Simultaneous Process Synthesis, Heat and Power Integration in a

Article pubs.acs.org/EF

Simultaneous Process Synthesis, Heat and Power Integration in a Sustainable Integrated Biorefinery Rex T. L. Ng,† Douglas H. S. Tay,‡ and Denny K. S. Ng*,† †

Energy Fuels 2012.26:7316-7330. Downloaded from pubs.acs.org by UNIV OF SUNDERLAND on 11/05/18. For personal use only.

Department of Chemical and Environmental Engineering/Centre of Excellence for Green Technologies, University of Nottingham, Malaysia, Broga Road, 43500 Semenyih, Selangor, Malaysia ‡ GGS Eco Solutions Sdn Bhd, Wisma Zelan, Suite G.12A & 1.12B, Ground Floor, No 1, Jalan Tasik Permaisuri 2, Bandar Tun Razak, Cheras, 56000 Kuala Lumpur, Malaysia. ABSTRACT: An integrated biorefinery is a processing facility that converts biomass feedstocks into a wide range of value added products (e.g., biofuels, specialty chemicals) via multiple technologies. To synthesize a sustainable integrated biorefinery, consumption of energy within such biorefinery should be self-sustained. The performances of integrated biorefinery can be improved via simultaneous process synthesis, heat and power integration. Due to the complexity of the process synthesis and integration problem, there is a need for a systematic approach to address the problem. In this work, the modular optimization approach, which breaks a large optimization problem into small models, is adapted to solve the complex problem. This allows engineers to ‘zoom in’ on specific key process units in a smaller model. Based on the proposed approach, selection of the optimum process alternatives/technologies and products, as well as integration of heat and power between process units, can be performed simultaneously. To illustrate the capability of proposed approach, two case studies are solved.

1. INTRODUCTION With the increasing fossil fuels price and depleting natural resources, many researchers are searching for sustainable alternative energy sources. In this context, renewable energies are believed to be able to meet the ever-increasing energy demand by reducing emission of greenhouse gases in order to promote greater energy efficiency. According to Energy Outlook 2030, substitution of fossil fuel with renewable energies is expected to account for more than around 6% of total world primary energy by 2030.1 Among all the potential renewable energy sources, cellulosic biomass has been identified as the most promising source to reduce the dependency of fossil fuel and minimize the negative impact to the environment. Cellulosic biomass mainly consists of three major components, namely, lignin, cellulose, and hemicellulose.2 These three main components are present in varying portion in different types of biomass; a typical range is 40−50% cellulose, 20−40% hemicellulose, 10−25% lignin.2 Note that biomass can be converted to value-added bioproducts as well as use for heat and power generation through biological, physical, and thermochemical conversions. To date, several research works have been conducted and improved in different conversion technologies. For example, empty fruit bunch (EFB) from palm oil mill can be converted into syngas through gasification process for heat and power generation. Syngas can also be further converted to various high value products such as dimethyl ether (DME), Fischer−Tropsch fuel (FT fuel), and mixed alcohol (mix−OH) via different synthesis processes.3,4 Besides, EFB can also be converted to dried fibers, which can then be converted to mattresses, seats, insulation, etc.5 In addition, solid fuels such as briquette and pellet can also be produced from EFB through densification via screw press technology.6,7 Moreover, the lignocellulose content in biomass can also be fermented to produce bioethanol, which can be © 2012 American Chemical Society

used as fuel additive to cut down a vehicle’s carbon monoxide and other smog-causing emission. This also reduces the dependency on fossil fuels.8 To convert the biomasses into value-added products, a biorefinery, or processing facility, is needed. Kamm et al.9 defined a biorefinery as a complex system of sustainable, environment- and resources-f riendly technologies for the comprehensive utilization and the exploitation of biological raw materials (biomass). It has similar concept to petroleum refineries, which integrate the biomass processing plant to produce numerous bioenergy, biobased chemicals, and biofuel.10 The concept of an integrated biorefinery, which integrates multiple platforms as a whole, was proposed to enhance the overall process and economic performances.11 To date, various approaches have been developed for process synthesis and screening of potential technology pathways for integrated biorefineries, which includes hierarchical approach,12 mathematical optimization approach,13−15 and graphical-based approach.16 In addition, systematic process synthesis of an integrated biorefinery, involving synthesizing process configuration, and raw materials and products allocation are presented.17−19 Furthermore, Ng20 presented an optimization-based targeting technique, known as automated targeting, to synthesize integrated biorefinery in determining the maximum biofuel production and revenue levels. Recently, Tay and Ng21 extended the previous work into multiplecascade automated targeting which considers multiple process parameters simultaneously. In addition, Reaction Network Flux Analysis (RNFA) is developed to access optimum production routes in term of reaction pathways.22,23 On the other hand, Received: July 31, 2012 Revised: October 18, 2012 Published: October 19, 2012 7316

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enhanced. A superstructure model for the simultaneous process synthesis and integration for a sustainable integrated biorefinery is developed. In addition, the modular optimization approach is adapted to solve the complex problem and reduce the computational efforts. Based on the model, selection of the optimum process alternatives/technologies and products as well as integration of heat and power between process units can be determined simultaneously. To illustrate the proposed approach, two illustrative case studies are solved. In this work, a commercial optimization tool, LINGO v13.0 with Global Solver, is used as a platform to develop and to optimize the developed models. LINGO v13.0 with Global Solver uses a branch-and-bound (B&B) algorithm that combined with linearization to find globally optimal solutions to nonlinear program (NLP) and multi-integer nonlinear programming (MINLP) problems.47

exergy analysis is also performed in generating and screening design alternatives to sustainable development of bioethanol production.24,25 Moreover, Chouinard-Dussault et al.26 presented new approach for the integration of process integration with life cycle analysis (LCA) simultaneously of biofuel production. Different design approaches and optimization of biofuel production technologies with heat and power systems have been studied. These include biodiesel production,27−29 bioethanol production,30,31 mixed alcohols and transportation fuels production.32 Besides, techno-economic evaluation of such systems in integrated biorefinery has also been conducted by the research communities.33−35 On the other hand, multiobjectives optimization is adapted in synthesizing integrated biorefinery with minimum environmental impact while maximizing economic performances.36−38 Pokoo-Aikins et al.28 included safety metrics evaluation along with process and economic metrics to guide the design, simulation, screening, and analysis of biorefineries. In addition, optimization approaches for simultaneous synthesis of supply chain network and biorefinery configuration have been presented.39−41 To synthesize a sustainable integrated biorefinery, simultaneous process synthesis and integration should be taken into consideration. Materials and utilities recovery via process integration within integrated biorefineries are presented.34,42 However, the utilities recoveries of previous works are solved sequentially. Duran and Grossmann43 proved that the simultaneous approach resulted in more efficient heat integration compared to sequential approaches. Consequently, simultaneous process synthesis heat and power integration of biorefineries are developed. For example, Elia et al.44 and Baliban et al.45 presented simultaneous process synthesis heat and power integration in thermochemical coal, biomass, and natural gas to liquids (CBGTL) facility. Although the previous works provided reliable solutions to the problem, complex mathematical models and integration approaches are computationally intensive, laborious, and required detailed process data. Recently, Tay et al.46 presented a modular optimization approach that breaks a large optimization problem into small models. Via modular optimization approach, each model consists of a process unit and its alternatives in different degrees of modeling details (i.e., white-, gray- or black-box). A white-box model is the most detailed type, but it is more complex and computationally intensive. In contrast, a black-box model is very simple and does not provide detailed information of the process; however, fine-tuning of the model is difficult.46 A gray-box model can be solved efficiently with the combination of detailed white-box models through the use of simplified relationships of white-box. Via this approach, the designer is allowed to ‘zoom in’ on specific key process units, while the rest of the model remains as black boxes. Thus, it will be able to reduce the computational effort significantly as compared to pure white-box modeling. Based on the modular optimization approach, the complex mathematic formulation can be simplified without losing the detail of the interest process. As proven in the literature,43−45 a simultaneous approach is more efficient in integrating heat and power compared to sequential approaches; thus, a systematic approach for simultaneous process synthesis and integration of a sustainable integrated biorefinery is proposed. Via the simultaneous process synthesis and integration, the process efficiency can be

