
2,398
North West (1)
73,424
6,487
13,264
North West (2)
48,303
8,926
12,617
North (1)
68,389
4,109
9,127
North (2)
164,315
12,618
11,593
Center (1)
38,355
15,733
11,998
Center (2)
24,938
6,861
5,217
South West (1)
197,866
22,925
14,839
South West (2)
54,784
7,470
6,870
South East (1)
41,595
6,490
3,076
South East (2)
31,895
5,187
4,371
South (1)
284,116
39,623
18,528
South (2)
16,038
4,247
2,565
Total
1,167,492
172,421
141,508
% out of all
79%
11%
10%
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The residuals types are mainly three: Foliage includes all green leaves and non-woody shrubs
and field crops biomass, Woody residuals include most orchards and forest branches and
trunks, and fruits include all fruits and vegetables (F&V) residuals. These three categories had
been identified based on the required feedstock for the waste treatment technologies. Saying
that, each treatment technology has a specific fine tuning requirements for the feedstock
included within each category, as detailed in the treatment technologies subsection (section
2.2).
2.2 Waste treatments and products' market
Recent regulation guidelines identify agricultural residuals which are being left at the field-side
or burnt in the open-air as a costly ecological risk (Greenhot et al, 2015). Therefore, the
objective of the treatment facilities examined in this research is to maximize the reuse of
agricultural residuals using economically feasible facilities, while converting the waste risk into
a worthwhile feedstock. The different waste treatment options include several technologies.
Each technology requires a different type of feedstock, including F&V, woody residuals, or
pruned branches and trimmed foliage. Some of the treatment facilities are best operating with
only a few kinds of field crops (for example animal feed is based on the animals' diet
requirements and physical digestion ability). The waste treatment facilities also generate
different products, which include heat and biochar for multiple purposes, charcoal for cooking,
steam for industrial processes, electricity, RDF (refused derived fuel), animal food or compost
(details in subsection 2.2.1). Bio oil is another application, although not part of this research's
scope.
Despite the theoretical feasibility of a treatment facility to reuse the available agricultural
residuals, the economic feasibility of the treatment facility is also dependent on the availability
of the local market for the products of the waste treatment facilities. Therefore, the criteria
defining the treatment facilities siting for the WMS design's analysis (chapter 3) is based on
both, the residuals (feedstock) availability and the market availability for the different waste
treatment products, within the different districts analyzed (subsection 2.2.1). The economic
feasibility of each treatment facility was the criteria to define which technology will eventually
be selected for each district (subsection 2.2.2). Saying that, this chapter does not take into
account the logistics (transportation) cost, or it's affect on the treatment facility's economical
feasibility. These crucial parameters will be assessed in chapter 3.
2.2.1 Feedstock and products
The following treatment technologies will potentially have the required feedstock and a market
for their generated products, within the different districts assessed in this research:
§ Anaerobic digestion is a thermophilic (about 60oC) process using microorganisms for
biomass digestion. The main feedstock for this facility is cattle manure, mixed with dry
vegetative biomass. In this research we assume that the vegetative biomass is 50% of one
ton of digested feedstock, generating biogas, which is then being used to generate
electricity (Cavinato et al, 2010; Hadas et al, 2013; Greenhot et al, 2015). The treatment's
leftover is biomass, which may be reused as a low quality compost or a feedstock for RDF
production (details below).
§ Chopping and pressing for RDF production is a physical process which includes chopping
and then sorting and pressing of biomass into briquettes or pellets which have a
homogenous calorific value (there are several levels of RDF quality, as defined by their
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calorific value). The RDF is being consumed as a fuel for energy generation in industrial
processes. Local experience of RDF production include several pilots, which are using
municipal solid wastei, forest residuals and paper industry waste as feedstock, while F&V
are also suitable in specific conditions (Srivastava et al, 2014; Ben-amram et al, 2015;
ECOENG and Redivivus, personal correspondence, 2015). All pilots are based on the
adjustment of the RDF calorific value to the energy consumption process. Forest residuals
have a similar calorific value to orchard residuals, and will be used as the basis for our
assumptions for RDF production (JNF, 2013; Mrus and Prendergast, 1978).
§ Chopping for land covering (also known as mulching) is a common treatment used mainly in
orchards (and regulatory forbidden in field crops due to ecological risk). In most cases,
while avoiding transport costs, the pruned branches are being chopped and left as a soil
cover and amendment between the orchard trees. The amendment benefit is that the
chopped biomass enables higher levels of soil humidity and reduced rates of erosion. The
risk is the potential effect of the additional biomass on the orchard trees, as land resources
required to disassemble the covering biomass are not available to support the orchard trees
development (Ben-Hur et al, 2011; Fine, Personal correspondence, 2015). In a different
application the chopped biomass is being relocated to be used for public gardens land
covering (JNF, 2013).
§ Combustion for steam generation is an aerobic process, which uses biomass as a fuel to
heat the water (boilers). The capacity of the steam generation is measured by units of "a ton
of steam" and it is usually required as part of specific industrial processes. Local research-
districts experience shows that dedicated steam generation facilities which are using
biomass as a fuel, will be operational only in the case that a ton of steam generation using
biomass combustion will cost less than a ton of steam using a different fuel type, for
example fuel oil, Mazut (Saygin et al, 2014; Kinamon-Gan-Shmuel and Hen-Veridis,
personal correspondence, 2016).
