An Economic and Environmental Assessment for Microalgal Energy Systems

Available online at www.sciencedirect.com

ScienceDirect Energy Procedia 105 (2017) 3051 – 3061

The 8th International Conference on Applied Energy – ICAE2016

An Economic and Environmental Assessment for Microalgal Energy Systems Po-Hana Wanga, Jo-Shu Changa, Wei Wu*a,b, Erdorng Wub a

Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan Department of Chemical Engineering, Wuhan University of Technology, Wuhan 430070, China

b

Abstract The superstructure modeling of microalgal energy system including cultivation, dewatering, harvesting, extraction, biodiesel production, gasification and power output is achieved by using Aspen Plus® and experimental data. To address the multi-objective optimization (MOO) of microalgal energy system with respect to e economic cost and life cycle assessment (LCA), the mixed-integer nonlinear programming (MINLP) is solved by GAMS®. Bilinear relaxation is applied for the nonconvex constraints to reach the global optimum by using the piecewise bilinear relaxation convex envelope. Finally, a Pareto-curve graph for evaluating the trade-offs between environmental impacts and economic feasibility is successfully investigated. © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review of under responsibility of ICAE Peer-review under responsibility the scientific committee of the 8th International Conference on Applied Energy. Keywords: Sustainable process design, MINLP, GWP, microalgal energy

1. Introduction In our research, we considered cultivation, harvesting, dewatering, extraction, biodiesel production, gasification and power output in the model described as Figure 1. We split microalgae into oil and residual part as two different biomass resources. Not only can algal oil make biofuel, the large amount of the residual part can also be solid fuel. The character of this process is that we reutilize the residue of extracted microalgae as solid fuel. Considering the scale of feasibility, we assume our system is developed in relative small scale. Based on the processes described previously. Microalgae absorb the flue gas which is emitted from gas turbine, and then go through the pre-treatment processes to separate algal oil and the residue. The oil part is further transformed into biodiesel via chemical way and the residual part is used by thermal conversion way through co-gasification with coal to generate syngas. Syngas could be used as the fuel to

* Corresponding author. Tel.: +886-6-2757575 ext.62689. E-mail address:[email protected].

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 8th International Conference on Applied Energy. doi:10.1016/j.egypro.2017.03.633

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generate electricity by solid oxide fuel cell (SOFC) and gas turbine. The model of these processes is built by either the simulation results from Aspen Plus® or the experimental data from the lab. The aim of microalgal energy system is to develop an optimized utilization of microalgal process, in other words, the most economic or environment-friendly utilization. 2 Options

Cultivation

7 Options

2 Options

4 Options

Harvesting

Dewatering

2 Options

Disruption

Oil To Biodiesel

Extraction

Biodiesel

Fluegaas

FAir Fwater

FAir

ASU

FO2

mRBM

mCOAL

Heat recovery

Gasification

FN2

Frsyn

Anode

Acid gas removal

Flue gas

Ffuel

SOFC

Frsyn

Gas turbine

Cathode Air

Acid gas FO2

Electricity

Sulfur Recovery

Fsteam

Flue gas

Electricity AirCathode Sulfur

G Fluegas

Figure 1. Microalgal energy system

2.Methodology MeOH-Reflux (1) (2)

MeOH + NaOH

MeOH-Reflux

(6)

Algal oil

(8) Design/Spec (5)

Q2

Trans-esterification reactor

Q1

Water

Dist1

(7)

(4)

(9) Washtower

Biodiesel

Q3

(3) Mix1

(10)

Gly + H2O

Waste water Neutralizer

(15) (14)

(11) Filter

Dist2

(13) (16)

(12) H3PO4

Glycerol

Q4

Solid waste

Figure 2. Biodiesel production process

2.1 Biodiesel production Algal oil from extraction units then is further transformed into biodiesel through several processes. These processes include transesterification, methanol recovery, water washing, neutralization, solid waste

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filtration and glycerol recovery. Each of these processes is simulated in Aspen Plus® v8.4 as Figure 2. We assume the algal oil of the system is composed mainly of triglyceride without considering other small proportion of fatty acids. Since the lack of thermodynamic data of components, the structure of components like triglycerides, diglycerides, monoglycerides and methyl-esters were entered manually for the parameter of thermodynamic data estimation. 2.2 Co-gasification Gasification process modelling in Aspen Plus® is described as Figure 3. The whole processes including drying, pyrolysis and gasification represents the reactions happened in the gasifier. water RStoic

