Life Cycle Assessment of Biobased p-Xylene Production - Industrial

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Life cycle assessment of biobased p-xylene production Zhaojia Lin, Vladimiros Nikolakis, and Marianthi G Ierapetritou Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/ie5037287 • Publication Date (Web): 12 Feb 2015 Downloaded from http://pubs.acs.org on February 18, 2015

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Industrial & Engineering Chemistry Research

Life cycle assessment of biobased p-xylene production Zhaojia Lin1, Vladimiros Nikolakis2, Marianthi Ierapetritou1,* 1 Department of Chemical and Biochemical Engineering, Rutgers - The State University of New Jersey, 98 Brett Road, Piscataway, New Jersey 08854-8058, United States 2 Catalysis Center for Energy Innovation & Department of Chemical & Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States KEYWORDS p-xylene, biomass, starch, glucose, heat integration, life cycle assessment

ABSTRACT

A general framework implementing process design, simulation, heat integration and Life Cycle Assessment (LCA) is illustrated to develop a sustainable route, which is particularly exploited to evaluate the environmental impacts of the p-xylene production from both generation biomass feedstocks. Noticeably, the lignocellulose-based p-xylene is comparable with the petroleumbased p-xylene whilst the starch-based p-xylene appears less environmentally friendly. In the latter case the cultivation and processing of maize starch and heating requirements dominate total environmental impacts. The main contributions for the lignocellulose-based p-xylene arise from the cultivation of biomass and from the large requirements of non-renewable chemicals (i.e. the 1

ACS Paragon Plus Environment


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makeup solvent (THF) and ethylene). Sensitivity analysis indicates high selectivity is also favored to achieve better environmental performance while the impacts of conversion are negligible and discovers a large variance from different biomass feedstocks. The uncertainties caused by the assumptions and developing technologies are assessed by uncertainty analysis.

INTRODUCTION Uncertainties about the availability of oil reservoirs, and petroleum price as well as rising environmental concerns have prompted the development of renewable energy sources, especially biomass that can be the source of both high-volume- low-value fuels, and high-value chemicals that are currently made from petroleum resources [1]. In addition, biomass can be beneficial to the environment, not only due to the reduction of CO2, sulphur and heavy metal emissions in the atmosphere but also by diminishing the environmental damage due to the fossil fuels and chemical production, leading to a sustainable production process. The biobased products will be more easily accepted in the market place if they can successfully compete with petroleum-based products in terms of economics, reliability and sustainability [2]. The idea of integrated biorefinery has been presented to provide a solution to employ various combinations of feedstock and conversion technologies to produce a variety of products, including biofuels, chemicals, animal feed, heat and power [3]. The U.S. Department of Energy has projected that biobased fuels and chemicals in the year 2030 will contribute to 20% of US transportation fuel and 25% of the production of US commodities, respectively, in comparison to 0.5% and 5% in 2001 [4]. Among biobased products, p-xylene (pX) has recently drawn considerable attention because it is the principal precursor to polyester polyethylene terephthalate (PET), a polymer resin broadly 2


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used in the synthesis of fibers, films, and beverage containers [5]. Several companies, such as Coca Cola, Pepsi and Procter & Gamble, have launched projects towards the utilization of biobased PET [6-8]. In our recent techno-economic analysis we found that the minimum cost of pxylene from starch is higher than that of the oil-based p-xylene [9, 10]. Even though a higher cost is not ideal, it can be tolerated if the biobased process is more environmentally benign. We expect that the cost can further be reduced via combination of new scientific developments and process synthesis, integration and optimization. However, it is important to evaluate environmental impacts and eventually to achieve an optimal balance between it and process economics. This work focuses on the use of LCA to evaluate the p-xylene production described in [9, 10] based on the discoveries of Catalysis Center of Energy Innovation (CCEI). Just to briefly summarize the CCEI-pX production involves three stages. The first stage is the conversion of starch to HMF, which involves first the saccharification of starch into glucose followed by the isomerization of glucose to fructose, and finally the dehydration of fructose to HMF using a biphasic reaction [11]. The next stage is the hydrodeoxygenation of HMF to 2,5dimethylfuran (DMF) [12-14]. At the last stage, p-xylene is produced by the dehydration of the intermediate formed from DMF and ethylene via a Diels-Alder cycloaddition reaction [15]. Given that the main driver for using biomass as an alternative source is sustainability, a large number of the existing studies have implemented LCA to quantify the environmental impacts and evaluate sustainability of the biobased chemical production processes [16]. Based on the definition by the International Organization for Standardization (ISO), LCA provides a systematic procedure designed to compile inventories of energy and material inputs and environment releases, and to assess the overall environmental impacts of a product or a process associated with economic, social and ecological impacts within the defined domain such as from3



