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Towards a computer-aided synthesis and design of biorefinery networks – data collection and management using a generic modeling approach PEAM CHEALI, Krist Gernaey, and Gurkan Sin ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/sc400179f • Publication Date (Web): 06 Aug 2013 Downloaded from http://pubs.acs.org on August 13, 2013

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ACS Sustainable Chemistry & Engineering

Towards a computer-aided synthesis and design of biorefinery networks – data collection and management using a generic modeling approach Peam Chealia, Krist V. Gernaeyb and Gürkan Sina*

a

CAPEC, Department of Chemical and Biochemical Engineering, Technical University of

Denmark (DTU), Building 229, DK-2800 Lyngby, Denmark.

b

PROCESS, Department of Chemical and Biochemical Engineering, Technical University of

Denmark (DTU), Building 229, DK-2800 Lyngby, Denmark.

KEYWORDS: Process synthesis, superstructure, biorefinery, thermochemical conversion, data management. AUTHOR INFORMATION *Corresponding author. Tel.: +45-45252806; Fax: +45-45932906; Email: [email protected] (Gürkan Sin)

ACS Paragon Plus Environment

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ABSTRACT

Recent research into biorefineries resulted in many competing concepts and technologies for conversion of renewable bio-based feedstock into an array of promising products including fuels, chemicals, materials, etc. The topic of this study is collection and management of the complex biorefinery data which are needed among others to support the superstructure based optimization studies. To this end, we first formulate an integrated thermo-chemical and biochemical biorefinery superstructure and then use a generic modeling approach to represent each processing technology in the superstructure. The generic model parameters includes reaction yield, utility consumption, and separation efficiency among others, which are identified on the basis of inputoutput data (generated from rigorous models) collected from detailed biorefinery case studies reported in the open literature. The outcome is a verified database for the extended biorefinery networks combining thermo-chemical and biochemical platforms which represents 2882 potential biorefinery routes. The validated biorefinery database is made public and can be used to cross-validate and benchmark new biorefinery technologies and concepts as well as in superstructure-based optimization studies.

INTRODUCTION The limited resources of fossil fuel feedstock form a serious challenge to future growth of the processing and chemical industries. This motivates the development of new and more sustainable technologies for processing renewable feedstocks, with the aim of bridging the gap for fuel, chemicals and material production. In a biorefinery, a bio-based feedstock is processed to produce various products such as fuel, chemicals, or power/heat. As there are several feedstock


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sources, as well as many alternative conversion technologies to choose from to match a range of products, this creates a large number of potential processing paths for biorefinery development.3,10 The characterisation of each process alternative requires a substantial amount of information: parameters, variables, models of known reactions, thermodynamic properties, process efficiencies or experimental data.1-2 In order to manage the complexity of designing a biorefinery, several publications have focused on simplification i.e. (i) to find an optimal processing route considering only the reactions;21-22 (ii) to limit the number of processing steps to five steps11; or, (iii) a systematic study of the superstructure of integrated biorefineries by using a combined process and economic modeling.16 Clearly, in the early stage of biorefinery planning and design − a phase that is often characterised by lack of detailed data − it is important to simplify and manage the complexity related to the huge amount of data that is to be processed prior to identifying the optimal biorefinery processing path with respect to economics, consumption of resources, and sustainability. A methodology to generate and identify optimal biorefinery networks was developed earlier.15,24 The methodology is based on superstructure optimization and consists of tools and methods including databases, models, a superstructure, and solution strategies to represent, describe and evaluate various processing network alternatives. The data collection and the modeling form a significant part of this methodology, which is the subject of this contribution. In this study, we expand the scope and the size of the biorefinery network problem by extending the database, the models and the superstructure of the methodology with thermochemical biomass conversion routes, and integrate them with the superstructure of the biochemical conversion network.24 In particular, in this paper we present a generic process modeling approach to collect and manage multidisciplinary and multidimensional data related to


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process alternatives in a biorefinery process network. We then perform a verification of the generic models and its parameters against the actual data source for quality control purposes. We also briefly introduce the MINLP-based problem formulation to indicate how the generic model and data developed in this contribution are embedded in the optimization problem setting. The solution and the analysis of the optimization problem itself, including the effect of data uncertainties, is however beyond the scope of this contribution and subject to further work. The paper is organized as follows: (i) the overall methodology used in this study is briefly introduced; (ii) the methodology is applied to the expanded biorefinery network, where the superstructure definition, the data collection and the data validation are presented.