§ Combustion for electricity generation is a process in which the biomass energy is being
converted three times, first physically into RDF, then combusted for steam generation and
then the steam is being used for electricity generation (Ganesh et al, 2013). Some of the
coal firing electricity turbines within the research districts are using coal firing steam
generators, which examined recently the co-firing of coal with RDF, meaning the addition of
municipal solid waste (MSW) based RDF to the coal combustion kilns (Ben-Amram et al,
2015; Litinzky-IEC, personal correspondence, 2016). The examination showed a potential
cost saving to the electricity plant due to fuel costs reduction (coal vs RDF production). The
electricity generation feedstock may also be based on RDF produced by vegetative
residuals, although MSW RDF, which include plastic materials, has a higher calorific value
then a vegetative based RDF. Saying that, this research assumes that the electricity
generation costs using biomass RDF are similar to the MSW RDF case, because: (a) The
MSW RDF process requires costly sorting efforts to eliminate metals and glass out of the
RDF, while these costs are not required with vegetative RDF, even in the case that the
vegetative waste is not homogenous (including plastic waste, etc.); (b) The average calorific
value of dry wood material is 16,000 kJ/kg while the average calorific value of coal is 21,000
kJ/kgii. In an application using a feedstock of 50% coal and 50% RDF, the expected calorific
value of the feedstock would be about 88% of the coal-only feedstock. These values fit the
original assumption of Ben-Amram's research analysis, assuming that the calorific value of
the co-firing feedstock is 75% of the original coal-only firing value.
§ Composting is an aerobic process using microorganisms to produce a soil amendment. The
feedstock for this treatment include cattle manure or sewage sludge on one hand mixed
with chopped vegetative residuals on the other hand. We assume that the vegetative
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material ratio is 50% of the feedstock capacity, and 33% of one ton of feedstock (Greenhot
et al, 2015). Recent applications include an in-vessel anaerobic compost production, which
is preferable due to better management of smell externalities, and the ability to place it
close to the crops field and avoid transportation costs (Roberts et al, 2007; Laor and Akiva,
Fine and Hadas, Personal correspondence, 2015). Compost addition to agricultural soils
has a benefit mainly in semi-arid districts with drought or in high slope areas with soil
erosion. The use of compost jointly with a fertilizer as a soil amendment, based on the soil
and crops characteristics, will drive higher yield rates through the provisioning of higher
carbon levels to the soil, reduction of pests in the soil, and better management of the water
nutrition for the agricultural crops (Vanden-Nest et al, 2014; Raviv, Bruner and Fine,
Personal correspondence, 2015).
§ Gasification is a thermal conversion of biomass feedstock (carbon based) at 500-1,500°C,
with a limited supply of air or oxygen and with the involvement of a chemical reaction. The
biomass is being converted into a synthetic gas (called syngas), which may be then re-
converted into a vehicles' fuel or into electricity. This research will focus on the electricity
generation application, which has a higher market availability potential (Ayalon et al, 2013).
The feedstock types required for this treatment include all kinds of woody or cultivated crops
residuals (Singh, 2015; Yagar and Shapira, Personal correspondence, 2015).
§ Mixing for animal feed is a process in which residuals are being added to cattle and sheep's
food. Cattle and sheep's diet may include between 10-50% of agricultural residuals, in
addition to their dedicated silage crops. The assumption in this research is that dairy cattleiii,
goats and sheep consume up to 10% of residuals as part of their diet, beef cattle 10-20%,
and mutton sheep - up to 50% (Shahamiv and Dayanv, personal correspondence, 2015).
Each group of animals are able to digest different biomass materials. Most of the animals
consume most of the F&V components (mainly leftovers of industrial processes, like peels
of pomegranate, grapes, or citrus fruits). Some of them are also able to benefit from specific
vegetables shrubs such as pepper, melon or tomato (Yosef et al, 2015). The main
challenge for animal feeding is the short storage time, since the wet feedstock needs to be
digested within a few days. Therefore, the siting of the animal feed facilities at the different
districts is defined by availability of both, the specific residuals and the number of cattle and
sheep heads consuming them (Greenboim, Halevi and Ambarvi
, personal correspondence,
2016). In this research the assumption is that all F&V residuals are first being used for
feeding purposes and only then for other treatment options.
§ Pyrolysis is a commonly used technology-name describing a process of anaerobic thermo-
chemical digestion of woody materials in the temperature of 300-900oC. The feedstock is
based on orchard pruned branches including palm fronds. Heat and biochar are the
products of a slow pyrolysis process (temperature change is less than 10oC per a second).
Biochar applications include a bio-sorbent of environmental contaminants in contaminated
soil (for example near gas stations, industrial areas, etc) or liquids, which do exist in the
researched districts. The biochar product may also be used as a soil amendment in
agricultural land allowing better moisture, nutrient and microbes management (Ahmed et al,
2016; Graber et al, 2014), or in hydroponic (no soil) crops and gardening. The heat product
applications are for a near-by heating application (like heat pumpvii
for a house or
greenhouse heating) or for the drying of the next feedstock capacity awaiting to be
processedviii
within the treatment facility (Pyreg, personal correspondence, 2015; Graber et
al, 2015; Ziv, personal correspondence, 2016).
§ Torrefaction is a thermochemical process (200-300oC), in the absence of oxygen, which
produces charcoal (Medic, 2012; Bar-Ziv et al, 2012; Zivix, personal correspondence, 2016).
The charcoal applications include a biomass for cooking (mainly in restaurants or outdoor
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grillx) or a biomass for electricity generation (adding charcoal to gasification or coal firing
kilnsxi).
2.2.2 Economic feasibility
The economic feasibility assessment of a standalone facility is dependent on the production
costs on one hand, and the benefits (income) following the merchandize of the products.
