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Convenient Product Distribution for a Lignocellulosic Biorefinery: Optimization through Sustainable Indexes Javier Larragoiti-Kuri,† Martín Rivera-Toledo,† José Cocho-Roldán,† Karina Maldonado-Ruiz Esparza,† Sylvie Le Borgne,‡ and Lorena Pedraza-Segura*,† †

Ingeniería y Ciencias QuímicasUniversidad Autónoma Metropolitana Cuajimalpa, 01120 México City, Mexico Chemical Sciences and Engineering, Universidad Iberoamericana, 01219 México City, Mexico

‡

S Supporting Information *

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

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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 fulfill 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 al.1 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 optimization.2−4 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; additionally, they introduce a simple mass index to quantify how efficient is the biorefinery in relation to the integral use of raw materials.1 Current objectives of biorefineries include: reducing the use of hazardous materials, decreasing the environmental impact by © 2017 American Chemical Society

employing mild operating conditions and lessening production services such as heat, and proving a solid economic potential. Previous studies have been made 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, ElHalwagi 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 Received: Revised: Accepted: Published: 11388

May 22, 2017 August 28, 2017 September 7, 2017 September 7, 2017 DOI: 10.1021/acs.iecr.7b02101 Ind. Eng. Chem. Res. 2017, 56, 11388−11397

Article

Industrial & Engineering Chemistry Research

Figure 1. Block diagram of the lignocellulosic biorefinery.

material: cellulose, hemicellulose, and lignin.12 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 energy.13 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 low digestibility make it unsuitable for animal feed15−17

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, the availability of electricity, transportation costs, market prices, etc. In this regard, Sahinidis8 published an interesting paper on state-of-the-art and opportunities for optimization under uncertainty. Additionally, Beyer and Sendhof9 give a detailed discussion on how to take account for design uncertainties to perform a robust optimization. Recently, some authors, such as Cheali and co-workers,10 have proposed to use a distribution function based on historical data, 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

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METHODS 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 Supporting Information. The

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DOI: 10.1021/acs.iecr.7b02101 Ind. Eng. Chem. Res. 2017, 56, 11388−11397

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Industrial & Engineering Chemistry Research Table 1. Conversion Yields and Operating Conditions of Upstream Processes process

feeding

product

yield (%)

pretreatment delignification SSF bioethanol SSF lactic acid xylitol production succinic acid production lingnosulfonates production

hemicellulose lignin cellulose cellulose xylose xylose lignin

xylose solubilized lignin bioethanol lactic acid xylitol succinic acid lignosulfonates

71 60 80.2 77.2 70 74 95

conditions 160 °C, 18 45 °C 37 °C, 100 37 °C, 120 27 °C, 150 27 °C, 300 60 °C

ref

min

19 20 21 22 23 24 25

rpm rpm rpm rpm

stage, potassium, ammonia, and magnesium salts, as well as yeast extract are added together with 0.11 g of enzyme per gram of dry matter. For this purpose, a load of 25% w/w of solids is employed. After 6 h, S. cerevisae is inoculated into the bioreactor to start the fermentation process, which lasts 72 h. 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 in this process. After 59 h of fermentation, cell biomass is separated with a centrifuge, and the clarified liquid containing xylitol is further purified. Downstream processing begins by decolorizing the xylitolrich 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 supersaturation. The supersaturated solution is crystallized for 48 h 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 h), 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 9 were done by employing the conversion and purification yields shown in Tables 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

description of the main blocks of such facility is described below. 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 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 h, P. acidilactici is inoculated into the system (20% v/v) decreasing the temperature to 37 °C. Fermentation lasts 72 h, and 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 occur. Ca(OH)2 is added to form calcium lactate, and 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

Table 2. Overall Purification and Recovery Yields for Downstream Processing

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product

recovery yield (%)

ref

ethanol lactic acid xylitol succinic acid lignosulfonates

95 70.6 60.02 70 95

26 27 28 29 30


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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 f i(y,u):Fn → F1, where n, m, and 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 the 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 f i(y) < f i(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 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

