Performance analysis of a biomass gasification-based CCHP system integrated with variable-effect LiBr-H2O absorption cooling and desiccant dehumidification

Energy 176 (2019) 961e979

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Performance analysis of a biomass gasification-based CCHP system integrated with variable-effect LiBr-H2O absorption cooling and desiccant dehumidification Xian Li a, Xiang Kan b, Xiangyu Sun c, Yao Zhao c, Tianshu Ge c, Yanjun Dai c, Chi-Hwa Wang b, * a b c

NUS Environmental Research Institute, National University of Singapore, Singapore, 138602, Singapore Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 June 2018 Received in revised form 3 April 2019 Accepted 7 April 2019 Available online 10 April 2019

A novel biomass gasification-based combined cooling, heat and power (CCHP) system, which is composed of a gas-fueled internal combustion engine, variable-effect LiBr-H2O absorption cooling, and dehumidification air-conditioning with desiccant coated heat exchangers, was introduced. The temperature and humidity independent strategy was applied in the gasification-based CCHP system to enhance cooling production, in which the variable-effect absorption chiller and desiccant dehumidification air-conditioning were driven by the exhaust heat and jacket heat of the gas engine based on energy cascade, respectively. The operation strategy of the system followed the electric load. Validated by experimental data, a zero-dimensional code of the gasifier with Gibbs free energy minimization, an artificial neural network model of the variable-effect absorption chiller, and a 1-D dynamic model of the dehumidification air-conditioning, were built with reasonable deviations. The results of energetic, economic, and environmental (3E) analyses for the proposed gasification-based CCHP systems that were applied in two different buildings indicate that woody chips are the most favorable feedstock under the climate of Singapore. The total performance is more sensitive to the feedstock cost than to the natural gas cost. This work enables to contribute valuable data to the practical application of the biomass gasification-based CCHP system in Singapore's building sector. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Biomass gasification CCHP Variable-effect absorption Desiccant dehumidification Thermodynamics 3E analysis

1. Introduction Energy supply and energy security have gained increasing attention throughout the world. It is attributed to that the energy demand is increasing year-by-year while the limited fossil fuel source is decreasing. Continuously exploring sustainable and clean energy sources and technologies has become a key driver to a great number of researchers in this sector, in order to find a more environmentally friendly approach to mitigate emissions (e.g. greenhouse gas). Biomass (e.g. biofuel and organic waste [1]) that dominates renewables shared 5.7% of world primary energy supply in 2016 [2]. In megacities like Singapore, organic solid waste e.g.,

* Corresponding author. Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117576, Singapore. E-mail address: [email protected] (C.-H. Wang). https://doi.org/10.1016/j.energy.2019.04.040 0360-5442/© 2019 Elsevier Ltd. All rights reserved.

paper/cardboard, horticulture waste, sewage sludge, etc. is annually generated in huge quantities. In 2016, Singapore annually produced around 7,814,200 tons of solid waste, of which 61% was recycled with the rest await disposal. Non-food organic solid waste (e.g. paper/cardboard, horticulture waste, and sewage sludge) occupied 33.5% of total waste that had to be disposed of in Singapore. Combustion, pyrolysis, and gasification are by far the main pathways of converting biomass to energy carries ‒ heat, power, synthesis gas or liquid fuels, in which thermochemical gasification [3,4] is an environmentally friendly approach of converting biomass feedstock to more diverse fuels e.g. syngas (mainly composed of H2 and CO) and liquid fuels via Fischer-Tropsch synthesis. These gaseous/liquid fuels can be more efficiently used for power generation or combined cooling, heat and power (CCHP) that is an attractive and mature technology of maximizing the overall energy efficiency at the systematic level. Biomass-fueled gasification CCHP systems [5,6] are playing an increasingly

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Nomenclature cp CCR CO2ER COP COST E ER F h HHV LHV M m_ f m_ air n Nne Ne OCRR PER Q r RMSDr Stotal T U V_

specific heat capacity, kJ/(kg K) cooling-to-cooling ratio CO2 emission reduction ratio coefficient of performance cost, $/kg, $/Nm3, $/kWh electrical consumption, kW equivalence ratio fuel energy, kW enthalpy, kJ/kg higher heating value, MJ/kg lower heating value, MJ/kg mass fraction of the element in biomass feedstock, % feedstock feeding rate, kg/s air feeding rate, kg/s moles of the species in the product syngas releasing from the gasifier, mol nominal electrical power of a natural gas fired ICE, kW power output of internal combustion engine, kW operation cost reduction ratio primary energy ratio energy flux, Kw sensible heat ratio relative root mean square deviation total performance temperature, K switching variable for HE-4

W

volume flow rate, m3/s switching variable for HE-3

Subscripts air B1 B2 c c,latent c,sensible col chw cw CGE D

air gas boiler #1 gas boiler #2 cold fluid latent cooling of buildings sensible cooling of buildings coolant water chilled water cooling water cold gas efficiency heat used by the DDAC unit

significant role in mitigating climate deterioration and in sustainable economics. Based on the upstream products (e.g. heat, gas, and liquid fuels) of biomass-to-energy processes, the prime movers e.g. internal combustion engines (ICEs) and gas turbines further convert the products to power, heat, and cooling. The gasifier is a key component in the gasification-based CCHP (G-CCHP) systems. During the past decades, the comprehensive gasification processes with carbonaceous feedstock materials have been widely studied in order to improve the gasification efficiency i.e. cold gas efficiency. Nipattummakul et al. [7] conducted an experimental study on syngas that was generated from sewage sludge by using steam gasification, where the effect of the reaction temperature on the gasification efficiency indicated that the reduction temperature over 1073 K was recommended for the steam gasification with sewage sludge. Umeki et al. [8] numerically analyzed the performance of an updraft biomass gasifier with the

e electricity eq,redwood equilibrium for redwood pellets eq,woody equilibrium for woody chips eq,sludge equilibrium for sewage sludge ext exhaust gas/heat f feedstock h hot fluid hw hot water HE heat exchanger in inlet jacket coolant water/heat load load loss heat loss N natural gas out outlet utility utility grid s producer gas trans transmission of utility grid V heat amount used by the VEAC unit Greek symbols efficiency εHE effectiveness of heat exchanger astoich stoichiometric ratio of oxygen-to-feedstock m CO2 equivalence rate u1 u2 , u3 weight factor used in total performance ∅ modified factor for electrical power efficiency d modified factor for exhaust gas temperature

h

Abbreviations 3E energy, economy, and environment CCHP combined cooling, heat and power DCHE desiccant coated heat exchanger DDAC desiccant dehumidification air-conditioning FEL following electrical load G-CCHP gasification-based combined cooling, heat and power HE heat exchanger HVAC heating, ventilation, and air conditioning ICE internal combustion engine PHE plate-type heat exchanger VEAC variable-effect absorption chiller RS reference system UPS uninterrupted power supply

high-temperature steam agent and investigated the effect of various steam-to-feedstock ratios. Xiao et al. [9] proposed twostage fluidized bed gasification to conduct the parametric and performance investigation by using the woody chips and pig manure. Contributed by the steam gasifying process, a high H2 content of 60% was obtained. Olgun et al. [10] designed and established a small-scale fixed-bed downdraft gasifier with agricultural and forest solid waste to study the effect of air-to-fuel ratios on the gasification performance. At the systematic level of the G-CCHP, most of the investigators aimed to assess the overall performance that involves in the energetic, economic and environmental (3E) impacts via the experiment, simulation, and multiple-scale demonstration. Wang et al. [11e13] have carried out modeling and performance analyses regarding optimization of the biomass gasification-based CCHP system, including produced energy supplied to buildings and

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hybrid utilization with natural gas. Li et al. [14] evaluated the yearly performance of a 20 kW micro-scale biomass gasification-based CCHP system running under U.S. climate. Patuzzi et al. [15] proposed the scale category for biomass gasification CHP systems, i.e. micro-scale systems (<50 kW), small-scale systems (50 kWe1 MW) and large-scale systems (>1 MW). They compared the various small-scale biomass gasification combined heat and power (CHP) systems utilized in Italy, in which the highest electrical efficiency of 25.3% can be reached corresponding to wood chips feedstock. Puig-Arnavat et al. [16] built a thermodynamic model of a biomass gasification system and conducted a comparative study on performances in different system configurations. Huang et al. [17] focused on the performance assessment on a biomass gasification tri-generation system via modeling prediction. In this paper, three different types of feedstock (willow chip, miscanthus, and rice husk) were compared with respect to energetic and economic benefits when the system supplied to energy profiles of selected buildings. The biomass-fueled G-CCHP systems that are integrated with ICEs are summarized in Table 1 covering the nominal power range of 15e1000 kW (from micro-scale to smallscale). The majority of research in the literature focused on the singleeffect or double-effect absorption cooling driven by the exhaust heat of ICEs, and on domestic hot water produced from coolant water. A biomass gasification-based CCHP system operates the offdesign mode frequently to match the load evolution that is affected by weather conditions and building features. However, the conventional single-effect or double-effect absorption cooling cycle has significant limitations in off-design operation e.g. the driving temperature restriction. In addition, buildings in tropical cities like Singapore are in high demands for cooling but not for domestic hot water and space heating. Thus, a novel biomass gasification-based CCHP system (G-CCHP) that adopted the temperature and humidity independent concept was introduced, where state-of-the-art technologies of the variable-effect absorption chiller (VEAC) [18]