Biomass & Removable water

Preliminary dried biomass

Flash RYield

H2OIN

Conversion

H2ODRY

Decomposed Biomass

Dry Biomass

Water Calculator (Fortran)

Input: Yield Ultinal Data

Yield Calculator (Fortran)

water RStoic

Flash RYield

H2OIN

Syn gas

Decomposed Coal

Coal & Removable Water

Preliminary dried coal

RGibbs

Mixer

Conversion

H2ODRY

Dry Coal heater

Ultinal Data

Water Calculator (Fortran)

Steam

Input: yield

water

Yield Calculator (Fortran)

Gasification process

O2

Figure 3.Cogasification process

Assumptions The following assumptions are employed to simplify the simulation of biomass gasification. (1) Biomass gasification process are isothermal and in steady state. (2) The gasifier is operated at the thermodynamic equilibrium state; that is, the residence time of reactants is sufficiently long so that the reactions in the reactor are in chemical equilibrium. (3) The feedstock is at normal conditions (i.e. 25oC and 1 atm). (4) Char contains solid carbon (C) and ash alone, and tar formation is disregarded. The data of feedstock is shown as Table 1 below. Table 1. Characteristic of feedstock Ultimate analysis C

H

Proximate analysis O*

N

S

Mois

VM

FC

Ash

HHV(MJ/kg)

Microalgae [1]

50.53

7.4

24.29

11.69

0.73

4.19

71.36

19.31

5.14

22.84

Coal [2]

74.12

4.22

6.93

1.9

0.41

6.67

27.25

54.5

11.58

26.82

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2.3 Solid oxide fuel cell system The SOFC model is based on the following main assumptions [3]: (1) the process is steady-state; (2) the system is zero-dimensional; (3) and pressure drop in the system is neglected; (4) chemical reactions reach the thermodynamic equilibrium at a given temperature; (5) the cell half reactions are replaced by the overall oxidation of hydrogen, since the process of ions crossing over through the electrolyte cannot be modelled in Aspen Plus. The Aspen Plus flowsheet of the SOFC process is shown in Figure 4. F8

Cathode (Splitter) Preheater

HEX1

Cathode-out

A1

A2 F7 Q5

Design/Spec (Ua)

Air

A4

Gas turbine (Compr) Calculator (Uf)

Comp2

A3

Q3

Afterburner F6

Ejector F2

Syn gas

Splitter Anode-out

F4 Cooler

Q4

Comp1

F3

Anode (RGibbs)

Anode Steam reforming: CH4 + H2O 烌 CO + 3H2 Water-gas shift: CO + H2O 烌 CO2 + H2 Electrochemical: H2 + O2- ɦ H2O + 2eOverall: H2 + 1/2O2 ɦ H2O

Q2

Reformer F5 Q1

Figure 4. SOFC process

3.Results The amount of gasifying agent plays an important role in the gas composition. Thus, the sensitivity analysis of different O/C, S/C ratio at fixed gasification temperature (i.e. 1200oC) to the composition of effluent from gasifier is shown as Figure 6. S/C and O/C ratio is in the range of 1.4-2.0, 0.2-0.8, respectively. Mole fraction of H2 and CO decreases when S/C and O/C ratio increased. S/C ratio has less impact on the concentration of CO2 than O/C does. We can see clearly the concentration of CO2 increases with increasing O/C, which means the reaction inside tend to be combustion. The concentration of steam increases with increasing O/C and S/C. These results summarize the profile of the equilibrium constant of water gas shift reaction (Gai and Dong 2012)[4], KWGS, which is used to model the gasification reaction in the system.

(a)

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Po-Han et al. / Energy Procedia 105 (2017) 3051 – 3061

(c)

(d)

Figure 5. Molar fraction of (a) hydrogen (b) carbon monoxide (c) carbon dioxide (d) water in the syngas from the gasification of different S/C, O/C

In our approach, cold gas efficiency (CGE) and energy conversion efficiency (ECE) are crucial indexes to account for the performance of biomass gasification that are defined as follows.[4] Gp ×LHVsyngas CGE(%)= ×100 HHVfuel Fsyngas ×G ×LHVsyngas p ECE(%)= ×100 Ffuel LHV +Q ሶ fuel

