Designing the scale of a woody biomass CHP considering local forestry reformation: A case study of Tanegashima, Japan

Applied Energy 198 (2017) 160–172

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Designing the scale of a woody biomass CHP considering local forestry reformation: A case study of Tanegashima, Japan Yuichiro Kanematsu a, Kazutake Oosawa a,b, Tatsuya Okubo a,c, Yasunori Kikuchi a,c,⇑ a

Presidential Endowed Chair for ‘‘Platinum Society”, The University of Tokyo, Ito International Research Center, 7-3-1 Hongo, Bunkyo-ku 113-0033, Tokyo, Japan Wakayama Prefectural Government, 1-1 Komatsubaradori, Wakayama City 640-8585, Wakayama, Japan c Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku 113-8656, Tokyo, Japan b

h i g h l i g h t s
Long-term profile of timber availability was estimated based on forest reformation.
The possible scale of biomass-CHP was constrained by local timber availability.
The effect of CHP scale on GHG emission was examined by LCA.
Biomass-CHP can mitigate fossil resource consumption with forestry reformation.
The scale expansion of CHP should be planned on the phase of forestry reformation.

a r t i c l e

i n f o

Article history: Received 2 August 2016 Received in revised form 30 March 2017 Accepted 11 April 2017

Keywords: Simulation Forest resource Biomass supply chain Life-cycle assessment District heating and cooling Remote island

a b s t r a c t Biomass has become a renewable resource for energy, but it needs continuous management to make it truly renewable. Planted forests in particular areas in the world are facing the challenges of reformation because of the severe maldistribution of forest age-classes caused by a stagnation in demand for wood or degradation by past deforestation. In these regions, the amount of wood processed and the overall biomass must be controlled by a schedule of forestry reformation. Particularly for energy plants, where a continuous and adequate supply of wood is required, the scale of the plants should be carefully designed to maintain a harmonized demand/supply balance in the region. In this study, we considered the scale design for a combined heating and power (CHP) system using woody biomass for district heating and cooling (DHC) in Tanegashima, a remote island in Japan, which requires immediate forestry reformation in addition to mitigating its fossil-fuel consumption. A process model representing material and energy flows associated with the life cycle of a biomass CHP system was developed, which considered the dependency of timber consumption on the scale of the CHP, together with other design parameters. The profile of the annual timber supply for the next 100 years to achieve sustainable forestry planning was calculated, which became the constraint on the feasibility of resource procurement for CHP. The ratio between the maximum and minimum timber supply in the next 20 years was determined to be about 1.8. Through simulation and life cycle assessment (LCA) using the developed model, CHP scales were specified to cover the overall heat and power demand of consumers for biomass-derived energy at 840 kWe with 1470 kWth. We found feasible and effective scale ranges for the various stages of forest-resource status by comparing LCA results, timber consumption, and profiles for timber supply. We have been able to demonstrate that woody biomass utilization in a specific area should address forestry reformation in a sustainable way. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction

⇑ Corresponding author at: Presidential Endowed Chair for ‘‘Platinum Society”, The University of Tokyo, Ito International Research Center, 7-3-1 Hongo, Bunkyo-ku 113-0033, Tokyo, Japan. E-mail address: [email protected] (Y. Kikuchi). http://dx.doi.org/10.1016/j.apenergy.2017.04.021 0306-2619/Ó 2017 Elsevier Ltd. All rights reserved.

Sustainability of primary industries and their related natural resources has become one of the most important issues in the utilization of biomass as an energy source. The essential purposes of such primary industries are food production by agriculture and materials production with forest conservation by forestry. Woody

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Nomenclature Main abbreviations CHP combined heating and power COP coefficient of performance DHC district heating and cooling FIT feed-in tariff GHG greenhouse gas LCA life-cycle assessment LC-GHG life-cycle greenhouse gas emissions MSF multistoried forest NRF natural regenerated forest SSF single-storied forest Additional abbreviations AC absorption chiller CWT cold-water tank Fos fossil fuels HW hot-water supply HWT hot-water tank SC space cooling SH space heating l energy source, i.e., DHC or Fos m energy usage categories, i.e., HW, SH, or SC n building number T heat storage tank, including HWT and CWT Variables coefficient of performance of device using energy source COPl;m n l for the usage category m in the n-th building F in flow rate into AC at time i (m3/h) AC ðiÞ F out CHP ðiÞ

flow rate of the stream from CHP at time i (m3/h)

F in T ðiÞ

flow rate of the stream into the tank T at time i (m3/h)

biomass utilization has become a practical option, particularly in countries of the European Union (EU), where woody biomassderived energy accounts for over 50% of renewable energy [1]. In some regions of the EU, forestry and wood industries have been developed as the major industry, where there is a mature market and technologies based on their circumstances. Nabuurs et al. [2] estimated the increase in future wood supply from EU forests over the next 50 years, with wood demand being expected to increase gradually and continuously. In contrast, the wood supply in Southeast Asia was estimated to decrease and the long-term promotion of rehabilitation and plantation is necessary because of the deforestation and forest degradation that took place in the last few decades [3]. In Japan, forest resources have aged and accumulated mainly because of the decrease of wood demand that was replaced by steel for building material and by fossil fuels for energy [4]. Because older trees have less capacity for carbon fixation because of the decline in growth rate, net greenhouse gas (GHG) emissions from an aged forest can result in positive values. In addition, the multiple functions of forests, including wood resource production, water resource conservation, landslide prevention, and soil conservation [5], will suffer a gradual loss with aging. A long-term approach to equalize the age-class distribution is therefore required [6]. The design of biomass-based energy systems will necessitate the reconsideration of wood supply from forest to consumers. Life cycle assessment (LCA) or optimization methods have been widely applied for the design of biomass-based energy systems to deal with the problem of the supply chain of biomass and bioenergy.

