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sTechno-Economic Evaluation of Integrated Bioethanol Plant for Sago Starch
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Processing Facility
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Yoke Kin Wana,b, Jhuma Sadhukhanc, Denny K. S. Ng*a
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a
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Department of Chemical and Environmental Engineering/
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Centre of Excellence for Green Technologies, The University of Nottingham, Malaysia
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Campus, Broga Road, 43500 Semenyih, Selangor, Malaysia.
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b
Crops for the Future Research Centre, The University of Nottingham, Malaysia Campus,
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Broga Road, 43500 Semenyih, Selangor, Malaysia.
c
Centre for Environmental Strategy, University of Surrey, Guildford, GU2 7XH, United
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Kingdom.
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Emails: [email protected], [email protected],
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[email protected]*
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*Corresponding author. Tel: +60389248606; Fax: +60389248017.
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Abstract
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To reduce reliance on fossil fuel and environmental issues, alternative energy sources such as
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biomass are vital to be recovered and converted into biofuel. In sago industry, a huge amount
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of sago biomass (i.e., sago barks and fibres) is generated during sago starch extraction
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process (SSEP). The biomass can be utilised as feedstocks for bioethanol, and energy
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production. In addition, environmental pollutants could also be reduced by recovering such
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biomass from disposed to the environment. Therefore, in this work, a conceptual integrated
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sago-based bioethanol plant (SBP) is envisioned and analysed based on the bioethanol plant
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study conducted by the National Renewable Energy Laboratory (NREL). Besides, techno-
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economic performance as well as environmental performance of this integrated SBP is also
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evaluated via Aspen Plus software and a spreadsheet based yield prediction model. For the
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performance evaluation, various feedstocks such as sago fibres, barks and combined biomass
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(fibres and barks) are considered.
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performance of the integrated SBP with on-site and off-site enzyme production as well as the
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impacts of labour cost on the economic performance of the integrated SBP is also evaluated.
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Based on the evaluation and analysis, the integrated SBP with combined biomass (fibres and
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barks) has the highest technical, economic and environmental performance amongst the sago
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biomass. A total of 4.75 t/d of bioethanol and 252 kW/d of electricity are expected to be
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produced; and reduction of 16.32 tCO2 equivalent/d of carbon dioxide emission is expected.
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In addition, the payback period of the integrated SBP with on-site enzyme production and
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using current available labour from SSEP is estimated as 6.6 years. Based on the analysis, it
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is noted that enzyme and labour costs are critical cost contributors to the new development of
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the integrated SBP and hence, a sensitivity analysis on such parameters is performed.
In addition, techno-economic and environmental
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Keywords: techno-economic evaluation; sago biomass; bioethanol production; process
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integration
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1.0
Introduction
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Rapid growth in economic and population worldwide has led to several global issues
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such as depletion of fossil fuel reserves and global warming. In order to decelerate the
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depletion of fossil fuel reserves and reduce the environmental issues, alternative energy
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sources are urgently sought to produce biofuel such as bioethanol. Biomass is one of the
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promising alternative energy sources to replace fossil fuel. By recovering the biomass,
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environmental pollutants also can be reduced. Sago biomass (i.e., sago barks and sago fibres),
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which is generated during sago starch extraction process (SSEP) in sago mill, could be
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converted into bioethanol (Adeni et al., 2013; Singhal et al., 2008). However, not many
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research works considered sago biomass as raw material for bioethanol production (Adeni et
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al., 2013; Kannan et al., 2013; Thangavelu et al., 2014). In the past decades, different works
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were presented on production of ethanol from sago starch. For instances, Kim et al. (1992)
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studied a simultaneous saccharification fermentation (SSF) for ethanol production in batch
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and semi-batch modes using sago starch as raw material, Amyloglucosidase as an enzyme,
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and Zymomonas mobilis as a bacterium. Later, the study was extended to continuous process
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using free, immobilised or co-immobilised enzyme and cells by Kim and Rhee (1993).
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Meanwhile, Aziz et al. (2001) investigated the effect of carbon to nitrogen (C/N) ratio and
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initial sago starch concentration on the performance of direct fermentation of sago starch into
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bioethanol by recombinant yeast, S. cerevisiae YKU 131. Besides, the effects of temperature,
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pH and time of fermentation were also investigated on SSF using sago starch as raw material
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and with different enzymes such as glucoamylase and Symomonas mobilis ZM4 (Ratnam et
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al., 2003), and Amyloglucosidase and Zymomonas mobilitis MTCC 92 (Bandaru et al., 2006).
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Apart from the abovementioned works, performance of a microwave assisted
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bioethanol production from sago starch has also been investigated (Saifuddin and Husain,
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2011). A series of studies on hydrolysis of sago starch for ethanol fermentation was also
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conducted by Sunaryanto et al. (2013). Note that sago starch is one of the important foods
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for human as it contains high amount of carbohydrate. In order to avoid shortage of food,
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sago starch should not be converted into bioethanol. To replace fossil fuel while to reduce
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environmental pollutants, sago biomass is important to be recovered and converted into
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bioethanol.
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Although the sago biomass is convertible into bioethanol via hydrolysis and
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fermentation process as reported by Adeni et al. (2013), Kannan et al. (2013), and
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Thangavelu et al. (2014), the biomass is not recovered by sago mill owner in current
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industrial practices. Instead, the biomass such as sago barks is deposited as flooring materials
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around the area of SSEP and the excess barks are burnt off. Besides, sago fibres are
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discharged as waste into nearest river together with sago wastewater without any treatment.
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These practices caused severe environmental issues (e.g., river and air pollutions) and
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wastage of valuable energy. Therefore, as mentioned earlier, sago biomass is vital to be
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recovered and converted into bioethanol to have more sustainable sago industry. In order to
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prioritise the sago biomass to be recovered, a novel material flow cost accounting (MFCA)-
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based prioritisation approach was introduced recently by Wan et al. (2015a). According to
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Wan et al. (2015a), MFCA-based prioritisation approach can be used as a selection tool to aid
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decision maker in selection of biomass to be recovered to improve the economic and
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environmental performance of SSEP. Besides, a fuzzy multi-footprint optimisation (FMFO)
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approach, which considered multiple footprints (i.e., carbon, water, and workplace footprint),
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was also developed recently to synthesise a sustainable sago value chain (Wan et al., 2015b).
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In line with the global efforts in sustainable development, process integration
