+ All Categories
Home > Documents > Wirth Htc Eu Bc&e 2011

Wirth Htc Eu Bc&e 2011

Date post: 28-Jan-2016
Category:
Upload: suruagy
View: 4 times
Download: 0 times
Share this document with a friend
Description:
Article
Popular Tags:
11
See discussions, stats, and author profiles for this publication at: http://www.researchgate.net/publication/260056047 Hydrothermal carbonization: Influence of plant capacity, feedstock choice and location on product cost CONFERENCE PAPER · JUNE 2011 DOI: 10.5071/19thEUBCE2011-VP3.2.6 CITATIONS 6 READS 105 6 AUTHORS, INCLUDING: Benjamin Wirth Leibniz Institute for Agricultural Engineering 15 PUBLICATIONS 65 CITATIONS SEE PROFILE Berit Erlach acatech - Deutsche Akademie der Technik… 13 PUBLICATIONS 170 CITATIONS SEE PROFILE Susanne Rolinski Potsdam Institute for Climate Impact Rese… 50 PUBLICATIONS 203 CITATIONS SEE PROFILE Available from: Benjamin Wirth Retrieved on: 15 October 2015
Transcript
Page 1: Wirth Htc Eu Bc&e 2011

Seediscussions,stats,andauthorprofilesforthispublicationat:http://www.researchgate.net/publication/260056047

Hydrothermalcarbonization:Influenceofplantcapacity,feedstockchoiceandlocationonproductcost

CONFERENCEPAPER·JUNE2011

DOI:10.5071/19thEUBCE2011-VP3.2.6

CITATIONS

6

READS

105

6AUTHORS,INCLUDING:

BenjaminWirth

LeibnizInstituteforAgriculturalEngineering

15PUBLICATIONS65CITATIONS

SEEPROFILE

BeritErlach

acatech-DeutscheAkademiederTechnik…

13PUBLICATIONS170CITATIONS

SEEPROFILE

SusanneRolinski

PotsdamInstituteforClimateImpactRese…

50PUBLICATIONS203CITATIONS

SEEPROFILE

Availablefrom:BenjaminWirth

Retrievedon:15October2015

Page 2: Wirth Htc Eu Bc&e 2011

HYDROTHERMAL CARBONIZATION:

INFLUENCE OF PLANT CAPACITY, FEEDSTOCK CHOICE AND LOCATION ON PRODUCT COST

B. Wirtha, G. Eberhardtb,c, H. Lotze-Campenb, B. Erlacha, S. Rolinskib, P. Rotheb

a) Technische Universität Berlin, Institute for Energy Engineering b) Potsdam Institute for Climate Impact Research

c) Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. a) Marchstraße 18, 10587 Berlin, Germany, Phone: +49 30 31428449, E-Mail: [email protected]

b) Telegraphenberg A 31, 14473 Potsdam, Germany, Phone: +49 331 2882699, E-Mail: [email protected] c) Max-Eyth-Allee 100, 14469 Potsdam, Germany, E-Mail: [email protected]

ABSTRACT: Pre-treatment of raw biomass with hydrothermal carbonization (HTC) increases the energy density, facilitates mechanical dewatering and improves properties, such as grindability, for use as a fuel in existing power plants. This paper analyses the effects of HTC plant size, feedstock choice and biomass supply logistics on the HTC product costs. A case study for the German federal state of Brandenburg is presented, where optimal locations for HTC plants are determined with a mathematical programming model based on the warehouse location problem. Straw, wood chips from short rotation forestry and wood chips from forest residues are considered as HTC feedstocks. The results indicate that, under present conditions, the production of HTC biocoal from regionally grown straw and wood in Brandenburg is not profitable in aggregate. Biomass supply is the dominant cost factor. For a location with a high area-wide availability of biomass, economy-of-scale effects regarding investment and operating cost outweigh the slight increase in feedstock cost due to longer transportation distances, making large infrequent HTC plants more attractive than small decentralized plants. Keywords: cost analysis, densification, pre-treatment, short-rotation forestry, storage, upgrading.

1 INTRODUCTION

Hydrothermal carbonization (HTC) is an artificial coalification process which converts biomass into biocoal, a lignite-like product. The slightly exothermic HTC reaction takes place in water at 180 to 250 °C at saturation pressure with residency times of 1 to 12 hours.

The higher heating value of the biocoal lies in the range of 22–28 MJ/kg (on dry basis). Compared to the raw biomass, biocoal is more homogeneous and has a higher energy density and carbon content. Unlike other biomass pre-treatment processes such as torrefaction or fast pyrolysis, HTC does not require prior drying. That and the fact that HTC facilitates mechanical dewatering, which consumes much less energy than thermal drying, makes HTC especially attractive for biomass with a high moisture content.

The as-produced biocoal can be milled or pelletized and may prove to be an excellent substitute for coal in existing power plants and other combustion applications such as industrial driers.

No commercial HTC plants are as yet in operation, and most published research focuses on the chemical reaction itself.

For HTC to become an economically viable technology, the most suitable areas of application need to be identified. Which biomass should be favored as a feedstock? Where would the biocoal be best utilized? How do the logistics of harvest, collection, processing and distribution interact?

One especially interesting question is whether HTC plants should be small and decentral or larger and more centralized. On one hand, larger plants generally benefit from lower specific investment costs due to economy-of-scale effects. On the other hand, larger plants require transport of the biomass over longer distances, which can be costly.