F waste ðiÞ flow rate of water discarded as overflow from the tank T T (m3) Bld;m Loadn ðiÞ hourly energy load for the usage category m in the nth building at time i (J/h) DHC;m Loadn ðiÞ hourly energy load covered by device using energy source l for the usage category m in the n-th building at time i (J/h) Q in heat flow of the stream into AC at time i (J/h) AC ðiÞ Q out AC ðiÞ

heat flow of the stream from AC at time i (J/h)

Q out CHP ðiÞ ðiÞ Q DHC;m n

heat flow of the stream from CHP plant at time i (J/h)

Q in T ðiÞ

heat flow into the tank T at time i (J/h)

Q loss T ðiÞ

heat flow by natural heat radiation from the tank T caused by (J/h)

Q out T ðiÞ

heat flow from the tank T at time i (J/h)

ðiÞ Q waste T

U T ðiÞ V T ðiÞ

discarded heat with overflow from the tank T (J/h) temperature of the water before cooling by AC (K) temperature of the water before heating by CHP (K) temperatures of the water entering the tank T at time i (K) available heat from the tank T at time i (J) volume of water stored in the tank T at time i (m3)

V MAX T

inner volume of the tank T (m3)

V out T ðiÞ ðiÞ V waste T

volume of water supplied from the tank T at time i (m3)

heat flow of the stream from device using energy source l for the usage category m in the n-th building at time i (J/h) Q 0CHP;m ðiÞ hourly energy demand for the usage category m in the n n-th building at time i under the assumption that all demands are covered by CHP-derived heat (J/h)

T C0 T H0 T T ðiÞ

volume of water discarded as overflow from the tank T (m3)

LCA studies of biomass-based energy systems were performed in Denmark based on the energy scenarios for 2030 and 2050 [7] and in Ireland for energy requirements and GHG emissions [8]. Impacts on the fossil and woody biomass-based energy production chains for North Karelia in Finland, focusing on economic, environmental, and social indicators, have been evaluated considering sustainability [9]. Optimization studies have considered supply-chain and logistics issues because transport costs have significant effects on the economic feasibility of the utilization of biomass, as bulky and low-energy-density resources [10]. For example, the objective functions adopted in previous studies have mainly involved minimizing costs, maximizing profit, or minimizing GHG emissions [11,12]. A tactical value-chain optimization model for a woody biomass power plant was developed by Shabani and Sowlati [13]. Optimization using geographical information systems was applied to estimating the wood supply and costs in Ontario in Canada [14] and Ivanjica in Serbia [15]. For any form of assessment and optimization, the modeling of unit processes to quantify material and energy flows within the life cycle is inevitable. At this time, a conventional LCA cannot consider the dynamic changes in the future availability of wood, as caused by an inventory analysis per functional unit such as the unit amount of product. However, the design of the appropriate scale of the biomass-based energy plants should be harmonized with various scenarios for the long-term promotion of forestry. Multigeneration of products including energies from biomass can add more value to wood than can monogeneration [16]. Combined heating and power (CHP) or district heating and cooling

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(DHC) with CHP can be an effective means to improve the resource-use efficiency of biomass, which has a low energy density compared to fossil fuels. Sophisticated analysis of the specification of CHP and DHC is strongly required, to check that heat demand matches supply through an investigation of potential users of CHP and DHC and of energy-demand fluctuations. The modeling of trigeneration (heating, cooling and power) including biomass gasification [17], a comparative evaluation of economic feasibility of biomass DHC for ten rural areas [18] and a performance analysis for energy storage for biomass CHP [19] have been conducted recently. These studies demonstrated that the evaluation results could be affected by various technical and social parameters, such as energy conversion efficiency, moisture content of biomass, specification of energy demand or price of product energy. The scale of an energy plant is one of the most important parameters among the aforementioned ones, because it has a direct effect on the required amount of the fuel biomass and energy output. The location and scale of a pellet production plant were optimized using a geographic information system [20]. An LCA study of power generation from forest biomass [21] showed that energy consumption and environmental loads by fuel transportation increased with the plant size, whereas other parameters were relatively constant. DHC/CHP were not considered in the studies above. Biomass-based multigeneration with different scales and several options of products were compared [22], which is one of the few studies considering both DHC/CHP and the production scale. Although large-scale energy plant will have a higher conversion efficiency [23], the total wood demand must be plausible in relation to the local availability of wood resources [24]. For regions where the demand for wood has been limited, biomassderived energy systems can become a stimulus for economically feasible harvesting and forestation. For example, a feed-in tariff (FIT) enforced in Japan in July 2012, has increased new constructions and plans for power plants based on woody biomass. A demand for wood as an energy resource is being affected greatly by the emerging reformation of the energy systems and the market in Japan. The transition of the forest state should be considered from the viewpoint of long-term management of the region including forest resources. The involvement of many decision makers in the supply chain must be addressed in the design of biomass CHP and DHC. They will include forestry companies, wood suppliers, forest owners, energy suppliers, local governments, and energy consumers, as demonstrated by Shimizu et al. [25] when visualizing the performance of an energy-cooperative network. A simulation-based approach to the change of material and energy flows following the installation of CHP and DHC will be useful for such multistakeholder decision making. At the same time, demand-side characteristics will be important considerations in the design and evaluation of biomass energy systems [26] and the design and operation of CHP in urban areas [27]. To involve industries in decentralized energy systems, the temperature ranges of their heat demands (e.g., Kikuchi et al. [23]) should be carefully matched with supply-side conditions to meet their exact needs for heat, not only in terms of quantity but also quality. In this paper, we tackle the scale design for woody biomass CHP and DHC, considering the temporal profile of the availability of local biomass resulting from forestry reformation aimed towards sustainability. A design case study is performed for the remote island of Tanegashima in Japan, where the age-class maldistribution and the decline of forestry have become severe issues. A lifecycle model for the simulation of material and energy flows related to woody biomass CHP and DHC is developed. It enables the quantification of the effects on material and energy flows caused by the changes of CHP scale and demand characteristics, which are required for an assessment of CHP installation in the local area.

The life-cycle model can take into account the temporal profile of local wood availability, which is examined in terms of long-term changes of wood supply originating in the difference between the present and target states of age-class distribution over the next 100 years. The acceptable and feasible range of CHP scales, constrained by resource supply, can be assessed by matching the estimated wood supply profile and the simulated material flows. Because the possible CHP scales could become design alternatives, various scenarios for installing biomass CHP and DHC in Tanegashima are discussed.