NREL databank.18 In that reference, the heat capacity and enthalpies of formation of hemicellulose, cellulose, lignin, and biomass are documented. 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, 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. Table 3. Market Size and Costs of Products product

market size (ton/year)

price (USD/kg)

ref

× × × × ×

0.9 1.3 3.3 3.4 1

33 34 35 36 37

ethanol lacticacid succinicacid xylitol lignosulfonates

71 71 90 19 70

106 104 103 105 104

Fn(y s ) = min{ ∑ [f p (y , u) − fk (y up , u)]2 }1/2

Table 4. Costs of Services and Main Raw Materials

y,u

a

product or service

price

ref

steam electricity corncob yeast extract NaOH HCl H2SO4

4.4a 0.045b 723.14c 827.63c] 176.50c 32.75c 186.36c

31 31 38 38 38 38 38

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 multiple-stakeholder decision-making.42 In order to assess the stakeholders’ satisfaction toward a decision and how they reflect the overall population’s opinions, the authors proposed a method which weighs each of the stakeholders’ 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 Collette;44 some authors such as Marler and Arora40 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 indicators as economic potential (EP), specific energy intensity (RSEI), and safety index (SI). The latter is represented by the addition of the individual

USD/ton. bUSD/(kWh). cUSD/batch.

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 Sigma-Aldrich) with bulk prices.32 Optimization. The general multiobjective optimization problem (MOP) is defined as min F(y , u) = [f1 (y , u), f (y , u), ..., fk (y , u)]T y,u

p ∈ Rk

(MOP)

subject to the constraints: h(y , u) = 0

g (y , u) ≤ 0 ylb ≤ y ≤ yub ulb ≤ u ≤ u ub

Table 5. Sustainability Indicators Considered for the Convenient Products Distributions for a Biorefinery47 indicator

formula

best target

economic potential (EP, $/kg)

revenue − raw material costs − utility costs total mass of products

1.5

0

specific energy intensity (RSEI, kJ/kg)

net energy used as primary fuel equivalent mass of product

0

1.949 × 106

safety index (SI)

ProbitH2SO4 + ProbitHCl

0

200

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Figure 2. Pareto frontier between RSEI and SI.

where f EPI, f RSEI, and f SI are the objective functions for economic potential, specific energy intensity, and safety index, respectively, y is the vector of decision variables, u corresponds to the vector of manipulated variables, wEPI, wRSEI, and wSI are the weights for economic potential, specific energy intensity, and safety index, respectively, Fi0 is the mass flow rate of component i in the system, mi is the mass of component i in the system, Xi is the conversion yield of component i, Cpi is the heat capacity of component i, T and T0 are the temperatures in the system and the input stream into the system, respectively, ΔHr is the heat of fermentation reaction and, finally, Q is the heat added to the reactor or another unit production (evaporation, crystallization, etc.). In the Supporting Information Data Collection and Modeling section, we have shown an example for the mass balance of the xylitol fermentation batch process, according to the block diagram of Figure 1.

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 reports2,7,45,46 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. El-Halwagi et al.7 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 flow rates 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

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RESULTS AND DISCUSSION The problem addressed is a mathematical formulation based on the diagram of the biorefinery shown in Figure 1. In this case we are looking for the convenient product distribution for this plant under economic, environmental, and safety objectives. The economic objective is measured by the economic potential index (EPI), the environmental objective is measured by the specific energy intensity (RSEI), and the safety objective is measured by a safety index (SI) given by the addition of the Probit functions for sulfuric and hydrochloric acids.48 A multiobjective nonlinear programming model (MONLP) was developed accounting for the major characteristics of each step of Figure 9, including raw material feed costs, density, heat capacity, the biochemical conversions such as separate hydrolysis and fermentation, and so on. This MONLP was solved using the Generalized Reduced Gradient method embedded in MS-Excel coupled with sensitivity analysis. In this case the decision variables were the ratio of xylose and cellulose-rich streams destined for the production of xylitol and succinic acid, and bioethanol and lactic acid, respectively. It is worth mentioning that in all cases 64.8 tons of lignosulfonates are produced, as lignin distribution is not a decision variable in our model. The resulting Pareto-optimal curves reveal the trade-off between the economic, environmental, and safety dimensions of the sustainable product distribution for the biorefinery. The Pareto frontiers are shown in Figure 2,

min wEPIfEPI (y , u) + wRSEIfRSEI (y , u) + wRSEIfSI (y , u) y,u

subject to the equality and inequality constraints given by the heat and mass balances: dmi = Fi0(1 − Xi), dt n