2. PROBLEM STATEMENT In this work, the problem statement for simultaneous process synthesis and integration in a sustainable integrated biorefinery is stated as follows: Given an existing processing facility with a set of raw material iRM ∈ IRM with the flow rate of Fexist iRM that generate a set potential biorefinery feedstock, i ∈ I with flow rate of Fi at a mass conversion of Yexist i . Fi is to be distributed to different biomass pretreatment technologies j ∈ J to produce pretreated biomass i′ ∈ I′ at a conversion of Yiji′, and then to be converted into useful intermediates p ∈ P at a given mass conversion Yi′j′p via different biomass conversion technologies j′ ∈ J′. The flow rates of pretreated biomass and intermediates are denoted as Fi ′ and Fp. In the case where unconverted intermediates p from the synthesis processes or intermediates p do not meet the requirement of the synthesis processes, they need to be reconditioned in processes j″ ∈ J″ to meet the process requirement of the synthesis process. The total flow rate that send for reconditioning process is denoted as FRec p . In most cases, the conditioning processes involved additional reactions, separations, or pressurization. Thus, a mass conversion Ypj″p′ is included for the production of conditioned intermediates p′ ∈ P′ that meets the specification of synthesis processes. Subjecting to meeting the process requirement of process j″′, conditioned intermediates p′ ∈ P′ can then be converted to final products, p″ ∈ P″ through synthesis processes j″′ ∈ J″′, at the given mass conversion of Yp′j‴p″. The total flow rates of conditioned intermediates and final products are denoted as Fp′ and Fp″, respectively. The consumption and generation of energy e ∈ E per unit of source (raw material iRM, biorefinery feedstock i, pretreated biomass i, raw intermediates processed p, and conditioned intermediates p′) through existing processing facilities, pretreatment processes, production of intermediates processes, conditioning of intermediates processes, and synthesis of final prod con prod , Yexist−prod , Ycon products are given as Yexist−con ije , Yije , Yi′j′e, Yi′j′e , iRM iRM e e con prod con prod Ypj″e, Ypj″e , Yp′j‴e, and Yp′j‴e, respectively. The total energy consumption rates in the existing processing facilities, pretreatment processes, production of intermediates processes, conditioning of intermediates processes, and synthesis of final , Epretreat−con , Einter−con , Econd−con , products are given as Eexist−con e e e e conv−con and Ee , respectively. Meanwhile, total energy generation rates in the existing processing facilities, pretreatment processes, production of intermediates processes, conditioning of intermediates processes, and synthesis of final products are 7317


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Figure 1. Generic superstructural representation of an integrated biorefinery.

given as Eeexist−prod, Eepretreat−prod, Eeinter−prod, Eecond−prod, and Econv−prod , respectively. e In this work, the optimization objective is given as maximize economic potential (EP). The EP of the synthesized integrated biorefinery with existing processing facility is determined by the difference of total revenue of each final product p″ and total cost of biorefinery feedstock i. Cost of importing energy Cexternal e will be taken into consideration if the energy generated is insufficient; meanwhile, the price of exporting energy Cexport will e be taken into consideration if the energy generated exceeds the total energy consumption.

producing bioproducts, energy can be generated (i.e., steam and electricity) via material and energy recovery. 3.1. Material Balance. The material balance for biomass, from the existing processing facility to the pretreatment, production of intermediates, conditioning of intermediates and synthesis of final products are computed via eqs 1−10. Production of biorefinery feedstock i with flow rate of Fi from the existing processing facility, Fexist iRM : exist Fi = Fiexist RM Y i

∀ i ∀ i RM

(1)

Splitting of biorefinery feedstock i with flow rate of Fi into biomass pretreatment technology j with the flow rate of Fij:

3. PROBLEM FORMULATION The objective of this work is to synthesize a sustainable integrated biorefinery with maximum economic potential via simultaneous heat and power integration with existing processing facility. Modular optimization approach is adapted in this work to break down a complex mathematical model into few simplified models without losing insights to the key parameters. The main steps of conversion of biomass into bioproducts are biomass pretreatment, intermediates production, conditioning of intermediates, and synthesis of final products. Additional heat and power integration is included in order to synthesize a sustainable integrated biorefinery. In this work, heat integration through steam allocation is considered. Processes that generate steams are considered as heat sources, while processes that demand steam are taken as heat sinks. Based on the specific level of steam required in heat sinks and generated in heat sources, the level of temperature for heat integration has been taken into consideration. Note that direct heat recovery or exchange within process streams is not considered. Six distinct simplified material and energy balance models (i.e., existing processing facilities; biomass pretreatment processes; production of intermediates processes; conditioning of intermediates processes; synthesis of final products; heat and power integration) are considered in this work (Figure 1). Different conversion technologies can be proposed in each simplified block. Note that all the models interact with each other through material and energy balances other than

Fi =

∑ Fij

∀i (2)

j

Production of pretreated biomass i′ with the flow rate of Fi′ from pretreatment technology j at the conversion rate of Yiji′: Fi ′ =

∑ ∑ FijYiji′ j

∀ i′ (3)

i

Splitting of pretreated biomass i′ into production of intermediate process j′ with the flow rate of Fi′j′: Fi ′ =

∑ Fi′ j ′

∀ i′ (4)

j′

Production of intermediate p at flow rate of Fp from production process j′ at the conversion rate of Yi′j′p: Fp =

∑ ∑ Fi′ j ′Yi′ j ′p j′

∀p (5)

i′

In the conditioning of intermediates, flow rate of intermediate p(Fp) and unconverted intermediate p(FRec p ) of from the synthesis processes are mixed at the flow rate of Fpj″: Fp + FpRec =