This paper comprises two parts. In the first part, we analyze the effects of HTC plant capacity on the product costs and assess the economic implications of different feedstocks and transportation distances for HTC in

general terms. In the second part, we present a case study for the German federal state of Brandenburg (which surrounds Berlin). Optimal locations for HTC plants are determined with a mathematical programming model based on the warehouse location problem, which considers technical potentials for different kinds of biomass feedstocks, potential customers for the biocoal, and three different sizes of HTC plant. 2 FEEDSTOCK CONSIDERATIONS 2.1 Thermodynamic and technical considerations

HTC removes oxygen from the feedstock through the formation of water and CO2, thereby increasing its higher heating value and carbon content. Higher reaction temperatures and longer residency times lead to a higher calorific value, but also to a lower energy yield, since more of the feedstock chemical energy is converted into heat of reaction. Higher heating values of 25–29 MJ/kg (d.b.) were reached within 4 hours at an operating temperature of 220 °C in laboratory experiments with straw and poplar wood conducted at Technische Universität Berlin and BIOPOS [9]. For poplar wood, mass yields ranged from 49–68% and energy yields from 74–85%. For straw, mass and energy yields were considerably lower (39–49% and 56–66%, respectively). The biggest loss in these laboratory experiments derives from organic compounds dissolved in the waste water.

About 15% of the feedstock carbon was found to be dissolved, mostly in the form of organic acids [8].

Experiments conducted with recirculated process water from prior experiments instead of distilled water indicate that these losses can be significantly reduced [9, 25].

Depending on the water content of the biomass pumped into the HTC reactor, more than 20% of the final biocoal energy is required for preheating the biomass [24]. This illustrates that an efficient heat recovery is essential for the HTC process. Simulation studies using continuous flow HTC plant indicate that more than half

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2001

Page 3: Wirth Htc Eu Bc&e 2011

the energy demand for preheating biomass and for drying biocoal to a moisture content of 5–10% can be covered by process heat recovery, if a complex heat recovery scheme is employed [5, 6, 24]. However, some additional process steam has to be supplied to the reactor to reach the required reaction temperature, and electricity is required to drive the slurry pumps, the press for dewatering the biocoal, drier fans and equipment for post-processing the biocoal such as pellet presses or hammer mills. The resulting overall energetic efficiencies lie in the range of 75 to 90% (HHV) [5, 6, 24]. In the plant design considered in this paper, the auxiliary energy comprises 0.02 MJ of electricity and 0.11 MJ of natural gas per MJ biocoal (HHV), based on simulation studies.

Losses through dissolved organic compounds are assumed to be 8% of the feedstock energy (HHV). A detailed description of the reference plant design can be found in [6].

One major benefit from HTC is the fact that biocoal, unlike raw biomass, can be mechanically dewatered to a dry matter content of approximately 60%. Thus part of the thermal energy required to evaporate excess moisture through thermal drying or during combustion is saved.

Dry matter contents of 55–64% for biocoal from wood [24] and 57–68% for biocoal from organic waste [22] after dewatering with a laboratory press are reported in literature.

For combustion applications without flue gas condensation, such as conventional power plants, only the lower heating value (LHV) of the fuel can be recovered as thermal energy. For very wet biomass like organic waste, this recoverable energy is increased through treatment of the biomass with HTC. From a thermodynamic point of view, HTC makes less sense for dry biomasses such as dried wood and straw.

This fact is illustrated in Figure 1, which depicts the higher (HHV) and lower (LHV) heating values for raw biomass and the resulting biocoal (after mechanical dewatering to a water content of 40%) for four different feedstocks, The values are normalized to one kilogram of dry biomass input. For fresh wood, 98% of the LHV can be recovered in the biocoal. For dried wood and straw, HTC means a loss of LHV-based energy. For organic waste with 70% water content, however, the LHV-based energy of the biocoal is higher than that of the feedstock.

In this latter case, HTC increases the energy that can be recovered from the feedstock in combustion applications. Note that auxiliary energy consumption is not included in Figure 1.

Figure 1: Energy content of raw biomass (BM) and the resulting biocoal (HTC) per kilogram of dry biomass, based on higher (HHV) and lower (LHV) heating values for different biomasses (water content in brackets).

Aside from the increase in energy density and potential increase in LHV energy, HTC leads to other changes in properties which may be advantageous for combustion and gasification applications. Since biocoal is more brittle than raw biomass, milling to the small particle sizes required for pulverized coal burners or entrained flow gasifiers is less energy intensive.

One aspect that deserves attention is the fate of mineral matter during HTC. Experimental results for threshed hay indicate that some elements including Cl and K are completely dissolved, whereas others, such as Ca, P, S and Si, remain partly or entirely in the solid product [21]. The final ash composition influences the combustion properties of a fuel, such as its slag melting temperature, as well as any potential utilization of the ash. HTC pre-treatment of biomass with a high chlorine content may well prevent chlorine-associated boiler corrosion.

Yan et al. [26] measured the equilibrium moisture content (EMC) of biocoal and found it to be significantly lower than that of the feedstock biomass. Yan et al. also argue that a reduced EMC indicates a lowered risk of biological deterioration, which would assist long term storage. 2.2 Cultivation, harvest and storage

Costs for cultivation and harvest may include costs for arable land, seeds, fertilizers and pesticides, water for irrigation, as well as the required machinery and labor.

Wood from short-rotation coppice, forest residues and straw are considered as feedstocks in this analysis.

Straw is a residue from grain production, and all costs associated with the cultivation can be allocated to the grain as the main product. However, as straw is presently used to sustain the nutrient cycle, the costs for nutrients which substitute the straw should be allocated to the straw. Therefore, the costs for straw depend on the price of fertilizer. As natural deposits of phosphate diminish, the explicit price of straw is likely to rise in the mid-term.