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approaches are important to adopt for more sustainable productions, competitive economic
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operation and environmental performance. In order to achieve these objectives, a conceptual
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integrated sago-based bioethanol plant (SBP), which consists of combined heat and power
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(CHP) system, and wastewater treatment plant (WWTP), is developed. Note that numerous
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economical studies were conducted for integrated biorefinery in previous research works
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(Misailidis et al., 2009; Moncada et al., 2013; Rosenberg et al., 2011; Sadhukhan et al., 2008;
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Sadhukhan and Ng, 2011). However, there is no techno-economical study of the integrated
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SBP. Therefore, a detailed techno-economic assessment of integrated SBP that also produces
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electricity using sago biomass as feedstock is conducted in this work.
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Note that the findings from a large-scale bioethanol plant study conducted by National
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Renewable Energy Laboratory (NREL) (Humbird et al., 2011) are consolidated for sago
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biomass. Such biomass is fed to the SBP to produce bioethanol (main product) and lignin
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(by-product).
The resulting wastewater from the SBP is then treated in WWTP for
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generation of biogas in anaerobic process. The generated lignin and biogas can be utilised as
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fuels in CHP system to generate steam and electricity. By converting the biomass feedstocks
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into bioethanol via integrated SBP, the waste discharge to the environment can be reduced.
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Besides, the electricity demand from the grid and subsequently the greenhouse gas (GHG)
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emissions to the atmosphere can also be reduced.
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In this work, a small scale sago mill from Sarawak, Malaysia with starch production
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capacity of 12 t/d taken from Wan et al. (2015a) is used as a base case for evaluation. Since
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the scale of integrated SBP is smaller than the bioethanol plant studied by NREL, it is noted
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that the choice and performance of the scaled down equipment may differ from the NREL’s
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process design. In order to simplify the analysis, the choice and performance of the scaled
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down equipment are assumed same as the NREL’s study. Apart from this assumption, the
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concept of economy of scale is also applied in this work for scaling purpose. The scaling
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factor is taken from Humbird et al. (2011) and Sadhukhan et al. (2014).
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Based on this capacity of sago mill, approximately 20.8 tonnes (wet basis) or 10.2
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tonnes (dry basis) of sago barks; and 16.9 tonnes (wet basis) or 6.5 tonnes (dry basis) of sago
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fibres are generated during starch extraction. With the given amounts, both technical and
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environmental performance of the integrated SBP with different feedstock (i.e., sago barks,
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sago fibres or both) is evaluated. The integrated SBP with the highest technical performance
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(highest yield of bioethanol and electricity production), is selected for detailed economic
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analysis.
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On the other hand, according to Linggang et al. (2012), sago biomass could be used as
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raw material to produce cellulase enzyme that is required in hydrolysis process. Therefore,
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scenarios with on-site and off-site enzyme production are considered in this work. Note that
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on-site enzyme production is referred to enzyme production is located in SBP. In contrast,
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off-site enzyme production is referred to all required enzymes are purchased from supplier.
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Besides, economic analysis for using the current available labour form SSEP or hiring of new
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labour to operate the bioethanol processing facility is performed.
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integrated SBP is making use of current available labour from SSEP, additional labour cost
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will not be considered in the economic analysis. In contrast, in case where new hiring is
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required, labour cost is included in the analysis.
In case where the
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The rest of this paper presents the process description of the integrated sago-based
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bioethanol plant in Section 2, which is followed by the introduction of sago-based bioethanol
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plant (SBP), combined heat and power (CHP) system, and wastewater treatment plant
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(WWTP). Next, methodology of performance evaluation for an integrated SBP is presented
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in Sections 3. This includes technical, environmental, and economic performance evaluation.
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The evaluation results and sensitivity analysis are discussed in Sections 4 and 5. Finally,
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conclusions and future works are given in Section 6.
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2.0
Process description: Integrated sago-based bioethanol plant
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In an integrated SBP, sago biomass can be converted into bioethanol. The resulting
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wastewater and lignin are sent to the WWTP and CHP system, respectively. In the CHP
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system, the lignin and biogas (produced from the WWTP) are used as fuel sources to generate
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steam and electricity. The generated steams are used in SBP to fulfil the process steam
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requirement before being used for electricity generation. The generated electricity is then
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supplied to the SBP, WWTP and existing SSEP for self-sustenance. Excess electricity (if any)
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can be sold to the grid to increase the overall economic performance of the integrated SBP.
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Meanwhile, the wastewater is sent to the WWTP to generate biogas and treat the wastewater
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to meet the discharge regulation. The treated water can be recycled in SBP to reduce the
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freshwater consumption. The proposed integrated SBP is shown in Figure 1.
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Figure 1: Conceptual block diagram of integrated sago-based bioethanol plant
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2.1
Sago-based bioethanol plant (SBP)
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In this work, a biochemical conversion technology studied by NREL and Harris
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Group Inc., (Humbird et al., 2011) is adopted for conversion of sago biomass into bioethanol.
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In this process, there are few main processes involved to convert the biomass into bioethanol,
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such as pretreatment, enzymatic hydrolysis, fermentation processes, and bioethanol recovery
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process (Figure 2). In the first stage of pretreatment process, sago biomass is fed to a
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pretreatment reactor and mixed with diluted sulphuric acid (18 mg acid/dry g of biomass) that
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catalyses the hydrolysis reaction at a temperature of 158 oC. High pressure (13 bars) steam is
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used in this stage to maintain the temperature. Most of the hemicellulose carbohydrates such
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as xylan in biomass are converted into xylose oligomers within a short residence time of 5
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minutes. Some other minor hemicellulose carbohydrates (arabinan, mannan and galactan)
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have the same reactions and conversions as xylan. The resulting slurry goes into a second
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stage of pretreatment, oligomer conversion step, where most of the xylose oligomers from the
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first stage are converted into monomeric xylose at a temperature of 130 oC and residence time
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of 20 – 30 minutes. The slurry is then flashed at atmospheric pressure. After the flash, the
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slurry containing 30 wt% of total solids is sent to a conditioning reactor, where water and
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ammonia are added to dilute the solid content to approximately 20 wt% and to increase the
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pH of the slurry to 5 – 6 to ensure miscibility for enzymatic hydrolysis. The slurry is cooled
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to 75 oC after a total conditioning residence time of 30 minutes. Note that ammonia helps to
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avoid sugar losses and eliminate the solid-liquid separation steps. This makes ammonia a
