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Convenient Product Distribution for a Lignocellulosic Biorefinery: Optimization through Sustainable Indexes Javier Larragoiti-Kuri, Martin Rivera-Toledo, José Cocho-Roldán, Karina Maldonado- Ruiz Esparza, Sylvie Le Borgne, and Lorena Pedraza-Segura Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b02101 • Publication Date (Web): 07 Sep 2017 Downloaded from http://pubs.acs.org on September 10, 2017

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Industrial & Engineering Chemistry Research

Convenient Product Distribution for a Lignocellulosic Biorefinery: Optimization through Sustainable Indexes Javier Larragoiti-Kuri[a], Martín Rivera-Toledo[a], José Cocho-Roldán[a], Karina MaldonadoRuiz Esparza[a], Sylvie Le Borgne[b] and Lorena Pedraza-Segura *[a] Universidad Autónoma Metropolitana Cuajimalpa, 01120 México, D.F,[b] and Universidad Iberoamericana, 01219, México, D.F, [email protected]*[a] biorefinery • sustainability indexes • optimization • safety • bioethanol

ABSTRACT: The current approach for technological projects must fulfill sustainability indexes. In this work, we use a multiobjective optimization framework to analyze the convenient product distribution in a lignocellulosic biorefinery, considering economic, environmental and saferty indexes. Corn cob was selected as feedstock due to the high annual volume produced as residue of maize crop. Products selection was based on the existing demand and economic value; bioethanol, lactic acid, succinic acid, xilitol and lignosulfonates were chosen. Through the multiobjective optimization strategy an efficient solution with an Economic Potential Index of 0.16 is achieved, generating an annual utility of nearly 70 kUSD when bioethanol and xylitol production is favored over succinic acid and lactic acid. This tool can be applied with different

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feedstocks and products in a biorefinery scheme, with kinetic and yield data for corresponding processes.

INTRODUCTION An imminent challenge of current technological projects is to enhance economic revenues while diminishing the environmental impact and safety risks. Considering such aspects in optimization tools is needed to increase the efficiency of product distribution, production logistics or operating parameters, and fulfil sustainability indexes. Among the diverse projects that could benefit from this optimization strategy, biorefineries are of particular interest as they must represent an attractive and eco-friendly business to favor their preference over petrochemical refineries, and boost their implementation. In this context, the design, development and optimization of biorefinery concepts remains a challenge and several methodologies have been proposed for this purpose, as extensively reviewed by Moncada et al1. These methodologies allow assessing feedstocks, technologies, products and processing routes through technical, economic, environmental and energy analysis to obtain the best process configuration. The three major approaches are superstructure and conceptual design, optimization and a combination of the two, and supply chain optimization2, 3, 4

, Moncada et al. propose a design strategy based on hierarchical decomposition, sequencing to

logically order technologies and products and integration of feedstocks, technologies and products and, additionally, they introduce a simple mass index to quantify how efficient is the biorefinery in relation to the integral use of raw materials1. Current objectives of biorefineries include: reducing the use of hazardous materials, decreasing the environmental impact by employing mild operating conditions and lessening production services such as heat, and proving a solid economic potential. Previous studies have been made


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to optimize production pathways and product distribution in biorefineries by including sustainable indexes as constraints or objective functions. Zondervan et al, optimized the design of a multiproduct biorefinery capable of generating ethanol, butanol, and succinic acid. Their approach utilized economic indexes to show that a project of such nature presents an economic potential with small revenues.5 Wang et al, utilized sustainable indexes for optimizing the design of a hydrocarbon biorefinery, demonstrating that the economic feasibility and the environmental impact can be addressed simultaneously by optimization tools.6 Among the papers in which safety indexes are considered, El-Halwagi et al, utilized safety and economic objectives when optimizing the distribution of a bio-hydrogen biorefinery, proving that these objectives can contradict over certain ranges.7 Although such studies have combined economic and environmental or economic and safety indexes to optimize diverse aspects of a biorefinery, no studies have been made to combine this three objectives simultaneously. Such approach is needed to address the interests of all the groups involved in a biorefinery project, like investors, workers and neighboring communities. The novelty of the present paper is that combine product distribution optimization of a lignocellulosic biorefinery when three sustainable indicators are optimized simultaneously. Nevertheless, it is important to highlight that we have not considered the uncertainties for design parameters, environmental and economics, for instance, the demand for feedstock, the prices of fuels, and the availability of electricity, transportation costs and market prices etc. In this regards, Sahinidis8 shows an interesting paper for state-of-the-art and opportunities for optimization under uncertainty. Besides Beyer and Sendhof