A FRAMEWORK FOR SYNTHESIS AND DESIGN OF BIOREFINERY This study uses an earlier developed framework,15 the so called integrated business and engineering framework (Figure 1). In particular, we highlight the data management and collection efforts with a generic process modeling approach13 to collect and manage the complexity of multidisciplinary and multidimensional data related to the different process alternatives in a biorefinery process network. The different steps which are part of the framework (Figure 1) will be explained briefly.


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Step 1. Problem definition

Step 2. Superstructure definition

Processing networks

Step 3. Data collection and estimation

Generic model & its parameters

Step 4. Models and data verification

Verified database for biorefinery network

Figure 1. The steps taken in the data management and collection framework: the dashed boxes indicate the outcome of each step of the workflow Step 1: Problem definition The first step includes the definition of the problem scope, the selection of suitable objective functions and optimization scenarios with respect to either business strategy, engineering performance, sustainability or a combination of such objectives.

Step 2: Superstructure definition A superstructure representing different biorefinery concepts and networks is formulated by performing a literature review. A typical biorefinery network consists of a number of processing steps converting or connecting biomass feedstocks to bio-products such as pretreatment, primary


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conversion (gasification, pyrolyis), gas cleaning and conditioning, fuel synthesis and product separation and purification. Each processing step is defined by one or several blocks depending on the number of unit operations considered in the step (several unit operations can be modeled using one process block). Each block incorporates the generic model to represent various tasks carried out in the block such as mixing, reaction and separation (Figure 2)15. Detailed presentation of the generic model itself is given below.

Step 3: Data collection and modeling Once the superstructure is defined, the data are collected and modeling is performed. Generally, the models for each processing technology are rigorous, non-linear and complex models (e.g. kinetics, thermodynamics). In this step, however, a simple input-output type generic block model is used which are identified from the data generated from the above mentioned complex model. This generic block thus consists of four parts of the typical simple mass balance equations: (i) mixing; (ii) reaction; (iii) waste separation; and, (iv) product separation.

Figure 2. The generic process block model


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, , ,

(1)

, ,, ∗ , ∗ ,

(2)

, , ∗ ∑ , ∗ , ∗ , /

(3)

, 1 ! ", ∗ ,

(4)

#$%, ", ∗ ,

(5)

&'$1, "()$, ∗ ,

(6)

&'$2, 1 ! "()$, ∗ ,

(7)

1,, + ", -,-- ∗ &'$1,

(8)

2,, + ", -,-- ! ", ∗ &'$1,

(9)

in, ∑ 1,, 2,,

(10)

The above equations 1-7 are the equations used for the generic block to estimate the outlet mass flow (&'$1, , &'$2, ) using simple mass balances. In Equations 1-2, the chemicals and utilities used (, ) for each processing technology are calculated by using the ratio ( ,, ) to the inlet mass flow rate ( , ). The parameter , represents the consumption of the utilities or chemicals: 0 corresponds to 100% consumption; 1 represents no consumption. In Equation 3, the reaction outlet mass stream (, ) is calculated based on stoichiometry, , and conversion fraction, , . In Equations 4-5, the waste stream (#$%, ) and the remaining stream (, ) are calculated on the basis of the removal fraction, ", .The product outlet streams are calculated in Equations 6-7 on the basis of a product separation fraction, "()$, . Moreover, in order to connect each generic block and thereby formulate the superstructure, the equations 8-10 are used. The mass outlet flows mentioned earlier (&'$1, , &'$2, ) are called primary and secondary outlet flow, respectively. The primary and


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secondary outlet flows are connected to the next generic blocks using binary variables ", , "), respectively. The outlet flows between the generic blocks (1,, , 2,, ) of each stream (primary and secondary) are summed up as the input of the next generic block. Note as well that recycle flows can be considered using Equations 8-10. There are two potential cases of recycle flows addressed: (i) recycle flows within the same processing step, i.e. internal recirculation - the simulation of the recycle flows and their impact on process performance needs to be done prior to estimating the parameter values for the corresponding generic model block (e.g. processing step 2, 4); and (ii) recycle flows to the previous processing step, which is handled by using Eq. 89 . The appropriate values for the above mentioned parameters can be collected in several ways including: (i) literature sources or technical reports; (ii) experimental data; (iii) simulation results; or, (iv) stream table or operating data of a designed flowsheet. The collected data are in the end organized in a multidimensional matrix form which represents processsing steps, alternatives, components, among others etc.

