Optimal Extractive Distillation Process for Bioethanol Dehydration

Optimal Extractive Distillation Process for Bioethanol Dehydration

Jiří Jaromír Klemeš, Petar Sabev Varbanov and Peng Yen Liew (Editors) Proceedings of the 24th European Symposium on Computer Aided Process Engineering...

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Jiří Jaromír Klemeš, Petar Sabev Varbanov and Peng Yen Liew (Editors) Proceedings of the 24th European Symposium on Computer Aided Process Engineering – ESCAPE 24 June 15-18, 2014, Budapest, Hungary. Copyright © 2014 Elsevier B.V. All rights reserved.

Optimal Extractive Distillation Process for Bioethanol Dehydration Anton A. Kiss,a,* Radu M. Ignat,b Costin S. Bildeab a

AkzoNobel Research, Development & Innovation, Process Technology ECG, Zutphenseweg 10, 7418 AJ Deventer, The Netherlands. b University Politehnica of Bucharest, Department of Chemical Engineering, Str. Gh. Polizu 1-7, 011061 Bucharest, Romania [email protected]

Abstract The large-scale production of bioethanol fuel requires energy demanding distillation steps to concentrate the diluted streams from the fermentation step and to overcome the presence of the ethanol-water azeotrope. The conventional separation sequence consists of three distillation columns performing several tasks with high energy penalties: preconcentration distillation (PDC), extractive distillation (EDC) and solvent recovery (SRC) columns. Remarkable, almost all papers on this topic focus on the azeotropic separation only, while neglecting the pre-concentration step. Usually, the ethanol concentration in the first distillate stream is arbitrarily considered close to the azeotropic composition. While the energy usage in the PDC increases as the distillate composition gets closer to the azeotrope, the energy requirements in the EDC-SRC units decreases as the feed to EDC becomes richer in ethanol – and the other way around. This paper addresses this key trade-off of the distillate composition – a fundamental issue that was not studied before. Aspen Plus simulations were used to investigate how this parameter affects the energy usage and investment costs of the complete system. This issue applies in any other methods using a pre-concentration column (e.g. extractive and azeotropic distillation). The optimal economics is reached at a distillate concentration of 91.0 %wt ethanol, where the specific energy use is only 2.11 kWh (7,596 kJ) per kg ethanol. Keywords: extractive distillation, economic optimum, process optimization

1. Introduction Bioethanol is one of the most promising alternative and sustainable biofuel. The bioethanol production at industrial scale relies on several processes, such as: corn-toethanol, sugarcane-to-ethanol, basic and integrated lignocellulosic biomass-to-ethanol. After the initial pre-treatment steps, the raw materials enter the fermentation stage where ethanol is produced (Vane, 2008). A common feature of all these technologies is the production of diluted bioethanol – about 5-12 %wt ethanol – that needs to be further concentrated to a maximum allowed water content of 0.2 %vol (EU), 0.4 %vol (Brazil) or 1.0 %vol (US) according to various bioethanol standards. Several energy demanding separation steps are required to reach high purities, mainly due to the presence of the binary azeotrope ethanol-water (95.63 %wt ethanol). The first step is carried out in a pre-concentration distillation column (PDC) that concentrates ethanol from 5-12 % up to near azeotropic compositions (Frolkova, 2012). The second step is the ethanol dehydration up to higher concentrations above the azeotropic composition, hence it is more complex and of greater research interest.

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Ethanol Solvent (Ethylene glycol) EDC Water PDC-TOP

SRC

PDC Feed

EDC-BTM

PDC – pre-concentration distillation column EDC – extractive distillation column SRC – solvent recovery column

Water

Solvent (recycle)

Figure 1. Flowsheet for bioethanol pre-concentration and dehydration by extractive distillation