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give a detailed discussion on how to take

account for design uncertainties to perform a robust optimization. Recently, some authors, as Cheali and coworkers10 have proposed to use a distribution function based on historical data,


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experiences, and realization to take account the input uncertainties, and Gargalo et al.11 have used a deterministic sensitivity analysis for identifying the sources of uncertainty which affect the economic performance. Here, the product distribution in a lignocellulosic biorefinery, was optimized using the following criteria: economic potential, specific energy intensity and safety indexes to obtain the most favorable pathway to produce bioethanol, lactic acid, succinic acid, xylitol and lignosulfonates. These products are obtained from the diverse fractions which compose any lignocellulosic material: cellulose, hemicellulose and lignin12.

Moreover the biorefinery

employed in this study is based on a biochemical platform, in which biomass is first submitted to a low-severity pretreatment to fractionate the cellulose, hemicellulose and lignin. Then, enzymatic or chemical hydrolysis is used to release the cellulosic or hemicellulosic monomers, glucose and xylose respectively, for further fermentation to ethanol or other products. The lignin fraction can be valorized into different materials, chemicals and energy13. The lignocellulosic feedstock selected was corn cob due to its fair availability in Mexico, It is estimated that 4.14 million tons of corn cob are annually produced. The composition of dry corn cobs is 38.2% cellulose, 32.5% hemicellulose, 22.2% lignin, 2.2% ashes and 4.9% extractives.14 Compared with corn stover, which is an important food for different ruminants, corn cob is used as a filler in diets for pigs and cattle in dry season mainly. Its low protein content and digestibility makes it unsuitable for animal feed 15,16,17

METHODS


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Description of the biorefinery The biorefinery process for the production of lactic acid, xylitol, bioethanol and lignosulfonates was summarized as shown in the block diagram depicted in Figure 1. A more detailed description of this block diagram is given in the supplementary information. The description of the main blocks of such facility is described below.

Figure 1. Block diagram of the lignocellulosic biorefinery. Pretreatment The first stage starts by the handling and conditioning of corn cob. First, corn cob is grinded to obtain an adequate particle size. Then, grinded corn cob is impregnated with an acid solution (20% wt. solids, 1.6% wt. H2SO4) for being processed through a thermochemical pretreatment. The resulting suspension is fed into a reactor where it reaches 160°C, for 18 min. Through this


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process, the hemicellulosic fraction is hydrolyzed into xylose, while cellulose and lignin remain mostly intact. Once this time is fulfilled, the reactor is depressurized and the resulting slurry is processed in a press filter to deal with the solid and liquid portions separately. The solid fraction (cellulose and lignin) is then treated through a peroxide-alkaline treatment for lignin extraction. In such treatment, the solid is mixed with an alkaline solution (NaOH, 45% wt. solids) and hydrogen peroxide to solubilize lignin. The resulting slurry is then filtered to separate the solid and liquid phases. The recovered solids contain cellulose, which is then neutralized prior to the SSF process to generate ethanol and/or lactic acid. The liquid fraction with solubilized lignin, is acidified to precipitate such component which will be further processed to generate lignosulfonates. The liquid fraction resulting of the pretreatment step (liqueur), is fed to a stirred tank for neutralization. Then, it is detoxified through a column packed with activated carbon. In this process inhibitors generated during the pretreatment, like furfural and phenolic compounds, are removed. Such liquid stream is mainly composed of xylose, which will be employed as carbon source for the production of xylitol and/or succinic acid. Lactic acid production Cellulose is transferred to a fermentation vessel for the SSF process. Potassium, ammonia and magnesium salts, and yeast extract are added to perform a pre-hydrolysis at a pH of 4.8, and 50°C with a 30% (w/w) load of solids. After 8 hours, P. acidilactici is inoculated into the system (20% v/v) decreasing the temperature to 37°C. Fermentation lasts 72 hours, then, the contents of the bioreactor are placed in a centrifuge to separate biomass. Downstream processing constitutes the most complicated step in the production of lactic acid. The supernatant is transferred to a mix vessel where precipitation and acidification occurs.