The availability of straw for energy purposes can also experience seasonal price fluctuations related to annual precipitation. For Germany in 2007, the straw yield varied between 1.7 and 6.4 tonnes of dry matter per hectare with an average value of 4.3. Furthermore, only 10 to 40% of this yield can be deployed for energy purposes [13].

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2002

Page 4: Wirth Htc Eu Bc&e 2011

The dry sandy soils found in Brandenburg offers the possibility of short-rotation forestry (SRF) using willow and poplar. SRF exhibits higher yields per hectare when compared to grain-based straw. The yield depends on the planting density and can reach annual gains of dry matter of up to 14 t/ha [20].

The costs for both crops were calculated after [7] and amount to 61.32 € per tonne of dry matter for straw in square bales, and 78.65 € per tonne of dry matter for poplar wood chips. The costs for wood chips from forest residues are presented with qualification due to the wide margin on yield from differing from tree species. Costs of 65.38 € per tonne of dry matter are assumed in this work, which accords with the spectrum of 50–80 €/t described in [1].

The storage of wet biomass causes mass loss due to biodegradation of dry matter and loss of moisture through evaporation (drying).

Within 4.5 to 6 months, the water content of wood chips is reduced by 10 to 24 percentage points [14, 23].

The water content of straw cannot be reduced further by storage since it is only 15% at harvest. Moreover, re-moistening can be prevented by beneficial storage.

Dry matter losses can be calculated from Equation (1) with respect to a monthly removal of biomass.

( )

−−−⋅⋅+=

St

S

SStotSL

LtL11

111, (1)

The total dry matter losses for one year of storage

LS,tot are defined by the length of storage tS in months and the monthly dry matter losses LS. To ensile the wood chips under airtight sheeting before storage can reduce the monthly dry matter losses to 0.5% [12] and presents an interesting option thereby, since HTC takes place in water. The annual dry matter losses of selected biomass types are summarized in Table I. The dry matter loss of wood chips from short-rotation forestry is twice as high as that of forest residues due to their higher water content (50–60% compared to 35%) and smaller particle size by virtue of the harvesting technology (30–45 mm compared to 200 mm).

Table I: Overview of dry matter losses during storage for different biomasses.

Biomass Yearly dry matter losses in %

Straw 3.5 Wood chips from forest residues 6.3 Wood chips from short-rotation forestry 13.3 Ensiled wood chips from short-rotation forestry 3.2 2.3 Logistics model for costs of biomass

We developed a transportation model for the calculation of the transport costs as a function of the capacity of the HTC plant and the availability of each crop.

The required biomass for the HTC plant is grown inside a circular area where cropland makes up only part and is randomly spread. After harvesting, the biomass will be transported to a seasonal storage place at the center of the circular area, next to the HTC plant. This

transportation route is an indirect route. The transportation distance rT is calculated using Equation (2).

223

2

1

+⋅

⋅=f

Ar

crop

T πδ (2)

The expression below the radical corresponds to the

radius of the whole circular catchment area and is calculated from the cropland for the dedicated crop Acrop in square kilometers. The cropland is calculated with respect to the energy respectively dry matter demand of the HTC plant and the yield of the corresponding biomass. It is then adjusted by the effective availability of the crop for energy purposes which can vary between 10 and 40% for straw [13] and is assumed to be 100% for wood chips from short-rotation forestry.

As can be seen from Equation (2) Acrop is then divided by the so-called area factor f1, which represents the percentage of Acrop of the whole area. In case of straw, data can be obtained from the Statistisches Bundesamt and is treated in detail for Brandenburg. On average, 17% of the total area of Brandenburg is used as cereal cropland. The available area for short-rotation coppicing is assumed to be 10% of the entire area.

The average linear transport distance (two thirds of the radius of the catchment area) is multiplied by a tortuosity factor δ, representing the deviation between actual and linear transport distances. Empirical observations show δ is well represented by root two (1.41). The result has to be adjusted by the addition of two kilometers to obtain the effective transportation distance [15].

The costs of biomass transportation depend on the water content and the bulk density of the transported material. While for biomass with a low bulk density the load factor is limited by volume, a higher water content leads to a mass-limited transportation and subsequently reduces the transported energy yield per transportation unit. The densities and higher heating values on a wet basis are shown in Table II and immediately reflect on the curves in Figure 2. The density of HTC coal powder is thereby estimated corresponding to values for pulverized lignite [17] and the HTC coal pellet density is sized referring to [16].

Table II: Bulk densities and the higher heating values on a wet basis of different biomasses and HTC coal derived from poplar as feedstock [12, 13, 16, 17].

Transported Bulk HHVar Energy material density density (w.c. in %) in kg/m³ in MJ/kg in GJ/m³

Organic waste (70%) 750 4.67 3.50 Gras (60%) 500 6.16 3.08 Straw – Square bales (15%) 151 16.21 2.45 Round bales (15%) 119 16.21 1.93 Poplar – Chips (50%) 290 10.04 2.91 Pellets (10%) 650 18.06 11.74 HTC coal – Powder (10%) 498 23.22 11.56 Pellets (10%) 808 23.22 18.76

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2003

Page 5: Wirth Htc Eu Bc&e 2011

Two different types of transportation are considered: tractor/trailer units for short distances and trucking for long distances. Analysis shows that for transport distances above 13 km in case of wood chips from short-rotation forestry and above 32 km in case of straw square bales, trucking is more cost efficient, despite the additional reloading from tractor/trailer to trucks [11].

Figure 2 collates the transportation costs by trucking for different biomasses and for biocoal derived from poplar.