963

that is capable of smoothly operating between the single- and double-effect cycles, and desiccant dehumidification (DDAC) with desiccant coated heat exchangers (DCHEs) [26] were integrated in order to maximize cooling output. To date, no such a G-CCHP system has been reported. The objective of this paper is to comprehensively assess the systematic performances of the proposed GCCHP systems with different feedstock materials (i.e., redwood pellets, woody chips, and sewage sludge) for two types of buildings in Singapore (i.e., data center and commercial building), via 3E analyses based on the experiment, simulation and comparative study with the conventional heating, ventilation, and air conditioning (HVAC) e electricity-driven vapor compression refrigeration. Firstly, the system configuration was introduced and the operation strategy was described subsequently. Secondly, the models of the gasifier, ICE, DDAC, and VEAC were developed and validated with the experimental data collected from on-field and lab test. In addition, the 3E evaluation criteria and total performance were defined. Thirdly, based on the gasifier model, the optimal parameters for the gasification process were obtained. The 3E and total performances of the proposed systematic schemes were evaluated. Sensitivity analysis was conducted to find the effect of the key factors on the total performance. Finally, the major findings were summarized. 2. System description 2.1. System configuration A G-CCHP system (see Fig. 1) that is fed with solid-waste carbonaceous feedstock is integrated with variable-effect LiBrH2O absorption cooling and desiccant dehumidification airconditioning that includes desiccant coated heat exchangers. The proposed G-CCHP system is mainly composed of two subsystems ‒ upper stream subsystem (i.e., a producer gas generation subsystem) and downstream subsystem (i.e., a CCHP subsystem), which

Table 1 Summary of biomass-fueled G-CCHP systems that are integrated with ICEs (the nominal power range of 15e1000 kW). Feedstock

Gasifier

CCHP configuration

Energy output

Research category

Source

Ligno-celulosic/eucaliptus wood

Downdraft

ICE, NH3-H2O absorption chiller

Experiment

[18]

Straw

Downdraft

ICE, two-Stage LiBr-H2O absorption chiller

Simulation

[19,20]

Wood, coconut, straw

N.A.

ICE, single-effect LiBr-H2O absorption chiller

Experiment and Simulation

[21,22]

Woody chips

N.A.

ICE, single-effect LiBr-H2O absorption chiller

Simulation

[12]

Woody chips

N.A.

ICE, mixed-effect LiBr-H2O absorption chiller

Simulation

[23]

Woody chips

Fixed-bed

ICE, Single-effect LiBr-H2O absorption chiller

Simulation

[24,25]

Woody chips and almond shells

Downdraft and Fluidized bed

ICE, single-effect or double-effect LiBr-H2O absorption chiller

Simulation

[16]

Willow chips, miscanthus, rice husk

Downdraft

ICE, NH3-H2O absorption chiller

Simulation

[17]

Woody chips

Downdraft

ICE, double-effect LiBr-H2O absorption chiller

Electricity: 15 kW Cooling: 17.4 kW Electricity: 446 kW Cooling: 1804 kW Heating: 595 kW Hot water: 335 kW Electricity: 1000 kW Cooling: 2800 kW Heating: ~2000 kW Electricity: 346 kW Cooling: 1010 kW Heating: 1063 kW Electricity: ~40 kW Heating: 60 kW Cooling: N.A. Electricity: 50 kW Heating: 120 kW Cooling: 20 kW Electricity: 250e1974 kW Heating: 2057e5217 kW Cooling: 151 kW Electricity: 250 kW Heating: 320 kW Cooling: 92.2 kW Electricity: 100 kW Heating: ~131 kW Cooling: ~76 kW

Simulation

[13]

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10 4

Hopper

Generator

5 18

HE-4

19 3

1 (Air)

HE-1 3 Cyclone Filter 11 Natural gas

Syngas 2 (feedstock)

B1 HE-3

6

20 21

DDAC Air outlet

8

Gasifier

25

B2

24 22 Air inlet

Natural gas Residue

ICE

PHE

Feedstock

23

HE-2

Cooling tower #2 Exhaust gas 13 9

VEAC 12

15

14

Cooling tower #1

17 16

HVAC Utility grid

Syngas Inlet/Outlet air

Cooling (fan coils) Building

RS

Pressure hot water Electricity

Electricity (Electrical equipment)

Chilled water

Exhaust gas

Jacket water

Cooling water

Fig. 1. The layout and stream flows of the G-CCHP and reference systems.

can simultaneously supply cooling and power to buildings. To be specific, the carbonaceous feedstock is fed into the air-based gasifier and is converted to producer gas and by-products (e.g., tar and solid residue). The gas-water heat exchanger (HE-1) is used to recover sensible heat of the producer gas, and then the cooled producer gas is purified by a cleaning system in which the cyclone and filter mainly remove particles and tar, respectively. Ultimately, the purified producer gas drives the ICE to generate electrical power accompanied by two types of waste heat ‒ the jacket heat of coolant water and the exhaust heat of the engine flue gas. In view of cascade utilization of waste heat, the exhaust heat drives a variableeffect LiBr-H2O absorption chiller to supply chilled water for end users (i.e. buildings in this work), while the jacket heat is used to drive the DDAC synchronously. Since the coolant water temperature is too high to directly serve as the heat source of regeneration of the DDAC, a plate-type heat exchanger (PHE) is set to achieve the required temperature level. The coolers (HE-3 and HE-4) are used to release superfluous heat if the waste heat is beyond the required amount based on the system output and load demand of buildings. Cooling towers #1 and #2 are equipped herein to eject the waste heat of cooling water for the VEAC and DDAC, respectively. For the DDAC, the waste heat of cooling water originates from two-fold aspects e the adsorption heat of the desiccant adsorbent material and the heat capacity caused by the regeneration cycle. The natural gas fueled boilers (i.e., B1 and B2) serving as the auxiliary unit are employed to increase the temperatures of the pressure hot water (VEAC) and hot water (DDAC) to the required parameters in the absence of the sufficient exhaust heat and jacket heat. The temperature and humidity independent strategy was adopted. Generally, the VEAC mainly handles the sensible cooling load, while the DDAC handles the latent cooling load. The HVAC with electricitydriven vapor compression refrigeration is commonly applied in

different kinds of commercial buildings or data centers in Singapore. Hence, it is selected as the conventional reference system (RS) in this work, in order to conduct the comparative evaluation with the proposed G-CCHP system. 2.2. Operation strategy In this work, the following electrical load (FEL) [27] served as the operation strategy of the G-CCHP system. The major descriptions and assumptions are listed below:
The power consumptions of the units in the G-CCHP system keep constant (the rated power consumption) under the offdesign (non-rated) operation.
Superfluous jacket/exhaust heat (QHE-3/QHE-4) is assumed to be ejected into the environment. Hence, its 3E (energetic, economic, and environmental) benefit is neglected herein.
The G-CCHP system is reliable for 0e100% of the rated capacity. The purified producer gas is assumed to be fully combusted in the ICE.
The gasification-based power generation system employs the uninterrupted power supply (UPS) to fulfill the power demand in the start-up operation. When the system reaches a steady state, the self-generated electricity enables a long-term operation. UPS power consumption is neglected in this work.
The efficiencies of the ICE, VEAC, and DDAC units vary with offdesign operation conditions to enhance the accuracy of the performance evaluation, which relies on the following models described in Section 3. The energy flows of the G-CCHP system are shown in Fig. 2. The energy input of feedstock for the gasifier is calculated by

X. Li et al. / Energy 176 (2019) 961e979

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Fig. 2. The energy flows and operation strategy of the G-CCHP systems.