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Ca(OH)2 is added to form calcium lactate, then,

H2SO4 is added to form lactic acid and

precipitate calcium sulfate. Acidification is followed by a filtration step in which the solids (calcium sulfate) are treated as waste. The liquid stream, rich in lactic acid, is transferred to an evaporator to increase the concentration of the product. Finally, the concentrated solution is decolorized in a granular activated carbon (GAC) column to remove impurities. Bioethanol production After the cellulose-rich stream has been divided for production of lactic acid and/or bioethanol, cellulose is transferred to a bioreactor. For the prehydrolysis stage, potassium, ammonia, and magnesium salts, as well as yeast extract are added together with 0.11 grams of enzyme per gram of dry matter. For this purpose, a load of 25% w/w of solids is employed. After 6 hours, S. cerevisae is inoculated into the bioreactor to start the fermentation process, which lasts 72 hours. Once the fermentation is over, a centrifuge is used to separate the biomass. The clarified liquid is then fed to a distillation column to purify the bioethanol. Finally, the distillate is treated in a zeolite column to remove water. Xylitol production Xylitol fermentation is a batch process in which detoxified xylose contained in the pretreatment liquid stream and other nutrients like yeast extract conform the culture media. Candida parapsilosis is the microorganism used and this is process. After 59 hours of fermentation, cell biomass is separated with a centrifuge and the clarified liquid containing xylitol is further purified. Downstream processing begins by decolorizing the xylitol-rich stream with activated carbon. Then, ion exchange columns are employed to remove salts and other impurities. The volume of the resulting solution is reduced by evaporation, increasing xylitol concentration to


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supersaturation. The supersaturated solution is crystalized for 48 hrs at -20°C. Finally, the crystals are washed with ethanol. Succinic acid production Part of the xylose stream is used for the production of succinic acid. For this purpose, nutrients are added and A. succinogenes is inoculated. After the fermentation is over (60 hours), the biomass is extracted via centrifugation and succinic acid is purified by decolorizing with activated charcoal, followed by a crystallization process. Lignosulfonates production Lignin obtained from the delignification process applied to the solid fraction of pretreated corncob, is first precipitated by acidification with sulfuric acid. Then, solid lignin is fed to a reactor, where H2O2, FeSO4, CH2O and Na2SO3 are added to generate lignosulfonates. Such product is purified by precipitation and filtration. Dimensioning, estimation of costs and utilities Mass balances of the biorefinery described in Figure 8 were done by employing the conversion and purification yields shown in Table 1 and 2 respectively. Energy balances were done to estimate energy consumption in equipment as reactors, fermenters, heat exchangers, among others. Thermodynamic properties of biorefinery compounds were taken from the NREL databank.18 In such reference, the heat capacity and enthalpies of formation of hemicellulose, cellulose, lignin and biomass, are documented.


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Table 1. Conversion yields and operating conditions of upstream processes. Yield (%)

Conditions

71

160 °C, 18 19 min

Solubilized lignin

60

45 °C

20

Cellulose

Bioethanol

80.2

37°C, rpm

100 21

SSF lactic acid

Cellulose

Lactic acid

77.2

37 °C, 120 22 rpm

Xylitol production

Xylose

Xylitol

70

27°C, rpm

Succinic acid Xylose production

Succinic acid

74

27 °C, 300 24 rpm

Lingnosulfonates Lignin production

Lignosulfonates 95

Process

Feeding

Product

Pretreatment

Hemicellulose Xylose

Delignification

Lignin

SSF bioethanol

Reference

150 23

60°C

25

Table 2. Overall purification and recovery yields for downstream processing Product

Recovery Yield (%)

Reference

Ethanol

95

26

Lactic acid

70.6

27

Xylitol

60.02

28

Succinic acid

70

29

Lignosulfonates

95

30

Sizing of equipment was computed by shortcut methods described elsewhere.31 A simulation which allowed to dynamically estimate the dimensions of equipment, fixed and variable costs,


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feeds of raw materials, and operating conditions was created to estimate changes given by the variations in the proportions of the xylose and cellulose streams destined to their corresponding products. Table 3 shows the economic costs of all products, while Table 4 shows the costs of the main raw materials and services. Costs of all raw materials were estimated by the method propose by Hart et al, which correlates the price of analytical grade chemicals (prices taken form SigmaAldrich) with bulk prices.32

Table 3. Market size and costs of products. Product

Market size (ton/year)

Price (USD/kg)

Reference

Ethanol

71 x 106

0.9

33

LacticAcid

71 x 104

1.3

34

SuccinicAcid

90 x 103

3.3

35

Xylitol

19 x 105

3.4

36

Lignosulfonates

70 x 104

1

37

Table 4. Costs of services and main raw materials. Product or service

Price

Reference

Steam

4.4[a]