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cradle-to-grave, from-cradle-to-gate and from-gate-to-gate [16]. The majority of previous biomass-related LCA studies have been performed on power-generation systems and on biofuels production [16-21]. In comparison, few studies have focused on the production of biobased chemicals [22, 23]. This work will explore environmental evaluation on biobased chemicals particularly pX and contribute to some extent on the assessment of biorefinery development. The existing LCA studies on biofuel production lead to controversial results indicating that such production is not always benign to the environment. Although bioethanol production reduces resource use and global warming, the impacts on acidification, human toxicity and ecological toxicity occurring mainly during the growth and processing of biomass are often unfavorable [21]. Therefore it is crucial to evaluate the environmental merits of the biobased products in order to assess their sustainability comprehensively. In our previous work [10], the production of pX started with corn-starch which is among the first generation biomass feedstock. However, previous studies showed that the first generation bio-ethanol (starch-based ethanol) is not as an environmentally friendly attractive alternative due to the large values of water and energy consumption in the biomass conversion [24]. The firstgeneration bio-ethanol (such as sugarcane in Brazil and corn in US) has also faced issues with low capacity of biomass feedstocks, and ethical problems related to the competition with raw materials use for food and with the land use devoted to the production of those raw materials [25, 26]. In comparison bio-ethanol from second-generation biomass feedstocks (i.e. lignocellulose) performs much better than the first generation biomass feedstock in all the impact categories and has more advantages than the petroleum-based ethanol at some categories. In addition, the second-generation lignocellulose including agricultural, industrial and forest residuals account for the majority of total biomass present in the world. Therefore the pX 4


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production from the second generation lignocellulose biomass feedstock is also studied in this work with the assumption that the yield from lignocellulose-derived glucose feedstock is the same as using starch-derived glucose to ensure the usage of the same conversion and separation units. The aim of this work is to investigate environmental impacts of the biobased CCEI-pX production to improve the process flowsheets towards more sustainable process and to compare with the petroleum-based production. The remainder of the manuscript is structured as follows. Initially the methodolgy used to perform the assessment are explained, followed by the detailed descriptions of four stages in LCA studies. In the results and discussion, the results of heat integration and process modification are presented and the LCA results are then discussed and analyzed for the scenarios based on first- or second- generation biomass feedstocks. Sensitivity analysis is used to address the impacts of variances of reaction conversion and selectivity and different environmental burden from different second-generation biomass feedstocks. Uncertainty analysis is then performed to address the process uncertainties. METHODOLOGY The general framework As a useful tool to analyze the environmental impacts of products and processes at all stages in their life cycle, LCA is applied to identify and quantify the environmental impacts and to guide the improvement of process flowsheet. It is noted from our preliminary LCA studies that energy consumption plays an important role in environmental impact although from an economic point of view utility cost accounts for a small fraction of operating cost [10]. Therefore heat integration is incorporated in this work with the processes developed in [9, 10]. The overall framework we used in our work is shown in Figure 1. The process is initially designed and simulated based on 5



