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Life Cycle Assessment (LCA) of Production and Fractionation of Bio-Oil Derived from Palm Kernel Shell: a Gate-to-Gate Case Study

  • Yi Herng Chan
  • Raymond R. Tan
  • Suzana Yusup
  • Armando T. Quitain
  • Soh Kheang Loh
  • Yoshimitsu Uemura
Original research paper
  • 448 Downloads

Abstract

This paper presents the life cycle assessment (LCA) of a novel process involving hydrothermal liquefaction of palm kernel shell (PKS) to produce bio-oil and subsequent extraction using supercritical CO2 (sc-CO2) to obtain a phenol-rich extract. In this study, five environmental impact categories, namely global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), human toxicity potential (HTP), and photochemical ozone creation potential (POCP), were considered. In addition, a qualitative analysis on technology readiness level (TRL) on the interpretation of the LCA results was included. Lastly, conventional production of crude phenol was chosen as the benchmark for comparison to identify the environmental impact margins that need to be bridged in the future through process improvements.

Keywords

Life cycle assessment Bio-oil Hydrothermal liquefaction Supercritical extraction 

Introduction

In response to the growing demand for energy, depletion of fossil-based resources, and environmental impacts associated with the utilization of non-renewable energy sources, the pursuit of alternative renewable energy sources is crucial towards achieving green and sustainable development. Among different types of renewable energy sources, biomass stands out as the only resource that can be converted to a wide range of high-value biofuel, biochemicals, and bioenergy via thermochemical conversion technologies, such as pyrolysis, gasification, liquefaction, torrefaction, carbonization, and combustion (Hamelinck and Faaij 2006). As far as sustainability issues are concerned, the environmental impacts of these conversion technologies with multiple types of biomass materials as feedstocks, which give rise to various forms of end products and applications, have been extensively assessed and compared using systematic approaches such as the life cycle assessment (LCA) (Muench and Guenther 2013).

LCA is a systematic procedure used to evaluate the performance of a product system or a technological process, based on its impacts to the environment throughout its life cycle (Zhou et al. 2017). The life cycle of such a system corresponds to the network of processes and activities that provide direct and indirect support. The International Organization for Standardization (ISO) has developed the ISO 14040 (2006) and ISO 14044 series LCA standards (2006). Based on the standards established, there are four major phases involved in a comprehensive LCA, which include (1) goal and scope definition, (2) life cycle inventory (LCI), (3) life cycle impact assessment (LCIA), and (4) interpretation. In general, the system boundary of LCAs of thermochemical conversion of biomass to useful end products involves three phases: (1) biomass cultivation, harvesting, and transportation; (2) plant site operation and upgrading of products; and (3) recycling and demolition of plant (Patel et al. 2016). Conventional LCA deals specifically with environmental issues, although recent developments have attempted to extend the approach to include social and economic aspects, under the generic framework known as life cycle sustainability assessment (LCSA) (Kloepffer 2008).

Bio-oil (or biocrude) is a liquid product derived through pyrolysis or liquefaction of biomass (Jena and Das 2011). Raw bio-oil contains a complex mixture of oxygenated compounds and possesses inferior quality, so that subsequent upgrading or refining to higher value-added chemicals or fuels is necessary (Chan et al. 2014). The purification steps depend on the specific component targeted for enrichment. In this context, LCAs of bio-oil production through pyrolysis and subsequent upgrading of liquid products have been widely reported in the literature. Ning et al. (2013) evaluated the production of biomass pyrolysis oil and its utilization in terms of cost, energy, and environmental performance, in which the environmental impact categories considered were global warming effect, acidification, eutrophication, ozone layer depletion, summer smog, winter smog, pesticides, airborne heavy metals, waterborne heavy metals, and carcinogenic substances. Dang et al. (2014) compared several upgrading (hydroprocessing) methods to produce biofuel from corn stover-derived pyrolysis oil. Snowden-Swan et al. (2016) reported the environmental impact of various catalysts used in hydrotreating of fast pyrolysis oil. Guo et al. (2017) compared and reported the performance and efficiency of various pyrolysis pathways to convert microalgae to aviation fuel.

On the other hand, there is a lack of literature on LCA of bio-oil production through liquefaction of lignocellulosic biomass (Patel et al. 2016). LCAs reported in the literature were mostly on bio-oil production from liquefaction of algae (Liu et al. 2013) and microalgae for applications as biofuel (Fortier et al. 2014) and bioenergy and biochemicals (Doren et al. 2017). Other than that, LCAs on biofuel (biodiesel) production from freshwater microalgae Scenedesmus dimorphus (Togarcheti et al. 2017) and switchgrass-based bioenergy systems (Bichraoui-Draper et al. 2015) have been reported. Studies on bio-oil extraction and fractionation are relatively limited in the literature as compared to other upgrading and processing methods. Kanaujia et al. (2016) optimized the liquid-liquid extraction procedure for bio-oil derived from Jatropha curcas seed cake via pyrolysis. Li et al. (2017) studied the extraction of chemical compounds from model bio-oil using ionic liquids. Furthermore, none of these works had evaluated the impacts to the environment using a systematic LCA.