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more economical alternative compared to lime due to reduced sugar loss and reduced capital
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cost (Jennings and Schell, 2011). On the other hand, the flashed vapour is condensed and
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sent to WWTP.
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Figure 2: Configuration of sago-based bioethanol plant
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The pre-treated slurry is sent to a sequential hydrolysis and fermentation process in
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batch operation.
In this process, enzymatic hydrolysis (also known as enzymatic
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saccharification) takes place. Cellulose fibres are broken down and converted into cellobiose,
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soluble gluco-oligomers and ultimately into glucose monomers using cellulase enzymes.
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Cellulase
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Endoglucanases attack the cellulose fiber to reduce the length of polymer chain;
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exoglucanases attack the ends of highly crystalline cellulose fibers; and β-glucosidase
enzymes
include
endoglucanases,
exoglucanases
and
β-glucosidase.
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hydrolyses the small cellulose fragments to glucose. Since the hydrolysis process is operated
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at elevated temperature, higher enzyme activity and higher conversion rate of cellulose to
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glucose is resulted as well as smaller amount enzyme is required. According to Humbird et
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al. (2011), a total cellulase loading of 20 mg enzyme protein/g cellulose is required to achieve
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90% conversion of glucose at a temperature of 48 oC. The yield of sugar increases with
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increasing load of enzyme, however, there is a significant cost implication of imported
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enzyme. To reduce the imported cost of enzyme, enzyme production could be implemented
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on-site.
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integrated SBP, the economic evaluation of such a case is considered in this work.
In order to evaluate the feasibility of an on-site production of enzyme in an
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Cellullase enzyme could be produced by Trichoderma asperellum and Aspergillus
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fumigates using sago fibres as substrate (Linggang et al., 2012). According to Linggang et al.
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(2012), the sago fibres obtained after hydrolysis can be used as a main carbon source for
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enzyme production. Since carbon is also contained in other sago biomass such as sago bark
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as well as the main product of sago industry, sago starch, namely, sago bark and starch could
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also be used for enzyme production as sago fibre.
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performance of integrated SBP using different sago biomass and completed with on-site and
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off-site enzymes production are evaluated to determine the most feasible option for sago
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industry.
Due to this reason, the economic
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After the hydrolysis process, the resulting slurry containing glucose and xylose is
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cooled and fermented to bio-ethanol. In the fermentation process, recombinant co-fermenting
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bacterium (Zymomonas mobilis) is used as fermenting microorganism or ethanologen. The
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ethanologen inoculums can be produced by mixing the slurry and nitrogen sources, i.e.
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Diammonium phosphate (DAP) in the fermentor. This type of fermenting microorganism
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can ferment glucose and xylose simultaneously to bio-ethanol. The minor hemicellulosic
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sugar arabinose is also fermented to ethanol with the same conversion as xylose, as reported
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in Humbird et al. (2011). Besides, some of the sugars (approximately 3%) are lost to
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contamination.
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concentration of 5.4%. It is then sent to distillation and molecular sieve adsorption for bio-
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ethanol recovery.
After the fermentation process, the fermentation broth has an ethanol
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In bioethanol recovery processes, water, bioethanol, and combustible solids are
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separated from the fermentation broth. Bioethanol with a concentration of 99.5% is obtained
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in the end of these processes. Firstly, fermentation broth is sent to a beer column to remove
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dissolved carbon dioxide and most of the water. This column is operated at approximately 2
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bar overhead pressure and low reboiler temperature in order to minimise fouling problem.
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About 99% of ethanol vapour with an approximate concentration of 40% is produced and
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removed from the side of the beer column and sent to a rectification column. The condensate
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from the top condenser of the column is returned to the column after venting out CO2. A
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small amount of ethanol is lost and is considered as permanent loss. To minimise the loss,
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the reboiler duty of the column needs to be kept relatively high, so there is a trade-off
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between ethanol loss and energy usage (Humbird et al., 2011). The bottom stream from the
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beer column contains unconverted insoluble and dissolved solids. This solid-rich stream is
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then directed to a pressure filter for dewatering. During the dewatering process, insoluble
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solids (lignin) with dryness 35% and filtrate are generated. Lignin is used as fuel in the CHP
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system, while filtrate is treated in WWTP, respectively.
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In the rectification column, ethanol vapour is concentrated to a near azeotropic
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composition. A vapour overhead stream of 92.5% ethanol and a bottom stream of 0.05%
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ethanol are obtained. The overhead ethanol stream is then further dehydrated to 99.5% via a
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molecular sieve adsorption process. The bottom stream from the rectification column is
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recycled to the pretreatment process as dilution water. Water is selectively adsorbed in the
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adsorbent bed of the molecular sieve adsorption process and removed together with a small
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amount of ethanol. The pure ethanol vapour (~99.5%) is produced and then cooled by heat
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exchange with the regeneration condensate from a regenerating column and then pumped to a
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storage tank. The low purity bio-ethanol generated from the regenerating column is recycled
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back to the rectification column to recover more bio-ethanol.
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2.2
Combined heat and power (CHP) system
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In the integrated SBP, a combined heat and power (CHP) system with biomass boiler
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is used to generate steam and electricity as shown in Figure 3. A normal pressure grate-fired
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biomass boiler (Huang et al., 2013), which consists of an economiser and a steam drum is
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used to generate high pressure (HP) superheated steam. This boiler is fed with lignin and
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biogas as fuel sources and air for full combustion. The boiler feed water (BFW) is pre-heated
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in the economiser and then turned into saturated and superheated HP steam (50 bar) in the
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steam drum within the boiler. Some of the resulting HP steams are sent to the SBP for
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bioethanol production. The remaining HP steam is sent to a back pressure turbine and a
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generator for electricity generation. The low pressure (LP) steam from the back pressure
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steam turbine is directed to the SBP to fulfil the steam requirement for bioethanol production.
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The balance of the LP steam can be further expanded in a condensing steam turbine and
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generator to generate more electricity. The generated electricity is supplied to the SBP and
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WWTP for self-sustenance of the integrated plant. Any excess electricity could be sent to an
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adjacent SSEP and sold to the grid. The generated condensate from the condensing steam
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turbine is recovered as BFW at ~0.1 bar and returned to the economiser via a pumping
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system in a closed cycle. The flue gas from the boiler is released to the atmosphere and the
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ash is collected in ash grate from the bottom of the boiler.
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Figure 3: Configuration of combined heat and power system