H

where G୮ is the volume of product gas from the gasification of per unit weight of fuel (Nm3 kg-fuel-1) and HHVfuel is the higher heating value of fuel (MJ kg-fuel-1), respectively. LHVsyngas is the lower heating value of syngas and expressed as [5]. QH is the heat required for the gasification process (kW). LHVsyngas ൫kJ Nm-3 ൯=(30.0xCO +25.7xH2 +85.4xCH4 )×4.2 where x୧ stands for the mole composition of i component in the syngas. Figure 6 (a) suggests that increasing O/C ratio lessens the value of CGE, the maximum CGE is nearly 90% in the low S/C and O/C ratio. O/C has more influence to CGE, thus O/C ratio should be controlled in low value. This profile reveals that CGE of gasification is consistent with the composition of H2, CO in the syngas (Figure.5 (a), (b)). The effect of the S/C and O/C ratio is presented in Figure 7 (b). The distribution of ECE shows an obvious growing trend with the decrease in the O/C ratio. Although higher O/C ratio reduces the heat required for gasification, the high content of oxygen also tremendously reduce the energy content in the product gas. The value of ECE approaches 70% roughly when O/C ratio stays in low value; on the other hand, ECE would decrease to nearly 50% as the value of O/C is near 1.0.

(a)

(b)

Figure 6. (a)Cold gas efficiency (b) energy conversion efficiency of gasification of different S/C, O/C

Figure 7 represents the results of heat consumption of the units in biodiesel production. The results obtained from the sensitivity analysis of Aspen Plus can be regressed as a multilinear model. Based on this, we can

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predict the heat consumption by a regressed model rather than entering rigorous physical equations. We used sufficient data points from the results of Aspen Plus to indicate the relevance between input variables and the result. By doing this; we do not necessarily consider factors like thermodynamic properties and physical equations, these factors are included in the results from the simulation instead. We built the model contains two variables, mass flow rate of feedstock in each unit and the recovery ratio of methanol (RMe). Since the excess amount of methanol was added to the transesterification reaction, the unreacted methanol recycles back to the reactor through the methanol recovery distillation column. Hence, RMe can be set as an important variable to consider the trade-off between the heat consumption of units and the cost of the usage of raw methanol.

(a)

(b)

(c)

Figure 7 Heat consumption of (a) methanol recovery column (b) glycerol recovery column (c) transesterfication with respect to feed flowrate and methanol recovery(RMe).

As shown in Figure 8, methanol recovery consumed more energy than the other two does. The value of each unit is in the range of 800-6800 (MJ/hr), 800-4000 (MJ/hr) and 60-320 (MJ/hr), respectively. We adopted the data of Weschler, Barr et al.[6] to estimate the energy consumption of the the pre-treatment processes. There are 2, 7, 4 options for cultivation, harvesting and dewatering, respectively. Racyway pond (RP) and flate-plate PBR are the options for cultivation. Gravitional settleing(GS), flocculation (F), micro strainer(MS), electrocoagulation and electroflotation(E&E), vibrating screen filter(VSF), dissolved air flotation(DAF) and tangential flow filtration (TFF) are the options for harvesting. Decanter centrifuge(DC), self-cleaning plate separator(SPS), belt filter press(BFP) and chamber filter press(CFP) are the optinos for dewatering.The results of energy consumption of each process are shown as Figure 6. We considered 9 strategies shown in Table 2 in the combination of different options of cultivation, harvesting and dwatering. The strategy combined with raceway pond, flocculation and chamber filter press has the lowest energy consumption which is 569 kWh per tonne microalgae. The strategy combined with flate-plate PBR, gravitional settling and chamber filter press consumes 3122 kWh per tonne microalgae. This strategy consumes the highest energy even though it has the lowest harvesting energy. The results match to reality, microalgae cultivate on raceway pond spent less energy, which means raceway pond is a more economic option. Table 2. Different scenario of pre-treatment processes Cultivation

Harvesting

Dewatering

Strategy 1

RP

SF

CFP

Strategy 2

RP

GS

CFP

Strategy 3

RP

MS

DC

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Strategy 4

RP

VSF

DC

Strategy 5

RP

VSF

CFP

Strategy 6

PBR

SF

CFP

Strategy 7

PBR

GS

CFP

Strategy 8

PBR

MS

DC

Strategy 9

PBR

VSF

DC

kWh/ton microalgae

3500 3000

Energy consumption of pre-treatment processes Cultivation

Harvesting

Dewatering

2500 2000 1500 1000 500 Strategy 1 Strategy 2 Strategy 3 Strategy 4 Strategy 5 Strategy 6 Strategy 7 Strategy 8 Strategy 9