The use of sub- and supercritical fluids in bio-oil production and extraction has been receiving great research attention recently due to lesser impacts to the environment and higher efficiencies (Xiu and Shahbazi 2012). In this study, to address the research gap and evaluate the potential of lignocellulosic biomass in value-added biochemicals production, LCA of a novel process of palm kernel shell (PKS) liquefaction for bio-oil production and subsequent extraction (fractionation) to a more concentrated extract of phenol using sub- and supercritical fluids is presented and discussed. Phenol is a valuable chemical that constitutes the major portion of PKS-derived bio-oil compounds due to high lignin content of the feedstock (Omoriyekomwan et al. 2016). A few studies on recovery of phenol from bio-oil using various techniques have been reported previously. Fu et al. (2014) reported the extraction of phenols from pyrolysis oil using switchable hydrophilicity solvent and up to ~ 70% of phenolic compounds were recovered in the extracts. Wang et al. (2014) studied a new method of reactive extraction to recover phenolic compounds from bio-oils by formation of intermediate complexes. Yang et al. (2015) performed the separation of phenols from bio-oil using extraction-column chromatography.

Technology readiness level (TRL) is a qualitative scaling system originally developed in the 1960s by the National Aeronautics and Space Administration (NASA) of the USA (Mankins 2009). TRL is used to access and compare the maturity of technologies conceived during the research and development process (Upadhyayula et al. 2018). In short, a particular technology is accessed on a TRL scale of 1 to 9, in which TRLs 1–4 involve conceptual development, technology research, proof of concept, and technology demonstration in laboratory scales; TRLs 5 and 6 represent technology development in terms of prototype demonstration and validation in simulated environment; and TRLs 7–9 require the demonstration of prototype in operational environment, system qualification, and technology competency to be commercialized and deployed (Mankins 2009). More recently, Straub (2015) has proposed the need for TRL 10 to indicate technological maturity at the level of extended, successful commercial use. As the core process data used in this study are mostly obtained from lab-scale experimental works, the process maturity level is currently still at the conceptual stage, TRL 4. However, use of LCA even in the early stage of development based on premature data can aid in identifying opportunities for cost-effective improvements. Furthermore, previous work suggests that LCA results based on low-TRL data such as laboratory scale tests overestimate impacts, usually by at least an order of magnitude (Gavankar et al. 2015). Hence, TRL is introduced as an indicator of technological uncertainty which can cause errors in life cycle inventory (LCI) estimates. This error is rarely addressed explicitly in literature. Despite developments over more than two decades, data quality is still a practical issue in LCA (Bicalho et al. 2017). In this study, subjective assessment on the projected environmental impacts (similar approach as data “pedigree matrix”) is provided and discussed based on previous reported trends when the maturity of the technology reaches TRLs 9–10 (Weidema and Wesnæs 1996). These LCA results are then benchmarked against those of conventional crude phenol production with TRLs 9–10.

Methodology

The conceptual process employed in this study is an extension of our previous works (Chan et al. 2015, 2016), with the addition of bio-oil extraction (fractionation) process. The computation of life cycle inventory and environmental impacts assessment are performed using GaBi software (version 8.1.0.29 equipped with Professional Database), supplemented with data reported in scientific literature.

Goal and Scope Definition

The goal of this LCA study is to assess the environmental impacts associated with bio-oil production via hydrothermal liquefaction (HTL) and subsequent fractionation using supercritical carbon dioxide (sc-CO2) to obtain bio-oil extract with improved concentration of phenol. Figure 1 shows the system boundary of this study and the functional unit is defined as 1 kg of bio-oil extract (containing improved phenol concentration of ~ 7–8 wt% as compared to ~ 5 wt% in raw bio-oil).
Fig. 1

System boundary defined by dashed-line box for hydrothermal liquefaction of PKS to bio-oil and subsequent fractionation process using sc-CO2

Limitations of This LCA Study

As this LCA study is conducted based on a general conceptual process of HTL of PKS and fractionation of bio-oil, some technical and real-time mature data are difficult to obtain. Hence, some assumptions and estimations need to be made based on proxy data available in the literature. Furthermore, this LCA study involves a gate-to-gate system boundary. Hence, there are still subsequent processes (further concentration of bio-oil extract to obtain high purity phenol) which are not included in this study. These excluded stages will further contribute to the environmental impacts of phenol production from bio-oil.