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2.3
Wastewater treatment plant (WWTP)
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Wastewater from the SBP is directed to a WWTP to produce treated water which can
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be reused in the SBP for bioethanol production. In this work, the design of the treatment
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process is adopted from Humbird et al. (2011).
The treatment process consists of an
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anaerobic digester, an aerobic digester, a membrane bioreactor (MBR), a reverse osmosis
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(RO) membrane unit and a sludge dewatering unit as shown in Figure 4. Wastewater with a
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chemical oxygen demand (COD) of 64 g/L is first channelled to the anaerobic digester to
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digest organic matter in the absence of oxygen.
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compounds in wastewater such as cellulose, xylan, and protein are present and can be
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removed by the pressure filter in the SBP. In the anaerobic digester, approximately 91% of
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each organic component is destroyed; 86% is converted to biogas containing methane that
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can be used as fuel in the CHP system; and 5% is converted to sludge. The production rate of
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methane is approximately 228 g methane/kg COD removed (Humbird et al., 2011). Sludge
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has a yield of 45 g sludge/kg COD digested (Humbird et al., 2011). To maintain the sludge
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loading in the anaerobic digester, a part of the sludge is returned to the anaerobic digester and
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the excess sludge is sent to a sludge holding tank. The resulting water from the anaerobic
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digester is pumped to the aerobic digester equipped with floating aerators that provide
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oxygen for aerobic digestion. In this process, a removal efficiency of soluble organic matter
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can go up to 96% (Humbird et al., 2011). Besides, ammonium ions are also removed in this
In addition, some insoluble organic
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process. The existence of ammonium ions is due to the usage of ammonia in the pretreatment
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process of the SBP. These ions are removed via a nitrification process by converting the ions
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into nitrate by nitrifying bacteria. Since nitric acid is formed in the nitrification process, pH
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in aerobic process is decreased. Due to this reason, caustic soda is added to the aerobic
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digester for neutralisation.
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generated. This sludge is carried forward with the digested water to the MBR and RO system.
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The main purpose of these systems is to separate the sludge from digested water and clarify
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the water to clear treated water which can be reused or recycled. The separated sludge is
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mostly returned to the aerobic process to maintain the required sludge loading.
During the aerobic process, significant amount of sludge is
The
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remaining sludge is pumped to the sludge holding tank and mixed with the sludge from the
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anaerobic process. This mixed sludge is then pumped to a centrifuge for dewatering to
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produce a nutrient rich stream that can be used as fertiliser or compost. The resulting water
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from the centrifuge is recycled to the aerobic process for additional treatment.
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Figure 4: Configuration of wastewater treatment plant
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3.0
Methodology of performance evaluation for an integrated SBP
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3.1
Technical performance evaluation
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The technical performance of the integrated SBP with different feedstock is first
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evaluated. Based on the available biomass from a sago starch processing, such as sago fibres
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(6.46 oven dried ton (odt)/d), sago barks (10.20 odt/d), or combined biomass (fibres and
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barks, 16.66 odt/d), production yield of bioethanol and total electricity generated are
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determined. In order to determine the feasibility of utilisation of biomass for bioethanol
24
production, a comparison study with bioethanol production from sago starch (12 ton/d) is
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performed. Note that the technical performance of the integrated SBP is dependent on the
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production yield of bioethanol and electricity.
Namely, the highest yield of bioethanol
2
production and electricity generation leads to the highest technical performance of the
3
integrated SBP. The integrated SBP with the highest technical performance is selected for
4
further analysis.
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In order to estimate the production yield of bioethanol and electricity of the integrated
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SBP, the composition of sago starch and sago biomass as well as the sugar contained in
8
hydrolysed sago starch is first determined from the experiment or literature. Tables 1 and 2
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show the properties of sago starch and sago biomass. Based on the properties, the production
10
of bioethanol and electricity can be estimated via a developed spreadsheet based yield
11
prediction model. This model is developed based on the large-scale bioethanol plant study
12
which is conducted by NREL and reported by Humbird et al. (2011). The details of this yield
13
model are discussed in the following section.
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Table 1: Sago starch and biomass composition
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Table 2: Sugars contained in hydrolysed sago starch sample (Dwiarti et al., 2007)
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In the spreadsheet based yield prediction model, mass and energy balances of the
20
integrated SBP as well as the total amount of bioethanol produced using the available
21
biomass feedstocks are determined based on the conversion rates, amounts of required
22
materials (e.g., sulphuric acid, HP steam, ammonia, etc.), and product ratios as applied in
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Humbird et al., 2011. Besides, conversion rates of hemicellulose carbohydrates (e.g., xylan,
24
mannan, galactan and arabinan) and some glucan contained in hemicellulose side-chains to
25
oligomers, soluble sugars (e.g., glucose, xylose, mannose, galactose and arabinose) and sugar
14
1
degradation products (furfural and HMF) as shown in Table 3 (in the column of pretreatment)
2
are also used in this evaluation. As shown, the conversion rates include acetate to acetic acid
3
and furfural and hydroxymethyl furfural (HMF) to tar as well as lignin to soluble lignin.
4
5
Table 3: Conversion rates for pretreatment, enzymatic hydrolysis and fermentation processes
6
(Humbird et al., 2011)
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8
The resulting product amounts are then inputted into the spreadsheet based yield
9
prediction model to determine the glucose that can be produced from cellulose based on the
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conversion rate as shown in Table 3 (in the column of enzymatic hydrolysis) after reacting
11
with either purchased or on-site produced cellulase enzyme with a feeding rate of 20 mg per
12
gram cellulose. In the case cellulase enzyme is produced on-site, some of the hydrolysate
13
slurries produced from the hydrolysis process rich in glucose and protein are sent to the
14
enzyme production process.
15
converted into ethanol and others products via the fermentation process based on the
16
conversion rates as shown in Table 3 (in the column of fermentation) and the other input
17
materials such as inoculums and DAP. The resulting streams are further used in the recovery
18
processes. In addition, the bioethanol concentrations as discussed in Section 2.1 and the
19
ratios as applied in Humbird et al. (2011) are manipulated in the developed spreadsheet based
20
yield prediction model to determine the mass flowrates of the produced bioethanol and all
21
other product streams (i.e., beer column, rectification column, molecular sieve adsorption
22
column, and pressure filter). Based on the determined mass flowrates, equipment can be
23
scaled down to estimate the required equipment size of integrated SBP. As mentioned earlier,
24
although the scale of integrated SBP is smaller than the NREL’s process design, the choice
25
and performance of scaled down equipment are assumed same as the NREL’s study.
The remaining sugars in the hydrolysate slurry are then
15
1
2
On the other hand, the resulting lignin and filtrate from the pressure filter are sent to the
3
CHP system and WWTP for CHP and biogas generation, respectively. Note that the amount
4
of generated biogas is determined based on the removal efficiency of chemical oxygen
5
demand (COD) (91%) and biogas production rate (228 g methane/kg COD removed)
6
(Humbird et al., 2011). In this work, approximately 26,500 kg/hr of total COD is entered the
7
anaerobic digester. The generated biogas is then fed into a CHP system (as described in
8
Section 2.2) with lignin and then utilised as fuel sources in the biomass boiler for heat and
9
power generation. Note that the proposed CHP system is deviated from the process given in
10
Humbird et al. (2011). This proposed CHP system is adapted from Huang et al. (2013). In
11
order to determine the potential of heat and power generation from the CHP system; the
12
boiler efficiency ( ηBoiler ) of 80% is set in this work (Thornley et al., 2009). Next, the
13
extractable energy from the biomass boiler can be determined theoretically based on the
14
calorific values of lignin (11.14 kJ/g) (Humbird et al., 2011) and biogas (12.54 kJ/g)
15
(Humbird et al., 2011) as shown in Equation (1).
16
17
E Out 
K
In Boiler
M In
k Ek η