∑ miCpi i=1

i = 1, 2, ..., c

n

dT = ∑ Fi0Cpi (T − T0) + Q − ΔHrFi0Xi dt i = 1

wEPI + wRSEI + wSI = 1

ylb ≤ y ≤ yub ulb ≤ u ≤ u ub 11392


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Figure 3. Pareto frontier between RSEI and EPI.

Figure 4. Pareto frontier between SI and EPI.

considering both RSEI and SI, in Figure 3, considering SI and EPI, and finally, in Figure 4 with RSEI and EPI. It must be stressed that all the depicted values of the objective functions discussed in these figures refer to scaled values, in other words we are considering the best target and the worst case according to the environmental efficiency, energy, and economic bases proposed by Smith et al.49 Pareto optimality is a special case of efficiency, but in terms of practical applications we are interested on a single solution point, a compromise solution. From a practical point of view, only one solution is selected. All the optimal solutions that take into account the economic, environmental, and social objectives lie on the Pareto curve. The solutions above the curve in Figures 2−4 are then suboptimal solutions. Figure 5 shows a surface corresponding to all the feasible solutions for the objective function, where the efficient solution and the utopia are highlighted. The values of the indexes in this graph have been scaled for easing a comparative analysis. Figure 2 shows that the efficient solution for RSEI has a value four times higher than its utopia when the SI has been minimized. Similarly, in Figure 3, it is shown that a bigger EPI is directly proportional to a higher consumption of energy (RSEI), which denotes the trade-off between these two parameters. Hence, from Figures 2 and 3, it can be stated that to fulfill a low

Figure 5. All the feasible solutions for the objective function. Scaled values of indexes employed are represented on each axis: x, economic (EPI); y, environmental (RSEI); and z, safety (SI). The utopia and the efficient solution have been highlighted.

safety risk and a high economic potential, minimizing the environmental impact must be sacrificed. In Figure 4 it can be observed that the SI and EPI are directly proportional, which means that high economic utilities are 11393


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Industrial & Engineering Chemistry Research associated with high safety risks. Therefore, minimizing safety risks results in lower utilities. Figures 2 to 4 show that the value of indexes in the efficient solution are closer to the minimum SI, lower by an order of magnitude to the utopia of EPI, and higher by an order of magnitude to the utopia of RSEI. Hence, the safety index has a bigger influence in the optimization function, while the specific energy consumption index has the lowest effect. From Figure 5, it is visible that the efficient solution is the point within the 3-dimensional space of feasible solutions that has the minimum distance to the utopia. The significant difference between the values of such solution and the utopia is explained by the trade-off of the simultaneous evaluation of the indexes while optimizing. To compare the influence of each objective function on the efficient solution, four different cases were studied. In the first case the RSEI was minimized, the second case considered maximizing the EPI, in the third case the SI was minimized, and the fourth case is represented by the efficient solution. Figures 6−8 depict the value of nonscaled indexes for each of these cases. From Figure 6, it can be seen that the RSEI in the efficient

Table 6. Results Obtained for Environmental, Safety, Economic, and Efficient Performancesa parameter xylose stream to xylitol (%) cellulose stream to lactic acid (%) xylitol (ton/ year) succinic acid (ton/year) bioethanol (ton/ year) lactic acid (ton/ year) H2SO4 (raw material) (ton/ year) HCl (raw material) (ton/ year) heat (kJ/year) electricity (kW/ year) variable costs ($/year) utilities ($/year)

case 2 (EPI max)

case 3 (SI min)

case 4 (efficient solution)

1.0

99.0

99.0

99.9

1.0

99.0

1.0

3.6

1.1

107.8

107.8

108.7

104.9

1.1

1.1

0.1

306.4

3.1

306.4

298.3

3.8

375.2

3.8

13.8

149.1

368.9

149.1

155.0

1371.9

13.9

13.9

1.4

2.5 × 1009 2.1 × 1005

1.5 × 1010 1.1 × 1005

1.0 × 1010 1.0 × 1005

1.0 × 1010 1.1 × 1005

1,235,739.7

747,784.4

652,318.2

653,113.8

−536,522.8

177,552.6

66,975.7

71,898.3

case 1 (RSEI min)

a

Data estimated for a processing capacity of 1100 tons of corn cob per year.