Life Cycle Inventory Data Collection and Process Description

The conceptual process constructed in this study is based on our previous lab-scale experimental approach and results obtained (Chan et al. 2017, 2018). Operating conditions of the process under investigation are summarized in Table 1. PKS is assumed to be collected from oil palm mill as biomass wastes to the processing site and transportation stage is not considered in this study. At the processing site, PKS will undergo pretreatment stage in which it is milled to particle size of < 710 μm prior to liquefaction process. The electricity consumption for milling and other auxiliary processing steps is adjusted based on the data reported in the literature (Iribarren et al. 2012).
Table 1

Process details and operating conditions

Process

Operating conditions/details

Reference

Pretreatment: milling

Particle size reduction to < 710 μm

(Chan et al. 2018)

Hydrothermal liquefaction of PKS (Fig. 1a)

T = 390 °C, P = 25 MPa, t = 1 h

(Chan et al. 2018)

Supercritical CO2-assisted hydrothermal liquefaction of PKS (Fig. 1b)

T = 300 °C, P = 25 MPa, t = 1 h

(Chan et al. 2018)

Supercritical CO2 fractionation of bio-oil

T = 70 °C, P = 40 MPa, t = 1 h, CO2 flowrate = 4 mL/min

 

As shown in Fig. 1, two HTL scenarios are compared in which one involves liquefaction using water only (Fig. 1a) while the other one involves the use of sc-CO2 as co-solvent (Fig. 1b). After the liquefaction process, bio-oil produced is extracted using toluene as solvent (Chan et al. 2015), while other byproducts such as aqueous bio-oil, char, ash, and gases produced in the process are not taken into consideration in this study due to the unavailability of the compositional data of byproducts. In this context, the bio-oil yields from PKS are based on our optimum result obtained in our previous work conducted (Chan et al. 2018). The electricity usage, thermal energy consumption (assumed to be supplied by natural gas (NG)-fired boilers with 80% thermal efficiency), and solvent make-up at this stage are adjusted from our previous published works (Chan et al. 2015, 2016).

The third stage considered in this study is the fractionation of raw bio-oil produced to obtain extract enriched in phenol. In this stage, concentration of phenol in the extract is enriched to ~ 7–8 wt% as compared to the original concentration of ~ 5 wt%. Due to lack of information reported in the literature in the context of supercritical extraction of bio-oil, it is estimated that the electricity consumption in this stage is similar to that of supercritical extraction of caffeine from coffee beans (De Marco et al. 2018).

In this study, the electricity consumption is based on Malaysia’s power generation mix, which is 50% natural gas, 40% coal, and 10% hydropower, while other types of electricity generation sources such as diesel and oil are considered negligible, i.e., < 2% of the total energy mix (Oh et al. 2018).

Life Cycle Impact Assessment

In this study, CML 2001 methodology is used for the LCIA of the conceptual process based on the emissions associated to each processing stage as specified in the system boundary (Fig. 1). The impact categories studied are global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), human toxicity potential (HTP), and photochemical ozone creation potential (POCP).

Interpretation and Benchmarking of LCA Results

The results of this LCA study are the environmental impacts of the conceptual process of PKS liquefaction to bio-oil and subsequent fractionation to intermediate bio-oil extract based on the impact categories investigated. The results are also interpreted and discussed in the context of higher TRL (TRLs 9–10) when the technology matures. In addition, the results obtained are benchmarked against the conventional production of crude phenol (data obtained from Gabi software version 8.1.0.29 Professional Database) to identify the margin exist. Hence, reducing this margin or “gap” could be the way forward in the future.

Results and Discussion

Interpretation of LCIA Results

Based on the established LCI (Table 2), various environmental impact categories resulting from the system boundary defined (Fig. 1) are evaluated. Table 3 shows the emissions of each processing stage and their contributions to the overall impacts are displayed in Fig. 2a–e. In the context of GWP, it is estimated that for every kilogram of phenolic-rich extract produced (extracted from 11.5 kg of crude bio-oil), a total of 42.69 kg of CO2 equivalent is produced through HTL process route using water only (Fig. 1a), whereas this amount is reduced slightly to 42.46 kg of CO2 equivalent for sc-CO2-assisted HTL (Fig. 1b). These results are comparable to those of our previous reported works in which an average of 3.55 kg of CO2 equivalent is produced per kilogram of crude bio-oil (Chan et al. 2015, 2016). The use of sc-CO2 as co-solvent enables the HTL process to operate at a lower temperature, due to the formation and dissociation of carbonic acid which result in a more reactive atmosphere for liquefaction (Chan et al. 2018). Hence, reduction in operating temperature from 390 to 300 °C (Table 1) leads to ~ 23% lower thermal energy requirement for sc-CO2-assisted HTL (Fig. 1b) compared to that of HTL (Fig. 1a), which translates to reduced emissions in this processing stage. The emissions from other stages of these two alternative processes are similar as the electricity and toluene consumptions are identical.
Table 2