k 1
(1)
18
19
In
where E Out is the total extractable energy from the biomass boiler; E In
k and M k are the
20
calorific value and the intake of dried biomass k fed into the boiler, respectively. Based on
21
the extractable energy, the total mass flow rate of steam generation, msteam can be determined
22
theoretically via Equation (2) (Sadhukhan et al., 2014):
23
24


E Out  m steam C p Tsat  TBFW   Δh vap  h sup  h v

(2)
16
1
2
where Cp is the heat capacity of water. Meanwhile, ∆hvap, hv and hsup are the enthalpy of
3
vaporisation of water, specific enthalpies of saturated steam and superheated steam,
4
respectively. In this process, the steam generation is determined based on the following
5
operating conditions.
6
7

Pressure of the HP superheated steam = 50 bar
8

Temperature of the HP superheated steam = 500 oC
9

Saturation temperature of steam, Tsat, at 50 bar = 264oC
10

Temperature of BFW, TBFW = 105 oC
11
12
The steam requirement by the SBP as shown in Tables 4 and 5 is supplied to the processes.
13
The remaining steam is then used for power generation via the back pressure and condensate
14
steam turbines. These unit operations are simulated using commercial software, Aspen Plus
15
V7.1, which is a standard process simulation tool and has been widely adopted to simulate
16
biomass CHP systems (Huang et al., 2013; Ng et al., 2011a; 2011b), to determine the total
17
power generated from the CHP system with following operating conditions.
18
19

Discharge pressure of back pressure steam turbine = 5 bar
20

Discharge pressure of condensate steam turbine = 0.1 bar
21
22
Apart from the steam, other utilities such as electricity as shown in Tables 4 and 5 are
23
also required for the integrated SBP to produce bioethanol regardless the enzyme production
24
is on-site or off-site. Based on these data, electricity to grid can be determined by deducting
25
the total usage of electricity in SBP and WWTP from the total generated electricity. Based
17
1
on the estimated amount of bioethanol produced and electricity generated from the integrated
2
SBP, the integrated SBP with the highest technical performance is selected as the most
3
feasible case for further evaluation.
4
5
Table 4: Utility consumptions of sago-based bioethanol plant (on-site enzyme production)
6
7
Table 5: Utility consumptions of sago-based bioethanol plant (off-site enzyme production)
8
9
3.2
Environmental performance evaluation
10
As mentioned previously, biomass (fibres: 16.92 t/d; barks: 10.20 t/d, on wet basis) is
11
discharged from SSEP into nearest river or deposited in the surrounding mill area. These
12
practices could cause severe environmental impacts such river and air pollution. Therefore, it
13
is important to recover such biomass for bioethanol production in an integrated SBP. In this
14
work, the environmental performance of integrated SBP is evaluated based on the reduction
15
in carbon dioxide (CO2) emission only. This is because the main products of the integrated
16
SBP i.e., bioethanol and energy can replace gasoline and grid electricity respectively, and
17
thereby reduce CO2 emission to the atmosphere. Based on the abovementioned assumptions,
18
the reduced CO2 can be determined using Equation (3).
19
20

d
ted
Grid
CFPmSBP_Reduce
 ELEC mSBP_Genera
 OPHRSBP
m'  EF
'
'

ted
 BETH mSBP_Genera
 CF L_T ON  LHV ET HANOL  EF GF
'


∀m'
(3)
21
22
_ Reduced
where CFPmSBP
(kg CO2/d) is the total reduced CO2 of integrated SBP with
'
23
_ Generated
biomass/starch m’ for bioethanol production. ELECmSBP
(kW) and OPHR SBP
m ' (hr/d)
'
24
are referred to the electricity generated, and operational hours of integrated SBP with
18
1
biomass/starch m’, respectively, while EFGrid (kg CO2/kWh) is the carbon emission factor of
2
grid electricity generated from fossil fuel in Sarawak, Malaysia. Note that the grid electricity
3
in Sarawak is a combined power from different power plants (i.e., combined cycle, coal-fired,
4
hydro, gas-turbine, and diesoline power plant) and supplied to most of the industry in
5
Sarawak (SEB, 2010). Hence, in this work, the carbon emission factor of the grid electricity
6
at Sarawak, Malaysia is taken as 0.8990 kg CO2/kWh (Wan et al., 2015b) with 24 hours of
7
operating basis.
8
bioethanol production in integrated SBP with biomass/starch m’ (t/d), conversion factor of
9
bioethanol volume from tonne to litre, lower heating value of bioethanol (MJ/l), and well-to-
10
wheels emission factor of gasoline use as transportation fuel (kg CO2 equivalent/MJ),
11
respectively. CFL_TON and LHVETHANOL are given as 1262 l/t and 21.1 MJ/l, respectively
12
(Bioenergy Feedstock Information Network, 2015); while, 0.086 kg CO2 equivalent / MJ of
13
EFGF is used in this work. Note that this well-to-wheel emission factor is extracted from the
14
GREET model, Version 2014, developed by Argonne National Laboratory.
ted
Meanwhile, BETH mSBP_Genera
, CFL_TON, LHVETHANOL, EFGF are the
'
15
16
3.3
Economic performance evaluation
17
To investigate the viability of integrated SBP utilising different biomass feedstocks
18
for bioethanol production, the economic evaluation is performed by adopting the economic
19
analysis methodology as presented in Sadhukhan et al. (2014). According to Sadhukhan et al.
20
(2014), a list of equipment for the integrated SBP is first to be compiled. The sizes of the
21
equipment are estimated based on the developed spreadsheet based yield prediction model
22
and Aspen Plus simulation model. Then, by applying the concept of economy of scale, the
23
base cost of equipment with a specific size adopted from Humbird et al. (2011) and
24
Sadhukhan et al. (2014) is scaled down to obtain the cost of equipment for the given plant
25
size via Equation (4):
19
1
2
COSTSIZE2  SIZE2 