Figure 6. Specific energy intensity for environmental, economic, safety, and efficient solution performances.

solution highly surpasses the minimum value that can be reached in the process for this index (case 1). Figure 6 also shows that when maximizing the EPI, the energy consumption is the highest. This can be explained by the data shown in Table 6, where it can be observed that for such case, xylitol and lactic acid production is favored for their corresponding streams (xylose and cellulose), as they represent the products with a higher economic value. However, their production is associated with a higher energy consumption, given by the use of evaporators in their purification processes. Similarly, Figure 7 shows that when minimizing the RSEI, the EPI is negative, denoting economic losses. In this case xylitol and lactic acid production is avoided to reduce the energy consumption of the evaporation step. However, the economic potential is compromised as the cheaper products (bioethanol and succinic acid) are favored. Therefore, the biorefinery employed in this study must have a high heat energy consumption to be economically feasible. It is worth highlighting that when EPI is maximized the SI rises considerably, as it can be seen in Figure 8. In this case, lactic acid production is preferred over bioethanol due to its higher cost as product. This implies a larger consumption of HCl, which is employed in the purification process of lactic

Figure 7. Economic potential index for environmental, economic, safety, and efficient solution performances.

acid, causing a higher risk of fatalities. In agreement, from Table 6 it can be observed that when minimizing the SI (case 3), bioethanol production is preferred to avoid using HCl. Similarly, xylitol production is favored over succinic acid to diminish the consumption of H2SO4 needed in the purification of the latter product. Figure 9 shows the global warming potential index (GWP) for each case. This index relates the mass of CO2 produced in the process per total mass of products. The mass of CO2 produced is based on the consumption of energy in the form of heat and electricity, which are considered to be generated through the combustion of natural gas. Contrary to expected, Case 1, in which heat energy consumption is minimized, has the highest GWP. This can be explained through the information shown in Table 6, where it is noticeable that 11394


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Through the multiobjective optimization approach employed in this study, an efficient solution with an EPI of 0.16 is achieved, generating an annual utility of nearly 70 kUSD. Such value is almost twice bigger than the optimized utility of 38 kUSD reported by Zondervan et al., who optimized the product distribution and production pathways of a biorefinery with ethanol, succinic acid, and butanol as main products.29 Result analysis also shows that the economic and safety targets have contrasting effects in the objective function, which means that the economic potential is diminished when reducing safety risks. Similar observations were made by El-Halwagi et al, who also employed Probit functions as safety indexes in a multiobjective optimization to evaluate the feasibility of a biohydrogen facility. The fact that the SI highly dominates the objective function can be explained by the approach employed for creating such index, through the addition of two independent Probit functions. Although this study is the first of its kind to employ 3 sustainability indexes for a simultaneous optimization, previously discussed results show that it is a fair approximation to take into account the economy, environmental impacts, and safety risks given by handling hazardous materials in the process. The low utilities estimated for the biorefinery employed in this study, may be related to the selection of the raw material (corn cob), and the theoretical conversion yields employed in the mass balances. Nevertheless, such yields were meticulously chosen from previous publications in which the processes were tested in pilot or large scale units. Mussatto et al, reported a techno-economic analysis for a biorefinery which employs brewer’s spent grain as raw material to produce lactic acid, xylitol, and phenolic compounds.50 Apart from a different raw material, their study considers higher theoretical conversion yields, proved only on lab scale, for the production of xylitol and lactic acid through fermentation. With such data, their results show an economic margin of 60% and a GWP of 0.96. Such results are better than our efficient solution, implying that our results could improve if considering other substrates and higher conversion yields. In this context, Gargalo et al.4,11 show that for a biorefinery employing glycerol as raw material, production of either lactic or succinic acid have the best economic potential. Their results differ to ours as we show that for corn cob valorization, bioethanol and xylitol production are the best options. Hence, the raw material selection is very influential in the best product distribution, which is expected due to the different conversion yields and the variable costs of the processes involved. Nevertheless, the latest investment announcement done by Mondelez International and S2G BioChem for production of xylitol from agricultural residues like corn cob shows that our results of giving preference to xylitol production are in agreement with current technological developments. Gargalo et al.11 also show that product distribution and selection in a glycerol biorefinery are highly influenced by the selection of the area or country in which it will be established and the economic uncertainty given by price variation. In this sense, further work is needed to analyze the influence of such considerations in this case study. Figure 10 shows a comparison of our results and previous studies, in terms of the utilities per mass of products and the GWP; data accounted for Gargalo et considered production of succinic acid from glycerol,4,11 while the reported data for Mussato et al.50 considered the simultaneous production of xylitol and lactic acid from brewer’s spent grain.