∑ Fpj ″ j″

∀p (6)

Production of conditioned intermediate p′ at flow rate of Fp′ via intermediates conditioning process j″ at the conversion of Ypj″p′: 7318


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∑ ∑ Fpj ″ Ypj ″ p ′ j″

Eeconv − prod =

∀ p′ (7)

p

j ″′

Conditioned intermediate p′ can be distributed to potential technology j″′ for synthesis of final products:

Fp ′ =

∑ Fp ′ j ′′′

(8)

The total production rate of final product p″ (Fp″) can be determined by converting intermediate p′ at the conversion rate of Yp′j″′p″ via the technology j″′: Fp ″ =

∑ ∑ Fp ′ j ′′′Yp ′ j ′′′ p ″ j ′′′

+ Eepretreat − prod + Eeexist − prod + Ee

Fp ″

∑∑

Yp ′ j ′′′ p ″

j ′′′ p ″

+ FpRec ′

∀ p′ (10)

In case where there is unconverted conditioned intermediate F p′Rec after the process synthesis, F p′Rec is recycled and reconditioned in the conditioning of intermediate j″. Thus, FRec is equal to the term of FRec as shown in eq 6 p′ p 3.2. Energy Balance. Based on the given information as described in the previous section, the total energy consumed (Eexist−con ) and generated (Eexist−prod ) for existing processing e e facilities can be determined via eqs 11 and 12, respectively. Eeexist − con =

∑ FiexistY iexiste − con RM

RM

+ Eepretreat − con + Eeexist − con

∑ ∑ FijY ijecon j

Eepretreat − prod =

j

Eeinter − con =

j′

∀e (15)

i′

j″

Eecond − prod =

Fpj ″Y pjcon″ e

p′

∑ e

(17)

e

EeexternalCeexternal

i

(25)

where Cp″ is the price per unit final product, Cexport is the price e per unit of energy e exported, Ci is the price per unit biorefinery feedstock i, and Cexternal is the price per unit energy e sourced e externally. The objective of this work is to synthesize a sustainable integrated biorefinery with maximum EP via simultaneous heat

∀e (18)

p

∑ Fp ″Cp ″ + ∑ EeexportCeexport − ∑ FC i i− p″

∀e

p

∑ ∑ Fp ′ j ″′Y pcon′ j ″′ e j ″′

EP =

(16)

∑ ∑ Fpj ″ Y prod pj ″ e j″

∀e

i′

∑∑

=

Eeconv − con =

(14)

∑ ∑ Fi′ j ′Yiprod ′j′e

Eeinter − prod = Eecond − con

∀e

i

∑ ∑ Fi′ j ′Y icon ′j′e j′

where e and e′ can be electricity (elec), ultrahigh pressure steam (HHP), high pressure steam (HP), medium pressure steam (MP), and low pressure steam (LP). Meanwhile, Ye−e′ is the factor of energy conversion where e is of higher quality energy (i.e., HP) than e′ (i.e., LP). 3.4. Economic Analysis. For the conceptual design of an integrated biorefinery, economic potential, EP (i.e., profit excluding the capital costs and operating and maintenance costs) can be used to determine the economic feasibility of product portfolios, the corresponding process pathways, and integration strategy.48 In this work, the EP can be determined by the equation below:

(13)

∑ ∑ FijYijeprod

(23)

(24)

∀ e′ ∀ e

∀e

i

∀e

Ee ′ = (EeTOTAL + Eeexternal − EeDEMAND − Eeexport)Ye − e ′

(12)

Based on a similar approach, the total energy for biomass pretreatment, production of intermediates, conditioning of intermediates, and synthesis of final products are computed via eqs 13 − 20. Eepretreat − con =

(22)

Advanced steam cycle with multiple pressure levels can be introduced with reference to the requirement of the processes in the integrated biorefinery. Excess steam of a higher pressure can be used to generate electricity and produces a lower pressure steam through a steam turbine. Such conversion can be represented by the generic equation as in eq 24.

∀e

RM

i RM

∀e

EeTOTAL + Eeexternal = EeDEMAND + Eeexport

(11)

∑ FiexistY iexiste − prod

Eeexist − prod =

(21)

EeDEMAND = Eeconv − con + Eecond − con + Eeinter − con

∀e

RM

i RM

∀e

where Ee is the total energy e produced from converting one form of energy to another. For example, medium pressure steam is produced from high pressure steam and electricity is generated from steam. In the case where the total amount of energy produced (ETOTAL ) is less than the energy demand (EDEMAND ) of the e e integrated biorefinery, external supply of energy Eexternal can be e sourced. On the other hand, for the case where the total amount of energy produced exceeds the energy consumption, the excess energy (i.e., ETOTAL − EDEMAND ) can be exported e e export (Ee ).

(9)

To track the material mass balance across the synthesis processes, an additional equation (eq 10) is included to allow unconverted intermediates or intermediates that do not met the required specification (i.e., Fp″ = 0, not recycled internally) to be recycled back to the conditioning processes. Fp ′ =

(20)

EeTOTAL = Eeconv − prod + Eecond − prod + Eeinter − prod

∀ p″

p′

∀e

p′

3.3. Heat and Power Integration. Based on eqs 11−20, all energy consumption and generation per unit of material processed in all processes can be determined. The total amount of energy produced, ETOTAL (i.e., steam at various pressure and e electricity), is as follows:

∀ p′

j ′′′

∑ ∑ Fp ′ j ″′ Y prod p ′ j ″′ e

∀e (19) 7319


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proposed approach. The superstructural representation of the problem is as presented in Figure 3. 4.1. Existing Sawmill. A given sawmill operates at the capacity of 90 000 kg log per day (=1 kg log/s). As shown in Figure 2, the amount of wood waste generated by the mill is approximately 50 wt % of the total log processed, in the form of log ends, bark, slabs, saw dust, and lumber edges.49 Electricity is consumed in the cutting, debarking, and sawing process at 273.6 kJ per kg log processed. However, HP, MP, and LP are not required in a sawmill. 4.2. Wood Waste Pretreatment. As reported in Asian Development Bank,49 wood waste has high moisture content at 50%. Prior to the gasification process, the wood waste needs to be predried to a moisture content of 25%. As shown in Figure 3, different options of pretreatment, j (i.e., air drying and steam drying) are taken into consideration. 4.3. Dried Wood Waste Gasification. In general, various biomass gasification technologies are available to convert biomass into syngas. In this case study, different gasification technologies are to be assessed. According to Gassner and Marechal,42 the product composition and yield of the syngas are often fixed on the basis of the type of gasification technologies. In this work, two types of gasification process j′ (i.e., direct heated (DHG) and indirect-heated gasification (InDHG)) are taken into consideration. The process data for sawmill, pretreatment, and wood waste gasification are adapted from Gassner and Marechal 42 and Tock et al.34 and summarized in Table 1. 4.4. Conditioning of Syngas. Syngas produced from biomass gasification may not meet the downstream requirement. Thus, conditioning of the syngas is required. In this case study, water−gas shift (WGS) and CO2 removal are identified as the key syngas conditioning processes, apart from the physical cleaning (i.e., filtration), which is not modeled in this work. As syngas composition is crucial for its further use in synthesis processes, water-gas shift process is included to adjust the composition of syngas to meet the specification required (i.e., H2/CO ratio) by additional input of steam. On the other hand, CO2 removal is to be considered as it is a standard operation in gas refining applications.34 In this case study, it is assumed that different composition of syngas is produced from different types of gasifier. The

and power integration with existing processing facility. The objective is shown as: Maximize EP