(a) 7000 kWh/ton microalgae

6000

Energy consumption of pre-treatment processes Cultivation

Harvesting

5000 4000 3000 2000 1000 Strategy 1 Strategy 2 Strategy 3 Strategy 4 Strategy 5 Strategy 6 Strategy 7 Strategy 8 Strategy 9

(b) Figure 8 Energy consumption of (a) cultivation, harvesting and dewatering (b) cultivation, harvesting, dewatering and drying of different strategies.

The optimization of microalgal energy system is based on following assumptions (1). Operating 8,150 hours per year. (2). SOFC stack is operated in nearly 1MW-AC

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(3). Fuel utilization/Air utilization: 0.8/0.167 (4). DC to AC efficiency: 0.92 (5). Isentropic efficiency of compressor/turbine: 0.85/0.95 (6). Separation units are set up with specific split fraction The results of electricity generation and consumption are shown as Figure 9 (a),(b). The total power generated from the system is 1.43 MW with 64% from SOFC and 36% from the gas turbine. Combined SOFC, gas turbine and heat exchange network, the system is an application of combined heat and power (CHP). The electricity efficiency can be defined as Pnet Șelec = Ffuel ×LHVfuel +Qሶ gis

where Pnet is the net power from the system and ܳሶ௚௜௦ is the heat consumption of gasification process. ‫ܨ‬௙௨௘௟ × ‫ܸܪܮ‬௙௨௘௟ is the energy content of feedstock. After calculation, Kelec is 52.17%. Although, the scale of power is limited (~1.5 MW), the efficiency is higher than a typical pulverised coal power plant, where its ߟ௘௟௘௖ is near 40% [7]. Moreover, carbon dioxide emission per unit energy (kW-1hr-1) is 0.4295 (kg CO2 kW-1hr-1), which is lower than 2.02-2.17(kg CO2 kW-1hr-1) where the typical coal power plants are.[8] This power would be used to support the electricity needed for the pre-treatment processes of microalgae. The pre-treatment processes used 0.627 MW in total where cultivation, drying, dewatering, extraction, harvesting, methanol production and transesterification is 13%,63%,1%,4%,3%, 7% and 7%, respectively.

(a)

(b)

Figure 9 Distribution of (a) the power output and (b) the electricity consumption

The results of carbon dioxide emission are presented as Figure 10 (a). 4,727 ton of equivalent carbon dioxide are emitted to atmosphere per year where 64% comes from flue gas, 33% comes from heat consumption and 3% caused by the raw material production. Figure 10.(b) is the detailed distribution of the heat consumption in the system. Heat used by methanol recovery and the gasification process is the largest part, which is 42%, 25%, respectively. Heat used by glycerol recovery, methanol production, drying and utility is 4%, 19%, 5% and 4%, respectively. Tranesterfication only consumes 1% of total amount.

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(a)

(b)

Figure 10 Distribution of (a) carbon dioxide emission and (b) the heat consumption

Eeconomic evalution is shown as Figure 11 (a) and (b). The summation of income is 0.8639 million per year with 74% and 26% contributed by the revenue of electricity and biodiesel, respectively. The raw material cost is 0.3510 million per year where 48%, 32% of total is used by purchasing hexane and methanol. Nature gas and coal spent 19% of the total cost, while neutralizer and alkai catalyst spent less than 1%.

(a)

(b) Figure 11 Distribution of (a) income and (b) raw material cost

To compare the performance of greenhouse gas(GHG) reduction with fossil fuel, the functional unit defined in the summation of electricity output and the energy content of the transportation fuel is used to standardize the results of biomass and fossil fuel. Figure 15 illustrates the two systems of a reference system using coal and fossil diesel and our system.[9]

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Figure 12 Comparison of the conventional fossil fuel and the microalgal energy system

We analyse the relation between profit and global warming potential (GWP). The relation is expressed as a pareto-curve (Figure 12) obtained by H-constraint multi-objective optimization. The results indicate that the trade-off between money and environment. The maximum revenue is $110,513 dollars, which causes 4839 tonne e-CO2 emission. While the carbon dioxide emission become more strict (the value of GWP is lower), the revenue would be decreasing. As GWP is lower than 4200 (tonne e-CO2/year), the net revenue will become negative. The performance of GHG reduction is shown in Figure 12. The efficiency is drop from 30% to negative value with the GWP constraint from 5000 to 4000 (tonne e-CO2/year). This result suggests it is not really beneficial to restrict the CO2 emission, because the operation in low carbon emission condition will lead to less product produced. Although the carbon emission is reduced, the overall reduction efficiency worsens. Figure 13 is the result of the price of biodiesel from $900 to $1500, obviously, the profit is increasing with the higher price. When the price is higher than $1,200, the profit will always be positive.