Figure 2: Energy-specific (HHV) transportation costs for different biomasses crops, for biodegradable waste and for biocoal derived from poplar as feedstock. 2.4 Biomass costs at the plant gate

The total costs for biomass at the plant gate derive from cultivation and harvest and from transportation and storage, as described in the previous subsections.

Table III collates these costs for different biomasses for the base-case plant with a thermal input of 11.15 MWHHV and 7000 full-load operating hours per year.

Table III: Overview of cost components of the total biomass costs at the plant gate for different biomasses and a plant capacity of 78 GWhHHV/a. All monetary values in €/GJ.

Wood Ensiled Cost component Straw bales chips wood from SRF chips

Cultivation & harvest 3.22 3.92 3.92 Transportation 0.58 0.36 0.35 Dry matter losses costs 0.14 0.58 0.14 Other storage costs 0.42 0.34 0.98

Total biomass costs 4.36 5.20 5.39

Dry matter losses were found to have a significant impact on the biomass costs, amounting to 11% of the total biomass costs for the storage of wood chips from short-rotation forestry. Storing the wood chips under airtight conditions can reduce the costs due to dry matter losses to a level similar to that of straw, as example of a dry biomass, amounting to 0.14 €/GJ. In contrast, the effort for storing wood chips as silage (e.g. construction of silos and need of different foils) results in an increase of storage costs (excluding dry matter losses) by 0.64 €/GJ, or 188%, respectively. This leads to higher total biomass costs of about 4%. Nevertheless, the

storage under an airtight atmosphere offers other benefits. It can reduce the exposure of adjacent workers to fungal and airborne spores [18].

To assess the influence of the biomass costs on the total costs for HTC, we also deploy an up-scale plant with a thermal input of 55.75 MWHHV. Table IV provides a comparison of the biomass costs for both plant capacities, using poplar as feedstock and also illustrates the main parameters for the cultivation and harvest costs (the dedicated cropland in km²) and the transportation costs (the transportation distance in km).

Table IV: Comparison of biomass costs for poplar wood chips and main parameters regarding cultivation, harvest and transportation for two different plant capacities.

Main parameter Plant capacity in GWhHHV/a 78 390

Dedicated cropland [km²] 16.7 83.6 Transportation distance [km] 8.9 17.4

Cost component (costs in €/GJ)

Cultivation & harvest 3.92 3.92 Tractor/trailer transport 0.36 0.01 Reload - 0.34 Truck transport - 0.13 Storage 0.92 0.87

Total biomass costs 5.20 5.27

Larger plants, with higher biomass demands, need larger catchment areas. This leads to increased transportation distances and consequently to higher transportation costs, including handling. Transportation costs rise by 33% in our case study, but despite this, the plant gate biomass costs increase by just 1%. 3 ECONOMIC ASSESSMENT

This section presents the assumptions used in the economic assessment. After sizing the equipment, plant costs are converted to 2009 euro using the chemical engineering plant cost index (CEPCI) and an exchange rate of 0.75 €/$US. The total capital investment (TCI), summarized in Table V, comprises the total module costs plus offsite costs (land, ancillary buildings, site development and utilities), fees and contingencies (15% of the module costs), working capital and start-up costs.

The HTC plant equipment costs were estimated based on an Aspen Plus process simulation developed in earlier work [6] and updated for this study. To assess economy-of-scale effects, equipment costs were estimated for a base-case plant with a capacity of 11.15 MW on biomass input and an up-scale plant of 55.75 MW, corresponding to 28,000 t/a and 140,000 t/a of short-rotation coppiced wood chips, respectively.

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2004

Page 6: Wirth Htc Eu Bc&e 2011

Table V: Break-down of the total capital investment (TCI) for the HTC plant design, developed in [6], with capacities of 11.15 MW and 55.75 MW on biomass input. All monetary values in thousand €.

Component Thermal input in MW 11.15 55.75

HTC reactor 1,145 3,348 Slurry pumps 271 690 Flash tanks 328 977 Biomass preparation 155 346 Boiler 107 330 Heat exchangers 400 952 Coolers 211 452 Filter press 822 3,020 Biocoal drier 892 2,712 Pulverizer 100 274 Coal storage & handling 194 510 Waste water treatment 182 462

Total module costs 4,806 14,073 Offsite costs 617 1,765 Fees & contingencies 721 2,111 Working capital 584 2,025 Start-up costs 251 745

Total capital investment 6,979 20,719

The investment annuity is calculated with an economic plant life (n) of 15 years and an interest rate (ieff) of 7% per year. Constant monetary values (in 2009 euro) are used with real escalation rates (rn) of 0.5% p.a. for natural gas and purchased electricity and 0.3% p.a. for biomass, excluding general inflation. The annual carrying charges are calculated as an investment annuity with a capital recovery factor (CRF) of 10.979, plus annual taxes and insurances (1% of TCI). The annual levelized costs for auxiliary energy, raw materials and operation and maintenance are calculated with the constant-escalation-levelization factor (CELF) as defined in Equations (3) and (4). All costs are then escalated to an assumed start of operation on 01 January 2011.

( )CRF

k

kkCELF

n

−−

=1

1 (3)

eff

n

i

rk

++

=1

1 (4)

The specific prices for natural gas and electricity

amount to 6.31 €/GJ and 22.22 €/GJ, respectively. The staffing requirement is estimated based on a pyrolysis plant of similar capacity [19] and amounts to 3.1 plant operators per shift for the base-case plant and 5.0 operators per shift for the up-scale design. This estimate is arguably conservative and labor costs might drop significantly if HTC plants can be better automated.