The economic feasibility is therefore represented by the net present value (NPV), which is
calculated as the sum of the benefits minus the sum of the costs, along the ten years of the
waste treatment facility operation. The sum of the benefits is represented by the market price
paid for the generated capacity of products (assuming fixed price along the ten years). The
sum of costs is represented by the construction and operational costs. The external
environmental costs are assumed to be internalized in the construction and operational costs,
as required by local environmental regulations.
Table 2: Yearly costs [in NIS*] and benefits for the different treatment facilities
Yearly costs and
benefits**
Maximum
yearly
theoretical
capacity of
feedstock
Average
investment
per a ton
Operational
costs per a
ton of
feedstock
Fixed &
variable
costs per
ton
Market price
of products
out of a ton
of feedstock
Net income
per a ton of
feedstock
Treatment facilities
[Ton]
[NIS per ton]
[NIS per ton]
[NIS per
ton]
[NIS per ton]
[NIS]
Pyrolysis for biochar
1,300
169
17
185
694
509
Torrefaction for charcoal
150,000
44
80
124
420
296
Mixing for animal feed
7,300
53
100
153
245
92
Composting for soil
amendment
54,000
66
130
196
272
76
Chopping and pressing
for RDF
25,000
62
57
119
195
76
Combustion for steam
120,000
37
37
73
137
63
Chopping for gardens'
land covering
60,000
40
26
66
90
24
Anaerobic digestion for
electricity
12,750
69
47
116
137
20
Combustion for electricity
91,000
1
100
101
107
6
Chopping for orchard's
land covering
1,744,000
1
26
27
15
(-12)***
Gasification for electricity
15,000
88
150
238
18
(-220)***
* NIS- New Israeli Shekel= 4.5 euro (June, 2016)
** The values in this table are based on academic articles as mentioned in the technologies review
section, supported by internet collected data and interviews with multiple local and non-local vendors, and
investors who has some kind of experience and could contribute with valuable practical information, rather
than theoretical one. Pyrolysis data is by Pyregxii and Peham-Ha'aretzxiii, Torrefaction by Bar-ziv (2012),
Animal feed by a few local facilities owners (Greenboim and Halevi), Ambarxiv and Shahamxv, Composting by
Raviv M. and Laor at Neve-Yaarxvi and in-vessel by the investor Akiva. RDF by Redivivusxvii, Steam by Gan-
Shmuelxv iii, Chopping for gardens' land covering by JNF, Anaerobic digestion by Eco-energy Golanxix,
Combustion for electricity by IECxx, Chopping for orchards' soil covering by Hadas et al (2013), Gasification
by Pyreg, Ludanxxi and investors Yagar and Shapira.
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*** Negative values represent no economic feasibility.
The yearly average benefits, costs and profit (benefits minus costs) of every treatment
facility are shown in table 2, in which the treatment facilities are ordered by the most profitable
to the less profitable one. The NPV calculation does not take into account the logistics to
relocate the residuals from the crops field to the treatment facility. These costs will be
discussed in chapter 3. Table 2 show the costs and benefits of one specific facility, while in
chapters 3 and 4 the WMS's design will include as many facilities as required to treat the
available residuals capacity.
The theoretically (standalone) identified most profitable facilities are pyrolysis, torrefaction,
animal feed and RDF production (and composting which we will drop from the energy focused
WMS design since it is mainly using municipal solid waste as a feedstock within the different
districts being researched). The production process of the four selected facilities are using one
cycle of energy conversion to generate their products, That makes them more efficient and
profitable in compare to the other processes, in which the products are generated using a
multiple energy conversion processes (with costly investments). The less profitable
technologies in table 2 are gasification for electricity, chopping for orchards' soil covering and
steam for electricity. The available technologies of gasification and combustion for steam or
energy generation, require multiple stages of costly energy conversion, which reduces the
treatment's profitability. Chopping for orchards' soil covering is a process which does not reuse
the residuals, while ignoring the potential of a woody biomass to become the feedstock of
profitable treatment facilities, and therefore it has no additional value for the WMS.
The four most profitable facilities are the ones selected by the optimization model as the
best potential means to treat the vegetative residuals, based on the assumptions of the
modeling for the WMS design (chapter 3). The WMS design will then be examined by the
sensitivity analysis, which takes into account the potential changes in the transport cost of the
residuals and the products market prices (chapter 4), while trying to minimize overall costs and
maximize social welfare.
2.3 The optimization model
The model objective is to search for the optimal allocation of resources for a WMS dedicated to
the treatment of vegetative agricultural residuals.
Figure 1: General layout of the agricultural waste model
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A mathematical programming model had been developed in order to analyze the potential
different versions of a nationwide agricultural WMS design.
The model is based on annual crop yields, vegetative waste production and waste treatment
facilities distributed among the territory, and the transport means to connect all. The model
integrates a wide range of economic, environmental and logistic factors in order to assess the
social welfare of each analyzed WMS (figure 1).
Farmers cultivate crops in agricultural lands optimizing their profits by maximizing the
difference between revenues (yields multiplied by market prices) and cost of inputs. As a side
effect, vegetative waste is produced as well. The vegetative waste (or residuals) must be
removed and can be reused as a feedstock by different waste treatment technologies. Some of
these technologies produce outputs not related to agricultural activity, selling their products in
the market. Other waste treatment products, as compost or bio-char can be used in the
agricultural sector as additional inputs (soil amendment), which may increase crop yields.
The objective of the model is to maximize the social welfare, defined as following:
WMCWMUAgCAgRSW −+−=
(1)
Where SW is the total social welfare, AgR and AgC stand for agricultural revenues and
agricultural costs respectively, WMU represents the WMS utilities (facilities), and WMC stand
for waste management costs.
The social welfare value is subject to several constraints, related to agriculture issues
(production factors limitations as land, water and labor availability), regional characteristics (for
example waste treatment facilities location, distances and availability of complementary waste
treatment resources, as animal manure) and to the WMS under study (used technology,
required feedstock, etc.).