Figure 13 Pareto-curve of the system

Because the percentage of oil (woil) of the microalgae may have effect on the profit, we did the sensitivity analysis to discuss how the oil ratio affects the profit and GHG reduction efficiency. We analysed woil ranged from 0.2 to 0.5 as Figure 14 is presented. The result indicates the profit is rising when woil does not തതതതതതത make the less profit exceed 0.3; while woil is greater than 0.3, the profit will fall down. The lower ‫ܹܲܩ‬ തതതതതതത make performance. In Figure 15, we can see clearly the higher value of woil and the lower value of ‫ܹܲܩ‬ the GHG reduction efficiency negative. The profile implies the biodiesel from algal oil has less contribution to GHG reduction; nevertheless, the usage of residual part can compensate the efficiency effectively.

Po-Han et al. / Energy Procedia 105 (2017) 3051 – 3061

തതതതതതത. Figure 14 Profiles of (a) profit and (b) GHG reduction with regard to woil and GWP

4. Conclusions In this article, the multi-objective optimization (MOO) of a microalgal energy system is addressed by the mixed-integer nonlinear programming (MINLP) model in GAMS®. The MOO is used to form a Paretocurve graph for evaluating the trade-offs problem between environmental impacts and economic feasibility. All algebraic model equations are obtained by Aspen models and experimental data. Global warming potential (GWP) is the factor account for the environmental impacts of the process. Bilinear relaxation is applied for the nonconvex constraints to reach the global optimum by adopting piecewise bilinear relaxation convex envelope. References [1] Wu, Keng-Tung, Tsai, Chia-Ju, Chen, Chih-Shen, & Chen, Hsiao-Wei. (2012). The characteristics of torrefied microalgae. Applied Energy, 100, 52-57. [2] Park, Sang-Woo, Jang, Cheol-Hyeon, Baek, Kyung-Ryul, & Yang, Jae-Kyung. (2012). Torrefaction and low-temperature carbonization of woody biomass: Evaluation of fuel characteristics of the products. Energy, 45(1), 676-685. [3] Doherty, Wayne, Reynolds, Anthony, & Kennedy, David. (2010). Computer simulation of a biomass gasification-solid oxide fuel cell power system using Aspen Plus. Energy, 35(12), 4545-4555. [4] Gai, Chao, & Dong, Yuping. (2012). Experimental study on non-woody biomass gasification in a downdraft gasifier. International Journal of Hydrogen. Energy, 37(6), 4935-4944. [5] Lv, P. M., Xiong, Z. H., Chang, J., Wu, C. Z., Chen, Y., & Zhu, J. X. (2004). An experimental study on biomass air-steam gasification in a fluidized bed. Bioresour Technol, 95(1), 95-101. [6] Weschler, Matthew K., Barr, William J., Harper, Willie F., & Landis, Amy E. (2014). Process energy comparison for the production and harvesting of algal biomass as a biofuel feedstock. Bioresource Technology, 153, 108-115. [7] Hirst, Neil, Seung-Young, Chung, Ciszewska, Aneta, Denysenko, Nataliia, Iwasaki, Takashi, Nishimura, Ikuo, Topper, John. (2010). Power Generation from Coal - Measuring and Reporting Efficiency Performance and CO2 Emissions: International Energy Agency (IEA). [8] (EIA), E. I. A. (2016). "How much carbon dioxide is produced per kilowatthour when generating electricity with fossil fuels." [9] Gutiérrez-Arriaga, César G., Serna-González, Medardo, Ponce-Ortega, José María, & El-Halwagi, Mahmoud M. (2014). Sustainable Integration of Algal Biodiesel Production with Steam Electric Power Plants for Greenhouse Gas Mitigation. ACS Sustainable Chemistry & Engineering, 2(6), 1388-1403.

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