Step 4: Models and data verification After the superstructure is defined and the parameters are collected, a validation of the selected models and parameters needs to be performed for quality and consistency check. The validation can be performed in this step by fixing the decision variables in the MINLP problem formulation – i.e. the vector y (see below) – and thereby to perform a simulation for each processing technology or path followed by comparison of the simulation results against the available data. Such data can originate either from experiments or from the literature. All the necessary equations and constraints relevant to each processing technology are also formulated in this step prior to be solved as MILP or MINLP problems in GAMS.


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The output of this step is a verified database representing the biorefinery superstructure formulated in step 2 and stored in an excel worksheet. To further highlight the use of this database, a simple optimization problem (MINLP) is briefly presented below to indicate how the generic models and parameters are embedded in the optimization problem formulation. The optimization formulation is presented in Eq. 11-16 which consists of the objective function (e.g. total annualized costs, Eq.11) subjected to process constraints, the process models and constraints (Eq.1-10) of the generic block mentioned earlier (0 is a process variable, the mass flow rate), structural constraints (Eq. 12 ! 13) representing the superstructure which allows selection of only one process alternative in each step and cost functions (eq. 14-16) to calculate the operating and capital costs using cost parameters (5167 , , waste treatment cost, 897/:;97

52,

, utility or chemicals cost, 53 , reactor investment cost, 53< , separation

investment cost, =#(%0 , capital expentidure).

As an example, the objective function is formulated such as to minimize total annualized costs (TAC), >?@ ∑ >5AB ! CD5ABE CD5ABF /$

(11)

Subject to: (i)

Process models of the generic block GH ,, , , , , , , , , ", , "()$, I 0 ,as mentioned earlier, eq. 17 and 10.

(ii)

Process constraints (gH", -,-- , ", I + 0 ,as mentioned earlier, eq. 8-9.

(iii)

Structural constraints,


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∑ K + 1

(12)

K ∈ M0; 1OP

(13)

(iv)

Cost constraints, 897/:;97

>5AB 52,

67 ∙ , 5167 ∙ , ,

CD5AB:;R:; ∑ =#(%0

TE TF CD5AB CO2 + H2

- O2 to biomass ratio - steam to %moist ratio

Bubbling fluidized bed gasifier

7

1

1

0.35 0.25

0.3

1

1

-

-

-

- H2, N2, CO, CH4 removal - H2O removal

-

-

-

-

-

1

0.26

0.4

-

- H2O, N2, CO2, NH3, H2S removal - COS removal - NH3, TAR, Ash, Char, H2O removal - H2S removal - CO2 removal H2O removal H2S removal CO2 removal - NH3, H2O, TAR, Ash, Char removal - H2S removal - CO2 removal

Ash, char removal

Ash, char removal

Ash, char removal

Ash removal

Ash, char removal

Ash, soot removal

1

Pyro-oil + 0.014H2O + 0.027H2 -> 0.003CO2 + 0.003CH4 +0.002gasoline +0.0012diesel

Pyro-oil + 0.02H2O + 0.01O2 -> 0.02CO2 + 0.0014gasoline + 0.0007diesel

CO + 1.2H2 -> 0.07H2O + 0.07CO2 + 0.1CH4 + 0.1CH4O + 0.2C2H6O + 0.017C3H8O

CO + H2 + 0.006H2O -> 0.36CO2 + 0.06CH4 + 0.04CH4O + 0.24C2H6O + 0.026C3H8O

CO + 2.1H2 -> 10.8C1 + 9.8C2 + 8.8C3 + 7.9C4 + 7.1C5 + 6.4C6 + 5.7C7 + 5.2C8 + 4.6C9 + 4.2C10 + 3.7C11 + 3.4C12 + 3C13 + 2.7C14 + 2.5C15 + 2.2C16 + 2C17 + 1.8C18 + 1.6C19 + 1.5C20 + 0.4 13Wax + H2O

TAR + 0.8CH4 + 0.02C2H6 + 0.14C2H4 + 0.8O2 + 0.2CH4O -> 2.5H2 + 1.3CO + 0.5CO2 + 0.1H2O

TAR + 0.7CH4 + 0.015C2H6 + 0.15C2H4 + O2 -> 1.5H2 + CO + 0.3CO2 + 0.4H2O

- CH4 + 0.1C2H6 + 0.2C2H4 + 1.6H2O -> 4.4H2 + 1.6CO - CO + H2O -> CO2 + H2

1

C + 0.11H2O + 0.34H2 + 0.5O2 + 0.002S + 0.007N2 '-> 0.36CO + 0.41CO2 + 0.002H2S + 0.01NH3 + 0.08CH4 + 0.01C2H6 + 0.02C2H4 + 1.2CHAR