Table 3: Waste treatment parameters
Name
Unit
Symbol
Comment
Input waste treatment costs
$ / Ton
oc
The cost per input waste (feedstock) includes
initial investment and operational costs
Output market price
$ / Ton
p
The output (product) market price is calculated as
a composition of all the treatment's products, and
expressed by input ton1
Maximal input allowed
Ton
MAX
I
Due to technological or logistic constraints or
market limitations
Local or national inputs
0 / 1
IN
L
If
1=
IN
L
only local inputs are allowed
Foliage feedstock input
0 / 1
IN
Y
If
1=
IN
Y
foliage input is allowed
Woody feedstock input
0 / 1
IN
T
If
1=
IN
T
woody residuals input is allowed
F&V input
0 / 1
IN
F
If
1=
IN
F
fruit input is allowed
This research focuses on waste-to-energy generation using these agricultural vegetative
1 For%example,%if%the%treatment%of%one%ton%of%vegetative%waste%produces%heat%and%biochar,%the%output%market%price%is%
the%sum%of%the%heat%and%biochar%value%produced%by%that%ton%of%waste%
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residuals, while not taking into account the possible feedbacks of the waste treatment products
on agriculture itself. Therefore, we assume that the agricultural activity is fixed (revenues, costs
and allocation of production factors), while ignoring the agricultural sub-model (Figure 1). The
whole model is thus reduced in this research to the waste allocation sub-model, and the social
welfare is represented by the WMS profits, defined as the difference of its benefits and costs.
Table 4: Logistic parameters
Name
Unit
Symbol
Comment
Distance between regions*
Km
ij
d
Distance from region i to region j
Transportation costs
$ / (Km * Ton)
tc
Foliage waste
Ton
K
wy
Total foliage waste produced in region K
Woody residuals waste
Ton
K
wt
Total woody waste produced in region K
F&V waste
Ton
K
wf
Total F&V waste produced in region K
* The distances between the different regions are available in the appendix.
In regards to transportation costs, the WMS is composed by two types of facilities: Local
facilities are assumed to be small scale plants, able to treat the waste stream produced locally
within the district, while minimizing transportation costs. In contrast, large-scale and extended
capacity waste treatment facilities are assumed to be located in a single location, while
potentially receiving their feedstock from all the regions. In this case, waste transportation
costs are higher, and they can prevent the use of wastes from relatively far away areas as
feedstock. Waste from the region of one district can be diverted both to local small-scale
treatment facilities and to large scale ones, while there are fifteen geographical regions in the
country.
The parameters characterizing the four selected treatment facilities, which are being used
for the modeling analysis, are described in table 3, while the logistic parameters are described
in table 4.
Using the symbols and units described in tables 3 and 4, the waste treatment facilities will
be geographically distributed in each region (district) j. If only local inputs (feedstock within
district j) are allowed, the selected utilities (treatment facilities) to treat the residuals are given
by:
( )
jINjINjIN wfFwtTwyYpWMU ⋅+⋅+⋅⋅=
(2)
And the costs are represented by:
( ) ( )
jINjINjINjj wfFwtTwyYdtcocWMC ⋅+⋅+⋅⋅⋅+=
(3)
If inputs from everywhere (all districts) can be used for feedstock, then WY, WT and WF are
defined as the horizontal vectors with the value of reused foliage, woody residuals and fruit
waste from all J regions. In addition there is a vertical vector
J
D
representing the distances
between region j and all others (a detailed distance table is included in the appendix).
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( )
( )
( )
Jj
Jj
Jj
wfwfwfWF
wtwtwtWT
wywywyWY
......
......
......
1
1
1
=
=
=
⎟
⎟
⎟
⎟
⎟
⎟
⎠
⎞
⎜
⎜
⎜
⎜
⎜
⎜
⎝
⎛
=
Jj
jj
j
J
d
d
d
D
...
...
1
(4)
In this case, waste management utilities (treatment facilities) are:
⎟
⎠
⎞
⎜
⎝
⎛⋅+⋅+⋅⋅=∑∑∑ i
iIN
i
iIN
i
iIN wfFwtTwyYpWMU
(5)
And waste management costs are:
( )
WFFWTTWYYDtcwfFwtTwyYocWMC INININJ
i
iIN
i
iIN
i
iIN ⋅+⋅+⋅⋅⋅+
⎟
⎠
⎞
⎜
⎝
⎛⋅+⋅+⋅⋅=∑∑∑
(6)
A specific WMS design is built for the model analysis. A WMS design is an array of waste
treatment facilities, some of them are small-scale facilities and others are large-scale facilities,
using different technologies and distributed siting within the different geographical districts. If
the WMS design includes N waste treatment facilities, each one located in a specific area, the
decision variables of the model are:
• Allocation of foliage waste per facility (
n
yw
)
• Allocation of woody waste per facility (
n
tw
)
• Allocation of F&V waste per facility (
n
fw
)
The objective function is then:
( )
∑
=
−
N
n
nn
fwtwyw WMCWMU
nnn 1
,,
max
(7)
Subject to the following restrictions:
• The quantity of vegetative wastes produced by agriculture is equal to the quantity of
inputs used as feedstock by all waste facilities:
( )
∑
=
++=
N
n
nnn wfwtwyalWasteAgricultur
1
(8)
• The quantity of vegetative wastes allocated to each facility is less than the maximal
allowed:
( )
nnnN wfwtwyIn MAX ++≥∀ ,
(9)
• Non negative waste inputs are allowed:
0,0,0, ≥≥≥∀ nnn wfwtwyn
(10)
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3. THE ASSUMPTIONS FOR THE WMS DESIGN
The selection of the facilities to treat the agricultural residuals (waste) is based on the
economical feasibility of the facility, the feedstock availability for the facility, and the market
available for the facility's products. Based on these criteria the facilities had been theoretically
placed in the different districts. The four most profitable waste treatment facilities and the type
of the residuals they will use as feedstock are detailed in Table 5. The distribution of the
different facility types in the different districts, by the assumptions of the WMS design, are
detailed in table 6. For example, only six districts out of the fifteen analyzed are suitable sites
for charcoal production, which is one of the profitable facilities based on table 2. In these six
sites, the woody materials for feedstock is available, based on the agricultural crops, and the
demand for charcoal from households and restaurants is potentially high enough to justify the
location of such facilities.