Figure 8. Safety index for environmental, economic, safety, and efficient solution performances.

Figure 9. Global warming potential for environmental, economic, safety, and efficient solution performances.

even though the heat consumption has been reduced, the electricity consumption is increased. Such increment in the use of electricity is explained by the product distribution. In such case succinic acid production is favored, and the motor of the fermenter employed in this process has a high energy requirement due to the agitation speed employed (300 rpm). In consequence, electricity consumption is highly increased. It is important to remember that the power requirement is a strong function of the agitation speed (P ∝ N3). As this product utilizes the highest agitation speed in its fermentation process (Table 2), it requires the highest consumption of electricity. In contrast, on the other three cases where succinic acid production is not favored, the electricity consumption is considerably lower. Xylitol fermentation is carried out employing an agitation speed of 150 rpm, consuming half of the electricity when compared to the fermentative process for obtaining succinic acid. Regarding the efficient solution, results from Table 1 show that xylitol production is preferred for the xylose-rich stream while bioethanol is preferred for the cellulose stream. Such selection reduces the consumption of acids as raw materials, minimizing the SI. The EPI is lower compared to its optimum value (case 2) as bioethanol, which is cheaper than lactic acid, is selected. Moreover, even though the RSEI is 4 times higher than its utopia value, the GWP is lower than in Case 1, where heat energy consumption is downsized. 11395



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ACKNOWLEDGMENTS

The authors acknowledge Universidad Iberomericana Ciudad de México for the facilities provided.

■ Figure 10. Comparison of the results obtained in this study and previous reports for similar biorefineries.

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CONCLUSIONS A multiobjective framework has been developed to compute the convenient product distribution for a lignocellulosic biorefinery. We have considered the economic, environmental, and safety objectives which are commonly conflicting targets in biorefineries. The Pareto set results clearly indicate that there is a trade-off between the economic potential index, the specific energy intensity, and the safety index. We have demonstrated that there is a feasible solution for positive utilities (71,898 $/year) when bioethanol and xylitol production is favored over succinic acid and lactic acid. Such preference is given by the influence of the safety index in the objective function, which avoids the use of sulfuric and hydrochloric acids. It is important to stand out that we have not considered any kinds of uncertainties, for instance: uncertainty with respect to the model parameters, in the input variables or in the initial conditions. In this regard, Gargalo et al.11 show a critical comparison for two market uncertainty scenarios: the first one takes account the fluctuation of product prices, and the second one considers the recent drop in oil prices. Further work will contemplate the input of all the groups involved in a biorefinery project (investors, neighboring communities, and workers) to find efficient solutions that suffice the needs of each. Finally, it may be extended to consider the effect on the decision-making under uncertainty, for instance, the demand for feedstock, the prices of fuels, and the availability of electricity.