(26)

4. CASE STUDY I: WOOD INDUSTRY Asian Development Bank49 found that a large part of the wood waste generated by sawmills are dumped or burned. Up to 50 wt % of wood logs processed in sawmills ended up as waste. The mass balance of a typical wood processing sawmill is as shown in Figure 2.49

Figure 2. Mass balance of wood processing at sawmill.49

Biomass gasification is recognized as one of the most promising options for the initial processing of biomass in an integrated biorefinery to produce value-added products (e.g., biofuels, biobased chemicals), as well as heat and power. Thus, a gasification-based integrated biorefinery case study adapted from Larson et al.32 and Tock et al.34 is solved to illustrate the

Figure 3. Superstructural representation of the synthesized wood-based integrated biorefinery with existing sawmill. 7320


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produced syngas is then sent to a predefined set of conditioning processes to meet the required specification. According to the previous work,42 the syngas produced by DHG and InDHG systems have H2/CO ratio at 1.52 and 1.66 respectively. Note that the composition of such syngas is able to meet the requirement of DME synthesis, which is specified at H2/CO = 1.3.34 Therefore, the syngas for DME synthesis can bypass the WGS process and proceed to CO2 removal directly. However, the syngas does not meet the minimum requirement of FT and mix−OH synthesis (H2/CO = 2.0). Thus, the syngas needs to go through WGS process to adjust the H2/CO ratio to 2, prior to CO2 removal process. Table 2 shows the process data for the conditioning of syngas adapted from Gassner and Marechal42 and Tock et al.34 As the operating pressure of FT, DME, and mix−OH synthesis are given as 25, 50, and 100 bar, therefore, the syngas has to be compressed to the pressure required for various synthesis processes upon going through WGS and CO2 removal processes. The process data is given in Table 2. 4.5. Synthesis of Final Products. To further convert the conditioned syngas into final product, synthesis processes such as Fischer−Tropsch (FT) liquids, dimethyl-ether (DME), and mixed alcohol (mix−OH) are included in the analysis (as shown in Figure 3). Larson et al.32 have carried out an extensive work to simulate various synthesis processes, and their alternative configurations, to produce key biofuels. Therefore, the results and data of process alternatives from Larson et al.32 are extracted as for the process synthesis process. As shown, the synthesis processes consume steams. However, heat from the synthesis reactions can also be recovered by generating HHP. The process data for these synthesis processes are as shown in Table 3.32 4.6. Heat and Power Integration. With the available data on processes and their alternatives, the model can be used to convert an existing biomass processing facility into an

Table 1. Process Data of Sawmill, Pretreatment, and Wood Waste Gasification sawmill49 utilities consumption (kW/(kg log/s)) electricity (Yexist−con iRMelec ) HP (Yiexist−con RM HP ) MP (Yexist−con iRMMP ) LP (Yiexist−‑con ) RM LP

iraw = logs, i = wood waste 273.6 -

0.5 kg wood waste/kg log mass conversion (Yexist i ) logs processed (Fexist 1 kg/s iRM ) pre-treatment (drying) i = wood waste; i′= dried wood waste utilities consumption (Ycon ij elec)

electricity HP (Ycon ij HP) MP (Ycon ij MP) LP (Ycon ij LP)

air drying j = 1

steam drying j = 2

56 kW/(kg/s) i -

0.0231 kg/(kg/s) i

mass conversion (Yiji′, kg i′/kg i) 0.75 dried wood waste gasification

0.75

i′ = dried wood waste; p = a, raw syngas utilities consumption electricity (Ycon i′j′elec) HP (Ycon i′j′HP, kg/(kg/s i′)) MP (Ycon i′j′MP) LP (Ycon i′j′LP) mass conversion (Yi′j′p, kg a/kg i′)

DHG pressure = 15 bar, j′ = 1′

InDHG pressure = 1 bar, j′= 2′

336.6 kW/(kg/s) i′ + 428 kW/(kg/s HP)a 0.6

217.8 kW/(kg/s) i′ + 428 kW/(kg/s HP)a 0.5

0.83

0.58

a

Additional heating required to generate superheated steam from high pressure steam.

Table 2. Process Data of Syngas Conditioning and Syngas Compression syngas conditioning34,42 p = a, raw syngas; p = b, syngas after WGS process; p = c, syngas after CO2 removal process WGS j″ = 1″

CO2 removal j″ = 2″

syngas from utilities consumption con electricity, kW (Ycon aj″elec, Ybj″elec) con con HP, kg (Yaj″HP, Ybj″HP) con MP, kg (Ycon aj″MP, Ybj″MP) con LP, kg (Ycon , Y bj″LP aj″LP) conversion (Ybj″a,Ycj″b, Ycj″a)

DHG (per kg/s a)

syngas through WGS from

InDHG (per kg/s a)

342.6

174.1

0.378 1.378 kg/s b

0.276 1.276 kg/s b

DHG (per kg/s b) InDHG (per kg/s b)

syngas bypassing WGS from DHG (per kg/s a)

InDHG (per kg/s a)

336.8

240

466.4

248

0.6184

0.4554

0.8564

0.5230

0.7519 kg/s c

0.5340 kg/s c

0.7150 kg/s c

0.6636 kg/s c syngas compression

p = c, syngas after CO2 removal process; p′ = a′ conditioned syngas syngas through WGS from utilities consumption

DHG

InDHG

syngas bypassing WGS from DHG

InDHG

Compression 1 to 15 bar, j″= 3″ [kW/(kg/s c)] electricity (Ycon pj″elec) electricity (Ycon pj″elec) electricity (Ycon pj″elec) electricity (Ycon pj″elec)

2061.17 Compression 15 to 25 bar, j″= 4″ [kW/(kg/s c)] 257.93 2907 284.09 Compression 25 to 50 bar, j″= 5″ [kW/(kg/s c)] 357.64 414.84 395.52 Compression 50 to 100 bar, j″= 6″ [kW/(kg/s c)] 3508 417.34 399.45 7321

2244.25 317.69 443.12 447.45 dx.doi.org/10.1021/ef301283c | Energy Fuels 2012, 26, 7316−7330