Table 5: Feedstock types and expected profits (NIS / ton input) for the waste treatment technologies
included in the WMS design
Waste treatment
technology
Feedstock type by technology
Profit per a ton
of feedstock
Maximum feedstock
per year**
Foliage
Woody
residuals
F & V
[NIS per Ton
Feedstock]
[ton / year]
Maximum capacity [ton]
1,167,492
172,421
141,508
1,481,421
Pyrolysis for biochar
+
+
n/a*
0 to 509
1,339,913
Torrefaction for charcoal
n/a
+
n/a
296
172,421
Chop and press for RDF
+
+
n/a
76
1,339,913
Mix for animal feed
+
n/a
+
92
1,309,000***
* The n/a means that this type of residuals can not be reused as feedstock for this treatment
** Not including transport costs
*** Theoretical assumption. Actual consumption depends on the the residuals nutrient value and the animals'
digestion ability
The two main uncertainties affecting social welfare in the proposed WMS are the
transportation costs and the product market price for those products which are not yet being
consumed in the analyzed districts.
The transportation costs are dependent on the residuals source location in relation to the
location of the facility which can reuse them. The selection of the type of treatment facility and
the feedstock capacity that this facility will use - are dynamically being re-assigned in the
model, for each scenario. On top of that, the actual profitability of the WMS depends on the
required costs to transfer these residuals from the crops land to the facilities.
In this research we assume that the cost to transfer a ton of feedstock by a truck for the
distance of one kilometer is about 0.6-0.7 NIS, which is about 0.16 USD (Ben-Amram et al,
2015; JNF, 2013). Saying that, the cost of the overall required transportation for a specific
scenario is an unknown for the WMS design, because it depends on the different values
assigned to the following parameters: (a) the technology selected per the residuals available,
(b) the distance between the treatment facility to the residuals crop-land location, (c) the
capacity of the feedstock delivered to this specific facility, and (d) the means of transport
(trucks, train, etc). Therefore, the transportation cost is an unknown to the model and should
be part of our sensitivity analysis to better define its effect on the WMS and on social welfare.
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Biochar is a product which has not yet been commercially produced or consumed in the
analyzed districts in this research. This research assumes that the maximum market price for
a bio-sorbent biochar is about 750 euro (Pyreg and Ziv, personal correspondence, 2016).
Saying that, the local market price of biochar is unknown in the districts analyzed in this
research, as it will be fluctuating dynamically based on supply and demand. On top of that, the
high market price of biochar contributes to the fact that pyrolysis is one of the most profitable
technologies found in this research. Therefore, the biochar price becomes a major uncertainty
in the potential profitability of the pyrolysis facilities the potential profitability of the WMS.
Based on that, biochar price will be part of our sensitivity analysis, along with the
transportation cost, as detailed in chapter 4.
Table 6: Waste treatment facilities siting in the different districts (regions), based on the availability of
the residuals feedstock and the market for the products.
Hosting district
Pyrolysis
Charcoal
production
RDF
production
Animal feed
North-East (1)
X
X
North East (2)
X
X
X
North East (3)
X
North West (1)
X
X
North West (2)
X
North (1)
X
X
X
North (2)
X
X
X
Center (1)
X
X
X
Center (2)
X
X
South West (1)
X
X
X
South West (2)
X
X
X
South East (1)
X
X
South East (2)
X
X
South (1)
X
X
X
South (2)
X
In order to take these two unknown parameters (transport cost and biochar price) into
account, the sensitivity analysis will include the change in their values as part of the social
welfare calculation (chapter 4).
4. RESULTS AND DISCUSSION
As discussed previously, there are two sources for the uncertainty in the WMS design: The
first is the transport costs, and he second is the biochar price. These parameters influence
both the selected technologies that will be effectively used (placed) in each district, and the
overall profitability of the WMS. However, the impact of the biochar price is stronger, because
of its potentially very high market price, in compare to the transport cost per a ton of residuals.
Therefore, in the staged analysis of the results (table 7), both variables vary gradually, while
the change in the biochar price is the main factor, and the change in the transport price is the
secondary factor.
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Table 7: Biochar market development scenarios
Scenario
Single
pyrolysis
plant
feedstock
capacity
Total
pyrolysis
feedstock
capacity
(national)
Total
potential
biochar
production
capacity
Biochar price
per ton, as %
of its maximal
potential
market price
Results in
figure #
[ton / year]
[ton / year]
[ton / year]
%
I
25,000
175,000
52,000
< 30%
Figure 2
II
25,000
175,000
52,000
40%
Figure 3
III
30,000
210,000
63,000
50%
Figure 4
IV
35,000
245,000
73,000
60%
Figure 5
V
40,000
280,000
84,000
70%
Figure 5
VI
45,000
315,000
94,000
80%
Figure 5
VII
50,000
350,000
105,000
90%
Figure 5
VIII
Limited only
by feedstock
availability
Limited only
by feedstock
availability
Depends on
feedstock
quantity
100%
Figure 6
Although assuming that biochar is a product with potentially large benefits, the local
consumption of biochar applications in the waste management districts are limited. This
means that the biochar market will be gradually developed, with initial low quantities of biochar
being produced and sold at a low price. As some benefits of biochar become evident, larger
quantities are demanded, and prices start to rise. At medium-long run, if the full potential of
biochar applications is demonstrated, its price is expected to reach the maximal value, and
most of the suitable vegetative waste will be redirected by the model into pyrolysis facilities, in
order to supply the expected demand and maximize profitability.