Ff ¼

ECCHP þ E hCGE ,he

(1)

Qjacket þ W,QB2 ¼ ð1  WÞ,QHE3 þ QD

(4)

where Ff represents the energy input of feedstock. ECCHP and E are the electrical power outputs for self-consumption and building load, respectively. hCGE and he are the cold gas efficiency of the gasifier and the power generation efficiency of the ICE, respectively. E ¼ Eload is followed by the electrical power demand of buildings. The heat drives the VEAC unit for sensible cooling is expressed as

where Qjacket represents the jacket heat generated from the ICE. QB2 is thermal energy supplied by the gas boiler B2. QHE3 is defined as the ejected heat by HE-3. QD is the heat amount used by the DDAC unit. In Eq. (4), the variable in the second term of the left side, W, is defined as a switching coefficient to control the operation statuses of both B2 and HE-3, and can be determined by

QHE1 þ QHE2 þ U,QB1 ¼ ð1  UÞ,QHE4 þ QV




(2)

where QHE1 and QHE2 represent the heat amounts recovered from HE-1 and HE-2, respectively. QB1 is thermal energy supplied by the gas boiler B1. QHE4 is defined as ejected heat by HE-4. QV is the heat amount required by the VEAC unit. The switching variable in the third term of the left side of Eq. (2), U, is used to control the operation status of the gas boiler B1 and HE-4, and it can be determined by




U ¼ 1; ðQHE1 þ QHE2 Þ,COPVEAC < Qc;sensible U ¼ 0; QB1 ¼ 0; ðQHE1 þ QHE2 Þ,COPVEAC  Qc;sensible

W ¼ 1; Qjacket ,COPDDAC < Qc;latent W ¼ 0; QB2 ¼ 0; Qjacket ,COPDDAC  Qc;latent

(5)

where COPDDAC is the coefficient of performance for the DDAC. Qc;latent is the latent cooling load of buildings. When the latent cooling capacity contributed by jacket heat of the ICE is insufficient (Qjacket ,COPDDAC < Qc;latent ), the gas boiler B2 serves as the backup to provide the necessary heat for the DDAC. In contrast, the excess jacket heat is ejected to the environment by HE-3. The fuel energy consumptions of the gas boilers B1 and B2 can be calculated by

(3)

where COPVEAC is the coefficient of performance for the VEAC. Qc;sensible is the sensible cooling load of buildings. In Eq. (3), operation statuses of the B1 and HE-4 are dependent of the comparison of the cooling capacity (contributed by HE-1 and HE-2) and the real-time sensible cooling load of the building. If the cooling capacity contributed by HE-1 and HE-2 is insufficient (ðQHE1 þ QHE2 Þ,COPVEAC < Qc;sensible ), the boiler B1 enables to supplement accordingly. On the contrary, the redundant heat is ejected to the environment by HE-4. The heat supplied to the driving heat source of the DDAC unit for latent cooling is expressed as

8 QB1 > > > < FB1 ¼

hB1

> Q > > : FB2 ¼ B2

(6)

hB2

where FB1 and FB2 are the fuel energy consumptions of B1 and B2, respectively. hB1 and hB2 are thermal efficiencies of B1 and B2, respectively. The energy balance of the cooling load for the G-CCHP system can be expressed as

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8 Qc;latent þ Qc;sensible ¼ QDDAC þ QVEAC > > > > > > QD < Qc;latent ¼ QDDAC ¼ COPDDAC > > > > QV > > : Qc;sensible ¼ QVEAC ¼ COPVEAC

ER ¼ (7)

where QDDAC and QVEAC are cooling capacities provided by the DDAC and VEAC, respectively. The energy balance of the cooling load for the RS can be expressed as

Qc;latent þ Qc;sensible ¼ QHVAC

(8)

where QHVAC denotes the cooling capacity of the HVAC unit. The electrical power consumption EHVAC of the HVAC unit for the RS can be calculated by

EHVAC ¼

QHVAC COPHVAC

(9)

where COPHVAC denotes the coefficient of performance for the HVAC unit. For the utility electrical supply in the RS, the electrical power balance is expressed as

Eutility ¼ ERS þ EHVAC

(10)

where Eutility is the total electrical power output from the utility grid. ERS ¼ Eload is the electrical power supplied to the buildings. 3. System modeling 3.1. Autothermal gasifier A zero-dimensional thermodynamic equilibrium model [28] with Gibbs free energy minimization (that originated from the Aspen Plus V9 Program [29]) was developed and applied in this work to predict the producer gas composition and yield generated from the gasifier. The equilibrium model is able to calculate the gas yield and composition with a maximum conversion efficiency, which is independent of gasifier configurations [30]. Major assumptions are listed below:
The feedstock residence time in the gasifier is long enough to reach chemical equilibrium.
Other mineral contents and low species mole fractions are neglected (e.g. H2S, HCN) except for the C, H and O contents.
The producer gas generated from the gasifier consists of H2, CO, CO2, H2O, CH4, and N2 neglecting other higher hydrocarbon. Subsequently, the equilibrium equation of the air-based gasification can be expressed as

CHx Oy þ ERastoich ðO2 þ 3:76N2 Þ þ wH2 O/nH2 H2 þ nCO CO þ nCO2 CO2 þ nH2 O H2 O þ nCH4 CH4 þ 3:76ERastoich N2 (11) where x and y are elemental mole ratios of H/C and O/C in the feedstock, respectively. nH2 , nCO , nCO2 , nH2 O , and nCH4 are the moles of the species in the producer gas. w represents the mole numbers of moisture per mol feedstock. astoich is defined as the stoichiometric ratio of oxygen-to-feedstock. ER is the equivalence ratio [31] that can be calculated by

nO2 nfeedstock astoich

(12)

where nO2 and nfeedstock are the moles of the species of feeding oxygen and feedstock, respectively. The higher heating value of carbonaceous feedstock (HHVf ) can be calculated using the following correlation [32]:

HHVf ¼ 0:3491MC þ 1:1783MH  0:1034MO  0:0151MN þ 0:1005MS  0:0211MA (13) where MC , MH , MO , MN , MS , and MA denote the mass fractions of carbon (C), hydrogen (H), oxygen (O), nitrogen (N), sulfur (S), and ash (A), respectively. The lower heating value of feedstock (LHVf) [33] with respect to the higher heating value is calculated by

LHVf ¼ HHVf  0:21978MH

(14)

Three carbonaceous feedstock materials e redwood pellets, woody chips, and sewage sludge pellets (shown in Fig. 3) e were selected in this paper. Table 2 lists the data of ultimate and proximate analyses. 3.2. Internal combustion engine (ICE) So far, a biofuel-fueled ICE conventionally stems from the natural gas-fueled ICE. Due to the low caloric value of the producer gas generated from autothermal gasification with biomass, the output performance of a producer gas fueled ICE is negatively influenced. To reasonably and accurately predict the output of electrical power, the jacket heat and exhaust heat of the producer gas fueled ICE, the fuel quality model of the ICE has been proposed [34,35]. However, this model overestimates the electrical efficiency and underestimates the exhaust gas temperature of the gas engines with low nominal electrical power. Combined with the fuel quality model, a modified ICE model is proposed and validated by experimental data collected from an ICE unit with a nominal electrical power of 20 kW. The energy equilibrium of a control volume ICE under the steady flow based on the first law of thermodynamics can be expressed by

Qs þ m_ air hair ¼ Ne þ Qjacket þ Qext þ Qloss

(15)

where Qs is the feeding energy of producer gas from the gasification process. Ne represents the electrical power output. m_ air denotes the mass flow rate of the air fed into the ICE. hair is the enthalpy of air. Qjacket , Qext , and Qloss are jacket heat, exhaust heat, and heat loss of the ICE, respectively. The feeding energy of producer gas can be calculated by

Qs ¼ V_ s LHVs

(16)

where V_ s is the volume flow rate of producer gas. LHVs is the lower heating value of producer gas. The electrical efficiency he of the ICE with respect to the nominal electrical power [36] can be calculated by

 0:102 he ¼ 28:08ØN0:0563 ne

LHVs þ 0:897 LHVN

 (17)

where Nne is the nominal electrical power (i.e., the maximum power) of a natural gas fired ICE. LHVN is the lower heating value of natural gas. The modified factor ∅ derived from experimental data

X. Li et al. / Energy 176 (2019) 961e979

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Fig. 3. Photos of solid waste feedstock materials: (a) redwood pellets, (b) woody chips, and (c) sewage sludge pellets.