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Table 3. Process Data of Biofuel Synthesis Processes and Heat and Power Integration in Study Case I synthesis processes p′ = a′, conditioned syngas; p″= final products utilities consumption electricity, kW (Ycon p′j″′elec) HP, kg (Ycon p′j″′HP) MP, kg (Ycon p′j″′MP) LP, kg (Ycon p′j″′LP) conversion (Yp′j‴p″) HHP generation, kg (Yprod p′j‴e)

electricity Ye′−e, kW steam Ye′−e

FT synthesis a j‴= 1″′, p″= a″ (per kg p′)

FT synthesis b j‴= 2″′, p″= a″ (per kg p′)

DME synthesis a j‴= 3″′, p″= b″ (per kg p′)

DME synthesis b j‴= 4″′, p″= b″ (per kg p′)

mix−OH synthesis j″′ = 5″′, p″= c″ (per kg p′)

0.462 kg a″

0.456 kg a″

0.1493 0.2883 0.934 kg b″

0.0416 0.1282 0.9 kg b″

0.2152 0.296 kg c″

38 bar 1.2890

38 bar 1.2955

38 bar 1.2632 heat and power integration

38 bar 0.5077

130 bar 1.118

e = 38 bar HHP, e′ = elec, HP per HHP

e = 130 bar HHP, e′ = elec, HP per HHP

e = HP, e′ = elec, MP per HP

e = MP, e′ = elect, LP per MP

31.21 0.9862 kg HP

189.2 0.8524 kg HP

165.0 0.9467 kg MP

166.4 0.9557 kg LP

4.7. Optimized Results. Cost data for final products and utilities of both case studies is tabulated in Table 4. It is

integrated biorefinery with heat and power integration. Heat integration through steam allocation is considered in this case study. As mentioned previously, processes that generate steam are considered as heat sources; while processes that demand steam are taken as heat sinks. Based on the specific level of steam required in heat sinks and generated in heat sources, the level of temperature for heat integration has been taken into consideration. Note that direct heat recovery or exchange within process streams is not considered. In this case study, e in eqs 21−24 is used to represent electricity (elec), HHP, HP, MP, and LP. Note that, based on eq 24, it allows excess steam of a higher pressure to be sent to a steam turbine to generate electricity and produce a lower pressure steam. As mentioned previously, the term e in eq 24 is used to represent the higher quality energy than e′. For this case study, HHP generated from synthesis reactions’ heat recovery is not used in the processes but will be used to generate electricity, HP, MP, and LP. To represent such problem, eq 24 is revised as the following equations:

Table 4. Cost Data for Raw Material, Final Products, and Utilities Cp″ (US$/kg) 0.51 0.39 0.62 Cp″ (US$/t)

DLF (1″) briquette (2″) pellet (3″) palm oil mill

160 70 100 Ci (US$/t)

EFB (1) 6 PKS (2) 50 PMF (3) 22 utilities Cexternal , Cexport (US$/kg steam, US$/kWh electricity) e e HP MP LP electricity (elec)

TOTAL external DEMAND export Eelec = (E HHP + E HHP − E HHP − E HHP ) TOTAL external DEMAND YHHP − elec + (E HP + E HP − E HP export TOTAL external − E HP + EMP )YHP − elec + (EMP DEMAND export − EMP − EMP )YMP − elec

sawmill FT liquid (a″) DME (b″) mix−OH (c″) palm oil mill

0.026, 0.017, 0.012, 0.102,

− − − 0.0714

assumed that there are no raw materials (wood waste) imported from external facilities and fully provided by existing mill, and thus, the raw material price Ci is neglected. It is also assumed that integrated biorefinery operates 8000 h per year. In this case study, eqs 1−23, 25, and 27−30 are solved with optimization objective of maximum EP (eq 26) based on the process and economic data in Tables 1 − 4. A total of 83 variables and 74 constraints (12 nonlinear variables and 8 nonlinear variables) in this NLP model is solved via LINGO v13.0 with Global Solver in HP Compaq 6200 Elite Small Form Factor with Intel Core i5−2400 Processor (3.10 GHz) and 4GB DDR3 RAM within few seconds. A global optimum result of Case Study 1 is tabulated in Table 5. As shown, the maximum EP is targeted as US$ 0.073/s (US$ 2.10 million/year). Note that DME is selected as the main product with the targeted production rate of 0.2658 kg/s. In addition, electricity, MP and LP have to be imported/ generated to meet the demand in the integrated biorefinery.

(27)

TOTAL external DEMAND export E HP = (E HHP + E HHP − E HHP − E HHP )YHHP − HP

(28) TOTAL external DEMAND export EMP = (E HP + E HP − E HP − E HP )YHP − MP

(29) TOTAL external DEMAND export E LP = (EMP + EMP − EMP − EMP )YMP − LP

(30)

where Eelec is the amount of electricity generated when excess HHP, HP, and MP are sent to the steam turbine at the conversion factors of YHHP−elec, YHP−elec, and YMP−elec. Meanwhile, EHP, EMP and ELP are the amount of HP, MP and LP produced from the steam turbine system with the conversion of YHHP−elec, YHP−MP, and YMP−LP. All the conversion factors are tabulated in Table 3. 7322


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Figure 4 illustrates the optimization pathway of wood industry. As shown, it is noted that DME is selected as the optimum product in sawmill integrated biorefinery. With a wood waste supply of 0.50 kg/s, 0.2658 kg/s of DME can be produced. The model simultaneously synthesizes an optimum integrated biorefinery configuration and generates a steam network consisting of HHP, HP, MP, and LP and gas turbine. Steam drying is selected in the biomass pretreatment, and 0.375 kg/s of dried wood waste is fed into DHG in order to produce 0.3113 kg/s of syngas. Conditioning of the syngas is required since raw syngas does not meet the downstream requirement. The syngas is compressed to 50 bar upon going through WGS and CO2 removal processes. Syngas after

Table 5. Results of Case Study I case study I: wood-based integrated biorefinery EP Fb″ (DME) Fexist iRM (logs) Fi (wood waste) Eexternal elec Eexternal HP Eexternal MP Eexternal LP

0.073 US$/s 0.2658 kg/s 1 kg/s 0.5 kg/s 889.8 kW 0 kg/s 0.186 kg/s 0.212 kg/s

Figure 4. Result of the conversion of sawmill into an integrated biorefinery. 7323


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Figure 5. Mass balance of palm oil processing in palm oil mill.52

Figure 6. Superstructural representation of the synthesized palm oil-based integrated biorefinery with existing palm oil mill.