COSTSIZE1  SIZE1 
R
(4)
3
4
where SIZE1 and SIZE2 is the capacity of the base system and the capacity of the system after
5
scaling down, respectively. COSTSIZE1 is the cost of the base system and COSTSIZE2 is the
6
cost of the system after scaling down; R is the scaling factor which can be taken from
7
Humbird et al. (2011) and Sadhukhan et al. (2014). Note that different R factors are used for
8
different types of equipment. To update the cost of equipment from their given base years,
9
Equation (5) and the Chemical Engineering Plant Cost Index of year 2014 (574.4) are used in
10
this work.
11
12
 I pr 

C pr  C o  

I
 o 
(5)
13
14
where Cpr is the present cost, Co is the original cost, Ipr is the present index value (574.4), and
15
Io is the original index value. Then, Guthrie’s method is applied to determine the total capital
16
investment (TCI) using installation factors of individual unit operations obtained from
17
Humbird et al. (2011) and Sadhukhan et al. (2014). The total capital cost is then annualised
18
using the discounted cash flow (DCF) analysis with plant life time of 30 years and interest
19
rate of 8%. In order to estimate the equipment cost, the concept of economy of scale as
20
discussed previously is used in this work.
21
22
Next, the annual operating cost is determined, which is the summation of the fixed
23
and variable operating costs. The fixed operating cost includes the costs of maintenance,
20
1
personnel, laboratory, supervision, plant overheads, capital charges, insurance, local taxes,
2
royalties, sale expense, general overheads and research and development (Sadhukhan et al.,
3
2014). These costs are determined based on the labour cost and indirect capital cost. The
4
working hours and salary of each worker are assumed 3330 hour/year and $30 per hour. The
5
SBP system is operated 24 hours a day and hence two shifts per day are assumed. Since the
6
SBP is integrated with the existing SSEP, some of the existing staff in the SSEP can be
7
allocated to the SBP and hence only one additional worker per shift can be employed. In this
8
case, labour cost need to be considered in the SBP, which is taken as hire new labour (HL) in
9
this work. In contrast, if the SSEP provides all manpower to the SBP, labour cost can be
10
excluded from the SBP, which is known as use current available labour (UCL). Besides, in
11
order to determine the indirect capital cost, the Lang’s method is used.
12
13
The variable operating cost is the total of the raw material cost, utilities cost and
14
transportation cost. Sago starch and sago biomass are supplied by the SSEP without any
15
charges. For other raw materials, their unit costs are as shown in Table 6. Note that the
16
cellulase enzyme cost is not accounted for the SBP case with on-site enzyme production. In
17
the case the enzyme is purchased from suppliers (off-site enzyme production), a unit cost of
18
enzyme is applied, as shown in Table 6. In addition, a unit cost of fresh water as shown in
19
Table 6 is used to determine the utility cost. The average transportation cost of biomass
20
feedstock is assumed at $0.60/GJ (Sadhukhan et al. 2014). To determine the revenue, an
21
electricity price of $0.1375/kWh (8.03 p/kWh) (Department of Energy & Climate Change,
22
2014) and an ethanol price of $0.92/kg (Sadhukhan et al., 2008) are used. Based on these
23
data, the profit and payback period of integrated SBP with different feedstock is determined.
24
25
Table 6: Unit prices of products, raw materials and utilities
21
1
2
4.0
Results and Discussion
3
4.1
Technical performance of integrated SBP
4
5
The technical performance of the integrated SBP with different biomass/starch, with
on-site and off-site enzyme production, is presented in Tables 7 and 8, respectively.
6
7
8
Table 7: Technical and environmental performance of integrated sago-based bioethanol
plant (on-site enzyme production)
9
10
11
Table 8: Technical and environmental performance of integrated sago-based bioethanol
plant (off-site enzyme production)
12
13
As shown in Table 7, the integrated SBP with on-site enzyme production and using combined
14
biomass (fibre + bark) as feedstock has the highest amount of bioethanol production (4.75
15
t/d). This is followed by sago starch (4.17 t/d of bioethanol), barks (2.75 t/d of bioethanol),
16
and fibres (2.01 t/d of bioethanol). However, combined biomass gives lower bioethanol
17
production yield (0.28 t of bioethanol/t of biomass) compared to sago starch (0.35 t of
18
bioethanol/t of biomass) as shown in Table 7. These results are reasonable as there is higher
19
sugar content in sago starch compared to sago biomass (Singhal et al., 2008). On the other
20
hand, it is noted that the integrated SBP using combined biomass produces the highest
21
amount of electricity (252 kW/d) (see Table 7).
22
23
Table 8 shows the technical performance of the integrated SBP with off-site enzyme
24
production. This technical performance has similar trend to the integrated SBP with on-site
25
enzyme production as shown in Table 7. The integrated SBP using combined biomass as fuel
26
source achieves the highest production of bioethanol (5.23 t/d) and this is followed by sago
22
1
starch (4.28 t/d), barks (2.95 t/d), and fibres (2.26 t/d). However, similar with on-site enzyme
2
production (Table 7), combined biomass has lower bioethanol production yield (0.31 t of
3
bioethanol/t of biomass) compared to sago starch (0.36 t of bioethanol/t of biomass). On the
4
other hand, highest amount of electricity (275.6 kW/d) can be generated from the integrated
5
SBP that is using combined biomass as fuel source.
6
7
As shown in Tables 7 and 8, the production yield of bioethanol from sago fibres is 0.31
8
t of bioethanol/t of biomass and 0.35 t of bioethanol/t of biomass for the integrated SBP with
9
on-site and off-site enzyme production, respectively. It is interesting to note that these results
10
are close to the expected theoretical ethanol yield from sago fibres (0.38 t of bioethanol/t of
11
fibres) as reported in Thangavelu et al. (2014). Note that approximately 60% of the fibres are
12
starch (Singhal et al., 2008) and hence fibres always have higher production yield amongst
13