1

1

C + 0.13H2O + 0.6O2 + 0.0017S '-> 0.13H2 + 0.0007N2 + 0.66CO + 0.34CO2 + 0.002H2S + 0.07SOOT + 1.3SLAG

Moisture removal

Moisture removal

Wax + 5.4H2 + 0.5C5 + 0.48C6 + 0.44C7 + 0.35C9 + 0.32C10 + 0.28C11 + 0.25C12 0.0257 + 0.23C13 + 0.2C14 + 0.18C15 + 0.15C17 + 0.14C18 + 0.12C19 + 0.11C20 1 -> 2Gasoline + 2.76Diesel

0.05

1.87 0.37

1

1.2

0.36

0.14

0.22 C + 0.47H2 + 0.04H2O + 0.4O2 + 0.0006S + 0.001N2 0.2 '-> 0.19CO + 0.39CO2 + 0.0006H2S + 0.002NH3 + 0.18CH4 + 0.01C2H6 + 0.003 0.013C6H6 + 0.55TAR + CHAR 0.004

C + 0.5O2 + 0.7H2 + 0.45ASH + -0.0001S -> 1.4H2O + 0.002CO + 0.25CO2 + 15PYRO-OIL

0.26 0.17 2.2 C + 0.48H2 + 0.6O2 + 0.0006S + 0.001N2 0.38 '-> 0.37CO + 0.39CO2 + 0.12H2O + 0.0006H2S + 0.002NH3 + 0.13CH4 + 0.02C2H6 1 0.003 + 0.03C2H4 + 0.23TAR 0.004

C + 0.3O2 + 0.6H2 + 0.007N2 + 1.5ASH + -0.001S '-> 0.05H2O + 0.05CO + 0.07CO2 + 0.01CH4 + 4.5CHAR + 15PYRO-OIL

0.35 0.48

- O2 to biomass ratio - steam to %moist ratio

Entrained-flow (free-fall) gasifier

-

6

-

4

Hammermill and rotary dryer - Electricity to biomass ratio (directly-contact with hot flue - waste heat from process gas)

-

.

, Waste separation

5

-

Reaction (stoichiometry, , .)

4 5.9

,

4

Mixing ( , 1)

Hammer mill, rotary dryer - Electricity to biomass ratio (indirectly-contact with steam) - steam to %moist ratio

No. Description

Waste heat

Steam

Product separation (in primary outlet)

- Ar, O2, N2 in primary outlet - H2O in primary outlet - CO2 in primary outlet

1 0.65 0.6

0.8

1

-

-

-

-

-

1

1

0 0.6 0.4

0 0.5

1

- CH4O in primary outlet - C2H6O in primary outlet - Higher alcohols in primary outlet

H2O, H2, CO, CO2 in primary outlet, light hydrocarbon

H2O, O2, CO2, CH4 in primary outlet

H2O, O2, CO2 in primary outlet

0.07 0.99 0.05

0

0

0

- H2, N2, CO, CH4 in primary outlet 0.01 - H2O, alcohols in primary outlet 1 - CO2 in primary outlet 0.08

One outlet stream

One outlet stream

- Ar, N2 in primary outlet - H2O, CO2 in primary outlet

0.98 0.8 0.6

-

One outlet stream

0.99 0.95 0.9

1

0 0.15 0.01

- H2, O2, N2, CO2 - H2O - CO One outlet stream

0 0.63

0.6

0.75 0.25 0

1

1

0

0

- H2, O2, N2, CO, CO2, CH4 - H2O

Two product stream (same component)

1 0.7

1

0.85

0.999

- H2O in primary outlet 0.999 - CO2 in primary outlet - O2, N2, Ar in primary outlet

0.999 One outlet stream

0.999 One outlet stream

0.96

0.6

",

"()$,

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Table 5. Summary table for the data collection (mixing, , , , , reaction, , , ., , , waste, ", , and product, "()$, , separation) for thermochemical processing networks