The highest estimation of the market price for biochar is 750 euro/ton, assuming a high
quality dry biochar, which fits the characteristics of a bio-sorbent for environmental
contaminants (Pyregxxii and Zivxxiii, personal correspondence, 2016). Different assumed values
for biochar market prices are described in Table 7 as percentages of the maximal value.
Initially (scenario I), biochar price is negligible and pyrolysis is mainly valued by its energy
output. Under these conditions pyrolysis is not profitable, and agricultural waste is being
reused mainly as a feedstock for animal feed. Since woody residuals are not suitable for this
purpose, they are being reused as a feedstock for torrefaction facilities, for charcoal
production. Transport costs change the equilibrium if they rise over 0.1 NIS / (ton-km). Since
the RDF production facilities are available in all districts, if transport is expensive, it becomes
more profitable to use foliage as input in local RDF facilities than to transport it to relatively far
away animal feed processing facility. The allocation of vegetative waste under different
transport costs in Scenario I is shown in figure 2.
When biochar price is above 30% of its maximal market price, pyrolysis becomes a
profitable waste treatment. In Scenario II, we assume that biochar price is 40% of its maximal
price, but the demand is relatively low. Pyrolysis facilities active in this scenario have a very
limited capacity, of only 25,000 tons of waste per year. These facilities work at full capacity,
processing all together 175,000 tons of waste and producing roughly 52,000 tons of biochar
yearly. Changes in transport cost have an impact on the share of waste diverted to all the
other technologies (animal feed, RDF and charcoal production), but not on pyrolysis, as shown
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in figure 3.
Figure 2: Allocation of waste as function of transport costs in Scenario I
Figure 3: Allocation of waste as function of transport costs in Scenario II
In Scenario III (Figure 4), we assume a further increase both in the demand for biochar and
in its market price. Now we assume that biochar price is 50% of its maximal price, and the
capacity of pyrolysis facilities has been increased to 30,000 ton per year in each region.
Therefore 210,000 tons of waste are processed and converted into 63,000 ton of biochar.
Again, changes in transport costs do not influence the allocation of agricultural waste to
pyrolysis, but do change the balance between animal feeding and RDF production.
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Figure 4: Allocation of waste as function of transport costs in Scenario III (see table 7 for scenarios'
details)
Assuming that this trend continues, biochar production and price gradually evolve according
to Scenarios IV, V, VI and VII: The capacity of pyrolysis plants expands consistently following
the biochar market development which created both growing demand and increasing market
price for the final product.
Figure 5 shows the changes in the WMS as the biochar market evolves following Scenarios
IV to VII. The only factor that constraint the biochar production is the facilities' capacity, which
expands gradually. As in previous scenarios, changes in transportation costs are negligible
compared with pyrolysis profits, and therefore they do not have an impact on the pyrolysis
feedstock capacity processed, which is always the maximum imposed by the facility's
characteristics. Charcoal production is not influenced either, but the quantity of waste treated
by this technology is limited by the availability of the feedstock (woody residuals).
Transportation costs are still important for the balance of wastes between animal feeding and
RDF production.
The next qualitative change emerges when pyrolysis technology develop enough to allow
the treatment of unlimited quantities of waste input (Scenario VIII). Biochar price is assumed to
reach its maximal expected value. The limiting factor in this scenario is not the facilities'
capacity anymore, but availability of input waste, meaning foliage and woody residuals as
feedstock. The impact of this assumption on the agricultural WMS is dramatic, as is shown in
figure 6. Pyrolysis and animal feeding are the only applicable facilities, with pyrolysis
consuming about 90% of the feedstock (all but F&V).
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Figure 5: Allocation of waste as function of transport costs in Scenarios IV, V, VI and VII (see table 7
for scenarios' details)
RDF and charcoal production facilities are less profitable than pyrolysis. When capacity
restrictions are not relevant, pyrolysis facilities use all the agricultural waste that fits their
feedstock characteristics (1,340,000 tons of foliage and woody residuals). Therefore, RDF and
charcoal technologies disappear. The only type of waste that cannot be used in pyrolysis is
F&V leftover, and this stream is used for animal feed (141,000 tons of waste).
In conclusion, according to up-to-date waste processing technologies and transportation
costs, if biochar price reflects its expected advantages, the agricultural vegetative WMS will be
dominated by this product. The profit per ton of input waste in pyrolysis facilities is so high, that
all available waste that is suitable as a feedstock for this treatment is effectively diverted to it.
Only wastes that are not applicable as a feedstock for pyrolysis (leftover F&V with higher
humidity levels) are used for the second best alternative, which is animal feeding.
Figure 7 shows the profit per ton of feedstock for each treatment technology, when biochar
price varies according to the values defined in Scenarios I to VIII. Biochar maximal value
(BMV) expresses the biochar price per ton, as % of its maximal potential market price.