LCI of 1-kg bio-oil extract

Parameter/item

Amount

Unit

Remark

Assumption

Reference

Pretreatment: milling

 Electricity

39.02

kWh

Calculated based on weight of biomass for pretreatment

(Iribarren et al. 2012)

Hydrothermal liquefaction

 Pretreated PKS

79.64

kg

 Bio-oil

11.5

kg

Based on the optimum yield obtained from experimental work

(Chan et al. 2018)

 Thermal energy for liquefaction and solvent recovery (for route Fig. 1a)

14.84

MJ

Natural gas fired boilers with 80% thermal efficiency

(Chan et al. 2015, 2016)

 Thermal energy for liquefaction and solvent recovery (for route Fig. 1b)

11.42

MJ

Calculated based on the reduction of process temperature with respect to route Fig. 1a

Natural gas fired boilers with 80% thermal efficiency

Extraction of organic biocrude

 Make-up solvent (toluene)

0.115

kg

Assume solvent loss is similar to that of soybean extraction using hexane

(Chan et al. 2015, 2016)

 Electricity

0.46

kWh

(Chan et al. 2015, 2016)

Fractionation of biocrude

 Electricity

16.74

kWh

Assume electricity consumption is similar to the process of supercritical extraction of caffeine from coffee beans

(De Marco et al. 2018)

 Bio-oil extract (with phenol concentration of ~ 7–8 wt%)

1

kg

Table 3

Contributions of process stages to the overall environmental impacts

Impact category

Unit

Electricity for milling

Electricity for HTL process

Electricity for fractionation process

Thermal energy for HTL process

Toluene (make-up)

Total

Fig. 1a

 GWP

kg CO2 eq.

28.832

0.342

12.42

0.996

0.101

42.69

 AP

g SO2 eq.

245

2.91

106

1.61

0.23

355.8

 EP

g PO43− eq.

11.5

0.136

4.96

0.34

0.0174

16.9

 HTP

kg DCB eq.

4.449

0.0528

1.916

0.0201

0.0078

6.4457

 POCP

g C2H4 eq.

13.7

0.162

5.88

0.186

0.0353

20.0

Fig. 1b

 GWP

kg CO2 eq.

28.832

0.342

12.42

0.766

0.101

42.46

 AP

g SO2 eq.

245

2.91

106

1.24

0.23

355.4

 EP

g PO43− eq.

11.5

0.136

4.96

0.262

0.0174

16.9

 HTP

kg DCB eq.

4.449

0.0528

1.916

0.0155

0.0078

6.4411

 POCP

g C2H4 eq.

13.7

0.162

5.88

0.143

0.0353

19.9

Estimated emissions when TRLs = 9–10 (with reference to process conditions of Fig. 1a)

 GWP

kg CO2 eq.

2.883b

0.0342b

1.242b

0.0996a

0.101a

4.36b

 AP

g SO2 eq.

24.5b

0.29b

10.6b

0.161a

0.23a

35.8b

 EP

g PO43− eq.

1.15b

0.0136b

0.496b

0.034a

0.0174a

1.711b

 HTP

kg DCB eq.

0.445b

0.00528b

0.192b

0.00201a

0.0078a

0.6521b

 POCP

g C2H4 eq.

1.37b

0.016b

0.588b

0.019a

0.0353a

2.0283b

Estimated emissions when TRLs = 9–10 (with reference to process conditions of Fig. 1b)

 GWP

kg CO2 eq.

2.883b

0.0342b

1.242b

0.0766a

0.101a

4.34b

 AP

g SO2 eq.

24.5b

0.29b

10.6b

0.124a

0.23a

35.7b

 EP

g PO43− eq.

1.15b

0.0136b

0.496b

0.026a

0.0174a

1.703b

 HTP

kg DCB eq.

0.445b

0.00528b

0.192b

0.00155a

0.0078a

0.6516b

 POCP

g C2H4 eq.

1.37b

0.016b

0.588b

0.014a

0.0353a

2.0233b

Conventional crude phenol production (TRLs 9–10)

 GWP

kg CO2 eq.

2.20

 AP

g SO2 eq.

4.03

 EP

g PO43− eq.

0.411

 HTP

kg DCB eq.

0.0961

 POCP

g C2H4 eq.