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lab-scale experimental data and information found in the literature. The simulation is performed using Aspen Plus® which is a widely used simulation software. Then the process is evaluated by Aspen Economic Analyzer®, as illustrated in our previous work [9, 10]. In this work the process stream results from the simulations are transferred into Aspen Energy Analyzer® to design and improve the heat exchanger network (HEN). The optimal HEN design is then integrated in the original process flowsheet developed in Aspen Plus®. In the following stage, the flow streams and the energy consumptions are translated into Life Cycle Inventory (LCI) and subsequently utilized to perform the preliminary LCA using the SimaPro® software. The preliminary results are then employed to guide the modification of the process design to improve the sustainability of the system. Based on the results of the modified process, all the steps are then repeated to arrive to a more sustainable and energy efficient production platform. If additional modifications are identified the entire steps can be repeated.

Economic analysis

Economics

Flow rates/ Operation units/ Utility rates

Process Design and Simulation

modification

Stream results Heat integration HEN

Inventory information

modification

Life cycle assessment

Environmental impacts

Figure 1. The overall framework for the design of an economic, sustainable and energy efficient process flowsheet 6


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Process simulation Description of pX production process The previously developed pX production flowsheet (see Figure 3 of publication [10]) is briefly described here. The starch is hydrolyzed into glucose in an aqueous HCl solution at reactor (R1). The product stream is then mixed with the solvent tetrahydrofuran (THF) and NaCl (necessary to generate a two liquid phase system with enhanced HMF partitioning to the organic phase) to produce HMF. At reactor (R2) glucose is catalytically converted to fructose by zeolite Sn-Beta, followed by fructose dehydration using Brønsted acid (HCl). The organic phase is fed to flash (FL1) to evaporate the excess THF to form a 10 wt% HMF solution in THF. The THF from the top of FL1 is fed to a distillation column (DC1) to remove formic acid and recycle the THF. Most of HCl stays in the aqueous phase and the rest goes with the THF solution. The solid byproduct – humins present in the aqueous phase is removed from the R2 using the filter. At the next stage the vapor-phase hydrodeoxygeneration of HMF to DMF using Cu-Ru/C catalyst occurs. Unreacted H2 is recycled using a flash (FL2). A distillation column (DC2) and a flash (FL4) are employed to recover and recycle THF to R2 from the CO2 which is formed by the formic acid degradation. Another two distillation columns (DC3) and (DC4) are then used to purify DMF, to recycle the intermediate and to separate the byproduct. At the last stage, the purified DMF is converted to pX with ethylene in the presence of the solvent n-heptane and ntridecane. Water is removed from the product stream in a decanter (D). Then the solvent is separated and recycled using a distillation column (DC5) and finally pX is separated from the other byproducts using another distillation column (DC6). Description of hydrolysis

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Due to cellulose recalcitrance to hydrolysis it is more difficult to obtain glucose from lignocellulose than starch unless harsher conditions are used. The four most-widely-studied types of lignocellulose saccharification methods include concentrated acid, dilute acid, ionic liquid and enzymatic process [27]. Concentrated acid saccharification hydrolyzes hemicellulose and cellulose at the same time with relatively mild temperature and with nearly theoretical yield of sugars [28]. The main drawbacks of this method are the acid corrosion of equipment, the recovery and re-concentrating of sulfuric acid and the extra steps required to separate C5 and C6 sugars which are produced simultaneously thus has not been considered further in this work [29]. The direct hydrolysis of lignocellulosic biomass in ionic liquids is not considered in this work either because of the issues of high costs of materials of ionic liquid and the subsequent separation and recovery [27]. The two-stage enzymatic saccharification starts with an initial alkaline deacetylation step followed by dilute acid hydrolysis to liberate hemicellulose sugars and an enzymatic hydrolysis step that breaks down cellulose to glucose [30]. The enzymatic saccharification can achieve similar high yield as concentrated acid hydrolysis in several steps. Although the enzymatic hydrolysis has higher overall yield of glucose (74% vs. 57% of dilute acid), more operating units and enzymatic conversion involved that offset the benefits of high yield. In addition, from our preliminary study the environmental impacts of enzymatic hydrolysis and dilute acid hydrolysis are close. Furthermore, in the dilute acid hydrolysis process it is easy to separate the hemicellulosic from the cellulosic sugar fractions using fewer units and simpler operation although it has the drawbacks of higher reaction temperature and lower overall yield as well as issues with equipment corrosion [31]. Thus in this work the dilute acid hydrolysis is selected for the further study.