2. Material and methods 2.1. Design procedure for the scale of woody-biomass CHP considering the forestry reformation Fig. 1 shows the proposed design procedure for the scale of woody-biomass CHP considering forestry reformation. This procedure consists of problem definition, forest resource calculation (Section 2.3), material and energy flow simulation followed by LCA (Section 2.2), supply/demand matching, and scenario planning of CHP installation (Section 4.1). Problem definition is required for setting the boundary of the problem and the fundamental settings for CHP scale design. For the supply side, the forest area sourcing the woody biomass should be set based on the policy of installing CHP, whether the woody biomass will be all supplied within the governing region or partly imported from other regions. For the demand side, the DHC area to supply CHP-derived energies and the candidate consumers will be examined. For energy conversion, the candidate plant location and the technology type, i.e., steam turbine, gas turbine, or gas engine, are examined based on the rough estimation of the demand/supply scales. The initial setting of the CHP scale range can be also roughly estimated from the demand scale examined in detail by the following analysis. Material and energy flows, including required timber amount, were simulated by changing the CHP scale incrementally within the scale range. The consumption of fossil fuels by energy consumers, which is expected to be gradually replaced with the scale-up of CHP, was quantified by this simulation. The values of flows in simulation results were used as the foreground data for LCA. The background data for LCA was sourced from existing LCA databases [28–31]. The environmental performance of the CHP/ DHC system at each scale were interpreted through indicators in the LCA results. The long-term profile of timber availability is estimated by forest resource calculation based on present and target forest status (Section 2.3). In other words, the timber availability calculated in this paper is the desired harvesting and thinning amount to achieve the target forest state in future. Thus, for forest sustainability, both the shortage and excess of timber demand should be dealt with against the supply. Supply/demand matching was judged based on a comparison of the required timber amount, timber availability profile and LCA results. If the matching is not satisfied, problem definition will be repeated to change the basic settings such as the CHP scale range, forest area for procurement or demand area. For example, if the calculated timber requirement by CHP was smaller than the maximum timber availability, the CHP scale range could be expanded to the larger side and a corresponding calculation would be added until the matching is satisfied. The installation scenario of the CHP scale is discussed (in Section 4.1) based on the comparison between timber availability and requirement or LCA results. The scenario aims to control the supply/demand balance of regional biomass by actively changing

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Fig. 1. Overview of the design procedure for the scale of woody-biomass CHP considering forestry reformation.

the CHP scale following the temporal increase and decrease in timber availability at the chance of device replacement. 2.2. LCA with the models for simulation of material and energy flows In Fig. 2, the conventional material and energy flows based on fossil resources are represented as ‘‘Conventional flow” with dotted-line arrows. Conventional energy supplies mainly depend on imported fossil fuels for both power and heat in Japan [32]. The energy demands by consumers were classified into space cooling (SC), space heating (SH), hot-water supply (HW), and other power demands. In conventional systems, SC, SH, and HW involve power or fuel through heat pumps, heating appliances such as stoves, or water heaters. The ‘‘Designed flow” in Fig. 2 represents

the proposals in this study to use woody biomass CHP, where wood chips made from timber are used as fuel. Although imported or transported timber and other biofuels can also be used in a CHP plant, local woody biomass was selected for the CHP fuel to promote forestry reformation in the region. Cold heat can be provided from heat via absorption chillers (ACs). Thermal storage with hotwater tank (HWT) and cold-water tank (CWT) are also considered. In this study, it is assumed that the energy supplied through DHC would be the preferred option for use by energy consumers, with conventional systems being utilized as backup energy sources if DHC could not meet all heat demands. CHP-derived heat can be supplied only to consumers connected with the heating pipelines. If the CHP-derived heat exceeds the heat demand of connected consumers, the excess heat is discarded. In contrast, CHP-derived

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Fig. 2. System boundary for woody biomass CHP and DHC with material and energy flows.

power can be supplied to both inside and outside of the DHC area on the assumption of connection to the existing power grid. All CHP-derived power is assumed to substitute the conventional power from the public grid. The life-cycle boundary for the LCA was specified in terms of the life cycles of related products in Fig. 2. The functional unit for the LCA was defined as one year’s operation of the energy systems that satisfies the energy needs of consumers included in the system boundary. In order to take into account the seasonal changes in energy needs, the annual operation should be included in the functional unit. The foreground data was specified as the material and energy flows obtained by the developed model. The background data was sourced from multiple databases [28–31]. The following subsections describe the detail of modules for simulation of the unit processes included in the energy system. 2.2.1. Wood chipping Energy-intensity data of wood chippers were extracted from the literature (e.g., Takanashi et al. [33]). The material yield was set as 0.90 by considering losses via chipping residues. The moisture content (wet basis) of timber both before chipping and as wood chips was set as 50%, as assumed in previous research (e.g., Kikuchi et al. [34]). Drying process of the wood chips occurs before burning in the CHP unit and the weight change by water loss is considered in the heat calculations. 2.2.2. CHP plants A module of CHP plants was developed to calculate the required heat of biomass based on the conditions of the CHP scale and the efficiency of power and heat generation. In this study, CHP scale design was investigated considering the temporal change of timber availability caused by the forestry reformation. To enhance the flexibility of changing the total CHP scale, a small-scale CHP package was selected as the candidate technology. Among the technologies applicable to CHP, diesel or gas engines offer a wide range of scales with relatively high power-conversion efficiency, even less than 1 MWe [23]. Considering the feasibility of technology options, gas engines converting gasified biomass into power

and heat were adopted for the small-scale CHP in this study because of the availability of commercialized products. 2.2.3. Demand data conversion In this study, the relationship between demand and load is defined in terms of a coefficient of performance (COP) for heating/cooling devices as

Load ¼ Demand  COP;

ð1Þ

where Load [J/h] is the energy requirement to satisfy the needs of consumers for SC, SH and HW, which is a constraint unaffected by any changes in the performance of installed devices. Demand [J/h] is the energy to be supplied to the devices as fuel, electricity, or heat from DHC, which is affected by the performance of the installed devices. The change in demand caused by the replacement of heating/cooling devices is addressed in the developed module when the devices should be changed to utilize the heat from CHP and DHC. For example, electric heat pumps are replaced by fan–coil units that exchange the heat in the building space using hot or cold water from DHC. Hourly demand and load data are necessary for calculating the detailed heat balances in the DHC module, which will be unavailable unless the energy consumers have advanced sensing devices and records such as building energy management systems. The demand-data conversion module in Fig. 1 converts the data from monthly energy demand to hourly profiles of energy demand and load based on information sources such as monthly bills for electricity or gas. The shapes of the hourly load profiles were obtained from archived statistics [35]. This data source includes statistical pattern data about monthly and hourly load profiles per unit floor area. They are classified by seasons and by building types for four usage categories, namely SC, SH, HW, and other electricity, as shown in Tables A2–A5. Even if monthly data are unavailable from some consumers, a typical monthly load can be estimated using the building floor areas given in Tables A2 and A3. A monthly profile can be converted to an hourly profile by allocating monthly values to the hourly profile by using the hourly profile in the statistics. For these cases of hourly data generated from monthly data, we