0.212 kg/s of LP are to be imported from external facilities to meet the demand in the integrated biorefinery.

conditioning with 0.2846 kg/s is synthesized through the pathway of DME synthesis a in the last stage of process to produce 0.2658 kg/s DME. Furthermore, heat from the synthesis reactions is recovered by generating 0.359 kg/s of HHP. HHP is then converted to 0.308 kg/s MP and 32.6 kW electricity. Total required electricity, HP, MP, and LP of this case study are 922.4 kW 0.225 kg/s, 0.308 kg/s, and 0.212 kg/s, respectively. As a consequence, 889.8 kW of electricity, 0.186 kg/s of MP, and

5. CASE STUDY II: PALM OIL INDUSTRY Oil palm is the major crop planted in Malaysia. Based on Department of Statistics, Malaysia, 50 48 538 000 ha of agriculture land in Malaysia is planted with oil palm in year 2011. The fruit of the oil palm, which known as fresh fruit bunch (FFB), can be used to produce two distinct types of oils: 7324


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Table 6. Process Data of Palm Oil Mill and Pre-Treatment of Palm-Based Biomass

Table 8. Heat and Power Integration in Case Study II raw material/pretreated palm-based biomass i

palm oil mill

electricity (Yexist−con iRMelec ) HP (Yiexist−con RM HP ) MP (Yexist−con iRMMP ) LP (Yiexist RM LP) mass conversion (Yexist i ) FFB processed (Fexist iRM )

pretreated EFB PKS PMF steam temperature (°C)

palm processing mill iraw = FFB, i = palm-based biomass

utilities consumption

13.5 kW/(t/h FFB)

HP MP LP

0.422 t/h LP/(t/h FFB) 0.234 t EFB/t FFB 0.073 t PKS/t FFB 60 t/h pretreatment (drying)

electricity (Ycon ijelec) hot air (Ycon ijHA) HP (Ycon ijHP) MP (Ycon ijMP) LP (Ycon ijLP) mass conversion (Yiji′, kg i′/kg i)

air drying j = 1

electricity Ye′−e, kW steam Ye′−e, t hot air Ye′−e, kW

steam drying j = 2

120 kW/(t/h) i 1 t/h/(t/h) i 0.636

45 12 37

calorific value (kJ/kg)54 18838 20108 19068 pressure (bar)

400 250 145 boiler e = HP, e′ = elec, MP per HP

i = palm-based biomass; i′ = pretreated palm-based biomass utilities consumption

moisture content (%)

e = MP, e′ = elec, LP per MP

0.9467 t MP

40 12 4 gasifier e = HP, e′ = elec, LP per LP

e = syngas, e′ = elec, LP per syngas

84.56

1121

1 t LP 3271

Table 9. Results of Case Study II

0.636

case study II palm oil-based integrated biorefinery EP Fp″=1″ (DLF) Fp″=3″ (Pellet) Fp″=4″ (PKS) Fexist iRM (FFB) Fi=1 (EFB) Fi=2 (PKS) Fi=3 (PMF) Eexternal elec Eexternal HP Eexternal MP Eexternal LP Eexternal HA Eexport elec

crude palm oil (CPO) from mesocarp and crude palm kernel oil (CPKO) from kernel.51 Both CPO and CPKO possess excellent cooking properties and widely used for food application (i.e., margarine, shortening, etc.) and nonfood items (i.e., soap, cosmetics, etc.).43 CPO and CPKO are extracted via a processing facility, known as palm oil mill. The typical mass balance of palm oil mill is illustrated in Figure 5.52 As shown in Figure 5, upon harvesting FFB, they are loaded into the sterilizer cages and fed to stripper where fruits are separated from bunches, pressed through twin screw presses to extract the CPO and then pumped to a clarification tank for oil separation. Meanwhile, palm mesocarp fiber (PMF) and nut are sent to depericarper for mesocarp fiber separation. The remaining nut shells are further processed in the nut cracker to separate the kernels from the shells. Throughout the palm oil milling process, palm-based biomasses (e.g., empty fruit bunch (EFB), palm mesocarp fiber (PMF), palm kernel shell (PKS),

182.61 US$/h 2 t/h 2 t/h 0.42 t/h 60 t/h 14.04 t/h 4.38 t/h 7.80 t/h 0 kW 0 t/h 0 t/h 0 t/h 0 t/h 1627.7 kW

sludge, etc.) are produced as byproducts, as highlighted in Figure 5.

Table 7. Process Data of Palm-Based Biomass Conversion and Products Synthesis conversion of palm-based biomass utilities consumption electricity (Ycon i′j′elec) HP (Ycon i′j′HP, t/(t/h i′)) MP (Ycon i′j′MP) LP (Ycon i′j′LP) mass conversion (Yi′j′p, t p/t i′)

i′ = pretreated palm-based biomass; p = 1, WLF; p = 2, shredded EFB; p = 3, HP; p = 4, syngas; p = 5, WSF primary sieving j′ = 1′, p = 1, or p = 5 separating and shredding j′ = 2′, p = 2 160 kW/ (t/h) p″ 76 kW/(t/h) p″ 1 (t/h)/(t/h) p″ 1 (t/h)/(t/h) p″ 0.48 WLF/i′, 0.237 WSF/i′ 0.611/i′ product synthesis

gasification j′ = 4′,p = 4 0.65/EFB + 0.723/PKS

p = p′; p″ = 1″, DLF; p″ = 2″, briquette; p″ = 3″, pellet briquetting j″″ = 2″′

pelletizing j″′ = 3″′

electricity, kW (Ycon p′j″′elec) HP, t (Ycon p′j″′HP) MP, t (Ycon p′j″′MP) LP, t (Ycon p′j″′LP)

50 -

63 0.21 -

106.5 0.21 -

conversion (Yp′j‴p″)

0.929 DLF/p, 0.071 DSF/p

0.921

0.921

utilities consumption

secondary sieving j″′ = 1″′

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Figure 7. Result of the conversion of palm oil mill into an integrated biorefinery.

In the current practice, palm based biomass (i.e., PMF, PKS, and EFB) are used as the main source of energy input to the biomass boiler and cogeneration system for production of steam and electricity in the mills. Furthermore, there are many mature processes and technologies that convert palm-based biomasses to value-added products through biological, physical, and thermochemical conversions. The superstructural representation of the palm oil industry is presented in Figure 6.