the sago biomass. Besides, it is also found that more bioethanol is produced from the
14
integrated SBP with off-site enzyme production.
15
hydrolysate slurry is sent to the fermentation process compared to the integrated SBP
16
completed with on-site enzyme production, where some of the hydrolysate slurry was used in
17
enzyme production. On the other hand, the integrated SBP with off-site enzyme production
18
has higher electricity generation for export through grid, compared to the integrated SBP
19
completed with on-site enzyme production.
20
consumption in the enzyme production.
This is because a higher amount of
This is due to the additional electricity
21
22
As an overall observation from the technical performance evaluation of the integrated
23
SBP, the integrated SBP with combined biomass has the highest production yield of
24
bioethanol and electricity. Besides, combined biomass also generates the highest amount of
25
lignin, biogas, total energy including HP steam and electricity, compared to sago starch,
23
1
fibres and barks individual performance. Therefore, it is selected for further analysis. Tables
2
9 and 10 show the mass flowrates of all resulting streams in the SBP extracted from the
3
model.
4
5
Table 9: Mass streams of sago-based bioethanol plant (on-site enzyme production)
6
7
Table 10: Mass streams of sago-based bioethanol plant (off-site enzyme production)
8
9
4.2
Environmental performance of integrated SBP
10
In addition to the technical performance analysis, Tables 7 and 8 also show the
11
environmental performance of the integrated SBP with on-site and off-site enzyme
12
production, respectively. As shown in Table 7, the integrated SBP using combined biomass
13
as fuel source has the highest environmental performance as it has the largest CO2 emission
14
reduction potential, (~16.32 t CO2 equivalent/d). This is followed by sago starch (~14.23 t
15
CO2 equivalent/), sago barks (~9.23 t CO2 equivalent/d), and sago fibres (~7.11 t CO2
16
equivalent/d). In the same order, about 17.93 t CO2 equivalent/d, 14.84 t CO2 equivalent/d,
17
9.96 t CO2 equivalent /d, and 7.77 t CO2 equivalent/d of CO2 are reduced, respectively, for the
18
integrated SBP with off-site enzyme production (Table 8). Similarly with the technical
19
performance evaluation results (Tables 7 and 8), combined biomass has the largest reduction
20
potential of CO2 due to its highest yield of electricity generation and bioethanol production.
21
22
4.3
Economic performance of integrated SBP
23
As mentioned previously, utilisation of sago starch as feedstock for bioethanol
24
production is not the intention of this work as it is one of the important foods for human.
25
However, it is used for comparison against the performance of sago biomass. Hence, detailed
26
economic evaluation is only focusing on sago biomass. To simplify the economic analysis,
24
1
only the integrated SBP with payback period less than 30 years are summarised and shown in
2
Figure 5. In others words, the payback period of the others scenarios, which are not shown in
3
Figure 5, is more than 30 years. As shown in Figure 5, for the integrated SBP with off-site
4
enzyme production (purchase enzyme), sago starch is the only feedstock that has a payback
5
period of less than 30 years for bioethanol production. In contrast, for the integrated SBP
6
with on-site enzyme production, all the integrated SBP which is using sago biomass as
7
feedstock including sago starch are projected positive outcome (less than 30 years of payback
8
period). As shown in Figure 5, the payback period is highly dependent on labour cost. Note
9
that the main purpose of comparing the results of hire new labour (HL) and use current
10
available labour (UCL) is to demonstrate the importance and impact of labour cost on the
11
economic evaluation. As shown in Figure 5, in case where new labour is hired or the labour
12
cost is included, the payback period is doubled for the integrated SBP using sago starch and
13
combined biomass, and more than 30 years for the integrated SBP using sago fibres or barks
14
as feedstock. Since, the combined biomass has the lower payback period (6.6 years) and the
15
highest technical performance amongst the sago biomass, it is chosen for further detailed
16
economic analysis.
17
electricity production is shown in Table 11. Based on the results above (Figure 5 and Table
18
11), it is noted that both enzyme and labour costs are the critical cost contributors to pay due
19
attention for the development of new integrated SBP as both costs give significant impact to
20
payback period. Therefore, a sensitivity analysis on the payback period of integrated SBP is
21
conducted due to variations in enzyme and labour costs.
Its detailed economic performance as feedstock for bioethanol and
22
23
Figure 5: Payback period of various scenarios
24
25
Table 11: Economic performance of integrated sago-based bioethanol plant
25
1
2
5.0
Sensitivity analysis
3
Based on the results of economic performance evaluation (see Table 11 and Figure 5),
4
it is noted that an enzyme cost of USD 3.12 /kg taken from Sadhukhan et al. (2008) gave
5
infeasible payback period to integrated SBP. Therefore, a lower range of enzyme cost (USD
6
0 – 3.12 / kg) is set in this sensitivity analysis to examine its impact on the payback period.
7
In addition, a range of labour cost (USD 0 – 30 / h / person of labour) is also used in this
8
analysis. The first base case is given to analyse the payback period of the integrated SBP
9
using combined biomass and with off-site enzyme production (purchase enzyme).
The
10
results are summarised and shown in Figure 6. Note that only the scenarios with feasible
11
payback periods (less than 30 years) are shown in Figure 6.
12
13
14
Figure 6: Sensitivity analysis on payback period based on variation of enzyme and labour
costs (off-site production)
15
16
As shown in Figure 6, when no new hiring labour is needed (USD 0 / h / person), to maintain
17
the feasible payback period (< 30 years), the maximum enzyme cost is USD 2.0 / kg. When
18
the enzyme is purchased at a cost higher than USD 2.0 / kg, the economic performance of the
19
system will be infeasible. Meanwhile, when the labour cost goes up to USD 5 – 15 / h /