0.646

Subjective assessment of estimation

aNot overestimated (< 10% deviation from true value)

bMildly overestimated (25% deviation from true value)

cModerately overestimated (50% deviation from true value)

dHighly overestimated (> 100% deviation from true value)

eExtremely overestimated (> 300% deviation from true value)

Fig. 2

Relative contributions of various processing stages to the overall impacts and the estimated emissions when TRLs = 9–10. a Global warming potential (GWP). b Acidification potential (AP). c Eutrophication potential (EP). d Human toxicity potential (HTP). e Photochemical ozone creation potential (POCP)

Compared to GWP, the emissions leading to other environmental impacts (AP, EP, HTP and POCP) are less severe, as shown in Table 3. For all these environmental impacts, a major portion of ~ 67–69% is contributed by the electricity consumption at the milling stage. Similar results are also reported in the literature (Iribarren et al. 2012). The second highest contributor for all environmental impacts is electricity consumption at the fractionation stage (~ 28–29%) due to intense energy input for compression of CO2 to supercritical state for extraction of bio-oil (Wang et al. 2016). Thermal energy used in the liquefaction process contributes to ~ 2.3% of total GWP for the case of HTL using water only (Fig. 1a) and ~ 1.8% for the case of sc-CO2-assisted HTL (Fig. 1b). The remaining minor contributors to the environmental impacts are electricity and toluene consumption at the HTL process. Among all environmental impact categories investigated, utilization of toluene in the process imparts highest impact to GWP (~ 0.24%), followed by POCP (~ 0.18%), HTP (~ 0.12%), EP (~ 0.10%), and AP (~ 0.06%).

The power generation mix in Malaysia is largely dependent on non-renewable sources, with a major of ~ 50% from natural gas and ~ 40% from coal, while the remaining small portion is supplemented by hydropower (~ 10%). Generation of power from natural gas and coal causes higher impacts to the environment due to the intense emissions of heavy metals to the atmosphere during coal mining (Akber et al. 2017) and release of sulfur contained during electricity generation process (Brizmohun et al. 2015), which contribute to HTP and AP. Besides, utilization of coal poses the highest threat to EP and POCP due to emissions of phosphates during coal mining and electricity generation (Akber et al. 2017; Brizmohun et al. 2015), whereas natural gas and hydropower contribute minimally to those impacts (Garcia et al. 2014). This signifies electricity generation from hydropower is one of the best options for lowest greenhouse gases (GHG) emissions (Turconi et al. 2013). However, it is important to note that hydropower would have significant effect to the changes in ecosystems (Garcia et al. 2014), which is not included in this study.

Estimated Effect of Technology Maturity

As this LCA study utilizes data obtained from lab-scale tests, various forms of mature plant-scale data are lacking. However, it is important to perform a preliminary LCA study in order to proactively access the potential environmental performance of such process. Estimation of LCA results at higher TRLs (TRLs 9–10) based on the results obtained in this study is shown in Table 3. Significant reductions in the overall environmental emissions of the entire process could be expected with higher technology maturity, scale, and efficiency, as evidenced from previous reported work (Lundin et al. 2000). It is estimated that the energy requirement of the process undertaken in this study will be reduced by at least an order of magnitude as the scale of the process ramps up from lab scale to industrial scale, as reported in a previous case study on carbon nanotube synthesis (Gavankar et al. 2015). The environmental impacts associated with the quantity of make-up toluene in this case study are scaled linearly from the lab-scale data due to lack of other information, hence the results might be slightly overestimated (Piccinno et al. 2018).

As a technology gradually matures and develops into larger production scale, various aspects and requirements of the process change significantly. Lab-scale experiments usually omit the recycling, reuse, and recovery of various forms of materials and energy, and they are not being optimized in terms of resource efficiency (Piccinno et al. 2016). In the context of industrial-scale operations, practices and applications of process integration involving energy and materials recovery and recycling significantly increase process efficiency, and hence improving the environmental performance of the process, as reported in a previous case study (Milutinović et al. 2017). In addition, in the case of thermal systems, scaling up of process reduces heat losses due to reduced surface area per unit volume of thermal equipment (Piccinno et al. 2016). Obligatory waste management systems in industrial processes further improve the environmental performance by treating industrial wastes and thus, minimizing the negative environmental impacts associated with the generation and disposal of industrial wastes. A case study on concrete waste management reported that waste minimization through recycling reduced environmental liabilities compared to direct disposal of the waste through landfill without treatment (Mah et al. 2018). Furthermore, the process develops and expands into a biorefinery in which multiple processes are available and on-site co-generation of electricity and thermal energy is possible, as pointed out by Zhang et al. (2018). Other than that, future technological improvements which increase the yield of production and efficiency of process and equipment for various unit operations (such as boiler and steam turbine) will further reduce the negative environmental impacts (Gavankar et al. 2015; Tagliaferri et al. 2018). Based on these arguments, there is significant potential to further improve the sustainability profile of the process analyzed in this study, as the TRL level matures. However, any such improvements need to be verified by subsequent LCA work as new data becomes available.

The estimated environmental impacts are compared to those of conventional crude phenol production, which are chosen as the benchmark (Table 3). For 1 kg crude phenol produced, the reported GWP and HTP is 2.2 kg CO2 eq. and 0.0961 kg DCB eq., while other impacts are considerably small. The environmental performance gap that exists between the estimated and established values could be addressed and reduced by future process improvements and up-scaling, and accuracy of the estimated values could be improved with the availability of data obtained from higher scales. A more matured and established bio-oil production process in commercial scales might compensate part of the environmental emissions at the use phase of biofuels as compared to that of fossil-based fuels (Vienescu et al. 2018).