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REFERENCES

(1) Moncada, J.; Matallana, L. G.; Cardona, C. A. Selection of process pathways for biorefineries design using optimization tools. a colombian case for conversion of sugarcane bagasse to ethanol, phb and energy. Ind. Eng. Chem. Res. 2013, 52 (11), 4132−4145. (2) Ponce-Ortega, J. M.; Pham, V.; El-Halwagi, M. M.; El-Baz, A. A. A Disjunctive Programming Formulation for the Optimal Design of Biorefinery Configurations. Ind. Eng. Chem. Res. 2012, 51 (8), 3381− 3400. (3) Geraili, A.; Salas, S.; Romagnoli, J. A. A Decision Support Tool for Optimal Design of Integrated Biorefineries under Strategic and Operational Level Uncertainties. Ind. Eng. Chem. Res. 2016, 55 (6), 1667−1676. (4) Gargalo, C. L.; Cheali, P.; Posada, J.; Gernaey, K.; Sin, G. Economic risk assessment of early-stage designs for glycerol valorization in biorefinery concepts. Ind. Eng. Chem. Res. 2016, 55 (24), 6801−6814. (5) Zondervan, E.; Nawaz, M.; de Haan, A. B.; Woodley, J.; Gani, R. Optimal Design of a Multi-Product Biorefinery System. Comput. Chem. Eng. 2011, 35, 1752−1766. (6) Wang, B.; Gebreslassie, B.; You, F. Sustainable design and synthesis of hydrocarbon biorefinery via gasification pathway: Integrated life cycle assessment and technoeconomic analysis with multiobjective superstructure optimization. Comput. Chem. Eng. 2013, 52, 55−76. (7) El-Halwagi, A. M.; Rosas, C.; Ponce-Ortega, J. M.; JiménezGutierrez, A.; Mannan, M. S.; El-Halwagi, M. M. Multiobjective Optimization of Biorefineries with Economic and Safety Objectives. AIChE J. 2013, 59, 2427−2434. (8) Sahinidis, N. V. Optimization under uncertainty: state-of-the-art and opportunities. Comput. Chem. Eng. 2004, 28, 971−983. (9) Beyer, H. G.; Sendhoff, B. Robust optimization − A comprehensive survey. Comput. Methods Appl. Mech. Engrg. 2007, 196, 3190−3218. (10) Gargalo, C. L.; Carvalho, A.; Gernaey, K.; Sin, G. Supply chain optimization of integrated glycerol biorefinery: GlyThink model development and application. Ind. Eng. Chem. Res. 2017, 56 (23), 6711−6727. (11) Pennington, D. Biomass Feedstocks and Energy Independence. Jan 31, 2014; http://articles.extension.org/pages/26620/biomassfeedstocks-and-energy-independence. (12) Saini, J. K.; Saini, R.; Tewari, L. Lignocellulosic agriculture wastes as biomass feedstocks for second-generation bioethanol production: concepts and recent developments. Biotechnology 2015, 5, 337−353. (13) Beckham, G. T.; Johnson, C. W.; Karp, E. M.; Salvachúa, D.; Vardon, D. R. Opportunities and challenges in biological lignin valorization. Curr. Opin. Biotechnol. 2016, 42, 40−53.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.7b02101. Data collection and modeling; sensitivity analysis; and additional references (PDF)

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ABBREVIATIONS PLA = polylactic acid PET = polyethylene terephthalate SSF = simultaneous saccharification and fermentation GAC = granulated activated carbon NREL = National Renewable Energy Laboratory EP = economic potential RSEI = specific energy intensity SI = safety index MONLP = multiobjective nonlinear program GWP = global warming potential P = power N = agitation speed

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: +52 (55) 5959-4000 ext. 4624. ORCID