8


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More specifically, a two-stage hydrolysis process is implemented in this work based on the data available in [31] and then modeled in Aspen Plus®. The process flowsheet is shown in Figure 2. The lignocellulose feedstock is initially charged to the first prehydrolysis stage mixed with a dilute sulfuric acid solution and heated using direct steam, which leads to hemicellulose hydrolysis. The soluble compositions are washed and separated from the solids using filtration. At the second stage, the insoluble components from the first stage are impregnated with acid to hydrolyze cellulose into glucose where higher temperature and acid concentration are needed. The products are again washed by water and separated from the lignin and unreacted residue [31]. The first-stage hydrolysis occurs at 170°C and we assume that hemicellulose is completely dissolved. The second-stage the reaction occurs at 230°C with the yield based on the compositions reported in [27] and [31] which are 57.2%, 2.34%, 2.73%, 2.73% and 1.84% for glucose, HMF, LA, FA, and unaccounted components, respectively.

Figure 2. Process block diagram for two-stage hydrolysis using dilute acid [31]

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Heat integration As discussed before, it is essential to perform heat integration to achieve the energy efficiency of the entire process and reduce the energy consumption. Intensive studies focused on the development of strategies of heat integration specifically heat exchanger network synthesis (HENS) problem and details can be found in the excellent reviews by Linnhoff [32], Gundersen and Naess [33], and Furman and Sahinidis [34]. The basic HENS problems are defined as:[35] Given •

A set of hot process streams to be cooled and a set of cold streams to be heated

•

The flowrates and of all the process streams

•

The inlet and outlet temperatures and the heat capacities of all the process streams

•

The available utilities and temperatures, and the units cost of the utilities

the basic problem is to develop the heat exchanger network with minimum the annualized cost of equipment investment and operating cost. The main approaches to HENS can be categorized into sequential synthesis and simultaneous synthesis. The sequential synthesis divides the HENS problem into a series of subproblems that reduce the computation efforts and achieve a network but does not guarantee the minimum annualized cost. The sequential synthesis contains two subcategories of evolutionary design methods such as pinch analysis and mathematical programing. The simultaneous synthesis is aimed to achieve the optimal network without decomposition of the problem via formulating a MINLP subject to various simplified assumptions. The drawback of the simultaneous HENS is no splitting or mixing of streams are allowed. [34] However, since the main focus of this work is to evaluate the sustainability of the process, an existing analysis tool within the process simulation software package – Aspen Energy Analyzer® will be used to implement heat integration. Aspen Energy Analyzer® can automatically generate 10


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the optimal HENS using a three-step procedure. Specifically the first step employs linear programming to simultaneously optimize heat exchange area and heat load for each utility in order to eliminate poor matches, which does not consider the number of heat exchange units in the objective function. At the second step a mixed integer linear programming problem based on the approach developed by Shethna [36] is incorporated to simultaneously optimize the number for heat exchanger units, heat exchanger area and heat loads on each utility so that the total annual cost is minimized subject to the heat balance constraints. The solution of the optimization model obtain an optimal approach temperature for each match pair, the heat loads on every match pair and each utility, however the position information of the exchangers are not determined. The last step is a superstructure model formulated using mixed integer linear programming, which is based on the robust approach proposed by Yee and Grossmann [37]. The approach uses stage-wise representation that each stream is divided into a given number of stages and in each stage a heat exchanger is placed on each match pair obtain from the second step. This model satisfies the optimal heat load distribution obtained from the previous step and generates an optimal network that simultaneously optimizes utility cost, the numbers of heat exchangers and selection of matches. [38] LCA The detailed systematic procedures of LCA involves four main steps: goal and scope definition, inventory analysis, impact assessment, and interpretation are described hereinafter [16, 39, 40]. Goal and scope definition The goal of the LCA of this work is to assess and to improve the environmental performance of the biobased CCEI-pX production. The results of the LCA study are used to evaluate the 11