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assumed that all days in the month would have the same hourly profile. 2.2.4. DHC system The heat balances between the CHP output and the heat loads of consumers are modeled in the DHC module. Based on the heat balances, the reduction in fossil-fuel consumption can be calculated. We now give an overview of the DHC system. The heat generated by CHP was assumed to be used for HW, SH, and SC. The heat carrier, water, is heated to 85 °C. Cold water (7 °C) for cooling is generated by ACs using the CHP-derived hot water as the heat source. Hot and cold water are carried through pipelines and are used to generate hot or cold air via heat exchangers at the demand sites. Separate storage tanks for hot and cold water are assumed to be installed and all hot and cold water once flow into these tanks. Heat balances across the CHP plant, the heat-demand sites, and the thermal storage tanks were mathematized as follows. 2.2.4.1. Allocation of heat from CHP to heating/cooling. The heat supply to each consumer was assumed to be distributed in proportion to the hourly heat demand by each building. The heat demands and loads at each consumer site can be specified by the demand-data conversion module. As shown in Eq. (1), the heat loads addressed DHC;m

by DHC or conventional fossil-based systems, i.e., Loadn Fos;m

Loadn

[J/h], respectively, are formulated as

DHC;m Loadn ðiÞ Fos;m

Loadn

and

¼ Q DHC;m ðiÞ  COPDHC;m ; n n

ð2Þ

ðiÞ ¼ Q Fos;m ðiÞ  COPFos;m ; n n

Q DHC;m n

ð3Þ

Q Fos;m n

where and [J/h] are the heat from DHC and fossilderived energy in the n-th building for category m (HW, SH, or SC) at time i, respectively. COPDHC;m and COPFos;m [–] are the COPs n n for devices connected to DHC and fossil-derived energy supply, respectively. DHC-derived heat has the first priority for use in satisfying the heat loads for consumers, followed by the heat derived from conventional fossil-based systems. For the Load variables, Bld;m

Loadn ðiÞ [J/h] is specified in the demand-data conversion module and defined for the n-th building at time i, as Bld;m

Loadn

DHC;m

ðiÞ ¼ Loadn

Fos;m

ðiÞ þ Loadn

ðiÞ:

ð4Þ

Hot water generated by CHP is divided into that required for heating, directed towards the HWT, and that required for cooling, directed towards the AC, according to the heating/cooling demands Q 0DHC;m ðiÞ, which assume that all loads are supplied by CHP-derived n heat. Heat balance for HW can be treated similarly as SH, which means that the water is firstly heated by DHC-derived heat and the residual part of the heat load is supplied by fossil-derived energy, as same as the space heating, as shown in Fig. 3(b).

Q 0DHC;m ðiÞ ¼ n

8 LoadBld;m ðiÞ COPFos;m n > ðiÞ COPDHC;m < COPnDHC;m ¼ Q Fos;m n n

> :

n

LoadBld;m ðiÞ n

COPDHC;m COPAC n

¼

ðm ¼ HW; SHÞ

COPFos;m Q Fos;m ðiÞ COPDHC;mn COPAC n n

ðm ¼ SCÞ

:

ð5Þ

For the heat flow towards the HWT, the allocation is determined as

F in HWT ðiÞ

¼

F out CHP ðiÞ

out Q in HWT ðiÞ ¼ Q CHP ðiÞ

F in HWT ðiÞ : F out CHP ðiÞ

out F in AC ðiÞ ¼ F CHP ðiÞ

P 0DHC;SC ðiÞ out nQ n ¼ F ðiÞ P 0DHC;m ; P CHP in ðiÞ Q in n mQ n HWT ðiÞ þ Q AC ðiÞ

out Q in AC ðiÞ ¼ Q CHP ðiÞ

Q in AC ðiÞ

F in AC ðiÞ : F out CHP ðiÞ

ð8Þ

ð9Þ

2.2.4.2. Energy and mass balances around heat storage tanks. Energy and mass balances around the thermal storage tanks are modeled to consider the temporal gaps between heat generated by CHP and the demands of consumers. DTðiÞ is the temperature drop/rise in the heating/cooling via hot/cold water caused by the consumers, and defined as

DTðiÞ ¼ T T ðiÞ  T H0 ðiÞ > 0;

ð10Þ

DTðiÞ ¼ T T ðiÞ  T C0 ðiÞ < 0;

ð11Þ

where the water temperature at the tank at time i is T T ðiÞ [K], subscription T refers to one of the storage tanks HWT or CWT, and T H0 or T C0 [K] are the water temperatures before entering CHP or AC, respectively. Therefore, the available heat from the tank at time i, U T ðiÞ [J], can be represented by the volume of water stored in the tank at time i, V T ðiÞ [m3], and DTðiÞ as

U T ðiÞ ¼ qcDTðiÞV T ðiÞ;

ð12Þ

where q [kg/m ] is the density of water in the tank, and c [J/(kgK)] is the specific heat capacity of water. The constraint for water volume in the tank is 3

0 6 V T ðiÞ 6 V MAX : T

ð13Þ

The variation of U T per unit time is

DU T ðiÞ out waste ðiÞ  Q loss ¼ Q in T ðiÞ  Q T ðiÞ  Q T T ðiÞ; Dt

ð14Þ

where Q waste ðiÞ is the heat [J/h] discarded via overflow from the tank T when the tank is full. Q loss T ðiÞ is the heat loss from tank and pipes caused by imperfect insulation. The relation between heat flow and volumetric flow of each flow excluding Q loss T ðiÞ is

Q Tj ðiÞ ¼ F Tj ðiÞqcDT T ðiÞ ðj ¼ in; out; wasteÞ:

ð15Þ

Q loss T ðiÞ,

For the temperature variation caused by heat loss from tank is represented as

DT loss T ðiÞ ¼ 

Q loss T ði  1ÞDt : qcV T ði  1Þ

ð16Þ

Stored heat in the tank is extracted according to the heat demand of buildings on the basis of DHC devices as