Lorena Pedraza-Segura: 0000-0002-3988-3897 Notes

The authors declare no competing financial interest. 11396


Article

Industrial & Engineering Chemistry Research (14) Pedraza, L.; Flores, A.; Toribio, H.; Quintero, R.; Le Borgne, S.; Moss-Acosta, C.; Martinez, A. Sequential thermochemical hydrolysis of corncobs and enzymatic saccharification of the whole slurry followed by fermentation of solubilized sugars to ethanol with the ethanologenic strain Escherichia coli MS04. BioEnergy Res. 2016, 9, 1046−1052. (15) Macedo, R.; Galina, M.; Zorrilla, M. Balance forrajero, ́ de un sistema de producción tradicional de energético y proteIco doble propósito en México. Zootecnia Trop. 2008, 6 (4), 455−463. (16) McGee, A. L.; Johnson, M.; Rolfe, K. M.; Harding, Terry, J. L.; Klopfenstein, J. Nutritive Value and Amount of Corn Plant Parts. Nebraska Beef Cattle Report, 2012; http://beef.unl.edu/632c1963c960-4b25-bf54-2b05f12c022c.pdf (17) INIFAP. Rastrojos: manejo, uso y mercado en el centro y sur de Mé x ico. 2013. https://www.zef.de/uploads/tx_zefportal/ Publications/tbeuchelt_download_ Rastrojos%20manejo,%20uso%20y%20mercados%20en%20el%20cen tro%20y%20sur%20de%20M%C3%A9xico.pdf (accessed August 28, 2017). (18) Wooley, R. J.; Putsche, V. Development of an ASPEN PLUS Physical Property Database for Biofuels Components. U.S. National Renewable Energy Laboratory, 1996; http://www.nrel.gov/docs/ legosti/old/20685.pdf (accessed Jan 20, 2017). (19) Schell, D.; Farmer, J.; Newman, M.; Mcmillan, J. Dilute-Sulfuric Acid Pretreatment of Corn Stover in Pilot-Scale Reactor. Appl. Biochem. Biotechnol. 2003, 105, 69−86. (20) Toribio-Cuaya, H. Production of lignin and xylan: a sustainable option for the generation of co-products in a biorefinery. Ph.D. Disstertation, Universidad Iberoamericana, Mexico City, 2011. (21) Delgenes, J. P.; Moletta, R.; Navarro, J. M. Effects of lignocellulose degradation products on ethanol fermentations of glucose and xylose by Saccharomyces cerevisiae, Zymomonasmobilis, Pichia stipitis, and Candida shehatae. Enzyme Microb. Technol. 1996, 19, 220−225. (22) Min, D.; Choi, K.; Chang, Y.; Kim, J. Effect of operating parameters on precipitation for recovery of lactic acid from calcium lactate fermentation broth. Korean J. Chem. Eng. 2011, 28, 1969−1974. (23) Wei, J.; Yuan, Q.; Wang, T.; Wang, L. Purification and crystallization of xylitol from fermentation broth of corncob hydrolysates. Front. Chem. Eng. China 2010, 4, 57−64. (24) Salvachúa, D.; Mohagheghi, A.; Smith, H.; Bradfield, M.; Nicol, W.; Black, B.; Biddy, M.; Dowe, N.; Beckham, G. Succinic acid production on xylose-enriched biorefinery streams by Actinobacillus succinogenes in batch fermentation. Biotechnol. Biofuels 2016, 9, 1−15. (25) Ouyang, X.; Ke, L.; Qiu, X.; Guo, Y.; Pang, Y. Sulfonation of Alkali Lignin and Its Potential Use in Dispersant for Cement. J. Dispersion Sci. Technol. 2009, 30, 1−6. (26) Ballesteros, I.; Ballesteros, M.; Cabañas, A.; Carrasco, J.; Martín, C.; Negro, M. J.; Saez, F.; Saez, R. Selection of thermotolerant yeasts for simultaneous saccharification and fermentation (SSF) of cellulose toethanol. Appl. Biochem. Biotechnol. 1991, 28-29, 307−315. (27) Min, D. J.; Choi, K. H.; Chang, Y. K.; Kim, J. H. Effect of operating parameters on precipitation for recovery of lactic acid from calcium lactate fermentation broth. Korean J. Chem. Eng. 2011, 28, 1969−1974. (28) Wei, J.; Yuan, Q.; Wang, T.; Wang, L. Purification and crystallization of xylitol from fermentation broth of corncob hydrolysates. Front. Chem. Eng. China 2010, 4, 57−64. (29) Li, Q.; Wang, D.; Wu, Y.; Li, W.; Zhang, Y.; Xing, J.; Su, Z. One step recovery of succinic acid from fermentation broths by crystallization. Sep. Purif. Technol. 2010, 72 (3), 294−300. (30) Lignin. Kirk-Othmer Encyclopedia of Chemical Technology, 4th ed.; Wiley: New York, 2001. (31) Peters, M. S.; Timmerhausin, D. K. Plant design and economics for chemical engineers; Wiley-VCH: Weinheim, 1996; pp 341−740. (32) Rivera-Toledo, M.; Flores-Tlacuahuac, A. A Multiobjective Dynamic Optimization Approach for a Methyl-Methacrylate Plastic Sheet Reactor. Macromol. React. Eng. 2014, 8, 358−373.