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Another important aspect to consider when collecting data is that there are uncertainties which could be related to technical, economical and environmental parameters. It is important therefore to address uncertainties in data, which is needed for making decisions under uncertainty when applying the computer-aided synthesis and design approach.14 In order to exploit this feature, sources of uncertainties in the data need to be identified and characterized. In this study, the feedstock cost and the product price are considered to have significant uncertainty associated with their reported range. After identifying the uncertain parameters, data were then collected for statistical analysis. For estimating the uncertainty on product prices namely gasoline, diesel and ethanol prices, historical data (year-2012) have been used.19-20 The historical data were statistically analyzed using the Matlab statistical toolbox which returned the correlation matrix (given in Table 6b) as well as empirical distribution functions (shown in Figure 6). Based on the empirical distribution function, a uniform distribution is selected to be appropriate to describe the uncertainty range for these data together with upper and lower range as reported in Table 6a.. For the characterization of uncertainty on the feedstocks, as no historical data was available for these, instead, the open literature was reviewed to find out lower and upper bound. and reported in Table 6a. Further, a uniform distribution was assumed for these parameters, which is common practice in the uncertainty and sensitivity analysis field to use non-informative priors in case of no data availability.8,17


20

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Table 6a. Input uncertainty for feedstocks and products Input uncertainty

Min.

Max.

References

Corn stover cost ($/dry ton)

60

100

NREL18

Wood cost ($/dry ton)

60

100

NREL5

Mean

Std.

References

Gasoline price ($/gal)

3.53

0.21

U.S. EIA

Diesel price ($/gal)

3.97

0.14

U.S. EIA

Ethanol price ($/gal)

2.24

0.18

U.S. Department of Agriculture

Table 6b. Correlation matrix between uncertain data.19-20 Correlation matrix

Stover

Wood

Gasoline

Diesel

Ethanol

cost

cost

price

price

price

Stover cost

1

0

0

0

0

Wood cost

0

1

0

0

0

Gasoline price

0

0

1

0.71

0.12

Diesel price

0

0

0.71

1

0.36

Ethanol price

0

0

0.12

0.36

1


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Figure 6. Fuel price ($/gal) in 2012 and the corresponding probability density function for gasoline (top), diesel (middle) and ethanol (bottom).


22

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Step 4: Models and data verification Seven processing paths based on five NREL reports5-6,12,18,23 and a PNNL report9 were used to validate the models and data used for each process interval and processing path. As explained in section 2.4, the verification can be performed by fixing the processing path and comparing the simulation results with the NREL and PNNL studies. Table 7 summarizes the short descriptions, processing paths and the amount of biofuel products generated for each of the seven base cases used in this study. The simulation results of each processing path were validated by comparing with the detailed results published in NREL-PNNL reports.

Table 7. The seven processing paths used as base cases.

Cases 1 2 3 4 5 6 7

Descriptions Corn stover-entrained flow gasifier-hot gas cleaning-Fischer Tropsch18 Corn stover-fluidized bed gasifier-cold gas cleaning- Fischer Tropsch18 Wood-fluidized bed gasifier-tar reformer-alcohol synthesis12 Wood-fluidized bed gasifier-tar reformer-alcohol synthesis6 Wood-fluidized bed gasifier-tar reformer-alcohol synthesis5 Corn stover-fast pyrolysis23 Wood-fast pyrolysis9

Processing path

Biofuels production (tpd)

1 4 6 12 16 21 83 84

111a, 262b

1 4 7 13 16 21 83 84

87a, 206b

2 5 8 14 17 22 85 91

429c

2 5 9 14 17 22 85 91

526c

2 5 8 15 18 22 85 91

549c

1 4 10 19 83 84 2 5 11 20 83 84

160a, 160b 245a, 311b

a

FT-gasoline, bFT-diesel, cbio-ethanol

The verification between the reported results from NREL-PNNL reports and the simulation results of this study (implemented in GAMS) is necessary in order to validate the quality of the collected data and the models used in this study. In the previous section, the data collection was presented as examples for (i) the entrained-flow gasifier and (ii) gas cleaning and conditioning processes. Here, the collected data for both examples are validated and presented in Table 8 and


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9, respectively. The validation results confirm that the quality of the collected data is good and the data are consistent. The full simulation results (implemented in GAMS) can be found in Supporting Information, in Tables S1-S7.

Table 8. Summary of the validation results for the entrained flow gasifier.