8 > < V 0 ðiÞ out out V T ðiÞ ¼ F T ðiÞ  Dt ¼ PT > Q 0DHC ðiÞDt : n n q c DT T

Q in HWT ðiÞ

in Q in HWT ðiÞ þ Q AC ðiÞ P 0DHC;HW P ðiÞ þ n Q 0DHC;SH ðiÞ n nQ n ; ¼ F out P P 0DHC;m CHP ðiÞ ðiÞ n mQ n

In these equations, the energy supply is indicated by a superscript out (DHC or Fos) and F in X ðiÞ and F X ðiÞ are the volumetric flow rates 3 [m /h] into X and out from X, respectively. The cold flow can be represented similarly as

X

if if

n

ðiÞDt Q 0DHC n

qcDT T

P n

Q 0DHC ðiÞDt n

qcDT T

> V 0T ðiÞ

6 V 0T ðiÞ ð17Þ

ð6Þ

ð7Þ

where V 0T ðiÞ ¼ V T ði  1Þ þ F in T ðiÞDt, which is the available water volume, becomes the upper limit of outflow from the tank at time i. When the heat demand for the building is relatively small and the stored water is near the top of the tank, the water over the tank capacity will overflow to release the excess heat generated from CHP under the constraint of Eq. (13), as

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Fig. 3. Heat balance around the DHC system and its components.

( V waste ðiÞ ¼ F waste ðiÞDt ¼ T T

MAX V 0T ðiÞ  V out T ðiÞ  V T

if

MAX V 0T ðiÞ  V out T ðiÞ > V T

0

if

MAX V 0T ðiÞ  V out T ðiÞ 6 V T

ð18Þ

Q waste ðiÞ ¼ F waste ðiÞqcDT T ðiÞ; T T

ð19Þ

waste V T ðiÞ ¼ V 0T ðiÞ  V out ðiÞ: T ðiÞ  V T

ð20Þ

The total heat flow from DHC to all buildings is determined by F out T ðiÞ in Eq. (17) as out Q out T ðiÞ ¼ F T ðiÞqcDT T ðiÞ:

ð21Þ

Q out T ðiÞ

is allocated to each building in proportion to the demand for each building and the usage category. Heat flow supplied by DHC to the n-th building for usage category m is

2.2.6. Background processes Other background unit processes, namely forestry, production or construction of equipment, and waste treatment, were included in the model. The forestry process includes cultivation, harvesting, and collection of trees. The process inventory for fossil-fuel consumption by forestry machinery and vehicles was obtained from existing literature (e.g., Nambu et al. [37]). The quantities for the production and construction processes in newly installed devices and infrastructures, such as CHP plants, pipelines, and ancillary facilities were estimated considering their lifetimes. The lifetimes were set individually in the case study. Chipping losses and uncollected logs in the forest were included in the waste-treatment process, assuming that they would decompose into CO2 and H2O eventually. 2.3. Forest resource calculation

Bld;m

Loadn ðiÞ=COPDHC;m n Q DHC;m ðiÞ ¼ Q out n T ðiÞ P P Bld;m ðiÞ=COPDHC;m n n m Loadn ðm ¼ HW; SH; or SCÞ:

ð22Þ

Any residual energy

Fos;m load not covered by DHC, Loadn ðiÞ is ðiÞ can (4), and (22). Consequently, Q Fos;m n

determined by Eqs. (2), be determined by Eq. (3). From these values, the fossil-fuel consumption can be calculated using the heating value for fuels or power fed to conventional heating/cooling devices. 2.2.5. Transport of timber The amount of timber transported increases with the enhancement of CHP scale. Because the selection of the location to harvest and collection, which becomes the start point of transport, is different from region to region, the detail of the transport route was set in the case study section. The inventory of fossil-fuel consumption in transportation by timber trailer was sourced from a governmental guideline [36]. The load factor of timber on the trailer was assumed to be 40 wt% on average with 4-ton trucks.

The profile of annual wood availability was calculated considering the difference between the target status of the forest and its current status. The transition period was set as 100 years because several decades at least are needed for a single cycle of forest metabolism. The forest status involves both ‘‘Forest-type composition” and ‘‘Age-class distribution.” ‘‘Forest type” has three categories, namely single-storied forest (SSF), multistoried forest (MSF), and natural regenerated forest (NRF) [38] (see Table A1). In Japan, the existing forest is composed of 41% SSF, 4% MSF, and 55% NRF [38]. The governmental target for forest-type composition 100 years later was set as 26% SSF, 27% MSF, and 47% NRF [38], which aimed to improve multiple functions of the forest, including land conservation, watershed conservation, and carbon fixation [4]. The target for forest-type composition in respective regions can be set by considering this governmental target, and accordingly, the areas of each forest type can be specified. As shown in Fig. 4, the present age-class distribution in Japan has highs and lows that result in fluctuations in wood availability and malfunctioning of the forest environment, whereas the target status is a more level

Y. Kanematsu et al. / Applied Energy 198 (2017) 160–172

Fig. 4. Present and target age-class distributions in Japan. Source data was extracted from MAFF [4].

distribution. A feasible target age-class distribution can be set by using the present age-class distribution, which is usually available in statistical form from local governments or the national agency in Japan. From the present and target forest status, target amounts of harvesting, thinning, or conversion to other forest types can be calculated inductively. The details of case-specific calculations and data acquisition is explained in Section 2.4. 2.4. Case study for the remote island of Tanegashima in Japan The case study involved the application of the developed model to the remote island of Tanegashima in southwest Japan, where forest reformation has become a more critical issue than in other areas of Japan because of the shrinking of the forestry, the population, and the local economy. The quality of wood resources in Tanegashima is inadequate for house building because of insect damage and bending, which is caused in part by the climatic characteristics of the island, including strong winds, long periods of rain, and typhoons. New demands for wood, e.g., fuel use for CHP, are ready to be explored to promote harvesting for sustainable forestry. In addition, the local power grid of Tanegashima has no connection beyond the island and its nonrenewable element is dominated by diesel-engine generation, which has high GHG intensity relative to the average for the Japanese grid. In this study, GHG emission through the life cycles related to CHP and DHC (LC-GHG) was selected as an indicator in the evaluation. For newly installed facilities, the lifetime for each facility was set as in Table 1 to calculate the LC-GHG caused by construction. The same environmental load from construction was allocated to each year of the lifetime. The calculation of the timber-availability profile was based on a previous study [6]. For the strategy aiming to achieve the target status for SSF in Tanegashima, MSF and NRF were excluded in this case study for the sake of simplicity. Information about the forest status in Tanegashima, such as the forest-type composition and age-class distribution, was acquired from a local forest owners’ cooperative, as shown in Tables A6 and A7. The total forest area in the target year was calculated by setting a target composition for forest type. Under the constraint of total forest area, the same Table 1 Lifetimes for newly installed facilities assumed in LCA. Facility