Conversely, personnel requirements may increase when using inhomogeneous biomass, like biodegradable waste, as feedstock due to labor intensive preparation techniques, like screening and sizing.

The product type also influences the analysis, be it powder or pellets The hazardous material (hazmat) transportation requirements of milled HTC biocoal are assumed to be identical to that of pulverized lignite. Thus transporting milled biocoal to a customer some 100km

away is 146% more expensive (0.69 €/GJ compared to 0.28 €/GJ) than the transportation of pelletized biocoal.

This is due to the lower density of biocoal powder compared to pellets and the further safety precautions needed during transportation by silo trucks and their restricted payload. Conversely, pelletizing will increase the investment cost of the HTC plant through the addition of a pellet press. Moreover, pellets cannot be used directly in pulverized-coal burners and would require milling by the customer with associated energy and possibly investment costs. 4 LOCATION OPTIMIZATION MODEL 4.1 Model description

We have developed a mathematical programming model for investigating the optimal location of HTC plants within Brandenburg. The model uses mixed-integer programming (MIP) and is based on the warehouse location problem (WLP) [2].

In contrast to a normal WLP, there are no classic production sites at the outset .Rather, the arable land and forest areas represent the production sites. Circular rings with an incremental radius of 5 km up to 30 km and an additional 50 km radius around every possible plant location embody the first step of the opening distribution.

Every circular ring represents a possible biomass supplier. Overall, there are seven biomass supply zones around every possible plant location, in a distance from 5 to 50 km.

First, we calculated technical potentials for different kinds of biomass feedstock (straw, wood chips from forest residuals, wood chips from short-rotation forestry) for all municipalities in Brandenburg. From this pool, we selected 25 possible locations for HTC plants near existing settlements under the condition that they are evenly distributed across Brandenburg. The quantity of the technical potential of each location depends on different factors [13]:

• Site conditions: including availability of water, annual precipitation amount, soil fertility, sunshine duration, temperature

• Cultivation Management: including cultivation intensity, annual gain of dry matter, percentage of straw remaining at the cropland to sustain the nutrient cycle

The circular rings around the potential HTC plant sites were evaluated by using a geographical information system (ArcGIS).

Second, we determined investment, operation and transportation costs for three sizes of HTC plants with capacities of 32,500, 45,000, and 60,000 tonnes of fresh biomass per year, based on the cost estimates described in section 3. The investment costs are estimated using Equation (5). The scaling exponent of 0.67 is based on the economic assessment also described previously. Note too the desired plant costs (C2), the costs of the base-case design (C1) and the ratio of plant capacities (P). In this case, the desired plant capacity (P2) was determined by the supply of fresh matter.

67.0

1

212

⋅=

P

PCC (5)

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2005

Page 7: Wirth Htc Eu Bc&e 2011

Third, we specified potential customers for HTC biocoal, namely six power plants and one cement factory with a specific remuneration of 0.045 €/kWh of electricity. Three of the power plants are biomass-fired combined heat and power facilities (BM-CHP) which receive an additional payment under the Renewable Energies Act (EEG) in Germany, resulting into a specific remuneration of 0.083 €/kWh of electricity.

The model minimizes total costs over all locations for a given coal demand by choosing optimal plant locations and scale, feedstock types and utilization and transport flows from HTC plants to identified customers. 4.2 Scenarios

Site availability is an important determinant in the process of location analysis. For biomass, there are many uncertainties regarding the available quantity. This variability is considered by investigating different scenarios.

The first scenario ATP (available technical potential) assumes that the entire technical potential can be used for HTC biocoal production. Hence, the alternative use of biomass by competing energy conversion technologies, including ethanol production, direct combustion in biomass plants, or the production of wood pellets for domestic heating, are not taken into account.

The available technical potential is reduced by 50% to account for competing uses and is included in the second scenario AATP (adjusted available technical potential).

A further scenario that focuses on an additional uncertainty represents the scenario Y2003 that considers the yield fluctuations in relation to a dry season. In recent years, Brandenburg was increasingly confronted with draughts, especially during spring. To acknowledge these events we reduced the straw availability in the adjusted available technical potential (AATP) by another 40%.

This scenario is instructive in relation to the possible consequences of climate change on any HTC production system [10].

We analyze all three scenarios with the developed location model. Figure 3 illustrates the different levels of distribution in the context of the WLP. The location optimization is restricted on the first level due to the fact that the HTC plant can only be delivered by the surrounding circular biomass supply zones.

Figure 3: Depiction of the planning problem as a network in the context of the warehouse location problem (WLP) [3].

Detailed information about the structure and formulation of the optimization model, including representative equations, can be found in [3, 4]. 4.3 Model Goal

The goal of the model is to select a set of HTC plant locations from the 25 potential sites which to maximize the collective surplus of the entire distribution chain. A mixed-integer programming (MIP) optimization model was established that enables such plant location planning.

The surplus consists of the revenue from selling HTC biocoal to the specified potential customers, their savings due to not having to purchase emission allowances (calculated with 15 € per tonne of carbon dioxide emitted), and deducting the HTC production and distribution costs, including biomass cost, and the transportation costs to costumers.

All revenues and costs form part of the objective function. Constraint equations regulate transportation flows, plant capacities and the degree of conversion from biomass into HTC biocoal. Average mass conversion factors (MCF) for the biomass types used in the calculations are based on laboratory-scale experiments for poplar wood and straw, with some adjustment for the conditions likely in an industrial-scale plant. The values, on a dry basis, are shown in Table VI. The MCF for wood chips from forest residues is an average of the values from the other biomasses.