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biobased pX based on sustainability criteria and to compare the biobased pX production from both first- and second- generation feedstocks with the conventional petroleum-based pX production. Given the information available the system boundaries are from-cradle-to-gate, shown in Figure 3, including two stages: the first stage contains the cultivation and the processing of the first- and second- generation biomass and the next stage is the production of pxylene including the biphasic dehydration, hydrodeoxygenation and cycloaddition. The inventory analysis is divided based on each stage. This work does not involve the transportation and distribution of pX as well as the usage of pX to produce PET and the subsequent recycling of PET. To compare the LCA results, one metric ton of p-xylene produced is selected as the functional unit.

Figure 3. Scope definition of bio-based pX production process * emissions or purge include the water or air emissions produced during the system such as acids, THF, NaCl, heptane, etc. Inventory analysis The data collection is the most critical stage in LCA. There are different databases that calculate life cycle inventory (i.e. Ecoinvent, US LCI, ELCD, US Input Output, EU and Danish 12


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Input Output, Swiss Input Output and etc. [41]). It is known that the LCI varies on different database due to location, technologies, emission level, etc. The comparison of LCI from different databases is out of the scope of this work, but an example of mixed xylenes with the scope from cradle to plant gate is considered to illustrate the differences of the impacts resulting from the use of different databases Ecoinvent® v2.2 [42] and US LCI v1.6 [43]. The characterization results using ReCiPe midpoint method [44] are shown in Table SI1. It should be noticed that the impacts of all the categories vary extensively. For example in the category of fossil depletion, water depletion, urban land occupation, agricultural land occupation and fresh water eutrophication the impact using US LCI are all zero while those using Ecoinvent have values. In the categories of marine ecotoxity, fresh water ecotoxicity and human toxicity the results from Ecoinvent are less than 10% of those from US LCI. Both databases use the technology of catalytic reforming of naphtha; however the results are quite different. Unfortunately, some important components in the pX production are not included in the US LCI database such as THF, corn starch, etc. Thus all the datasets needed from external sources are retrieved from Ecoinvent® v2.2 in order to avoid issues due to lack of database compatibility since Ecoinvent® is the largest database and its LCI has been undergone review and validation with consideration of uncertainties [42]. Most components from Ecoinvent are still limited to European data in terms of technologies, emission level, etc. though some datasets such as electricity depend on the regions. Maize starch is selected as the example of the first-generation biomass feedstock. The LCI of the cultivation and the processing of maize is directly selected from Ecoinvent® V2.2. The cultivation process consists of the processes of soil cultivation, sowing, weed control, fertilization, pest and pathogen control, harvest and drying of the grains and the starch processing 13



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includes mechanical separation steps, swelling in process water, milling of the swelled corns, desiccation and drying of the extracted starch. The red oak is used as an example of the secondgeneration biomass. The LCI of the cultivation of red oak is retrieved from Ecoinvent® V2.2 as well whilst the LCI of the production of glucose from oak utilizing the two stage dilute acid hydrolysis (shown in Figure 3) is derived from the simulation performed in Aspen Plus® based on the available information in the literature [31]. When lignocellulose is used as feedstock the excess heat from the lignin combustion is employed as the heating source for the downstream production. The inventory of flow rates and energy consumptions of the two stage dilute hydrolysis, corresponding to the production of 1 metric ton of pX, is listed in Table 1. Table 1. Life cycle inventory of two stage dilute acid hydrolysis (corresponds to the production of 1 metric ton pX) COMPONENTS/ENERGY

ton/GJ

COMPONENTS/ENERGY

INPUT MATERIALS

ton/GJ

OUTPUT

BIOMASS (DRIED1)