Inlet flow Total (tpd) 2222.22 222.22 H2O 101.2 H2 812.6 O2 16 N2 4.4 S 945.6 C 120 ASH CO CO2 H2S NH3 COS AR CH4 SLAG SOOT CHAR

The reported results from NREL The simulation results of this report18 study R(i) waste(i) Fout1 Fout2 R(i) waste(i) Fout1 Fout2 1704 106 3819 0 1704 106 3818 0 960 988.4 960 988 122.8 123 700 0 700 0.1 17.7 17.7 0 0.1 0 0.1 0 0.1 1457 1457 1184 1184 4.5 4.5 0.1 0.3 43.7 43.7 43.7 43.7 100 6

100 6


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Table 9. Summary of the validation results for the gas cleaning and conditioning step of case 3: tar reformer, water scubber and acid removal.12

Inlet flow Total (tpd) H2O H2

2534

2302

3123

1089

3063

3940

2302

3123

1089 3063 3940

901.3 37.7

68.8

60.9

514.7

9.2 168.7

604

68.8

60.9

515

43.1

708.6

86.4

43.1

708.6

O2 N2 CO CO2 H2S NH3 TAR COS AR CH4 C2H6 C2H4 C2H2 C6H6 C3 C4 C5 C1-ol C2-ol C3-ol

The reported results from NREL The simulation results of this study report waste waste Fout Recycle R(i) Fout1 Fout2 Recycle R(i) Fout1 (i) (i) 2

2313 874.3 408 1.8 3.9 19.8

903.6 1153.8

180.5 5.2 86.8 8.2 6.6

84.9 3.5 6.6 0.6 17.4 3.2 0.6 4.3 4.3 11.6

1.5

45.4

2315

2346 903.6 572.7 405.3 895.6 1153.8 1.3

9.2 168.7

604 86

2313

45.4

2315

1.5

2346 405.3

896

573 1.3

0.1 39.4

39.4 43.0 0.1 6.9 0.7 17.4 3.2 0.6 4.3 11.6 17.4

39.4 84.9 3.5 6.6 0.6 17.4 3.2 0.6 4.3 4.3 11.6

39.4 43 0.1 6.9 0.7 0.1 17.4 3.2 0.6 4.3 11.6 17.4

The expanded network provides an expanded space for optimization studies meaning that it can generate more scenarios to compare a large number of processing alternatives before generating an optimal decision for biorefinery designs.


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Since the problem of optimal biorefinery design is data intensive with several categories of data (thermodynamic properties, kinetics, operating conditions or processing technologies), it is therefore important to organize the data in a compact and generic way. This is achieved by defining and using a generic process block model. In this way, it becomes relatively easy to collect and summarize different types of data (kinetics, experimental data, thermodynamics properties, simulation results, operating conditions, etc.) from many resources (literatures, reports, etc.) following the generic data structure. Indeed, the generic model reduces the data needs to six parameters representing mixing ( ,, , , ), reaction (, , , ), waste (", ) and product separation ("()$, ), which are obtained from experimental and rigorous simulation studies reported. Secondly, the resulting database and its structure can be used for cross-checking and validating data. Of course, availability of informative data resources is important, also with the use of the generic model blocks, since the quality of the results strongly depends on the quality of the input data. In this study, therefore, peer-reviewed sources and reports from national and renowned institutes such as NREL-PNNL studies were used for several reasons:(1) the data are in general considered to be objective and of high quality as the data source (i.e. NREL-PNNL) confirms to quality check and assurance and remains impartial to technology developers; (2) the studies are easily accessible through public resources (open literature, books, reports); and, (3) the commercial technologies together their improved process designs (heat integration, technoeconomic analysis, etc.) are represented in the alternatives. For these reasons, the superstructure defined and data collected represents a technically realistic and validated database of biorefinery relevant processing technologies. The database can be accessed from the following link:e (http://www.capec.kt.dtu.dk/documents/biorefinery/InputData_biorefinery_for_public.xls).


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The database also features an option to include uncertainties for data by defining an appropriate statistical distribution function together with their parameters (e.g. lower and upper bounds for uniform distribution). This provides a means to assess quality of the data source – the larger the uncertainty, the lower the reliability of the data hence the performance of the included technical alternatives. In this study, we considered raw material costs and product prices to be major sources of uncertainty and provided a corresponding uncertainty characterization. Such uncertainty information is valuable for making robust decisions as discussed elsewhere.14 The database will be maintained and expanded with more biorefinery relevant technology development efforts to keep it up-to-date and use it in our research for identifying optimal biorefinery concepts with respect to technical, economic and environmental objectives using computer-aided synthesis and design toolbox.