Conclusions and Perspectives

In this work, the LCA of a conceptual process of hydrothermal liquefaction of PKS and subsequent fractionation using sc-CO2 to obtain a phenol-rich extract is presented. The environmental performance of this process in terms of various environmental impacts is estimated on a gate-to-gate basis. Results show that the use of sc-CO2 as a co-solvent in HTL process of PKS reduces the energy consumption by ~ 23%. As the technology matures and the TRL improves, significant reductions in the environmental impacts of the process could be expected. The results obtained are also benchmarked against those of crude phenol production using commercially available technology, highlighting the margins or “gaps” which need to be bridged through future process improvements. In particular, further process development aimed at enhancement in bio-oil yield, purification efficiency of bio-oil to obtain pure phenol, and use of renewables for electricity generation is needed in order to improve the environmental performance of this process, and thus enable the viable production of crude phenol from biomass. Besides, a rigorous quantitative framework needs to be developed for incorporating effects of TRL on LCA calculations.

Notes

Funding Information

This research is supported by Long-Term Research Grant Scheme (LRGS) under the Ministry of Higher Education (MOHE), Malaysia and Japan Society for the Promotion of Science (JSPS) Bilateral Joint Research Program in collaboration with Universiti Teknologi PETRONAS, Malaysia, De La Salle University, Philippines, and Kumamoto University, Japan.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. Akber MZ, Thaheem MJ, Arshad H (2017) Life cycle sustainability assessment of electricity generation in Pakistan: policy regime for a sustainable energy mix. Energy Policy 111:111–126CrossRefGoogle Scholar
  2. Bicalho T, Sauer I, Rambaud A, Altukhova Y (2017) LCA data quality: a management science perspective. J Clean Prod 156:888–898CrossRefGoogle Scholar
  3. Bichraoui-Draper N, Xu M, Miller SA, Guillaume B (2015) Agent-based life cycle assessment for switchgrass-based bioenergy systems. Resour Conserv Recycl 103:171–178CrossRefGoogle Scholar
  4. Brizmohun R, Ramjeawon T, Azapagic A (2015) Life cycle assessment of electricity generation in Mauritius. J Clean Prod 106:565–575CrossRefGoogle Scholar
  5. Chan YH, Dang KV, Yusup S, Lim MT, Zain AM, Uemura Y (2014) Studies on catalytic pyrolysis of empty fruit bunch (EFB) using Taguchi’s L9 Orthogonal Array. J Energy Inst 87:227–234CrossRefGoogle Scholar
  6. Chan YH, Yusup S, Quitain AT, Tan RR, Sasaki M, Lam HL, Uemura Y (2015) Effect of process parameters on hydrothermal liquefaction of oil palm biomass for bio-oil production and its life cycle assessment. Energy Convers Manag 104:180–188CrossRefGoogle Scholar
  7. Chan YH, Tan RR, Yusup S, Lam HL, Quitain AT (2016) Comparative life cycle assessment (LCA) of bio-oil production from fast pyrolysis and hydrothermal liquefaction of oil palm empty fruit bunch (EFB). Clean Techn Environ Policy 18:1759–1768CrossRefGoogle Scholar
  8. Chan YH, Yusup S, Quitain AT, Uemura Y, Loh SK (2017) Fractionation of pyrolysis oil via supercritical carbon dioxide extraction: optimization study using response surface methodology (RSM). Biomass Bioenergy 107:155–163CrossRefGoogle Scholar
  9. Chan YH, Quitain AT, Yusup S, Uemura Y, Sasaki M, Kida T (2018) Optimization of hydrothermal liquefaction of palm kernel shell and consideration of supercritical carbon dioxide mediation effect. J Supercrit Fluids 133:640–646CrossRefGoogle Scholar
  10. Dang Q, Yu C, Luo Z (2014) Environmental life cycle assessment of bio-fuel production via fast pyrolysis of corn stover and hydroprocessing. Fuel 131:36–42CrossRefGoogle Scholar
  11. De Marco I, Riemma S, Iannone R (2018) Life cycle assessment of supercritical CO2 extraction of caffeine from coffee beans. J Supercrit Fluids 133:393–400CrossRefGoogle Scholar
  12. Doren LGV, Posmanik R, Bicalho FA, Tester JW, Sills DL (2017) Prospects for energy recovery during hydrothermal and biological processing of waste biomass. Bioresour Technol 225:67–74CrossRefGoogle Scholar