Lifetime (years)

Notes

CHP units ACs Pipelines for DHC Heat storage tanks

15 15 20

Used legal durable years in Japan [39] Used legal durable years in Japan [39] Used technical information by a manufacturer [40] Assumed replacement at the same time with pipelines

20

167

area for each age-class from the 1st to 10th class was set, and then, with the area for older classes decreasing linearly. That is, the 26th class has zero area, as shown in Fig. A1, with the decrease starting from the 11th class. The deficiencies at the 21st and 22nd classes in the target status are a consequence of the small areas for the 1st and 2nd classes in the present status. The area difference caused by these deficiencies was reallocated equally to other classes. After setting the target state, the felling requirement for each year can be calculated. In this study, the amount of harvesting was set so that the area of forests belonging to the same age-class decreases linearly every year. In other words, the difference between the area in present status and target status were divided equally into every year for each age-class group. The regulations for harvesting were that harvesting would not be undertaken until the trees reached the 5th age-class. Thinning amounts were calculated by multiplying the volumetric proportion of yield to standing tree, 0.86 [41], by the tree volume for thinning age-classes, which are the 5th, 7th, 9th, and 11th age-classes, according to the forest management plan for Tanegashima. A specific product [42] was selected as the CHP technology in this study, which offered 40 kWe electrical output and 110.5 kWth thermal output per unit. The available heat for DHC is 70 kWth for hot-water supply, because 40.5 kWth is needed for drying the wood chips from 50% to 15% wet-based moisture content. These specification data for the device were based on a preliminary feasibility study for Tanegashima and acquired through interview. The minimum unit scale for the CHP was defined as this 40 kWe unit, with multiple installations being adopted for larger scale CHP systems, such as 80 kWe, 120 kWe, 160 kWe, or more. The total conversion efficiency from fuel (i.e., wood chips available on the market) to power and heat was mathematized as the CHP module shown in Fig. 1, where the material and energy balances can be specified on the basis of necessitated CHP scales. Note that demand fluctuation was not considered for the CHP module because the installed CHP is assumed to be operated with a constant operating load in this study in order to achieve the timber consumption according to the target harvesting amount. Demand fluctuation and supply control are treated in the DHC module. The early phase of the installation of pipelines and other infrastructures related with DHC systems is assumed as the design phase in this study, which means that small number of demand sites are accessible to DHC. Regarding the demand for heating and cooling, the city center of Nishinoomote area in Tanegashima has a concentrated energy requirement. Two major hotels, a city government office, and a hot-spring facility were chosen as potential consumers of the DHC proposed in this case study. Monthly energy demands for the potential users were obtained through on-site investigation. The specifications of heating/cooling devices at consumer sites are summarized in Table A8. The capacities of the HWT and CWT were set individually as 20% of the heat generated daily by CHP, according to Eq. (12). This means that the tank capacity will change with the CHP scale. The flow temperatures were set as T HWT ðiÞ ¼ 85 °C, T H0 ðiÞ ¼ 60 °C, T CWT ðiÞ ¼ 7 °C, and T C0 ðiÞ ¼ 20 °C for time i. The heat loss from any tank was assumed to be negligible (by being well ¼ 0 in Eq. (14). By expanding the scale of insulated) giving Q loss T CHP, conventional power and fossil fuel consumption is gradually replaced by biomass-derived energy. If all heat demands in the DHC area are satisfied by biomass-derived heat, the excess heat from the CHP was assumed to be discarded. On the other hand, CHP-derived power was assumed to be available to the public power grid. Therefore, if power demands in the DHC area are satisfied by biomass-derived power, the excess power from the CHP was assumed to be made available outside the DHC area. The heat and power were balanced hourly using hourly demand data.

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Fig. 5. Estimated profile of annual timber availability for the SSF in Tanegashima, based on forest-resource calculations.

For timber transport in Tanegashima, it was assumed that timber was collected and brought from three major forest areas (north, central, and south) in Tanegashima. The transportation distance was measured along the route by setting the start and end point on the map. The start points were set simply as the centroids of the forest areas for each area, based on the assumption that harvesting and thinning are both undertaken in a geographically uniform manner across each forest area. That is, any deviations in the forest-resource distribution in any forest area were ignored.

timber supply in early years is caused by the initial age-class distribution of the forest, which has many trees in the thinningtarget age-classes. Although some timber is generated during the conversion of SSF to NRF or MSF, the total quantity of timber decreases to 15,700 m3/year (the minimum value) in 2036. After this, the timber from harvesting increases with the thinning, which results in a gradual increase in the total timber supply to approach a constant value after 100 years. From the availability profile through 100 years, the average timber availability was calculated as 21,100 m3/year.

3. Results 3.2. LC-GHG affected by the scale of CHP 3.1. Forest resource calculation Fig. 5 shows the results of the available-timber supply profile for Tanegashima, where the horizontal lines correspond to (a) maximum, (b) average, and (c) minimum levels over the next 100 years. The maximum value (27,600 m3/year) occurs for the first year, which is largely dominated by thinning activity. The timber supply continues to decrease over the next 20 years. The large

Fig. 6 shows the LC-GHG for a range of CHP-system scales. The LC-GHG attributable to the power and fuel consumption shown in Fig. 6 involves just the four target users of DHC. The bars in Fig. 6 were drawn for every 40 kWe increase, corresponding to the output of single unit. The bar at 0 kWe refers to the emissions associated with power and fuel consumption with no CHP and no DHC, namely 2568 t-CO2eq/yr and 1176 t-CO2eq/yr, respectively. The

Fig. 6. LC-GHG emissions from energy consumers for different scales of the CHP system. The timber requirement is also shown with vertical dashed lines (a), (b), and (c) corresponding to the lines in Fig. 5.

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Y. Kanematsu et al. / Applied Energy 198 (2017) 160–172 Table 2 Timber consumption levels and corresponding CHP scale.