Table 2 Ultimate and proximate analyses of the carbonaceous feedstock materials. Feedstock Ultimate analysis C (wt%) H (wt%) O (wt%) N (wt%) S (wt%) Proximate analysis Moisture (wt%) Volatile (wt%) Ash (wt%) Fixed carbon (wt%) HHVf (MJ/kg) LHVf (MJ/kg) Chemical composition

astoich

Redwood pellets

Woody chips

Sewage sludge pellets

47.1 5.5 36.1 0.50 0.10

43.8 5.8 33.4 1.5 0.8

35.0 4.8 24.7 5.2 1.7

9.3 87.9 1.4 1.4 19.2 18.0 CH1.4O0.57 1.065

8.4 68.5 6.3 16.8 18.6 17.3 CH1.59O0.57 1.113

5.8 50.8 22.8 12.8 14.9 13.8 CH1.65O0.53 1.148

is used to improve prediction accuracy for electrical efficiency when Nne  20 kW, and it can be calculated by




Ø ¼ 6:62  103  6:41  103  0:824Ne for Nne  20 kW Ø ¼ 1 for Nne > 20 kW (18)

The composition of exhaust gas relies on the air-fuel ratio and the complicated combustion in the ICE. In this work, the producer gas was assumed to be completely combusted associated with equilibrium state. Therefore, the temperature of exhaust gas from the ICE [36] can be calculated by the following correlation:

  LHVs 2 Text ¼ d 0:025 þ 0:974 2  105 Nne  0:0707Nne LHVN  þ 758:33 (19) The modified factor, d, for Eq. (19) is used to improve the accuracy of the existing model [34,35] for the cases of Nne  20 kW, and it can be expressed as




d ¼ 1:152  0:204  0:87Ne for Nne  20 kW d ¼ 1 for Nne > 20 kW

the specific heat of jacket water. Tjacket;in and Tjacket;out are the inlet and outlet temperatures of jacket water, respectively. 3.3. Variable-effect LiBr-H2O absorption chiller (VEAC) An artificial neural network (ANN) simulation model of the variable-effect LiBr-H2O absorption chiller, based on experimental data, has been previously reported by Xu and Wang [37]. This code was adopted herein to predict the thermodynamic behavior of the VEAC in terms of the driving hot water and cooling water, in which the cooling water temperature of the VEAC was affected by the heat and mass transfer behavior of cooling tower and the local weather condition including the dry-bulb temperature and relative humidity. In this work, a constant inlet temperature (16  C) of chilled water was used to improve the COP of the VEAC that handles sensible cooling load individually. The restrictive conditions of the VEAC code, with respect to the inlet temperatures of driving hot water, cooling water and chilled water, were derived from experimental data (i.e. the parametric range of all temperatures in the experiment) and are listed in Table 3. 3.4. Desiccant dehumidification air-conditioning (DDAC)

(20) A 1-D mathematical model of the desiccant-coated heat exchanger that serves as a critical component in the DDAC system

The jacket heat of the ICE can be calculated by

  8Q _ jacket cp;jacket Tjacket;out  Tjacket;in < jacket ¼ m   Ne Q s for Nne  20 kW : Qjacket ¼ 0:103 þ 0:354  0:905

Table 3 The restrictive conditions of the VEAC code.

Qjacket ¼ 0:17Qs for Nne > 20 kW

(21) where m_ jacket denotes the mass flow rate of jacket water. cp;jacket is

Category

Unit

Value

The inlet temperature of driving hot water The inlet temperature of cooling water The inlet temperature of chilled water



85.3e141.0 24.7e34.3 9.0e20.8

 

C C C

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has been proposed by our team [38]. The deviation of the experimental and simulated data was within the range of ±15% for the DCHE with overall tube number of 16. In this work, a 24-tube DCHE was adopted to enhance the heat transfer performance between the processed air and cooling/hot water. The geometric and material parameters of the DCHE are listed in Table 4, in which the geometric configuration of the DCHE has been elaborated by Sun et al. [39]. Silica-gel column chromatography (Macro pore, ZCX-II) serves as the desiccant adsorbent material coated at the surface of the fin-tube heat exchanger. The critical parameters of the desiccant adsorbent material used in the DubinineRadushkevich equation are presented in Table 5.

Table 5 The critical parameters of the desiccant adsorbent material used in the DubinineRadushkevich equation [38]. Parameter

B

C

D

Silica gel

0.5998

0.4866

0.3416

efficiency [41] is defined as the ratio of the producer gas energy to the energy consumption of feedstock with respect to the feeding rate of feedstock (m_ f ):

hCGE ¼

qs V_ s ,LHVs ¼ Ff m_ f ,LHVf

(24)

3.5. Heat exchanger The effectiveness minimum capacitance approach [40] was adopted in the thermodynamic performance prediction of heat exchangers. The gas-water heat exchangers (e.g., HE-1, HE-2, HE-3, and HE-4) have the shell-tube structure, whereas the plate-type heat exchanger (i.e., PHE) is structured by counterflow. The unified actual heat transfer rate of all types of heat exchangers can be expressed as

8   < qHE ¼ εHE Cmin Th;in  Tc;in C ¼ m_ c cp;c if m_ c cp;c < m_ h cp;h : min Cmin ¼ m_ h cp;h if m_ c cp;c  m_ h cp;h

(22)

where m_ c and m_ h are the mass flow rates of cold fluid and hot fluid, respectively. cp;c and cp;h are the specific heat capacities of cold fluid and hot fluid, respectively. εHE represents the effectiveness of heat exchangers. The outlet temperatures of the hot and cold fluid streams are calculated by

  8  Cmin  > > Th;in  Tc;in ¼ T  ε T > HE h;out h;in < Ch   >   C > > : Tc;out ¼ Tc;in þ εHE min Th;in  Tc;in Cc

(23)

where Tc;in and Tc;out are the inlet and outlet temperatures of cold fluid, respectively. Th;in and Th;out are the inlet and outlet temperatures of hot fluid, respectively. 3.6. Evaluation criteria 3.6.1. Cold gas efficiency (CGE) To evaluate the performance of the gasification process, cold gas

3.6.2. Primary energy ratio (PER) Primary energy ratio (PER) [27] of the G-CCHP system is defined as the ratio of the energy output to primary fuel consumption:

PER ¼

ECCHP þ E þ QVEAC þ QDDAC m_ f ,LHVf þ V_ N ,LHVN

where V_ N is the volume consumption rate of natural gas. LHVN is the lower heating value of natural gas. 3.6.3. Operational cost reduction ratio (OCRR) To assess the economic index of the G-CCHP system, OCRR [42] that is defined as the ratio of the saved operation cost of the GCCHP system to that of the RS, which can be expressed by

OCRR ¼ 1 

_ f þ COSTN ,V_ N COSTf ,m COSTe ,Eutility

Parameter

Unit

Value

Width of the fins Length of the fins Depth of the fins Fin pitch Fin thickness Inner diameter of the tube Outer diameter of the tube Fin layer number Transversal distance of two adjacent tubes Longitudinal distance of two adjacent tubes Tube number Surface area density The ratio of minimum free-flow area to frontal area Hydraulic diameter Desiccant weight

mm mm mm mm mm mm mm e mm mm e m2/m3 e mm kg

300 300 44 2 0.105 9.87 10.5 142 21.3 24.6 24 863 0.451 2.09 0.632

(26)

where COSTf , COSTN , and COSTe represent the costs of feedstock, natural gas, and electricity from the utility grid, respectively. Table 6 gives the costs of three feedstock materials in Singapore. The cost of natural gas was assumed to be equal to the liquid natural gas price (about $9.9 per MMBTU) in Singapore [43] via neglecting other unspecified costs (e.g. the reloading cost). 3.6.4. CO2 emission reduction (CO2ER) Reduction of greenhouse gas (GHG) has been regarded by the whole world, in which carbon dioxide (CO2) is the most significant emission in the power systems. The CO2 emission reduction of the G-CCHP system [42] can be written as

CO2 ER ¼ 1 

Table 4 The geometric and material parameters of the DCHE [39].

(25)

_ f þ mN ,V_ N mf ,m mgrid ,Eutility

(27)

where mf , mN , and mgrid are defined as the CO2 equivalence rates of the feedstock, natural gas and utility grid, respectively. mgrid ¼ 0:431 is referred to the Singapore Energy Statistics 2016 [45]. The value of mN is 1.876 [46]. Table 6 Costs of feedstock, natural gas, and electricity from the utility grid. Item

Cost

COSTf of redwood pellets ($/kg) COSTf of wood chips ($/kg) COSTf of sewage sludge ($/kg) COSTN ($/Nm3) COSTe ($/kWh)

0.150 0.058 [44] 0.061 [44] 0.360 [43] 0.150

X. Li et al. / Energy 176 (2019) 961e979

3.6.5. Total performance (Stotal) Stotal that comprehensively considers all indexes is used to assess the total performance of the G-CCHP system and is calculated by Refs. [27,47].

Stotal ¼ u1 ,PER þ u2 ,OCRR þ u3 ,CO2 ER

(28)

where u1 , u2 , u3 are the weight factors of PER, OCRR, and CO2ER, respectively. u1 ¼ u2 ¼ u2 ¼ 1=3 were applied in this paper considering the equal contribution of energy, economy and environment [27,47].