  13. Fortier M-OP, Roberts GW, Stagg-Williams SM, Sturm BSM (2014) Life cycle assessment of bio-jet fuel from hydrothermal liquefaction of microalgae. Appl Energy 122:73–82CrossRefGoogle Scholar
  14. Fu D, Farag S, Chaouki J, Jessop PG (2014) Extraction of phenols from lignin microwave-pyrolysis oil using a switchable hydrophilicity solvent. Bioresour Technol 154:101–108CrossRefGoogle Scholar
  15. Garcia R, Marques P, Freire F (2014) Life-cycle assessment of electricity in Portugal. Appl Energy 134:563–572CrossRefGoogle Scholar
  16. Gavankar S, Suh S, Keller AA (2015) The role of scale and technology maturity in life cycle assessment of emerging technologies: a case study on carbon nanotubes. J Ind Ecol 19:51–60CrossRefGoogle Scholar
  17. Guo F, Wang X, Yang X (2017) Potential pyrolysis pathway assessment for microalgae-based aviation fuel based on energy conversion efficiency and life cycle. Energy Convers Manag 132:272–280CrossRefGoogle Scholar
  18. Hamelinck CN, Faaij APC (2006) Outlook for advanced biofuels. Energy Policy 34:3268–3283CrossRefGoogle Scholar
  19. Iribarren D, Peters JF, Dufour J (2012) Life cycle assessment of transportation fuels from biomass pyrolysis. Fuel 97:812–821CrossRefGoogle Scholar
  20. ISO 14040 (2006) Environmental management—life cycle assessment—principles and framework. International Organization for Standardization, GenevaGoogle Scholar
  21. ISO 14044 (2006) Environmental management—life cycle assessment—requirements and guidelines. International Organization for Standardization, GenevaGoogle Scholar
  22. Jena U, Das KC (2011) Comparative evaluation of thermochemical liquefaction and pyrolysis for bio-oil production from microalgae. Energy Fuel 25:5472–5482CrossRefGoogle Scholar
  23. Kanaujia PK, Naik DV, Tripathi D, Singh R, Poddar MK, Konathala LNSK, Sharma YK (2016) Pyrolysis of Jatropha curcas seed cake followed by optimization of liquid-liquid extraction procedure for the obtained bio-oil. J Anal Appl Pyrolysis 118:202–224CrossRefGoogle Scholar
  24. Kloepffer W (2008) Life cycle sustainability assessment of products. Int J Life Cycle Assess 13:89–95CrossRefGoogle Scholar
  25. Li X, Kersten SRA, Schuur B (2017) Extraction of acetic acid, glycolaldehyde and acetol from aqueous solutions mimicking pyrolysis oil cuts using ionic liquids. Sep Purif Technol 175:498–505CrossRefGoogle Scholar
  26. Liu X, Saydah B, Eranki P, Colosi LM, Mitchell BG, Rhodes J, Clarens AF (2013) Pilot-scale data provide enhanced estimates of the life cycle energy and emissions profile of algae biofuels produced via hydrothermal liquefaction. Bioresour Technol 148:163–171CrossRefGoogle Scholar
  27. Lundin M, Bengtsson M, Molander S (2000) Life cycle assessment of wastewater systems: influence of system boundaries and scale on calculated environmental loads. Environ Sci Technol 34:180–186CrossRefGoogle Scholar
  28. Mah CM, Fujiwara T, Ho CS (2018) Life cycle assessment and life cycle costing toward eco-efficiency concrete waste management in Malaysia. J Clean Prod 172:3415–3427CrossRefGoogle Scholar
  29. Mankins JC (2009) Technology readiness assessments: a retrospective. Acta Astronautica 65:1216–1223CrossRefGoogle Scholar
  30. Milutinović B, Stefanović G, Ðekić PS, Mijailović I, Tomić M (2017) Environmental assessment of waste management scenarios with energy recovery using life cycle assessment and multi-criteria analysis. Energy 137:917–926CrossRefGoogle Scholar
  31. Muench S, Guenther E (2013) A systematic review of bioenergy life cycle assessments. Appl Energy 112:257–273CrossRefGoogle Scholar
  32. Ning S-K, Hung M-C, Chang Y-H, Wan H-P, Lee H-T, Shih R-F (2013) Benefit assessment of cost, energy, and environmental for biomass pyrolysis oil. J Clean Prod 59:141–149CrossRefGoogle Scholar
  33. Oh TH, Hasanuzzaman M, Selvaraj J, Teo SC, Chua SC (2018) Energy policy and alternative energy in Malaysia: issues and challenges for sustainable growth—an update. Renew Sust Energ Rev 81:3021–3031CrossRefGoogle Scholar
  34. Omoriyekomwan JE, Tahmasebi A, Yu J (2016) Production of phenolic-rich bio-oil during catalytic fixed-bed and microwave pyrolysis of palm kernel shell. Bioresour Technol 207:188–196CrossRefGoogle Scholar