(a) Maximum (b) Average (c) Minimum

Timber requirement [m3/y]

Number of CHP units [–]

Electrical output [kWe]

Thermal output [kWth]

27,600 21,100 15,700

22.0 16.9 12.6

882 675 502

1540 1180 879

thermal output consistent with the power output is also shown along the horizontal axis. The sum of GHG emissions from the processes of ‘‘Waste” and ‘‘Combustion” is assumed to be equal to ‘‘Absorption by forest”. As the CHP scale increases, power and fuel consumption gradually decreases and others increase. Power consumption is fully met by biomass-derived energy when the number of CHP units becomes 19. Similarly, fuel consumption drops to zero when 21 units have been installed. To connect the LC-GHG with the timber requirement and availability for CHP, the annual timber requirements equals to (a) maximum, (b) average, and (c) minimum levels of timber availability (see Fig. 5) are drawn as vertical lines in Fig. 6. The correspondence between the CHP scale and the timber requirement is shown in Table 2. (Note that fractional numbers of CHP units appear in Table 2, while only integer numbers of CHP units are assumed for the proposed model.) The results show that conventional power and fuel consumption by DHC users could be replaced by biomass CHP for scales near the point that consistent with an average level of timber supply. In Section 4, we discuss the details of a feasibility check on these CHP scales and the scenario design for CHP installation.

4. Discussion 4.1. CHP installation scenario based on resource-procurement feasibility Comparing the CHP scale with timber availability and requirements for the three cases of (a) maximum, (b) average, and (c) minimum shown in Figs. 5 and 6, we make the following observations regarding the CHP installation scenario and the feasibility of resource procurement: (a) The maximum timber supply enables a maximum scale for the CHP, but the procurement cannot be sustained from Tanegashima alone, as shown in Fig. 5. However, this scale of CHP can treat all wood in the target harvest amount from Tanegashima for the next 10 years, when considering the lifetime of the CHP (i.e., 15 years), as shown in Table 1. The import or transport of wood chips, timbers, or other biomass for fuel can compensate for the shortfall of local fuel. Alternatively, it could be operated under partial load, resulting in lower efficiency and operation ratios. (b) If the CHP scale corresponds to the average supply level, there is an excess of available timber before 2026 and after 2066 and insufficient timber between 2026 and 2066. Any excess remaining timber must be treated by manufacturing or other processes to prevent the age-class distribution deteriorating, following the stagnation of reforestation. However, if the available timber is insufficient, importing or transportation from other areas could address the shortage, as discussed for the maximum case. Partial load operation of the CHP is another option. (c) The operation of CHP is stabilized most easily at a scale corresponding to the minimum supply, although the effect of the CHP installation on the mitigation of LC-GHG is less than for the other CHP scales. Again, any excess timber not used as CHP fuel must be consumed in other ways.

To make the installation scenarios of CHP practical, two questions should be considered carefully under the constraint that forest reformation must be conducted. First, how should excess timber be treated by other demands for wood? Second, how should any shortfall in timber supply be met by timber from other areas? These questions relate strongly to the installation scale, timing, and lifetime for CHP systems. The energy systems should be examined carefully around the minimum point in timber availability, i.e., when the timber-availability gradient turns from negative to positive in Fig. 5 (year 2036 in this case). For CHP installation before the minimum point, CHP scales of less than level (c) can be recommended if other demands for timber exist, such as wood products, pellets, or export to other areas. If the excess supply of timber cannot be utilized via other demands, level (a) should be adopted for the next 15 years, with an additional procurement of fuel from other areas. For CHP installation after the minimum point, gradual scale-up is possible by adding CHP units corresponding to the increase of timber availability from level (c) to level (b) or above. The packaged CHP units adopted in the case study could be used in the adjustment of total capacity along with the longterm profile of timber availability. The installation scenario for the local community should be adapted by considering the stage of forestry reformation. Note that the above two questions cannot be answered fully by the analysis in this paper, because the evaluation of actual risks of malfunction in the forest, e.g., landslip in an old forest, was outside the scope of this work. The shortage of timber demand can lead to the stagnation of forestry, as has been happening in Japan. The supply chain for wood from outside Tanegashima, such as from overseas, appears difficult to establish at present. There are additional issues to be considered in making decisions about the CHP scale. For such a complicated design process for public energy systems, the developed model will enable the visualization of material and energy flows.

4.2. Scale-up effect on GHG reduction efficiency By increasing the CHP scale for a fixed energy demand, the LCGHG mitigation per the addition of one CHP unit decreases. Fig. 7 shows the change in this scale-up effect on the LC-GHG reduction. LC-GHG(n) refers to the GHG emissions (t-CO2eq/yr) for the case of n units of CHP (40 kWe/unit + 70 kWth/unit). Therefore, the difference LC-GHG(n  1)  LC-GHG(n) represents the effect of an increase of one CHP unit in the case study. Although the line graph for total LC-GHG in Fig. 6 appears to be a straight line, Fig. 7 shows the nonlinearity in the gradient of this line. In Fig. 7, the gradient of GHG reduction is flat from 1 to 7 units, where energy supply from CHP is much smaller than the energy demand and all generated energy is consumed. From 8 to 23 units, the level of GHG reduction drops steadily. This is because the energy supply from CHP can temporarily exceed the hourly energy demand, resulting in an increase of the excess heat from the CHP together with the scaling up. The gradient of GHG reduction is again flat for more than 23 units of CHP. This means that almost all heat demand, previously satisfied by fossil fuels, has been replaced by biomass-derived heat. For this range, the reduction in GHG is caused by the biomassderived electricity being connected to the local power grid. All biomass-derived electricity can be consumed for the range of

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Fig. 7. Scale-up effect on the amount of GHG reduction.