4. Results and discussion 4.1. Model validation As reported in our previous works [14,48], thermodynamic analysis and 3E (energetic, economic and environmental) benefits evaluation of a 20 kW gasification-based CHP system using redwood pellets have been implemented. Fig. 4 illustrates the 20 kW micro-scale G-CCHP system integrated with a DDAC unit. This system has been demonstrated in Singapore local site to convert woody chips to cooling, heat and power. This system implemented desiccant dehumidification without the VEAC unit. All relevant data collected from this G-CCHP system and labs were used to validate the developed models including the gasifier, ICE, VEAC and DDAC units. Table 7 lists the specifications of major experimental instruments. Considering the complex and time-varying gas composition, it is difficult to find a reasonable and accurate flow meter for such producer gas generated from wood-based gasification. The yield of cold producer gas was estimated based on mass conservation of input N2 and the N2 content of producer gas.

4.1.1. Gasifier The autothermal gasification model was experimentally validated with the producer gas compositions (i.e., CO, CO2, H2, CH4) generated from the gasifier shown in Fig. 4, using the redwood pellets, and the mixture of woody chips and sewage sludge. Tables 8 and 9 respectively compare the experimental and simulation data of the schemes of redwood pellets and the mixture of woody chips and sewage sludge. In addition, RMSDr values between experiment and simulation were reported. The lower heating value of the cold producer gas is the key parameter of the gasifier, which affects the cold gas efficiency and operation of the ICE. An RMSDr span of 3.6e18.1% for LHVs was observed from Tables 8 and 9, which proves the reliability of the developed gasification model. The discrepancies are mainly attributed to the adiabatic condition in the model whereas heat loss existed in the experiment.

Fig. 4. A 20 kW micro-scale G-CCHP system with a DDAC unit.

4.2.

a

969

20% sewage sludge, 80% woody chips

4.2.1. ICE Since the accuracy and reasonability of the ICE model under the nominal power more than 20 kW has been validated [36], in this work, the experimental validation was accomplished by using the ICE with a nominal power of 20 kW (see Fig. 5). The collected data of the jacket heat, exhaust temperature, and electrical power efficiency [14] under different power loads and producer gas compositions were used to validate the proposed ICE model. The comparison results show that the RMSDr values of 1.6%, 13.1%, and 12.1% were observed from the exhaust gas temperature, jacket heat, and electrical power efficiency, respectively. 4.2.2. VEAC The experimental data [37] of a VEAC with a nominal cooling capacity of 60 kW was utilized to validate the ANN model of the VEAC. Table 10 compares the experimental and simulation data of the VEAC. The maximum values of RSMDr reach 13.9%, 4.4%, and 27.4%, for the outlet temperature of chilled water (RSMDr,Tchw), the outlet temperature of cooling water (RSMDr,Tcw), and COP (RSMDr,COP), respectively. It proves that the ANN model has reasonable deviations under the restrictive conditions listed in Table 3 and is able to predict the thermodynamic behavior of the VEAC. 4.2.3. DDAC The DCHEs are critical components in the DDAC system. The experimental parameters listed in Table 11 served as input parameters of the DDAC model to obtain simulation output. The comparison results (see Fig. 6) of the air outlet temperature and humidity ratio indicate that the maximum discrepancy of RSMDr ¼ 11.1% was observed in the humidity ratio. In addition, the dynamic response has a good agreement. The proposed 1-D model is able to predict the dynamic behavior of the DCHE reasonably. The further comparison of the latent and sensible cooling capacities and thermal COP for the latent cooling in one dehumidificationregeneration cycle can be found in Table 12. 4.3. Determination of ER The objective of this section aims to evaluate the effect of ER on producer gas composition, LHVs, mole flow rate, reaction temperature, and cold gas efficiency (see Figs. 7e9), and subsequently explore the most appropriate ER for each feedstock material used in the following analyses. Observed from both redwood and woody chips, the CO content peaked at the maximum cold gas efficiency, whereas the CO2 content reached the nadir at the same cold gas efficiency. Besides, the CH4 and H2 contents reduced with the increasing ER, accompanied by the increasing N2 content. LHVs decreased with the increasing ER, which is mainly attributed to gradual strengthening of the exothermic reaction caused by the increasing oxygen-to-feedstock ratio. The similar justification can be used to explain the increasing trend of reaction temperature. For sewage sludge (see Fig. 9), it is interesting to note that, except for continuous declination in the CH4 content, other contents of producer gas reached peak or nadir points at the peak of cold gas efficiency. With respect to reaction temperatures of different feedstock materials, Teq,redwood > Teq,woody > Teq,sludge was observed under the same reaction temperature, which is owing to the lower heating value of feedstock. Based on the optimum cold gas efficiency, the optimized ER of 0.35, 0.4, and 0.6 can be obtained for redwood, woody chips, and sewage sludge, respectively. In the gasification, the major issue is one of the by-products, namely, tar. Under such optimized ER (based on maximum cold gas efficiency),

970

X. Li et al. / Energy 176 (2019) 961e979 Table 7 Specifications of experimental instruments. Parameters

Instruments

Uncertainty

Feeding rate of feedstock Producer gas composition

Weighing balance Gas analyzer Gasboard-3100P [49]

Temperatures Volume flow rate Electrical output Relative humidity Air velocity Air feeding rate

K-thermocouple Electromagnetic flowmeter [50] Power load bank [51] Relative humidity sensor [52] Hot-wire anemometer [53] Thermal mass flow meter [54]

±50 g ±1% for CO/CO2/CH4 ±2% for O2/H2 ±0.1  C ±0.5% ±0.1 kW ±2% ±0.004 m/s ±1%

Table 8 Comparison of the experimental and simulation data for the gasifier model with redwood pellets. Cold producer gas

CO (vol%) CO2 (vol%) H2 (vol%) CH4 (vol%) LHVs (MJ/Nm3) a b

Experiment [48]

Model

#1a

#2b

#1

#2

#1

RMSDr (%) #2

18.5 ± 0.4 10.0 ± 0.3 14.1 ± 0.7 2.4 ± 0.3 4.72 ± 0.14

19.5 ± 0.4 10.3 ± 0.3 15.0 ± 0.7 2.1 ± 0.4 4.83 ± 0.17

20.2 9.3 14.5 0.5 4.29

19.6 9.9 12.8 0.3 3.96

9.3 7.2 4.1 79.3 9.2

1.3 4.2 14.8 86.3 18.1

8 kW power output. 6 kW power output.

Table 9 Comparison of the experimental and simulation data for the gasifier model with cogasification of woody chips and sewage sludge. Cold producer gas

Experiment [41] a

CO (vol%) CO2 (vol%) H2 (vol%) CH4 (vol%) LHVs (MJ/Nm3)

Model b

RMSDr (%)

#1

#2

#1

#2

#1

#2

15.6 ± 0.5 12.7 ± 0.8 16.8 ± 1.5 2.1 ± 0.4 4.53 ± 0.22

12.0 ± 0.6 12.5 ± 1.2 13.4 ± 1.0 1.8 ± 0.5 3.61 ± 0.22

15.1 11.4 16.6 0.8 3.98

11.5 13.2 15.5 1.4 3.63

3.7 10.7 5.2 62.2 12.4

5.0 8.2 16.6 25.2 3.6

the by-product tar cannot be removed efficiently caused by low reaction temperatures. In order to minimize tar yield, a common approach is to improve the reaction temperature to thermally crack tar. However, in the autothermal gasification process, improving reaction temperature leads to continuously decreasing cold gas efficiency (as shown in Figs. 7e9). To enhance char conversion and tar cracking, a reactor temperature above 1073 K [31] served as the selection criteria to further determine the best ER parameters of 0.37, 0.45, and 0.7 (listed in Table 13), for redwood, woody chips, and sewage sludge, respectively. The producer gas quality (e.g. higher LHVs) that was generated from redwood was significantly superior to other materials, however, an obvious disadvantage in the mole flow rate affected its contribution to waste heat recovery. The worst quality of the producer gas was found in sewage sludge with LHVs ¼ 1.855 MJ/Nm3 accompanied by the largest gas yield of 4.35 mol/s per mol feedstock. These parameters, listed in Table 13, were adopted to the basic inputs of the gasifier model in the following analysis. 4.4. Case A: a typical data center with a micro-scale G-CCHP system Data centers are dramatically increasing with the rapid development of information and computer science technologies, e.g. big data and artificial intelligence (AI). Centralized facilities in the data centers require large energy consumption, which is higher than that of commercial offices. In this case, a data center in Singapore