31

Electricity

0.045[b]

31

Corncob

723.14[c]

38

Yeast Extract

827.63[c]

38

NaOH

176.50[c]

38


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HCl

32.75[c]

38

H2SO4

186.36[c]

38

[a] USD/ton, [b] USD/(Kwh), [c] USD/batch Optimization The general multiobjective optimization problem (MOP) is defined as: min , = , , , , … , , ,

(MOP)

Subject to the constraints: ℎ , = 0

, ≤ 0

≤ ≤

≤ ≤ As stated elsewhere, y is the vector of decision variables y ∈Rn, u corresponds to the vector of manipulated variables u ∈Rm,F∈Rk is a vector of objective functions fi(y,u):FnF1 where n, m, k refer to the number of states, manipulated variables and objective functions, respectively, and the equality and inequality constraints are given by h(y,u) and g(y,u) with its corresponding lower and upper bounds.39 Due the multiobjective nature of the optimization, there is no single solution to the problem and a set of feasible points needs to be determined.40 This is accomplished through the Pareto solution. The Pareto optimality states that any feasible point y* is said to be Pareto optimal if and only if there exists no other feasible point (y) such that Fk(y) ≤ Fk(y*) and fi(y) < fi(y*) for at least one function. All the Pareto points lie on a feasible performance space for the objective function, defined as the Pareto frontier. Another important definition is the utopia point whose


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solution yiup is obtained from min Fk(y) subject to h(y)=0, g(y)≤0 and their boundary conditions.[41] The utopia point is unattainable as it lies outside the Pareto frontier, but it is used as a reference point. Within the Pareto frontier, the efficient or compromise solution ys of the objective function Fn(ys) is defined as: #

/#

= min , − , " ' ,

∈% &

Recently, Dowling et al, proposed a variant of a multiobjective optimization (MO) problem by presenting a conditional-value-at-risk (CVaR) framework when dealing with multiplestakeholder decision-making.42 In order to assess the stakeholders’ satisfaction towards a decision and how they reflect the overall population’s opinions, the authors proposed a method which weighs each of the stakeholder`s preferences and from them formulates dissatisfaction functions which account for the deviations of the ideal solution. Following the framework, situations where a single stakeholder dictates a solution are avoided; enabling to get a solution that complies with economic, sustainability and safety targets, such approach would be very convenient when the MO for the full production process of biomass derivatives is tackled. During the past three decades, many methods have been proposed to deal with MO problems. Some interesting reviews can be found in the books published by Liu and co-workers43 and Collette44; some authors as Marler and Arora[40] show a valuable review methods for engineering area. Thus the implementation of a range of process engineering tools, such as sustainability indicators, sensitivity analysis, and optimization, would allow for the determination of the best distribution for the products of this biorefinery. In our case study, three objective functions have considered for the convenient products distributions for a biorefinery refer to both sustainability


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indicators as Economic Potential (EP), Specific Energy Intensity (RSEI) and the Safety Index (SI). The latter is represented by the addition of the individual Probit functions of the most hazardous materials employed in the biorefinery: HCl and H2SO4. Probit functions are the quantile function associated with normal standard distribution, and they are used to provide a link between the probability of expected response and the exposure of a population to a specific event. In this case, they relate the probability of accidents by operating effluents containing such acids. Previous reports

2, 7, 45, 46

show that operating conditions like high temperatures and

pressures strongly influence high probabilities of accidents. As in this case the core and longest processes operate at ambient conditions, it is fair to assume that safety risks are represented by issues related with the selected hazardous materials and the failure of the equipment involved in their transport and storage; which is addressed by the Probit functions employed in the SI. ElHalwagi et al7 used a similar approach to create a quantitative SI to assess the safety of a biorefinery for production of hydrogen; however, in their case, the Probit function employed related the probability of explosion by operating with flowrates of hydrogen. The sustainability indicators employed in the objective function are shown in Table 5. To solve our MOP, we used the weighted sums method, an a posteriori multicriteria method39 where the vector of objective functions is converted into a scalar optimization problem as a convex combination of the different objectives. The problem is then defined as: min )*+, *+, , + )%.*, %.*, , ,

+ )%.*, ., ,

Subject to the equality and inequality constraints given by the heat and mass balances:

/01 = 13 1 − 51 , 6 = 1,2, … , 8 /2


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1;

1;

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/< = 13 9:1 < −

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