CHP scales being investigated because the power-supply area is not limited to the heat-demand site but set as the whole island. Considering the design of the demand scale for connecting DHC to residential houses in the area as an example, the curves of the gradients for GHG reduction are moved. This is interpreted in terms of timber availability. We can label the two transition points in the scale-up effect as the scale-up effectiveness ‘‘peak” and the scale-up effectiveness ‘‘bottom.” The unit number for the scale-up effectiveness peak is much less than the minimum wood-supply level (c) in Fig. 6, which means that a small-scale CHP has an advantage in installation efficiency with respect to LC-GHG. Here, a larger scale CHP would also be acceptable, because it can reduce GHG emissions even for excess heat, as shown for the post-23 range in Fig. 7. For larger energy demand, a large-scale CHP has the advantage of installation efficiency, because the scale-up effectiveness peak shifts to the right in Fig. 7. Even though a larger scale CHP makes a larger contribution to the mitigation of GHG in Fig. 7, the advantage of CHP as a power-generation technology is dependent on the LC-GHG of the public power grid, as measured in kg-CO2eq/kW h. If low-carbon energy sources, such as photovoltaics, wind turbines, or geothermal power generation, become dominant across the power grid, the effects of CHP installation could become negative. This would occur if there was no local demand for the co-generated heat. This has also been discussed in a future-scenario analysis of distributed energy technologies [23]. 4.3. Effect of changing the capacity of HWT and CWT We now analyze the effects on GHG reduction of the capacity of the thermal storage tanks. In the case study, the capacities of the HWT and CWT were each set as 20% of the daily heat generated by CHP. By changing the tank capacity, the GHG reduction changes as shown in Fig. 8. The calculation was performed for six tank capacity ratios over CHP scales from 1 to 20 units. The six capacity ratios were 0%, 20%, 40%, 60%, 80%, and 100% of the heat generated daily by CHP for both the HWT and CWT. The actual heat amounts for the CWT are different from HWT because of the difference of DT in Eq. (12). The tank capacity (m3) is shown on the horizontal axis, which is scaled according to the capacity of the HWT. The vertical axis shows the reduction in LC-GHG from the no-tank case (in tCO2eq/year) for each CHP scale. This shows that the effectiveness of the enlargement of the tank capacity depends on its combination with the CHP scale. The coordinates of the peak point of LC-GHG reduction in each plot series differed for each CHP scale, as shown in Fig. 8. From 1 to 6 units, energy demands are larger than the energy supply from CHP for almost all periods, resulting in no scale advantage and an increase in LC-GHG with the increase of tank capacity caused by

the additional tanks’ production and construction. The effects of enlarging the tank capacity start to turn positive for 7 or more units. In the range of 10–16 units, all calculated points had positive values. The peak point in the plot series continues to shift rightwards until the 13-unit case, and then shifts to the smaller-capacity values after the 14-plus case with increasing CHP scale. The point with a tank capacity of 80% of daily CHP generation for the 13-unit case achieved the highest LC-GHG reduction effect from the no-tank case in this analysis. It seems that heat storage has the largest effect when temporal gaps between heat supply and demand are sufficiently wide and prolonged. For larger CHP scales, the peak of LCGHG reduction becomes small and even negative for 17 or more units. This is because CHP-derived heat becomes excessive for much of the time, and there is less room for consuming stored heat in the tanks. In such situations, unused heat is discarded and larger tanks will have less effect on LC-GHG reduction, and there is the additional negative effect of the production and construction of the tanks. The relationship between CHP scale and heat-storage capacity is clearly demonstrated by this analysis.

5. Conclusions This case study considers the design of an appropriate scale for CHP in a region facing challenges to forestry reformation, namely Tanegashima Island in Japan. Long-term timber availability was estimated by envisioning a target state for the forest over the next 100 years. The required amounts of harvesting and thinning to achieve the target state were quantified to indicate the transition from a maldistribution of forest age-classes in Tanegashima to an ideal distribution. During the transition, much of the timber should be utilized effectively under feasible conditions. The energy use of such timber can be regarded as a solution that adds values to the timber. By examining the life-cycle impacts originating from such use of the timber, a mathematical model of unit processes and its connection to the life cycle of materials and energies associated with CHP systems was developed. This model enabled the calculation of material and energy flows as a function of CHP scale and other design variables. By comparing the calculated timber requirement for various CHP scales with timber availability from the local forest, we investigated the feasibility of CHP/DHC systems based on their installation scenarios. We could demonstrate the importance of planning the CHP scale and installation timing in harmony with forestry management practices. As shown in the discussions, the suggested strategies differed significantly for each phase of the forestry reformation. Particularly for designing the CHP scale, the timber-availability profile should be considered, together with the CHP lifetime and the state of forestry reformation. For the design of CHP/DHC systems in rural areas, the accessibility and availability of the data required to run calculations via mathematical models must also be considered, to make the simulation usable for its various users. Simulations executed with accessible data will accelerate the expansion of models and methods for application for a variety of regions. Data accessibility has importance in CHP design because CHP usually involves distributed and small/medium-scale energy systems, which may be designed for various regions and by various decision makers. Data accessibility will be different for different types of decision makers, who might be local governments, energy consultants, or forestryrelated companies. In this study, for example, the input energydemand data were collected from monthly bills for electricity or fuel, or from interviews at each demand site, and monthly data were converted to 24-h data by a conversion module developed to produce a profile for hourly demand. Additional indicators for system evaluation need to be examined in future work. Although only GHG emissions were

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Fig. 8. Effect of changing the capacity of the HWT. The points on each series of plots correspond to the heat capacity of HWT with 0%, 20%, 40%, 60%, 80%, and 100% of the heat generated daily by CHP.

considered in this paper, economic and social criteria are also needed for actual decision-making. The developed model can then supply information about the changes of the material and energy flows required for economic and social evaluation.

[4] [5]

Acknowledgements [6]

The authors are grateful to the Tanegashima Forestry Cooperative, Nishinoomote city and Sustainergy Company for their cooperation in data collection and discussions with them. Part of this study was financially supported by JSPS KAKENHI Grant Numbers 25870163 (Grant-in-Aid for Young Scientists B), 26285080 (Scientific Research B), 15H01750 (Scientific Research A), 16H06126 (Young Scientists A), Environment Research and Technology Development Fund (1RF-1503), and project of New Energy and Industrial Technology Development Organization, Japan (NEDO) ‘‘Hikashokusei baiomasu wo katsuyousuru smart kagaku seisan system ni kansuru tyousa (in Japanese) [Investigation of smart chemical production system utilizing inedible biomass]”. Activities of the Presidential Endowed Chair of the Platinum Society at the University of Tokyo are supported by the KAITEKI Institute, Inc., Nippon Telegraph and Telephone Corporation, Fujifilm Holdings Corporation, Mitsui Fudosan Co., Ltd., LIXIL Corporation and Shin-Etsu Chemical Co., Ltd. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.apenergy.2017. 04.021.

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