Table VI: Overview of mass conversion factors (MCF) on a dry basis for different biomasses, obtained by laboratory-scale experiments. The value for wood chips from forest residues is an average of the MCF of straw and SRF wood chips.

Biomass MCF in %

Straw 66.10 Wood chips from forest residues 68.93 Wood chips from short-rotation forestry 71.77

The model is formulated using the General Algebraic Modeling System (GAMS) language and translator and solved with the CPLEX solver. It provides the following data:

• Surplus (aggregate profit)

• Number and capacity of HTC plants built

• Location and intake radius of biomass

• Amount of used biomass

• Transportation flows of HTC coal 5 RESULTS 5.1 Economy-of-scale effects

The overall annual levelized production costs of HTC biocoal decrease by 26% in case of straw and by 24% for poplar wood chips between the base-case and the up-scale plant design. This is due to a higher plant capacity and lower specific investment and labor costs. Table VII gives an overview of the calculated annual levelized product costs for different biomasses in case of a biomass input of 11 MW and 55 MW.

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2006

Page 8: Wirth Htc Eu Bc&e 2011

Table VII: Overview of the calculated annual levelized product costs for different biomasses in case of a biomass input of 11 MW and 55 MW.

Biomass Annual levelized product costs in €/tdm in €/GJ

Straw, 11 MW 347.84 13.30 Straw, 55 MW 257.29 9.84 SRF wood chips, 11 MW 379.99 14.68 SRF wood chips, 55 MW 287.50 11.11

As shown in Figure 4 the labor costs of the HTC plant are increasingly important for smaller plants despite the fact that the influence of scaling on staff demand has already been taken into account after [19]. For plants with larger capacities, the total biomass costs represent up to two thirds of the total product costs. In the scenarios analyzed here, the economy-of-scale effects on investment and labor costs for larger plants clearly outweigh the slight increase in biomass costs due to longer transportation distances.

For the case considered here, which implies a high degree of biomass availability in the plant catchment area, HTC plants with high capacity are therefore favorable. This result is in accordance with similar studies for fast pyrolysis with optimal plant capacities between 250 and 1000 MW thermal input [19] and more complex technologies, like the production of advanced biofuels (for instance, methanol, hydrogen and Fischer-Tropsch liquids) with scale-up plants of up to 2000 MW thermal input [11].

Figure 4: Cost components as a function of plant size (wood chips from short-rotation forestry as feedstock).

Figure 5 illustrates the composition of the costs more in detail for poplar wood and straw as the feedstock.

Figure 5: Composition of the specific annual levelized product costs for both determined plant capacities in case of straw and poplar wood chips from short-rotation forestry as biomass feedstock.

The remaining operating and maintenance (O&M) costs from Figure 5 comprise the costs for fresh water and for waste water disposal after treatment as well as materials and parts for maintenance. 5.2 Results of the location optimization model

The model indicates that, given the types of biomass under consideration and the remaining assumptions, there is no possibility to derive a collective profit from selling HTC coal in Brandenburg. Given the high potentials for biomass in Brandenburg it seems unlikely that the situation would be more favorable for HTC in other regions of Germany. Under our study conditions, biocoal can only be produced profitably if revenues from selling carbon credits accrue. The price for emission allowances amounts on average to 15 € per tonne of carbon dioxide emitted for the year 2010. Taking straw as the feedstock, a minimum price of 30 € per tonne is necessary. And taking wood chips, the floor price rises to 50 € per tonne.

In order to further analyze the cost structure, the role of biomass availability and the price of the biomass, the model was modified such that at least one HTC plant must be built. The chosen plant was picked from the smallest of the plant capacities determined in the reference situation. The distance to potential customers and related transport costs prove to be the dominant criterion for the optimal location choice. The availability of feedstock is less important, as different feedstock potentials are relatively high and fairly evenly distributed across study region.

One of the often voiced questions regarding the influence of the biomass availability on the total product costs can be answered by the results displayed in Table VIII. This table gives an overview of the biomass transportation costs as a percentage of the total costs for the resulting biocoal itemized by scenario.

Table VIII: Percentages of the biomass transportation costs on the total HTC costs for the determined scenarios.

Scenario Share of the total costs in %

ATP 6.00 AATP 6.12 Y2003 6.66

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2007

Page 9: Wirth Htc Eu Bc&e 2011

The range of the transportation costs is very small from scenario to scenario. The decrease of the availability of biomass is compensated by an increase of the catchment area and only raises the transportation costs. It should be added, supply and demand price movements, were not considered. In reality, shrinking biomass disposability would most likely lead to a significant increase in the market price of the respective biomass. 5.3 Sensitivity analysis

In order to assess the model reaction to varying input data and additional optimization constraints, a sensitivity analysis was performed. As shown in Figure 6, four parameters were altered in a range between 50 and 150% of the base values determined by the results of the scenario ATP.

Figure 6: Change in overall costs for the variation of different parameters determined by the GAMS model for the scenario ATP.

The parameters labor costs and overhead costs, including the total investment annuity of the HTC plant, were varied for to different arrangements of staff demand and labor costs and uncertainties assigning the bare module costs due to economic fluctuations.

Variability in transportation costs can overwhelm the effect of different transportation logistics and other assumptions, including the maximum payload of the transportation units.

As the results of the economic analysis have already shown, the biomass costs have a large impact on the total product costs. This effect can also be seen in the location optimization model as evidenced by the steepest slope on the graph illustrating the costs for straw in Figure 6. The deviation of the curve at about 120% of the base value can be explained by the increasing substitution of straw by wood as the HTC feedstock.