20
person, the maximum enzyme cost is decreased to USD 1.5 / kg. Note that the maximum
21
enzyme cost is further decreased to USD 1.0 / kg if the labour cost increases to USD 20 – 30 /
22
h / person.
23
24
For the second base case, the integrated SBP with combined biomass as feedstock and
25
completed with on-site enzyme production is further analyzed. Since the enzyme is produced
26
1
on-site, no external enzyme is needed. Therefore, only sensitivity analysis on labour cost
2
(USD 0 – 30 / h / person) is performed and the result is shown in Figure 7.
3
4
5
Figure 7: Sensitivity analysis on payback period based on variation of labour costs (on-site
enzyme production)
6
7
As shown, the lowest payback period is ~6.6 years when no new labour is needed. Note that
8
the payback period is increased proportionally with labour cost. When the labour cost
9
increases to USD 30 / h / person, the payback period increases to 12 years (see Figure 7).
10
Besides, it is also noted that to achieve a payback period less than 10 years, the labour cost
11
should be lower than USD 20 / h / person.
12
13
6.0
Conclusions and future works
14
Sago biomass needs to be recovered and utilised as feedstock for bioethanol
15
production to resolve immediate environmental concerns, to decelerate depletion of fossil
16
fuel reserves and for security of energy supply. Therefore, a conceptual integrated SBP has
17
been envisioned and analysed for integration with existing sago starch extraction process.
18
The first detailed techno-economic and environmental performance analyses of sago biomass
19
utilisation for bioethanol production in integrated SBP are presented in this work. Integrated
20
SBP with different types of biomass as feedstock, with on-site and off-site enzyme
21
production has been analysed.
22
spreadsheet-based yield prediction models, detailed techno-economic and environmental
23
analyses were performed to arrive following conclusions:
24
(1)
Based on the process simulation and the developed
Apart from sago starch, combined biomass (fibres + barks) has the highest technical,
25
economic, and environmental performance compared to individual usages of sago fibre
26
and barks in integrated SBP.
27
1
(2)
Approximately 37.7 t/d of wastes on wet basis (20.8 t/d of sago barks; 16.9 t/d of sago
2
fibres) and 16.32 – 17.93 tCO2 equivalent/d of CO2 could be reduced when combined
3
biomass is used as feedstock in integrated SBP.
4
(3)
By using combined biomass in the integrated SBP with on-site enzyme production and
5
making use of existing man power from the existing SSEP its economic performance
6
can be improved (6.6 years of payback period).
7
(4)
Enzyme and labour costs are critical cost contributors in the economics of the
8
integrated SBP. Hence, an on-site enzyme production is vital to be implemented in
9
bioethanol plant to achieve a higher economic performance.
10
(5)
Process integration as shown in Figures 1– 4 is important to implement in a new
11
development of bioethanol plant in order to achieve higher economic performance. In
12
case the sago-based bioethanol plant is stand-alone, the costs of biomass and utilities
13
are expected to increase and leading to infeasibility of the bioethanol plant.
14
Note that in this work, only CO2 emission reduction is used to assess the environmental
15
performance. However, conversion of such biomass into bioethanol and CHP in integrated
16
SBP also reduces other environmental issues, such as river pollution.
17
environmental assessment could be further extended by considering other assessments such
18
as water footprint, suitability index, etc. which will be considered in future work. Besides, it
19
is noted that the environmental performance evaluation conducted in this work is limited to
20
the emissions from CHP system and bioethanol plant of integrated SBP. This could be
21
further extended by having a more complete environmental analysis of the system using life
22
cycle analysis (LCA).
Therefore, the
23
24
Acknowledgement
28
1
The financial supports from the Crops for the Future Research Centre (CFFRC) via
2
CFFRCPLUS postgraduate studentships (BioP1-003) and the UK Natural Environmental
3
Research Councils (NERC) grant (NE/L014246/1) are gratefully acknowledged.
4
5
Nomenclature
6
List of abbreviation
7
BFW
boiler feed water
8
CHP
combined heat and power
9
CO2
carbon dioxide
10
COD
chemical oxygen demand
11
DAP
diammonium phosphate
12
FMFO
fuzzy multi-footprint optimisation
13
GHG
greenhouse gases
14
HL
hire new labour
15
HMF
hydroxymethyl furfural
16
HP
high pressure
17
LP
low pressure
18
MBR
membrane bioreactor
19
MFCA
material flow cost accounting
20
NREL
National Renewable Energy Laboratory
21
odt
oven dried ton
22
RO
reverse osmosis
23
SBP
sago-based bioethanol plant
24
SSEP
sago starch extraction process
25
TCI
total capital investment
26
UCL
use current available labour
27
VHP
very high pressure
29
1
WWTP
wastewater treatment plant
2
3
List of symbol
4
ted
BETH mSBP_Genera
'
bioethanol produced in integrated SBP using raw material m’, t/d
5
CFL_TON
conversion factor of volume from litre to tonne
6
_ Reduced
CFPmSBP
'
reduced carbon footprint of the integrated SBP, kgCO2/d
7
E Out
total extractable energy from biomass boiler, kW
8
E In
k
calorific value of biomass k fed into the boiler, kJ/g
9
10
EFFuel_Power
carbon emission factor of electricity generation from fossil fuel,
kgCO2/kWh
11
EFGF
emission factor of gasoline as transportation fuel, kgCO2 equivalent/MJ
12
_ Generated
ELECmSBP
'
electricity generated applied by an integrated SBP using raw material
13
m’, kW
14
hsup
specific enthalpy of superheated steam, kJ/kg
15
hv
specific enthalpy of saturated steam, kJ/kg
16
∆hvap
enthalpies of vaporisation of water, kJ/kg
17
LHVETHANOL
lower heating value of ethanol, MJ/l
18
m’
set of raw material
19
msteam
mass flow rate of steam generation, kg/s
20
M In
k
intake of biomass k fed into the boiler, kg/d
21
OPHR SBP
m'
operation hour applied by an integrated SBP using raw material m’,
22
hr/d
23
Qsteam
heat required for steam generation, kW
24
ηBoiler
boiler efficiency, %
25
TBFW
temperature of BFW, oC
26
Tsat
saturation temperature of steam, oC
30
1
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