was selected to investigate the 3E benefits of the proposed G-CCHP system. The energy profile of the data center [55], including electricity and cooling demands, is depicted in Fig. 10, where daily mean electricity demand was composed of the total amount of critical IT, less critical IT, and UPS loss consumptions. Here, sensible heat ratio, r, is defined as the ratio of sensible cooling load to total cooling load. In this case, the sensible and latent cooling loads were derived from the electricity consumption of the HVAC unit with an electrical COP of 2.1 [55], accompanied by r ¼ 0.8. As shown in Fig. 10, slight variations of the electrical consumption and cooling load were observed in the consecutive days, which was due to a typically tropical climate with a yearly stable ambient temperature and humidity ratio. The corresponding daily mean dry-bulb and wet-bulb temperatures are given in Table 14. Based on the energy profile and operation strategy (established in section 2.2), the rated design parameters of major components in the G-CCHP systems, including the gasifier, ICE, DDAC, VEAC, gas boilers, heat exchanger, and RS, are listed in Table 15, for redwood, woody chips and sewage sludge. The rated capacities of all components were to fulfill the peak of total energy requirement of the data center and selfconsumption electricity in the G-CCHP system. The results of performance indexes, embracing PER, OCRR, and CO2ER, in the proposed G-CCHP systems, are depicted in Fig. 11. Daily average total performances for the proposed feedstock materials are compared in Table 16, in terms of the feeding rate and natural gas consumption. As depicted in Fig. 11, the comparison result of PERredwood > PERwoody > PERsludge was visibly observed, which was attributed to the higher cold gas efficiency and LHVs (see Table 13). The OCRR indicator mainly relied on the impacts of the feeding rate and feedstock cost. Based on the observation of OCRRwoody > OCRRsludge > OCRRredwood, it can be noted that the key factor leading to the lowest OCRR of 0.35 was the feedstock cost of 1.5 $/kg for the redwood scheme even if it had the lowest feeding rate of 22.67 kg/h. For the environmental benefit, a comparison finding of CO2ER was similar to that of PER aforementioned, obeying the regulation of feeding rates. Another observation is that there's no obvious effect of uncertainty of the wet-bulb temperature on the system performance, which was attributed to the ample heat recovered from HE-1 and HE-2 as well as the sufficient jacket heat. In general, based on the highest total performance of 0.66, woody chips are the most favorable feedstock used in the G-CCHP system for the data center. Since the FEL scenario was adopted, in order to parametrically assess energy matching between energy supply and demand, a cooling-to-cooling ratio (CCR) of the G-CCHP system is defined as the ratio of the ideal output (no waste heat ejected to the environment, and no natural gas consumption) of the sensible and latent cooling to the cooling load of end users. Note that the mean CCR values in case A was 2.0, 1.5 and 3.3 for woody chips, redwood, and sewage sludge schemes, respectively, which led to a large amount of waste heat ejected to the environment, and finally resulted in a low PER.

X. Li et al. / Energy 176 (2019) 961e979

971

Fig. 5. Validation of the ICE model with experimental data of an ICE with a nominal power of 20 kW for the diverse power loads: (a) RMSDr of exhaust gas temperature, (b) RMSDr of jacket heat, and (c) RMSDr of electrical power efficiency.

Table 10 Validation of the experimental and simulation data of the VEAC with a nominal cooling capacity of 60 kW. Parameter

#1

#2

#3

#4

#5

#6

Th,in ( C) Tchw,in ( C) Tchw,out,ex ( C) Tcw,in ( C) Tcw,out,ex ( C) COPex Tchw,out,sim ( C) Tcw,out,sim ( C) COPsim RSMDr,Tchw (%) RSMDr,Tcw (%) RSMDr,COP (%)

115.2 ± 0.4 10.2 ± 0.3 7.5 ± 0.2 32.5 ± 0.3 35.9 ± 0.5 0.70 ± 0.24 6.6 36.6 0.78 12.1 2.1 27.4

119.4 ± 0.5 16.4 ± 0.4 13.2 ± 0.4 29.8 ± 0.3 34.2 ± 0.4 0.78 ± 0.19 11.4 35.2 0.86 13.9 3.0 19.8

125.9 ± 0.6 15.0 ± 0.4 10.7 ± 0.3 32.4 ± 0.2 37.2 ± 0.3 0.85 ± 0.20 9.9 37.5 1.02 7.6 1.5 27.1

130.8 ± 0.5 14.7 ± 0.5 10.5 ± 0.5 30.9 ± 0.4 35.1 ± 0.3 1.0 ± 0.31 9.5 36.3 0.94 9.8 3.8 18.1

133.8 ± 0.4 11.0 ± 0.5 7.2 ± 0.4 28.2 ± 0.5 32.0 ± 0.4 1.02 ± 0.35 6.6 32.7 0.99 8.8 2.6 20.2

135 ± 0.3 13.5 ± 0.3 9.0 ± 0.4 27.6 ± 0.3 31.9 ± 0.2 1.08 ± 0.30 8.3 33.3 0.9 8.1 4.4 21.0

Table 11 Experimental parameters of the DCHE for validation. Parameter

Unit

Value

Inlet air temperature Relative humidity of inlet air Inlet air velocity Cooling water temperature Hot water temperature for regeneration Flow rate of cooling/hot water



25 ± 0.3 77 ± 2.0 2.8 ± 0.004 21 ± 0.3 50 ± 0.3 356 ± 0.2

C % m/s  C  C kg/h

4.5. Case B: a typical commercial building with a small-scale GCCHP system A typical commercial building [58] with a gross floor area of

25,822 m2 served as a large-scale building sector in Singapore. Typically, electricity consumptions in the majority of nonresidential buildings are attributed to cooling (60%) and mechanical ventilation (10%) [59]. The remaining share goes to lighting (15%), lifts & escalators (10%) and other sources (5%). The representative sensible heat ratio of the commercial buildings in Singapore climate is 0.6, which means 60% sensible load and 40% latent load. The energy profile of this building with ambient drybulb and wet-bulb temperatures is shown in Fig. 12. Note that the variation of monthly average energy consumption was slight but more distinct than that of the data center in case A. The rated design parameters of the G-CCHP system matching the energy profile are listed in Table 17, which were inputted into the simulation model to generate the results of monthly average

972

X. Li et al. / Energy 176 (2019) 961e979

Fig. 6. Validation of outlet air parameters: (a) outlet air temperature and (b) humidity ratio of outlet air.

Table 12 Validation of the experimental and simulation results for the cooling capacity and thermal COP in one cycle. Parameter

Experiment

Model

RMSDr

Latent cooling capacity (kW) Total cooling capacity (kW) Thermal COP for latent cooling

2.12 ± 0.11 2.28 ± 0.12 0.78 ± 0.13

2.00 2.29 0.67

6.3% 3.1% 16.1%

performance indexes shown in Fig. 13. Table 18 gives the yearly average data of performance indexes for different feedstock materials under the climate of Singapore. The variation trends of month-to-month values obeyed the electrical energy consumptions shown in Fig. 12. It is indeed expected that, for PER (see Fig. 13a), the same finding was observed as the data center, which was mainly affected by the cold gas efficiency and power efficiency of the ICE. As shown in Table 18, the natural gas consumption of the redwood scheme was 65.9 Nm3/h that was higher than other schemes. However, a distinctly low feeding rate of feedstock overcame the negative effect of natural gas consumption, leading to the highest yearly PER value of 0.93. For OCRR (see Fig. 13b), the woody chips scheme presented a more favorable performance due to the lowest cost in feedstock and inbetween consumptions of the feedstock and natural gas. Based on the yearly average data, the redwood scheme had the lowest

To explore the effects of the critical parameters on the total performance, sensitivity analyses of the G-CCHP systems proposed in case A and case B were conducted. For a given G-CCHP system, two parameters significantly influenced by the market, namely, the feedstock cost (COSTf ) and natural gas cost (COSTN ), were investigated [27,47]. The natural gas price in Singapore based on the

4.0 1200

(b) 10

LHVs

70

8

60 6

50 40

4

30 20

2

3.5

1000 800

3.0

600 2.5 80 2.0

60 40

Mole flow rate

1.5

10

CEG

0

0 0.1

0.2

0.3 ER

0.4

0.5

Reaction temp. 1.0 0.1

0.2

0.3

0.4

0.5

20

0 0.6

Reaction temp. ( C) & cold gas efficiency (%)

80

H2 CH4

Mole flow rate (mol/s)

CO CO2 N2

(a)

LHVs (MJ/Nm3)

Producer gas composition (%)

4.6. Sensitivity analysis

12

100 90

feeding rate of 428.3 kg/h but the highest feedstock cost of 0.15 $/kg (see Table 6), which resulted in its disadvantage in OCRR. For CO2ER (see Fig. 13c), the woody chips scheme and sludge scheme were comparable based on the monthly and yearly average data. The uncertainties of ambient conditions affecting the cooling capacities of the DDAC and VEAC units had a slight impact on system performance indexes, which is shown as the error bar in Fig. 13. Subsequently, based on the yearly average data, total performances of redwood, woody chips, and sludge schemes were 0.73, 0.81 and 0.69, respectively. Analysis results of case B presented a similar finding with that of case A, which indicated woody chips are the most feasible feedstock used in the proposed G-CCHP system for a commercial building of Singapore. Note that yearly average CCR values of 0.7, 0.5 and 0.9 were respectively achieved for the woody chips, redwood and sewage sludge schemes in case B, which significantly differed from the CCR values in case A.