(33) Bioethanol as a growth market. http://www.cropenergies.com/ en/Bioethanol/Markt/Dynamisches_Wachstum/ (accessed Jan 15, 2017). (34) Grandview Research. Lactic Acid and Poly Lactic Acid (PLA) Market Analysis by Application (Packaging, Agriculture, Transport, Electronics, Textiles) and Segment Forecasts To 2020. http://www. grandviewresearch.com/industry-analysis/lactic-acid-and-poly-lacticacid-market (accessed Jan 1, 2017). (35) Deloite Access Economics. Economic impact of a future tropical biorefinery industry in Queensland, 2014; https://cms.qut.edu.au/__ data/assets/pdf_file/0004/482728/ife-biorefinery-report.pdf (accessed Jan 4, 2017). (36) Industry Experts. Xylitol − A Global Market Overview, 2017;http://industry-experts.com/verticals/food-and-beverage/xylitola-global-market-overview (accessed Feb 1, 2017). (37) Global Market Insights. Lignosulfonates market size by product (calcium, sodium, magnesium) by application, 2016; https://www. gminsights.com/industry-analysis/lignosulfonates-market (accessed Feb 1, 2017). (38) Hart, P. W.; Sommerfeld, J. T. Cost Estimation of Specialty Chemicals From Laboratory-Scale Prices. Cost Eng. 1997, 39, 31−35. (39) Rivera-Toledo, M.; Del Río-Chanona, E.; Flores-Tlacuahuac, A. Multiobjective dynamic optimization of the cell-cast process for poly (methyl methacrylate). Ind. Eng. Chem. Res. 2014, 53, 14351−14365. (40) Marler, R. T.; Arora, J. S. Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization. 2004, 26, 369−395. (41) Rivera-Toledo, M.; Flores-Tlacuahuac, A. A Multiobjective Dynamic Optimization Approach for a Methyl-Methacrylate Plastic Sheet Reactor. Macromol. React. Eng. 2014, 8, 358−373. (42) Dowling, A. W.; Ruiz-Mercado, G.; Zavala, V. M. A framework for multi-stakeholder decision-making and conflict resolution. Comput. Chem. Eng. 2016, 90, 136−150. (43) Yang, J.; Liu, B.; Whidborne, J. F. Multiobjective Optimisation and Control; John Wiley and Sons: New York, 2003. (44) Collette, Y.; Siarri, P. Multiobjective Optimization: Principles and Case Studies, Decision Engineering; Springer: Berlin, 2003. (45) Chen, Y.; Song, G.; Yang, F; Zhang, Y; Liu, Z.; Zhang, S. Risk Assessment and Hierarchical Risk Management of Enterprises in Chemical Industrial Parks Based on Catastrophe Theory. Int. J. Environ. Res. Public Health 2012, 9, 4386−4402. (46) Khan, F. I.; Abbasi, S. A. Risk assessment in chemical process industries; Discovery Publishing House: New Delhi, 1998. (47) Ruiz-Mercado, G. J.; Smith, R. L.; Gonzalez, M. A. Sustainability indicators for chemical processes: I. Ind. Eng. Chem. Res. 2012, 51, 2309−2328. (48) Bailey, H. C.; Liu, D. G.; Javitz, H. A. Time/Toxicity Relationships in Short-Term Static, Dynamic and Plug-Flow Bioassays. Aquatic toxicology and hazard assesment: Eight Symposium. 1985, 193− 212. (49) Ruiz-Mercado, G. J.; Smith, R. L.; Gonzalez, M. A. Sustainability Indicators for Chemical Processes: I. Taxonomy. Ind. Eng. Chem. Res. 2012, 51, 2309−2328. (50) Mussatto, S. I.; Moncada, J.; Roberto, I. C.; Cardona, C. A. Techno-economic analysis for brewer’s spent grains use on a biorefinery concept: The Brazilian case. Bioresour. Technol. 2013, 148, 302−310.

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