As shown by the two left columns in figure 7, RDF production technology can compete only
with animal feed. It is less profitable, but, since RDF facilities may be located everywhere and
animal food processing plants are concentrated in a few specific places, transport costs have
an impact on the distribution of waste inputs among them. In the case that transport of waste
from a region relatively far away to an animal food processing plant is too costly, it is better to
use this waste for a local RDF production facility. The only vegetative waste stream that can
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be shifted between these two treatments is foliage residuals.
Figure 6: Allocation of waste as function of transport costs in Scenario VIII (see table 7 for scenarios'
details)
Figure 7: Profit per ton input waste in NIS for waste treatment technologies (BMV = Biochar Maximal
Value)
F&V wastes can be used exclusively for animal feed, since they are not suitable for any
other treatment in our WMS design. This means that this stream is secured for animal feeding
BMV=0%'
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facilities input. Foliage residuals may also be used as a feedstock for animal feeding facilities,
in the case that biochar prices are low enough to affect pyrolysis profitability, to be lower than
animal feeding profitability. If pyrolysis is profitable (only when biochar price is above 40% of
its maximal expected price), all available foliage will be used as a pyrolysis feedstock.
At biochar prices of 40%, charcoal production can compete with pyrolysis (columns 3 and 4
from the left in figure 7). If transport costs are high, it is preferable to use woody residuals for
charcoal production in regions where there is enough demand for the final product, than to
transport it to pyrolysis facilities which are located far away (this effect is evident in figure 2).
However, if biochar prices are above 40%, the profit from pyrolysis is so high that, potentially,
all woody residuals and foliage waste may be used by it. The only constraint for pyrolysis, in
this case, is the processing capacity of the treatment facility, as shown in scenario II to VII. If
no capacity constraints are present, the WMS is well defined by Scenario VIII (figure 6).
In terms of total profits from the whole agricultural vegetative WMS, the results are
dominated by the biochar price, per the different transport costs, as shown clearly in figure 8.
Figure 8: Waste management profits in all scenarios as a function of transport costs
The strength of the link between biochar prices and the whole waste management profits is
evident in Figure 8. Only in scenario I (the lowest line) biochar is not playing a role in the
system. The intermediate lines, below profits of 300 million NIS, reflect the profits of waste
systems in which the biochar price gradually increases, but pyrolysis is constrained by
processing capacity. If biochar reaches its maximal price and there are no pyrolysis capacity
constraints, the WMS profits are expected to grow roughly four-fold, from 166 million NIS (at
transport price of 0.1 NIS / ton-km in Scenario I) to 690 million NIS (in Scenario VIII with the
same transport cost).
To conclude, as it was clearly reflected by the results, the effect of biochar price at high
levels is crucial on the WMS for the vegetative residuals, the technologies which are being
implemented, the way waste streams are allocated for feedstock and the final profit.
However, it is not completely clear if biochar price can really reach the high market price
values within the researched districts. It is possible that biochar prices will be much lower than
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the potential maximal value. In that case, one of the most difficult-to-deal-with scenarios is
that, in which the biochar price fluctuate around the prices that are at the verge of pyrolysis
profitability. In other words, the question is what will happen to the WMS if such an influential
technology is profitable intermittently along large periods of time.
Using the WMS design defined previously, it is easy to find what the boundary conditions
are for pyrolysis and biochar production. For simplicity we assume that transport costs are
fixed at 0.1 NIS / ton-km. The results show that pyrolysis become profitable when its output
prices (including biochar) are between 30% and 40% of their maximal value. This means that,
at a certain point between pyrolysis output price of 240 and 305 NIS per input ton of waste, the
pyrolysis technology is more profitable than any other defined in the WMS design. Zooming in
this profitability range, the exact break-even point is found. Figure 9 shows how the optimal
vegetative waste system evolve in this range.
Figure 9: Zoom-in on the impact of pyrolysis profits on the WMS
Up to a market price of 272 NIS per input ton, pyrolysis is less profitable than the competing
treatment technologies. Therefore, the system is dominated by animal feeding plants which
treat 88% of the vegetative agricultural waste (F&V and foliage), while torrefaction for charcoal
generation treats the other 12% (woody residuals). However, as market prices increases
above 280 NIS per input ton, pyrolysis is the best option for 62% of the agricultural waste
(foliage, in this case) at the expense of animal feeding which shrinks to 27%. If the market
price increases to the level of 283 NIS or more, the optimal allocation of feedstock is 79% for
pyrolysis and only 12% for animal feeding facilities. In other words, if the market price for
pyrolysis products fluctuate by only 11 NIS (between 272 and 283 NIS), the optimal waste
treatment allocates 80% of its inputs to different treatment technologies.
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5. CONCLUSIONS
The economic feasibility of any WMS is sensitive to transportation costs, especially if the
waste has a wide spatial distribution, as in the case of vegetative agricultural residuals. If a set
of waste treatment technologies have similar production costs and their final products have
similar market values, transportation costs are a critical variable that may define whether a
facility sited in a certain location is profitable or not. This is the rationale for the transport cost
sensitivity analysis. However, if there are substantial profitability gaps between the treatment
technologies themselves, the market value differences can be more influential on the optimal
WMS than the transport costs, as seen in this research.
Therefore, the results conclusion is that with social welfare maximization analysis, the cost
of transportation become minor in compare to potential changes in the market price of the
generated products, especially with products like biochar with high market value.
Another implication of this analysis is that the challenge to optimally manage a treatment
system is growing when some of the system's variables (in this case, market price of pyrolysis
outputs) are near boundary values which dominate the decision of whether or not to build the
treatment facility. This problem is not theoretic, as similar issues were raised in the past in
relation to the profitability of plastic recycling (for example) in times of declining oil pricesxxiv.
Local researched districts experience also show that dedicated vegetative residuals
combustion facilities are no longer profitable as the generation of one ton of steam using oil
combustion costs less than the reuse of agricultural residuals for combustion (Kinamon,
personal correspondence, 2016)xxv.