10.04

H2SO4

0.32

WATER

38.82

LIME

0.51

AIR Electricity

PRODUCTS

PREHYDROLYSATE (DRIED2/WATER)

3.94/18.29

GLUCOSE

UTILITY

(DRIED3/WATER)

2.77/19.11

Heat

-109.974

WATER

3.42

35.15

GLUCOSE

0.12

0.41

CO2

7.39

CaSO4

0.38

Na2CO3

0.32

N2

26.67

EMISSION

14


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O2

2.45

1 BIOMASS (dried) contains extractives, hemicellulose, cellulose and lignin 2 PREHYDROLYSATE (dried) contains the soluble hemicellulose, the soluble compositions of biomass such as extractives 3 GLUCOSE (dried) coexists with mannose and other components 4 Negative represents that the process provides heating energy The process flowsheet of the production of p-xylene is modified based on our previous work [10] with heat integration to reduce the energy consumption. The inventory of input and output flows is shown in Table 2. Table 2. Inventory analysis of pX production from starch (not include components with mass 50% of the 1.02 sites relevant for the market considered, over an adequate period even out normal fluctuations

3

Representative data from only some sited 1.05 (≤50%) relevant for the market considered or >50% of sites but from shorter periods

4

Representative data from only one site 1.10 relevant for the market considered or some sites but from shorter periods

5

Representativeness unknown or data 1.20 from a small number of sites and from shorter periods

1

Less than 3 years of difference to the 1.00 time periods of the dataset

2

Less than 6 years of difference of the 1.03 time period of the dataset

3

Less than 10 years of difference to the 1.10 time period of the dataset

partly

based

38


Selection

on 1.10 √

1.50

√

√

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U4-Geographical correlation

U5-Furthe technological correlation

4

Less than 15 years of difference to the 1.20 time period of the dataset

5

Age of data unknown or more than 15 1.50 years of difference to the time period of the dataset

1

Data from area under study

2

Average data from larger area in which 1.01 the area under study is included

3

Data from area with similar production 1.02 conditions

4

Data from area with slightly similar production conidtions

5

Data from unknown different area

1

Data from enterprises,

1.00

or

distinctly 1.10 1.00

processes and materials under study (i.e. identical technology) 2 3

Data on related processes

1.20

or materials but same technology, OR Data from processes and materials under study but from different technology 4

Data on related processes or materials but different technology, OR data on

39


1.50

√


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

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laboratory scale processes and same technology

U6-Sample size

5

Data on related processes or materials but 2.00 on laboratory scale of different technology

1

>100, continous

√

1.00

measurement, balance of purchased products 2

>20

1.02

3

>10

1.05

4

≥3

1.10

5

unknown

1.20

Table SI3. Heat duty of each unit

Heat Duty

GJ/ton pX

GJ/ton pX

Original Flowsheet

Modified Flowsheet

R1

144.28

144.28

R2

-178.06

-176.46

R3

-2.26

-1.83

R4

-1.92

-0.88

FL1

33.14

37.84

FL2

-10.78

-10.82

FL4

-2.00

-1.66

FL5

-0.08

40


√

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D

-3.66

-5.48

EVAP

4.41

4.21

condenser

reboiler

condenser

reboiler

DC1

-84.73

50.05

DC2

-15.84

18.63

-15.89

18.25

DC3

-1.22

1.28

-1.47

1.33

DC4

-0.11

0.12

-0.12

0.13

DC5

-6.95

10.13

-3.47

6.69

DC6

-0.72

0.18

-1.18

0.48

Before HEN

Heating

Cooling

Heating

Cooling

262.23

-308.24

213.21

-219.26

Heating

Cooling

Heating

Cooling

112.47

-158.79

68.11

-73.84

After HEN

1 Negative represents that cooling utility usage

AUTHOR INFORMATION Corresponding Author Phone: (848) 445-2971. Fax: 732-445-2581. E-mail: [email protected]

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REFERENCES 1. 2.

3. 4. 5. 6.

7. 8.

9.

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