Against the backdrop of growing prices for mineral fertilizers and concern over the need to preserve soil fertility, it is difficult to make reliable predictions on the amount of available straw for energy purposes and its price. Therefore, the costs for straw have been varied in detail. Figure 7 illustrates the substitution of straw by other feedstocks in the context of an increasing price for the scenario ATP.

Figure 7: Composition of the biomass feedstock chosen by the GAMS model for the scenario ATP.

Up to a price of 56 € per tonne of fresh matter, the required demand of 32,500 tonnes biomass can be fulfilled solely by straw inside the circular catchment area of radius 20 km. Between 58 and 62 € per tonne of fresh matter, straw is increasingly substituted by wood chips from forest residuals. Wood chips from short-rotation forestry do not appear as part of the total biomass usage on the basis of its relatively high price. Beyond 64 € per tonne of fresh matter there is no more usage of straw as feedstock. This value can also be found in Figure 6 equalizing the 120%-mark of the curve of the costs for straw.

A similar variation for the price of straw has been performed within the scenario Y2003, the dry climate scenario. In reality, the increasing costs for straw in this sensitivity analysis would be initiated by decreasing yields due to years with unusually low precipitation.

These results are presented in Figure 8.

Figure 8: Composition of the biomass feedstock chosen by the GAMS model for the scenario Y2003.

The radius of the circular catchment area in the scenario Y2003 extends to 30 km due to the lower yields. The substitution of straw by other feedstocks occurs in a wider range between 56 and 68 € per tone of fresh matter compared to the scenario ATP.

Again, straw is mainly substituted by wood chips from forest residuals. SRF wood chips play only a minor role in the composition of the selected biomass. Beyond 70 € per tonne of fresh matter there is no more usage of

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2008

Page 10: Wirth Htc Eu Bc&e 2011

straw as feedstock. The threshold price at which straw is displaced as feedstock is slightly increased because of altered transportation logistics within the larger catchment area.

These results clarify that it is not the transportation distance but the cultivation costs that are the dominant criterion for choosing the composition of the biomass feedstock. Transportation emissions naturally increase with longer haulage distances and, as a consequence, a larger catchment lowers the potential for CO2 emissions savings. 6 DISCUSSION

Our results indicate that, under present conditions, the production of HTC biocoal from regionally grown straw and wood in Brandenburg is not profitable in aggregate. This study focused on straw and wood chips from short-rotation forestry, in part because of data availability.

The dominant cost component is the cost of the raw biomass. In light of this result, the use of biodegradable waste as the feedstock might afford improved opportunities. Assuming such material is available free, the final HTC cost may drop to below 10 €/GJ [6]. This cost would further reduce if the HTC plant operator was paid for disposal, as is currently the case for composting plants in Germany. HTC is well matched to wet biomass including household green waste. Indeed, wet feedstock makes best use of the thermodynamic advantages shown in Figure 1. Further study could investigate the potentials for wet biomasses by extending the methodology presented here.

With regard to national policy objectives for Germany, an additional model constraint could represent the establishment of a promoted biocoal demand for Brandenburg in order to contribute to national renewable and biomass-based energy sourcing targets. This would naturally lead to more HTC plants being selected and a richer transportation network for biocoal feedstocks in particular.

Beyond that, it would be interesting to extend the logistics model to include transportation modes other than road haulage. Rail freight could offer interesting opportunities in conjunction with very high capacity HTC plants with the lowest specific product costs. And taking a global context, HTC biocoal could be produced and shipped, for example, from Canada or Brazil for use in Europe.

Regarding end-usage, it remains to be confirmed that the combustion systems of existing power plants — taken here to be the customers — can handle milled biocoal.

Furthermore, differentiating between biocoal powder and pellets within the model could provide insights into the most suitable form of product delivery.

Including other biomass pre-treatment technologies such as torrefaction and wood pelletizing in the model would show under which conditions HTC might best prevail among competing technologies.

We are currently planning (June 2011) to extend our optimization model to include some of these aspects.

7 CONCLUSIONS

Hydrothermal carbonization (HTC) is an artificial coalification process which converts biomass into a lignite-like product, within several hours. The product, biocoal, is more homogeneous than the raw biomass, has a higher energy density and is easier to store and handle.

Since HTC takes place in water and does not require prior drying, it is especially suited to biomass with a high moisture content.

For HTC to become an economically viable biomass upgrading technology, it is first useful to identify which feedstocks and applications are the most suitable, and whether the processing plants should be small and decentral or larger and less frequent.

This study represents an early attempt to determine the role that HTC might play in German federal state of Brandenburg, a region well suited to the production of biomass for energy. A key motivation for this study is also to demonstrate the contribution that optimization models can make in this regard.

The first part of this paper estimated HTC plant costs in some detail and in relation to scale. A continuous HTC plant design created earlier using the Aspen Plus process simulation package was further developed for this paper.

The key findings are that plant gate feedstock costs dominate the design (37–59% of the total biocoal production cost) and that a total cost of operation (TCO) scaling exponent of 0.67 can be applied over a feedstock capacity range of 10–60 MW. Hence, very considerable economies-of-scale apply to the plant itself.

The second part of this paper presented a case study for Brandenburg and used mixed-integer programming (MIP) and the GAMS/CPLEX package to develop and optimize a plant location and logistics model. The technical potentials for different biomass feedstocks in Brandenburg were quantified and input, along with the plant cost information from part one. The biomass feedstocks under investigation comprised agricultural straw and wood chips from forestry slash and from dedicated short-rotation forestry.