CONCLUSIONS AND FUTURE WORK The development of a superstructure and a database for thermochemical conversion and its integration with a biochemical conversion route were presented. The intensive data requirement of the biorefinery network design problem was addressed by using a structured and generic model to represent process alternatives. The structured and generic approach is important to manage and check the quality and consistency of multidimensional data. In the future, the database will be maintained and expanded with more biorefinery pathways and process alternatives, and will be used to perform multi-criteria evaluation to identify optimal biorefinery concepts under various applications and optimization scenarios including sustainability metrics. The biorefinery database features also characterization of important sources of uncertainties in


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data, which is valuable for assessing risk associated with biorefinery design as well as supporting risk-based decision making during early project planning/development stages.

SUPPORTING INFORMATION The summary table of the simulation results (7 cases) verified by NREL technical reports. This information is available free of charge via the Internet at http://pubs.acs.org/.

ABBREVIATIONS Indexes , component; o, component; p, process interval (origin); pp, process interval (destination); %#=$, key reactact; , reaction number;

Parameters

,, , ratio of utility or chemical mixed with inlet process stream; , , specific consumption of utility or chemical; , molecular weight; , , reaction stoichiometry; , , conversion fraction; ", , waste separation fraction; "()$, , fraction of component in primary outlet 897/:;97

stream; 5167 , ,waste treament cost; 52,

, utility or chemicals costs; 53 , 1 ,

investment cost parameter for reactor; 53 < , 2, investment cost parameter for separation; ", -,-- , superstructure split factor for primary outlet; , ", , superstructure split factor for all outlets; >5AB , operating cost ; CD5AB , capital cost.

Variables


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


, , component flow of inlet process stream; , , component flow of utility or chemical; , , component flow after mixing; , , component flow after reaction; , , component flow after waste separation; #$%, , component flow of waste stream; &'$1, , component flow of primary outlet stream; &'$2, , component flow of secondary outlet stream.

REFERENCES (1)

Baliban, R.C.; Elia, J.A.; Weekman, V., Floudas, C.A. Process synthesis of hybrid coal,

biomass, and natural gas to liquids via Fischer–Tropsch synthesis, ZSM-5 catalytic conversion, methanol synthesis, methanol-to-gasoline, and methanol-to-olefins/distillate technologies. Computers & Chemical Engineering. 2012, 47, 29-56. (2)

Baliban, R.C.; Elia, J.A.; Floudas, C.A. Optimization framework for the simultaneous

process synthesis, heat and power integration of a thermochemical hybrid biomass, coal, and natural gas facility. Computers & Chemical Engineering. 2011, 35 (9), 1647-1690. (3)

Bauen, A.; Berndes, G.; Junginger, M.; Londo, M.; Vuille, F. Bioenergy – A sustainable

and reliable energy source: a review of status and prospects; IEA BIOENERGY: ExCo: 06, 2009. (4)

Cheali, P.; Gernaey, K. V.; Sin, G. Synthesis and design of optimal biorefinery using an

expanded network with thermochemical and biochemical biomass conversion platforms, Proceedings of the 23rd European Symposium on Computer Aided Process Engineering. 2013. (5)

Dutta, A.; Talmadge, M.; Hensley, J.; Worley, M.; Dudgeon, D.; Barton, D.;

Groenendijk, P.; Ferrari, D.; Stears, B.; Searcy, E. M.; Wright, C. T.; Hess, J. R. Process Design


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and Economics for Conversion of Lignocellulosic Biomass to Ethanol; NREL Technical Report No, NREL/TP-5100-51400. 2011. (6)

Dutta, A.; Phillips, S. D. Thermochemical Ethanol via Direct Gasification and Mixed

Alcohol Synthesis of Lignocellulosic Biomass; NREL Technical Report No. TP-510-45913. 2009. (7)

Foust, T. D.; Aden, A.; Dutta, A; Phillips, S. An economic and environment comparison

of a biochemical and a themochemical lignocellulosic ethanol conversion processes. Cellulose. 2009, 16, 547-565. (8)

Helton, J.C.; Davis, F.J. Latin Hypercube Sampling and the Propagation of Uncertainty in