ER

Fig. 7. The effect of ER on producer gas quality and yield for redwood pellets: (a) gas composition and lower heating value, and (b) mole flow rate and cold gas efficiency.

X. Li et al. / Energy 176 (2019) 961e979

H2

CO2

CH4

N2

LHVs

4.5

10

70

8

60 6

50 40

4

30 20

4.0 3.5

600 3.0 2.5

400

2.0

80

1.5

60

10

0.5

0

0.0

0 0.2

0.3

0.4

800

Mole flow rate Reaction temp. Cold gas efficiency

1.0

2

0.1

1000

(b)

0.5

0.1

0.2

ER

0.3

0.4

40 20 CEG

0 0.6

0.5

Reaction temp. ( C) & cold gas efficiency (%)

80

CO

Mole flow rate (mol/s)

(a)

LHVs (MJ/Nm3)

Producer gas composition (%)

90

5.0

12

100

973

ER

12

100

80

CO

H2

CO2

CH4

N2

LHVs

800

(b)

4.5

700

10

600 4.0

70

8

60 6

50 40

4

30

Mole flow rate (mol/s)

(a)

LHVs (MJ/Nm3)

Producer gas composition (%)

90

500 400

3.5

300

3.0

80 60

2.5 Mole flow rate

20

2

10

CEG

2.0

0

0 0.4

0.5

0.6

0.7

Reaction temp.

40 20 0

0.3

0.4

0.5

0.6

Reaction temp. ( C) & cold gas efficiency (%)

Fig. 8. The effect of ER on producer gas quality and yield for wood chips: (a) gas composition and lower heating value, and (b) mole flow rate and cold gas efficiency.

0.7

ER

ER

Fig. 9. The effect of ER on producer gas quality and yield for sewage sludge pellets: (a) gas composition and lower heating value, and (b) mole flow rate and cold gas efficiency.

Table 13 The determined parameters of the gasifier for the feedstock materials. Parameter

Notation

Redwood

Wood chips

Sewage sludge

ER Hot producer gas composition

Lower heating value of cold producer gas Mole flow rate

ER CO (vol%) H2 (vol%) CO2 (vol%) CH4 (vol%) N2 (vol%) H2O (vol%) LHVs (MJ/Nm3) V_ s (mol/s) per mol feedstock

0.37 21.97 18.67 8.25 0.01 44.78 6.32 5.111 3.30

0.45 16.74 15.5 9.66 0.02 50.03 8.05 4.123 3.46

0.70 7.42 6.69 13.2 0.01 62.38 10.3 1.855 4.35

Cold gas efficiency

hCGE

0.89

0.82

0.59

statics data during the recent 5 years [60] indicated a variation span of ±40%. Hence, ±40% of the proposed data listed in Table 6 was selected for both feedstock cost and natural gas cost to generate 3-D map results of sensitivity analyses shown in Fig. 14. Note that the woody chips scheme was always more favorable than other schemes, which was insensitive to fuel costs. It was benefited from its lowest feedstock cost and comparable PER. The total performance in the case B was always higher than that in case A, under

Feedstock

the identical conditions, which was mainly attributed to higher PER values affected by two-fold factors: (1) upgraded electrical power efficiency e a larger nominal power capacity of ICE used in case B, and (2) more reasonable CCR e no waste heat was ejected to the environment. It indicates that CCR < 1 is acceptable for obtaining a reasonable PER value, accompanied by the ideal value of CCR ¼ 1.0. When comparing redwood pellets to sewage sludge, the total performance as a function of the natural gas cost is shown in Fig. 15.

X. Li et al. / Energy 176 (2019) 961e979 Table 15 Rated design parameters of the G-CCHP system for the data center.

50

40

HVAC UPS loss Less critical IT Critical IT

Total cooling load 50 Sensible cooling load Latent cooling load 40

30 30 20

20

10

Gasifier

Feedstock temperature T2 ( C)

25

ICE

Outlet temperature of producer gas from the gasifier T3 ( C) Nominal electrical power of the ICE Nne (kW) Inlet temperature of producer gas fed into the ICE T5 ( C) Exhaust temperature of the ICE T6 ( C) Inlet/outlet temperature (T19 =T18 ) of jacket water from ICE ( C) Flow rate of the jacket water (m3/h) Lower heating value of natural gas LHVN (MJ/Nm3)

800

Cooling load (kW)

Daily mean electricity consumption (kW)

974

10

DDAC

0

0 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7

Fig. 10. Energy profile of the data center during seven consecutive days (10e16 June 2003) [55].

With the variation of feedstock costs, the total performance of the redwood scheme was always superior to that of the sludge scheme, except for the comparable area labeled by the dotted line. Fig. 16 shows the variation of the total performance (Stotal) as the cost variations of natural gas and feedstock within the span of ±40%. It is pointed out that the effect of the feedstock cost on the total performance is more significant than that of the natural gas cost. In the case A, the total performance of the G-CCHP system was not affected by natural gas price, due to zero natural gas consumption. Another sensitivity analysis was to investigate the sensitivity of total performance (Stotal) to other factors, embracing the sensible heat ratio (r), the electrical power efficiency of ICE (he ), the effectiveness of heat exchangers (εHE ), thermal COPs of the DDAC (COPDDAC) and VEAC (COPVEAC), and electrical COP of the reference HVAC (COPRS). In fact, the above parameters are mainly dependent on building property and components of G-CCHP systems. In this work, it was assumed that the variation span of each parameter was on the basis of the uncertainties reported in section 4.1. Figs. 17 and 18 show the effects of such factors on the total performance for the case A and case B, respectively. For COPRS, the total performance decreased with the increasing COPRS, which was attributed to the decreasing electricity consumption in the RS. In the case A, the total performance increased with the increasing he , whereas, it decreased in case B. In general, it was attributed to that the increasing electrical efficiency he reduced the feedstock consumption. In the case A, the decrease in the feedstock consumption led to higher values of PER, OCCR and CO2ER, which was due to sufficient cooling converted from waste heat ‒ zero natural gas consumption. However, in the case B, the waste heat was insufficient to fulfill the required heat source of thermal driven cooling. The decrease in the feedstock consumption resulted in the increase in the natural gas consumption, which finally led to the decrease in the total performance. Since the electrical COP of the VEAC was superior to that of the DDAC, electricity consumptions of the GCCHP systems decreased with the increasing sensible heat ratio,

VEAC

Heat exchangers

Gas boiler G-CCHP RS

20 40 570 74/89

0.964 38.4 [57] Rated cooling capacity of the DDAC (kW) 7.2 Thermal COP of the DDAC 0.93 Electrical COP 6.0 Inlet/outlet temperature (T20 =T21 ) of cooling water for 27/29 DDAC ( C) Volume flow rate of the cooling water for DDAC (m3/h) 1.36 Cooling tower #2 efficiency (%) 65 Inlet/outlet temperature (T20 =T21 ) of hot water for 75/70  DDAC ( C) 3 1.36 Volume flow rate of the hot water for DDAC (m /h) Air flow rate (m/s) 1.5 Dehumidification/regeneration time (s) 360 Air inlet temperature T22 ( C) 30 Air inlet humidity ratio (g/kg) 21.1 Air outlet humidity ratio (g/kg) 11.75 Rated cooling capacity (kW) 29 Inlet/outlet temperature (T8 =T9 ) of pressure hot water 155/ for VEAC ( C) 150 Volume flow rate of the pressure hot water (m3/h) 4.7 Inlet/outlet temperature (T12 =T13 ) of cooling water for 28/33 VEAC ( C) 9.4 Volume flow rate of the cooling water (m3/h) Volume flow rate of the chilled water (m3/h) 4.7 Cooling tower #1 efficiency (%) 65 Thermal COP 1.16 Electrical COP 20.0 Effectiveness of HE-1, HE-2 and PHE εHE 0.8 Outlet temperature of the exhaust gas from HE-2 T7 234  ( C) 151 Water outlet temperature (T10 ) from HE-2 ( C) Water outlet temperature (T11 ) from HE-1 ( C) 155 Efficiency (hB1 and hB2 ) 0.9 Power usage (kW) 1.51 Inlet/outlet temperature (T17 =T16 ) of chilled water for 12/7 RS ( C) 97 Gross floor area (m2) Electrical COP of HVAC 2.1

which led to the increase in the total performance. Compared to the case A, the effect of the sensible heat ratio on the total performance was more significant in the case B. As shown in Fig. 18, it can be found that, when the sensible heat ratio varied within a range of 0.12e0.33, the sewage sludge scheme presented the higher total performance than the redwood scheme. It is expected that increasing values of COPVEAC, COPDDAC and εHE enable to improve the total performance. However, in the case A, the effects of such factors on the total performance were invisible due to the sufficient cooling capacity contributed by waste heat. The total performance was most sensitive to the electrical efficiency he in the case A,