Given the three feedstock options considered and a number of other modeling assumptions, the key finding is that even a modest HTC infrastructure for Brandenburg did not create an aggregate profit. This result applies to the most optimistic land use and yield scenarios but can be reversed if the carbon price climbs 30 € per tonne and beyond. Conversely, if the 2003 dry year weather pattern become the norm, as may happen under climate change, then the economics worsen.

The use of municipal biomass waste as a feedstock, for which disposal may even be free or paid for, would of course be attractive. But virtually no experimental work has been reported on the suitability of HTC in this context.

The paper concludes with a number of areas in which the logistics model might be usefully extended, including the addition of new biomass feedstocks, a consideration of rail freight and still larger plant, and the inclusion of other biomass upgrading technologies.

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2009

Page 11: Wirth Htc Eu Bc&e 2011

8 REFERENCES [1] S. Busch, Mobilisierbares Waldholz zur ener-

getischen Nutzung am Standort Baruth (2004) [2] W. Domschke, A. Drexl, Einführung in das

Operations-Research (2007) [3] G. Eberhardt, Standortwahl einer Anlage zur

Hydrothermalen Carbonisierung in Brandenburg (2010)

[4] G. Eberhardt, Rentabilität der Hydrothermalen Carbonisierung unter besonderer Berücksichtigung von Transportkosten (2011) – Berichte über

Landwirtschaft (submitted)

[5] B. Erlach, G. Tsatsaronis, Upgrading of Biomass by Hydrothermal Carbonisation: Analysis of an Industrial-scale Plant Design. Proc. ECOS – 23rd International Conference; 2010 Jun 14–17, Lausanne, Switzerland

[6] B. Erlach, B. Wirth, G. Tsatsaronis, Co-production of electricity, heat and biocoal pellets from biomass: a techno-economic comparison with wood pelletizing. Proc. of World Renewable Energy Congress; 2011 May 08–13; Linköping, Sweden

[7] Fachagentur Nachwachsende Rohstoffe e.V., Leitfaden Bioenergie – Planung, Betrieb und Wirt-schaftlichkeit von Bioenergieanlagen (2009)

[8] M. Gerhardt, M. Berg, B. Kamm, Hydrothermal carbonization of lignocellulosic biomass and its precursors. Proc. International Conference on Polygeneration Strategies with special Focus on Integrated Biorefineries; 2010 Sep 07–09, Leipzig, Germany

[9] M. Gerhardt, S. Kieseler, pers. comm. [experiments conducted at Technische Universität Berlin and Forschungsinstitut Bioaktive Polymersysteme, Berlin], Jun-Nov 2010

[10] F.-W. Gerstengarbe, PIK Report No. 83: Studie zur klimatischen Entwicklung im Land Brandenburg bis 2055 und deren Auswirkungen auf den Wasserhaushalt, die Forst- und Landwirtschaft sowie die Ableitung erster Perspektiven (2003)

[11] C. Hamelinck, Outlook for advanced biofuels (2004)

[12] M. Kaltschmitt, Energie aus Biomasse – Grundlagen, Techniken und Verfahren (2001)

[13] M. Kaltschmitt, Energie aus Biomasse – Grundlagen, Techniken und Verfahren (2009)

[14] N. Kanswohl, Trocknungsverhalten und Kosten bei der Lagerung von Holzhackschnitzeln

[15] G. Kappler, Systemanalytische Untersuchung zum Aufkommen und zur Bereitstellung von energetisch nutzbarem Reststroh und Waldrestholz in Baden-Württemberg – Eine auf das Karlsruher bioliq-Konzept ausgerichtete Standortanalyse (2008)

[16] J. Kiel, ECN BO2-technology for biomass upgrading (2007)

[17] R. Kurtz, Braunkohlenstaub: Herstellung, Eigen-schaften und Verwendung, Braunkohle, 5 pp. 11-17 (1991)

[18] P. Kofman, Storage and Handling of Willow from Short Rotation Coppice (1997)

[19] S. Lange, Systemanalytische Untersuchung zur Schnellpyrolyse als Prozessschritt bei der Produktion von Synthesekraftstoffen aus Stroh und Waldrestholz (2007)

[20] D. Murach, DENDROM – Zukunftsrohstoff Dendro-masse (2008)

[21] J.R. Pels, P.C.A. Bergman, TORWASH. Proof of Principle. Phase 1, Technical Report, ECN-E-06-021, Energy research Centre of the Netherlands (ECN), Petten (2006)

[22] H. Ramke, Hydrothermale Carbonisierung organischer Siedlungsabfälle, 22. Abfallwirtschafts-forum, Gießen (2010)

[23] R. Schaidhauf, Systemanalyse der energetischen Nutzung von Biomasse (1998)

[24] J. Stemann, F. Ziegler, Assessment of the energetic efficiency of a continuously operating plant for hydrothermal carbonization of biomass. Proc. of World Renewable Energy Congress; 2011 May 08–13, Linköping, Sweden

[25] J. Stemann, F. Ziegler, Hydrothermal carbonization (HTC): Recycling of process water. Proc. of the 19th European Biomass Conference and Exhibition. 2011 Jun 6–10 – Accepted for publication

[26] W. Yan et al., Thermal Pretreatment of Lignocellullosic Biomass, Environmental Progress & Sustainable Energy, 28 (3), pp. 435-440 (2009)

9 ACKNOWLEDGEMENTS

• This work has been funded by the German Federal Ministry of Education and Research as part of the joint research project 01LS0806.

19th European Biomass Conference and Exhibition, 6-10 June 2011, Berlin, Germany

2010


Recommended