Palm-based biomasses produced from palm oil mill either sent to pretreatment or directly to production of intermediates (i.e., primary sieving, separation and shredding, gasification, or combustion). The intermediates are then further processed through drying and secondary sieving, briquetting, pelletizing, steam turbine, or gas engine before final products are produced. 5.1. Existing Palm Oil Mill. A palm oil mill with the capacity of 60 t/h is utilized to illustrate the proposed 7326


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pressure steam and electricity. Thus, the term of hot air (HA), EHA is added in eqs 13−24. The process data of heat and power generation of palm oil industry is tabulated in Table 8. Note that the process data from Tables 6−8 are obtained from interviews with industry partners and do not consider economies of scale. In the boiler system, PMF, PKS, and pretreated EFB can be directly combusted in the boiler for HP production at 40 bar, 400 °C. The steam is sent to steam turbine for production of electricity and MP at 12 bar, 250 °C or LP at 4 bar, 145 °C. Note that, in this case study, HHP (eq 27) is neglected, as there is no HHP produced from boiler. The mixtures of biomass feedstock Fbiomass to boiler are required to meet the maximum moisture content of 40%. Feedstock with moisture content higher than 40% will cause incomplete combustion and leads to low boiler efficiency.53 Due to different calorific value (CV) of EFB, PKS, and PMF,54 as shown in Table 7, it is worth further analyzing the boiler system and examining the heat produced from different ratio of biomasses. The overall moisture content of mixtures of biomass feedstock MCoverall (eq 31) and the constraint of moisture content of the boiler (eq 32) are added in the optimization model. Noted that eq 31 is only applicable for the boiler (j = 4).

approach. As shown in Figure 5, the amount of EFB, MF, and PKS generated by the mill are 23.4 wt %, 18.0 wt %, and 7.3 wt % of total FFB processed, respectively (DOE, 1999). In this case study, EFB, PKS, and PMF are taken into consideration as feedstocks to be fed into integrated biorefinery. It is assumed that electricity and LP required in the palm oil mill are 13.5 kW and 0.3 t/h per t/h of FFB processed. In addition, HP and MP are not required in a palm oil mill. 5.2. Palm-Based Biomass Pretreatment. Because EFB has high moisture content at approximately of 65% after stripping process, EFB needs to be predried to a moisture content of 45% before sent to further process. Meanwhile, PKS has lower moisture content, thus, it can be directly sent to boiler or directly sold as final product without pretreatment. As mentioned earlier, different options of pretreatment, j (i.e., air drying and steam drying) are taken into consideration. The process data for the palm oil mill and pretreatment are as shown in Table 6. 5.3. Conversion of Palm-Based Biomass. In general, various biomass conversion technologies are available to convert biomass into value added products (i.e., dried long fiber (DLF), pellet, and briquette). In this case study, three different conversion technologies j′ (i.e., DLF processing, briquetting, and pelletizing) are considered. In the primary sieving process, throughout the processing of EFB to DLF, wet short fiber (WSF), and wet long fiber (WLF) are produced. Besides, EFB can be sent to separation and shredding processes before sending to further briquetting and pelletizing process. 5.4. Conditioning of Intermediates. In this case study, as the intermediates (WSF, WLF, and shredded EFB) do not need to be conditioned for downstream processes. Therefore, FRec p , con , Y in eqs 6, 10, 17, and 18 are neglected. Meanwhile, the Yprod pj″e pj″e term Ypj″p′ in eq 7 is equal to 1, as no conversion is involved in this stage. 5.5. Synthesis of Final Products. The byproduct WSF produced from primary sieving can be mixed with separated and shredded EFB and further processed to briquette and pellet through briquetting and pelletizing process. In addition, throughout the processing of DLF, dry short fiber (DSF) is produced. DSF can then be recycled into briquetting process, pelletizing process, boiler or taken as another source, to be gasified in gasifier for generation of heat, electricity, and steam. Based on the current practice, production capacity of each technology is determined by rounding up the production rate of final product to the nearest integer. Thus, general integer variables are included in this model. The process data of production of intermediates and synthesis of final products is summarized in Table 7. 5.6. Heat and Power Integration. Similar to the previous case study, the model can be used to convert an existing palm oil mill and palm-based biomass processing facility into an integrated biorefinery with heat and power integration. In this case study, there are two options for heat and power generation available (boiler and gasifier). As mentioned earlier, DSF can also be gasified in gasifier for generation of heat and electricity. In this work, 1.5 MW gasification power generation plant is constructed if gasification pathway is selected. A general integer variable is included in gasification pathway whereby if the DSF produced is insufficient to produce 1.5 MW electricity, extra PKS can be fed to gasification system. Furthermore, heat can be either recovered from gasification system and can be sent to pretreatment and drying system or used to boil water, creating steam for a steam turbine-generator and generate lower

MCoverall Fbiomass =

∑ MCijFij ij

MCoverall ≤ 0.4

(31) (32)

To determine HP produced from boiler, the dry flow rate of biomass mixtures is multiplied with CV of each palm based biomass and boiler efficiency ηboiler and divided by net heat required. Net heat required is determined by the enthalpy H of HP subtract the enthalpy H of feedwater at 1 bar, 100 °C, as shown in eq 33. In this work, it is assumed that the boiler efficiency is 55%. Additional equation is added in the model as follows: E HPS =

ηboiler × ∑ij (1 − MCi)(Fij)CVi HT = 400 ° C, P = 40bar − HT = 100 ° C, P = 1bar

(33)

5.7. Optimized Results. In the second case study, it is assumed that raw materials (EFB, PKS, and PMF) are purchased from existing mill, and thus, the raw material price Ci is taken into consideration. It is assumed that the operating hour of integrated biorefinery is 8000 h per year. Furthermore, the energy consumption within the sustainable integrated biorefinery has to be self-sustaining prior to the consideration of energy exportation.33 The constraint is added in this case study has shown as follows:

EeTOTAL ≥ EeDEMAND

(34)

Equations 1−5, 7−9, 11−16, 19−23, 27, and 29−34 are solved with the objective shown in eq 26 based on the cost data in Table 4 and process data shown in Tables 6−8. A MINLP model with a total of 99 variables and 83 constraints (2 nonlinear variables, 4 integer variables, and 1 nonlinear constraints) are solved via LINGO v13.0 with Global Solver in HP Compaq 6200 Elite Small Form Factor with Intel Core i5-2400 Processor (3.10 GHz) and 4GB DDR3 RAM within few seconds. A global optimum solution is found in this palm oil industry case study, and its result is tabulated in Table 9. The maximum EP is targeted as US$ 182.61/h (US$ 1.46 million/year) and production pathways of 2 t/h DLF and 2 t/h 7327


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pellet are selected. Excess electricity generated from steam turbine can be sold to external facilities. Note that the proposed model is able to target the economic potential of the integrated biorefinery and it serves as a useful benchmark and platform for the detailed design. In this case study, DLF and pellet production pathways are selected to achieve the maximum EP. The optimized pathway of this case study is illustrated in Figure 7. With a FFB supply of 60 t/h, 2 t/h of DLF and 2 t/h of pellet can be produced. It is noted that 14.04 t/h of fresh EFB with moisture content 65% is sent to steam drying pretreatment and total of 8.93 t/h pretreated EFB with moisture content 45% is pretreated. 4.49 t/h and 1.56 t/h of pretreated EFB are transferred to the primary sieving and shredding process, respectively. The remaining 2.89 t/h of pretreated EFB is fed into boiler system to generate steam and electricity. 2.15 t/h of WLF is produced from primary sieving and further processed in secondary sieving process before produce final product DLF. Meanwhile, 1.06 t/h of WSF, which is a byproduct from primary sieving for every 2 t/h of DLF production, is collected. WSF is fully utilized as feedstock fed into pelletizing process with 0.95 t/h shredded EFB to generate pellet. On the other hand, 0.42 t/h of PKS can be directly sold as final product; thus, PKS bypassed all technology levels. In the heat and power generation block, boiler and steam turbine are selected to generate steams and power. A total of 38.11 t/h of HP is generated from boiler to convert HP to 36.08 t/h MP. 18.04 t/h of MP produced is used in the process and remaining 18.04 t/h MP is further sent to steam turbine to generate 18.04 t/h LP. Note that 18 t/h LP is supplied to existing palm oil mill. Meanwhile, the balance of 0.04 t/h LP (Eexport LP ) is exported; however, as the amount of LP is too small; therefore, it is released to the environment. Total electricity of 3222.7 kW is produced from conversion of HP to MP and MP to LP through the steam turbine. Besides, pathway of gasification is not selected; thus, no air drying (pretreatment) is required in this integrated biorefinery. Total electricity of 1595 kW is necessitated for self-consumption within the entire integrated biorefinery and existing palm oil mill. So, 1627.69 kW of electricity can be exported to nearest grid.