In any case, this type of data analysis is essential to support policy makers and regulative
instruments aiming to plan the required WMS, while maximizing social welfare and market
growth.
Recommended further research may focus on the market analysis of the treatment facilities'
products, in order to avoid future market issues which will risk the WMS profitability and the
maximization of social welfare.
ACKNOWLEDGEMENTS
We wish to thank the Israel Ministry of Agriculture and Rural Developmentxxvi for supporting
this research.
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UNEP (2009). Converting Waste Agricultural Biomass into a Resource - Compendium of
Technologies. United Nations Environment Programme, Division of Technology, Industry and
Economics, International Environmental Technology Centre, Osaka/Shiga, Japan
Vanden Nest T., Vandecasteele B., Ruysschaert G., Cougnon M., Merckx R., Reheul D.
(2014). Effect of organic and mineral fertilizers on soil P and C levels, crop yield and P
leaching in a long term trial on a silt loam soil. Agriculture, Ecosystems and Environment 197,
309–317
Yosef E., Weinberg Z., Elyahu D., Nakbahat M., He Y., Solomon R., Meron S. (2015). The
potential of agricultural residuals in animal feed. The Israeli agricultural research organization -
Volcani center [Hebrew]. www.icba.org.il
VENICE2016
Sixth International Symposium on Energy from Biomass and Waste
Appendix
Distance table, in Km, between the researched districts:
Distances in km
NE-1
NE-2
NE-3
NW-1
NW-2
N-1
N-2
C-1
North-East (1)
20
37
94
87
118
84
99
147
North East (2)
37
20
84
70
102
69
84
133
North East (3)
94
84
20
76
73
43
47
79
North West (1)
87
70
76
20
58
54
56
100
North West (2)
118
102
73
58
20
60
47
66
North (1)
84
69
43
54
60
20
36
84
North (2)
99
84
47
56
47
36
20
70
Center (1)
147
133
79
100
66
84
70
20
Center (2)
204
149
99
112
74
101
86
41
South West (1)
218
203
148
166
129
154
140
91
South West (2)
204
190
133
157
120
141
127
78
South East (1)
190
177
117
153
120
130
119
75
South East (2)
189
175
116
148
114
127
115
69
South (1)
247
234
174
205
169
186
173
125
South (2)
322
311
249
287
253
265
253
208
Distances in km
C-2
SW-1
SW-2
SW-1
SE-2
S-1
S-2
North-East (1)
204
218
204
190
189
247
322
North East (2)
149
203
190
177
175
234
311
North East (3)
99
148
133
117
116
174
249
North West (1)
112
166
157
153
148
205
287
North West (2)
74
129
120
120
114
169
253
North (1)
101
154
141
130
127
186
265
North (2)
86
140
127
119
115
173
253
Center (1)
41
91
78
75
69
125
208
Center (2)
20
76
67
75
67
117
201
South West (1)
76
20
40
72
65
72
155
South West (2)
67
40
20
53
45
71
156
South East (1)
75
72
53
20
30
78
155
South East (2)
67
65
45
30
20
79
159
South (1)
117
72
71
78
79
20
105
South (2)
201
155
156
155
159
105
20
VENICE2016
Sixth International Symposium on Energy from Biomass and Waste
Personal correspondance sources:
i RDF facility using municipal solid waste - http://www.hiriya.co.il/len/apage/75704.php
ii Calorific values of fuel materials http://www.engineeringtoolbox.com/fuels-higher-calorific-values-d_169.html
iii ICBA - Israel Cattle Breeders Association - http://www.icba-israel.com/index.asp
iv Shaham - http://shaham.moag.gov.il/Unit/animal/Pages/default.aspx
v AMBAL - Beef cattle breeders of Israel - http://www.meat.org.il/cgi-webaxy/item?info_meatan_meatgt_315
vi Ambar - http://www.mmambar.co.il/
vii PYREG Pyrolysis http://www.pyreg.de/machinery-en.html
viii For example Batch Kiln 500-MK 1 - http://www.biochar.info/biochar.large-scale-biochar-production.cfml
ix Peham-Ha'aretz - http://www.permacultureisrael.org/
x http://www.sviva.gov.il/InfoServices/ReservoirInfo/DocLib4/R0301-R0400/R0337.pdf
xi http://www.btgworld.com/en/rtd/technologies/torrefaction
xii PYREG Pyrolysis http://www.pyreg.de/machinery-en.html
xiii Peham-Ha'aretz - http://www.permacultureisrael.org/
xiv Ambar - http://www.mmambar.co.il/
xv Shaham - http://shaham.moag.gov.il/Unit/animal/Pages/default.aspx
xvi Neve-Yaar - http://www.agri.gov.il/en/units/regionalcenters/9.aspx
xvii Redivivus - http://www.redivivus.co.il/
xviii Gan Shmuel industrial processes using steam - http://www.ganshmuel.com/
xix Eco-energy Golan - https://www.youtube.com/watch?v=dI5ahgAEfY8
xx IEC - https://www.iec.co.il/en/karat/pages/newprojects.aspx
xxi Ludan - http://ludan-env.com/case-studies/
xxii PYREG Pyrolysis http://www.pyreg.de/machinery-en.html
xxiii Peham-Ha'aretz - http://www.permacultureisrael.org/
xxiv http://www.wsj.com/articles/recycling-becomes-a-tougher-sell-as-plastic-prices-drop-1428279575
xxv Gan Shmuel industrial processes using steam - http://www.ganshmuel.com/
xxvi Israel Ministry of Agriculture and Rural Development - www.moag.gov.il