  35. Patel M, Zhang X, Kumar A (2016) Techno-economic and life cycle assessment on lignocellulosic biomass thermochemical conversion technologies: a review. Renew Sust Energ Rev 53:1486–1499CrossRefGoogle Scholar
  36. Piccinno F, Hischier R, Seeger S, Som C (2016) From laboratory to industrial scale: a scale-up framework for chemical processes in life cycle assessment studies. J Clean Prod 135:1085–1097CrossRefGoogle Scholar
  37. Piccinno F, Hischier R, Seeger S, Som C (2018) Predicting the environmental impact of a future nanocellulose production at industrial scale: application of the life cycle assessment scale-up framework. J Clean Prod 174:283–295CrossRefGoogle Scholar
  38. Snowden-Swan LJ, Spies KA, Lee GJ, Zhu Y (2016) Life cycle greenhouse gas emissions analysis of catalysts for hydrotreating of fast pyrolysis bio-oil. Biomass Bioenergy 86:136–145CrossRefGoogle Scholar
  39. Straub J (2015) In search of technology readiness level (TRL) 10. Aerosp Sci Technol 46:312–320CrossRefGoogle Scholar
  40. Tagliaferri C, Evangelisti S, Clift R, Lettieri P (2018) Life cycle assessment of a biomass CHP plant in UK: the Heathrow energy centre case. Chem Eng Res Des 133:210–221CrossRefGoogle Scholar
  41. Togarcheti SC, Mk M, Chauhan VS, Mukherji S, Ravi S, Mudliar SN (2017) Life cycle assessment of microalgae based biodiesel production to evaluate the impact of biomass productivity and energy source. Resour Conserv Recycl 122:286–294CrossRefGoogle Scholar
  42. Turconi R, Boldrin A, Astrup T (2013) Life cycle assessment (LCA) of electricity generation techniques: overview, comparability and limitations. Renew Sust Energ Rev 28:555–565CrossRefGoogle Scholar
  43. Upadhyayula VKK, Gadhamshetty V, Shanmugam K, Souihi N, Tysklind M (2018) Advancing game changing academic research concepts to commercialization: a life cycle assessment (LCA) based sustainability framework for making informed decisions in Technology Valley of Death (TVD). Resour Conserv Recycl 133:404–416CrossRefGoogle Scholar
  44. Vienescu DN, Wang J, Le Gresley A, Nixon JD (2018) A life cycle assessment of options for producing synthetic fuel via pyrolysis. Bioresour Technol 249:626–634CrossRefGoogle Scholar
  45. Wang D, Li D, Liu Y, Lv D, Ye Y, Zhu S, Zhang B (2014) Study of a new complex method for extraction of phenolic compounds from bio-oils. Sep Purif Technol 134:132–138CrossRefGoogle Scholar
  46. Wang T, Hsu C-L, Huang C-H, Hsieh Y-K, Tan C-S, Wang C-F (2016) Environmental impact of CO2-expanded fluid extraction technique in microalgae oil acquisition. J Clean Prod 137:813–820CrossRefGoogle Scholar
  47. Weidema BP, Wesnæs MS (1996) Data quality management for life cycle inventories—an example of using data quality indicators. J Clean Prod 4:167–174CrossRefGoogle Scholar
  48. Xiu S, Shahbazi A (2012) Bio-oil production and upgrading research: a review. Renew Sust Energ Rev 16:4406–4414CrossRefGoogle Scholar
  49. Yang H-M, Zhao W, Norinaga K, Fang J-J, Wang Y-G, Zong Z-M, Wei X-Y (2015) Separation of phenols and ketones from bio-oil produced from ethanolysis of wheat stalk. Sep Purif Technol 152:238–245CrossRefGoogle Scholar
  50. Zhang D, Rio-Chanona EA, Wagner JL, Shah N (2018) Life cycle assessments of bio-based sustainable polylimonene carbonate production processes. Sustain Prod Consumption 14:152–160CrossRefGoogle Scholar
  51. Zhou H, Qian Y, Kraslawski A, Yang Q, Yang S (2017) Life-cycle assessment of alternative liquid fuels production in China. Energy 139:507–522CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Biomass Processing Lab, Center of Biofuel and Biochemical Research, Institute of Self-Sustainable BuildingUniversiti Teknologi PETRONASBandar Seri IskandarMalaysia
  2. 2.Chemical Engineering DepartmentUniversiti Teknologi PETRONASBandar Seri IskandarMalaysia
  3. 3.Chemical Engineering DepartmentDe La Salle UniversityManilaPhilippines
  4. 4.Department of Applied Chemistry and Biochemistry, Faculty of Advanced Science and TechnologyKumamoto UniversityKumamotoJapan
  5. 5.International Research Organization for Advanced Science and TechnologyKumamoto UniversityKumamotoJapan
  6. 6.Energy and Environmental Unit, Engineering and Processing DivisionMalaysian Palm Oil BoardKajangMalaysia

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