Table 14 Daily mean dry bulb temperatures and wet bulb temperatures for the data center during the consecutive days [56]. Data 

Daily mean dry bulb temperature ( C) Wet bulb temperature ( C)

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

29.1 26.0 ± 1.5

28.8 25.7 ± 1.5

28.4 25.4 ± 1.4

29.6 26.0 ± 1.0

29.0 25.9 ± 1.5

29.3 26.2 ± 1.5

28.4 25.4 ± 1.4

X. Li et al. / Energy 176 (2019) 961e979

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Table 17 Rated design parameters of the G-CCHP system for the commercial building. Component

Parameter

Redwood

Gasifier

Feedstock temperature T2 ( C) Outlet temperature of producer gas from the gasifier T3 ( C) Nominal electrical power of the ICE Nne (kW) Inlet temperature of producer gas fed into the ICE T5 ( C) Exhaust temperature of the ICE T6 ( C) Inlet/outlet temperature (T19 =T18 ) of jacket water from ICE ( C) Flow rate of the jacket water (m3/h) Lower heating value of natural gas LHVN (MJ/Nm3) Rated cooling capacity (kW) Thermal COP of the DDAC Electrical COP Inlet/outlet temperature (T20 =T21 ) of cooling water for DDAC ( C) Volume flow rate of the cooling water for DDAC (m3/ h) Cooling tower #2 efficiency (%) Inlet/outlet temperature (T20 =T21 ) of hot water for DDAC ( C) Volume flow rate of the hot water for DDAC (m3/h) Air flow rate (m/s) Dehumidification/regeneration time (s) Air inlet temperature T22 ( C) Air inlet humidity ratio (g/kg) Air outlet humidity ratio (g/kg) Rated cooling capacity (kW) Inlet/outlet temperature (T8 =T9 ) of pressure hot water for VEAC ( C) Volume flow rate of the pressure hot water (m3/h) Inlet/outlet temperature (T12 =T13 ) of cooling water for VEAC ( C) Volume flow rate of the cooling water (m3/h) Volume flow rate of the chilled water (m3/h) Cooling tower #1 efficiency (%) Thermal COP Electrical COP Effectiveness of HE-1, HE-2 and PHE εHE Outlet temperature of the exhaust gas from HE-2 T7 ( C) Water outlet temperature (T10 ) from HE-2 ( C) Water outlet temperature (T11 ) from HE-1 ( C) Efficiency (hB1 and hB2 ) Power usage (kW) Inlet/outlet temperature (T17 =T16 ) of chilled water for RS ( C) Gross floor area (m2) Electrical COP of HVAC

25 800

ICE

DDAC

Fig. 11. Comparison of the performance indexes of the redwood, woody chips, and sewage sludge schemes.

Table 16 Comparison of feeding rate, natural gas consumption and total performance.

VEAC

Feedstock

Parameter

Value

Redwood pellets

m_ f (kg/h) V_ N (Nm3/h) Stotal m_ f (kg/h) V_ N (Nm3/h)

22.67 0

Stotal m_ f (kg/h) V_ N (Nm3/h)

0.66 44.56 0

Stotal

0.48

Woody chips

Sewage sludge

0.55 25.45 0

Gas boiler G-CCHP RS

40

Lifts & escalators mechanical ventilation HVAC Others

1400 1200

Lighting Wet-bulb temp. Dry-bulb temp.

38 36 34

1000

32

800

30

600

28 26

400

Ambient condition ( C)

Monthly electricity consumption (kW)

1600

24

200

22

0

20

1

2

3

4

5

6

7

8

9

Heat exchangers

10 11 12

Month Fig. 12. Energy profile of the representative commercial building in Singapore [58].

whereas it was most sensitive to COPVEAC in the case B. Note that, in both case A and case B, the woody chips are the most favorable feedstock used in the G-CCHP system due to the advantage in the total performance.

705 40 536 74/89 18.6 38.4 [57] 881 0.93 6.0 27/29 167 65 75/70 167 1.5 360 30 21.1 11.75 1321 155/150 212 28/33 424 212 65 1.16 20.0 0.8 228 151 153 0.9 73.4 12/7 25,822 3.0

5. Conclusions A biomass gasification-based CCHP system that was integrated with variable-effect LiBr-H2O absorption for sensible cooling and desiccant dehumidification with desiccant coated heat exchangers for latent cooling was proposed and investigated on potential applications in two types of Singapore's buildings (i.e., data center and commercial building). Models of four key components, including the gasifier, internal combustion engine, desiccant dehumidification air-conditioning, and variable-effect absorption chiller, were established and validated with experimental data collected in a micro-scale gasification CHP system and lab test. In this work, three carbonaceous waste materials embracing redwood pellets, woody chips and sewage sludge pellets, served as feedstock of the autothermal gasifier with the air gasifying agent. The energetic, economic and environmental (3E) benefits were analyzed accordingly. Ultimately, in terms of the total performance indicator, sensitivity analyses of the natural gas and feedstock costs, as well as other factors (i.e. sensible heat ratio, coefficient of performances of

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X. Li et al. / Energy 176 (2019) 961e979

Fig. 13. Monthly average performances of the proposed G-CCHP systems in the case B.

Table 18 Yearly average performances of the G-CCHP system in the case B. Feedstock

Parameter

Value

Redwood pellets

m_ f (kg/h) V_ N (Nm3/h) PER OCRR CO2ER Stotal m_ f (kg/h) V_ (Nm3/h)

428.3 65.9 0.93 0.50 0.75 0.73 475.3 51.5

PER OCRR CO2ER Stotal m_ f (kg/h) V_ N (Nm3/h)

0.91 0.73 0.80 0.81 832.2 50.0

PER OCRR CO2ER Stotal

0.69 0.60 0.80 0.69

Wood chips

N

Sewage sludge pellets

dehumidification air-conditioning, varaible-effect chiller and reference system, electrical power efficiency of the gas engine and effectiveness of heat exchangers), were performed. The optimal equivalence ratios of 0.37, 0.45 and 0.7 were respectively obtained from the air-based autothermal gasifier with redwood, woody chips, and sewage sludge, under the restriction

criteria of a reactor temperature above 1073 K, accompanied by cold gas efficiencies of 0.89, 0.82 and 0.59. A micro-scale gasification CCHP system (with 20 kW electrical power, 29 kW sensible cooling and 7.2 kW latent cooling) and a small-scale system (with 635 kW electrical power) were designed for the case A (data center) and case B (commercial building) to conduct 3E benefit studies, based on the energy profile and operation strategy. In the case A, the total performances (Stotal ) of 0.55, 0.66 and 0.48 were achieved for the redwood, woody chips and sewage sludge systems, respectively. Whereas, in the case B, the total performances (Stotal ) increased up to 0.73, 0.81 and 0.69. Based on the sensitivity analysis, the total performance in the case B was always higher than that in the case A, which was due to the increase in the primary energy ratio (PER) contributed by the upgraded electrical power efficiency and more reasonable coolingto-cooling ratios (CCR  1). In both case A and case B, woody chips are the most favorable feedstock used in the proposed gasificationbased CCHP system under Singapore's climate. The total performance of the redwood scheme is comparable to that of the sewage sludge scheme. Comparison data of variations of fuel costs indicate that the total performance is more sensitive to the feedstock cost. The uncertainty of ambient conditions has a slight impact on all performance indexes. This work can contribute valuable data to limited research on such a gasification-based CCHP system applied in Singapore's building sector. Besides, the major findings and conclusions are able to guide the design, optimization, and deployment of the system in the future.

X. Li et al. / Energy 176 (2019) 961e979

Fig. 14. 3-D maps of total performance as a function of the costs of natural gas and feedstock materials: (a) case A, and (b) case B.

Fig. 15. 2-D maps of total performance as a function of the costs of natural gas and feedstock materials: (a) case A, and (b) case B.

Fig. 16. Comparison of redwood pellets and sewage sludge pellets with the effects of the feedstock and natural gas cost: (a) case A, and (b) case B.

Fig. 17. Total performance as a function of variations of other factors in the case A: (a) woody chips, (b) redwood pellets, and (c) sewage sludge.

977

978

X. Li et al. / Energy 176 (2019) 961e979

Fig. 18. Total performance as a function of variations of other factors in the case B: (a) woody chips, (b) redwood pellets, and (c) sewage sludge.

Acknowledgement This research is supported by the National Research Foundation (NRF), Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme (Grant Number R-706-001-101-281). We thank Dr. Zhenyuan Xu from Shanghai Jiao Tong University for the discussion of modeling the variable-effect absorption chiller. Thanks are extended to Dr. Alexander Lin for his kind help on improving the quality of this article.

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