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Jones, SB.; Valkenburg, C.; Walton, CW.; Elliott, DC. Production of gasoline and diesel

from biomass via fast pyrolysis, hydrotreating and hydrocracking: a design case. U.S. Department of Energy, Pacific Northwest National Laboratory, PNNL-18284. 2009. (10) King, D., Hagan, A., Löffler, K., Gillman, N., Weihe, U., Oertel, S. The Future of Industrial Biorefineries. World Economic Forum, REF: 210610. 2010. (11) Pham, V.; El-Halwagi, M. Process Synthesis and Optimization of Biorefinery Configurations. AIChE Journal. 2012, 58 (4), 1212–1221. (12) Phillips, S.; Aden, A.; Jechura, J.; Dayton, D.; Eggeman, T. Thermochemical Ethanol via Indirect Gasification and MixedAlcohol Synthesis of Lignocellulosic Biomass. NREL Technical Report No. NREL/TP-510-41168. 2007.


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(13) Quaglia, A.; Sarup, B.; Sin, G.; Gani, R. Design of a Generic and Flexible Data Structure for Efficient Formulation of Large Scale Network Problems. Computer Aided Process Engineering – ESCAPE 23 2013a. (14) Quaglia, A.; Sarup, B.; Sin, G.; Gani, R. A systematic framework for enterprise-wide optimization: Synthesis and design of processing networks under uncertainty. Computers and Chemical Engineering. 2013b, http://dx.doi.org/10.1016/j.compchemeng.2013.03.018 (15) Quaglia, A.; Sarup, B.; Sin, G.; Gani, R. Integrated Business and Engineering Framework for Synthesis and Design of Enterprise-Wide Processing Networks. Computers and Chemical Engineering. 2012, 38, 213-223. (16) Sammons Jr., N.E.; Yuan, W.; Eden, M.R.; Aksoy, B.; Cullinan, H.T. Optimal biorefinery product allocation by combining process and economic modeling. Chemical Engineering Research and Design. 2008, 86, 800-808. (17) Sin, G.; Gernaey, K.V.; Neumann, M.B.; van Loosdrecht M.C.M; Gujer, W. Global sensitivity analysis in WWTP model applications: a critical discussion using an example from design. Water Research. 2011, 45, 639-651. (18) Swanson, R. M.; Satrio, J. A.; Brown, R. C.; Platon, A.; Hsu, D. D. Techno-Economic Analysis of Biofuels Production Based on Gasification. NREL Technical Report No. NREL/TP6A20-46587. 2010. (19) United States Department of Agriculture (USDA) website. http://www.ams.usda.gov/AMSv1.0/LPSMarketNewsPage.


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(20) United States Energy Information Administration (EIA) website. http://www.eia.gov/petroleum/gasdiesel/. (21) Voll, A.; Marquardt, W. Reaction network flux analysis: Optimization-based evaluation of reaction pathways for biorenewables processing. AIChE Journal. 2012, 58 (6), 1788–1801. (22) Voll, A.; Marquardt, W. Benchmarking of next-generation biofuels from a process perspective. Biofuels, Bioproducts and Biorefining. 2012, 6 (3), 292–301. (23) Wright, M. M.; Satrio, J. A.; Brown, R. C.; Daugaad, D. E.; Hsu, D. D. Techno-Economic Analysis of Biomass Fast Pyrolysis to Transportation Fuels. NREL Technical Report No. NREL/TP-6A20-46586. 2010. (24) Zondervan, E.; Nawaz, M.; de Haan, A. B.; Woodley, J.; Gani, R. Optimal Design of a multi-product biorefinery system, Computers and Chemical Engineering. 2011, 35, 1752-1766.


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For Table of Contents Use Only The manuscript title: “Towards a computer-aided synthesis and design of biorefinery networks – data collection and management using a generic modeling approach” Names of authors: Peam Cheali, Krist V. Gernaey and Gürkan Sin A brief synopsis: The collection and management of the multidisciplinary and multidimensional biorefinery data which are needed among others to support the superstructure based optimization studies are presented.

Feedstock

Biomass corn stover, wood

Data management ∙ Collection ∙ Estimation ∙ Verification

Output ∙ Verified database for process synthesis of biorefinery ∙ The extended biorefinery networks

Output

Biorefinery ∙ Data (multidimensional, multidisciplinary) ∙ Models (nonlinear, nonconvex)

Input

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Conversion platforms

Products

Thermochemical (pyrolysisgasification)

Fuels Ethanol, Butanol, Gasoline, Biodiesel

Biochemical (hydrolysisfermentation)

Chemicals Succinic acid, Acetone

Figure. Graphical abstract


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Toward a Computer-Aided Synthesis and Design ... - ACS Publications

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