Article

AUTHOR INFORMATION

Corresponding Author

*Telephone: +6(03) 8924 8606. Fax: +6(03) 8924 8017. Email: [email protected]. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The financial support from Global Green Synergy Sdn. Bhd., Malaysia, and University of Nottingham Research Committee through the New Researcher Fund (NRF 5021/A2RL32) is gratefully acknowledged.

■

NOMENCLATURE

Sets

i = index for potential biorefinery feedstocks, prior pretreatment i′ = index for pretreated biomass iRM = index for raw material e = index for energy streams (i.e., HHP = ultra high pressure steam, HP = high pressure steam, MP = medium pressure steam, LP = low pressure steam, elec = electricity) j = index for biomass pretreatment technologies j′ = index for biomass conversion technologies to produce intermediates j″ = index for technologies for the conditioning of intermediates j‴ = index for technologies for the synthesis of final products p = index for intermediates p′ = index for conditioned intermediates p″ = index for final products Parameters

Cp″ = price per unit final product p″ Ci = price per unit biorefinery feedstock i Cexport = price per unit of energy e exported e Cexternal = price per unit energy e sourced externally e MCoverall = overall moisture content MCi = moisture content of biorefinery feedstock i Yiji′ = conversion of pretreated biomass i′ per unit of biorefinery feedstocks i processed through technology j Yi′j′p′ = conversion of intermediates p per unit of pretreated biomass i′ processed through technology j′ Ye−e′ = conversion for converting one form of energy e to another e′ Ypj″p′ = conversion of conditioned intermediates p′ per unit of intermediates p processed through technology j″ Yp′j‴p″ = conversion of final products p″ per unit of conditioned intermediates p′ processed through technology j‴ Ycon ije = energy e consumption per unit of potential biorefinery feedstocks i processed by technology j Ycon i′j′e = energy e consumption per unit of pretreated biomass i′ processed by technology j′ Ycon pj″e = energy e consumption per unit of intermediates p processed by technology j″ Ycon p′j‴e = energy e consumption per unit of conditioned intermediates p′ processed by technology j‴ Yexist = conversion of biorefinery feedstocks i per unit of raw i material iRM Yexist−con = energy e consumption per unit of raw material iRM iRMe processed

6. CONCLUSION The paper presents a systematic approach of simultaneous process synthesis and integration of a sustainable integrated biorefinery with existing biomass processing facilities (e.g., sawmill and palm oil mill). Modular optimization approach is adapted in this work to simplify the overall formulation without losing the insights of interest for the effective design, synthesis, and integration of the processes. Thus, the computational effort of complex mathematical models can be significantly reduced. Based on application of different optimization objectives, the preliminary conceptual design of a sustainable integrated biorefinery, including the optimum final products allocation, steam, and electricity networks, can be synthesized. Direct heat recovery or exchange within heat sinks and sources, the usage of excess heat as part of the cogeneration system (e.g., wasteheat recovery, preheating of boiler feedwater, optimization of steam system), and the enhancement of cogeneration system performance through plus/minus rule shall be extended in future works. In addition, economic, environmental, and social aspects shall be solved simultaneously in conceptual design of a sustainable integrated biorefinery. 7328


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Yexist−prod = energy e production per unit of raw material iRM iRMe processed Yprod ije = energy e production per unit of potential biorefinery feedstocks i processed by technology j Yprod i′j′e = energy e production per unit of pretreated biomass i′ processed by technology j′ prod Ypj″e = energy e produced per unit of intermediates p processed by technology j″ prod Yp′j‴e = energy e produced per unit of conditioned intermediates p′ processed by technology j‴ ηboiler = efficiency of boiler

■

Fpj″ = flow rate of intermediates p distributed to various intermediates conditioning processes j″ Fp′j‴ = flow rate of conditioned intermediates p′ distributed to various synthesis processes j‴ Fp′ = flow rate of conditioned intermediates p′ Fp″ = flow rate of final products p″

REFERENCES

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Variables

CV = calorific value EP = economic potential Ee = total energy e produced from converting one form of energy to another Econd−con = total energy e consumed by the intermediates e conditioning processes j″ per unit of intermediates p processed Econd−prod = total energy e produced by the intermediates e conditioning processes j″ per unit of intermediates p processed Econv−con = total energy e consumed by the products synthesis e processes j″′ per unit of conditioned intermediates p′ processed Econv−prod = total energy e produced by the products synthesis e processes j″′ per unit of conditioned intermediates p′ processed EDEMAND = total demand of energy e e Eeexist−con = total energy e consumed by the existing processing facilities per unit of raw material iRM processed Eeexist−prod = total energy e produced by the existing processing facilities per unit of raw material iRM processed Eexport = total amount of excess energy e that is exported total e demand of energy e Eexternal = total amount of energy e that is sourced externally e to meet EDEMAND e Einter−con = total energy e consumed by the process j′ per unit e of pretreated biomass i′ processed Einter−prod = total energy e produced by the process j′ per unit e of pretreated biomass i′ processed Epretreat−con = total energy e consumed by the pretreatment e process j per unit of potential biorefinery feedstock i processed Epretreat−prod = total energy e produced by the pretreatment e process j per unit of potential biorefinery feedstock i processed ETOTAL = total amount of energy e produced e Fexist = flow rate of raw material in an existing processing RM i facilities Fbiomass = flow rate of biomass fed into boiler Fi = flow rate of potential biorefinery feedstocks i Fi′ = flow rate of pretreated biomass i′ Fij = flow rate of potential biorefinery feedstocks i distributed to different technology j for pretreatment Fi′j′ = flow rate of pretreated biorefinery feedstocks i′ distributed to different technology j′ for conversion to intermediates Fp = flow rate of intermediates p FRec = flow rate of unconverted intermediates p from the p synthesis processes or intermediates that do not meet the requirement of the synthesis processes FRec p′ = unconverted conditioned intermediate 7329


Energy & Fuels

Article

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