Several alternatives are also available and well described in the literature (Frolkova, 2012): pervaporation, adsorption, pressure-swing distillation, extractive distillation (ED), azeotropic distillation (AD), as well as hybrid methods (Vane, 2008). Extractive distillation (ED) remains the option of choice in case of large scale production of bioethanol fuel, and it involves an extractive distillation column (EDC) and a solvent recovery column (SRC) for the ethanol dehydration – see Figure 1. Almost all reports focus only on the separation of ethanol-water azeotrope, neglecting the preconcentration step. Typically, the ethanol concentration in the first distillate stream is arbitrarily considered close to the azeotropic composition. Though the energy usage in the PDC increases as the distillate composition approaches the azeotrope, the energy requirements in the EDC and SRC units decrease correspondingly as the feed to EDC becomes richer in ethanol. This paper addresses this key trade-off of the distillate composition – a fundamental issue that was not studied before. A mixture of 10 %wt (4.2 %mol) ethanol is concentrated and dehydrated using ethylene glycol as solvent. Rigorous Aspen Plus simulations were used to investigate how this parameter affects the energy usage and investment costs of the complete system. Note that this important issue applies in any other dehydration methods using a pre-concentration column.

2. Problem statement The composition of the distillate from the PDC unit is a key design optimization variable that was so far neglected in the optimal design of extractive distillation systems for ethanol dehydration. For example, Ryan and Doherty (1989) assumed a composition of 94.9 %wt (88 %mol) ethanol which is rather close to the azeotropic composition, while other authors (Kiss and Suszwalak, 2012; Li and Bai, 2012) selected more practical compositions of about 93.5 %wt (85 %mol). The problem is how to select this key design parameter such that the energy requirements and the capital cost of the two sections of the process (pre-concentration and dehydration of ethanol) are economically balanced to minimize the overall costs. To solve this problem, we investigate here the effect of the PDC distillate composition and prove that the optimal value is lower than what was considered so far in the literature.

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3. Results and discussion Extractive distillation performs the separation of close boiling components or azeotropes in the presence of a miscible, high boiling, relatively non-volatile component that forms no azeotrope with the other components in the mixture. For the ethanol-water mixture, ethylene glycol remains the most common entrainer used in extractive distillation processes. However, the use of ethylene glycol could become restricted in the future due to its toxicity. For this reason novel solvents are currently explored, as for example: glycerol, hyperbranched polymers and ionic liquids. In this work, Aspen Plus simulations were performed using the rigorous RADFRAC unit for distillation. NRTL (non-random two-liquid) was used as the most adequate property method, due to the presence of a non-ideal mixture containing polar components. The ternary mixture ethanol-water-glycol presents a single binary azeotrope and no liquid phase splitting – as shown in previous work (Kiss and Suszwalak, 2012). The feed used here is the diluted bioethanol stream (10 %wt or 4.2 %mol ethanol) obtained by fermentation. This is distilled to a composition below the azeotropic one, and then dehydrated to a purity of over 99.8 %wt ethanol, to comply with all the bioethanol standards. The production rate considered in this work is 100 ktpy bioethanol. The conventional sequence presented in Figure 1 consists of three distillation units: preconcentration distillation column (PDC), extractive distillation column (EDC) and solvent recovery column (SRC). The first column (PDC) in the sequence has the function to separate water as bottom stream and a near-azeotropic composition mixture as distillate – sent afterward to the second column (EDC). In the EDC unit, ethylene glycol is added on a stage higher than the feed stage of the ethanol-water mixture. Due to the presence of the solvent the relative volatility of ethanol-water is changed such that the separation becomes possible. High purity ethanol is collected as top distillate product of the EDC, while the bottom product contains only solvent and water. The solvent is then completely recovered in the bottom of the third column (SRC), cooled in a heat recovery system, and then recycled back to the extractive distillation column. An additional water stream is obtained as distillate of the SRC unit. The bottom product of the SRC unit constitutes the solvent recycle stream. The SQP optimization method and the effective sensitivity analysis tool from Aspen Plus® were used in the optimization procedure of all processes. The SQP (sequential quadratic programming) method has become one of the most successful methods for solving nonlinearly constrained optimization problems (Bartholomew-Biggs, 2008). In this particular study, the objective of the optimization is to find the optimal trade-off between the energy requirements and the equipment cost, both translated into the total annual cost (TAC). The objective function that is used approximates very well the minimum of total annualized cost of a conventional distillation column. The procedure was described in detail in our recently published work (Kiss and Ignat, 2012). min NT (RR+1) = f (NT,i, NF,i, SFR, RRi, Vi) Subject to

(1)

& & y m t xm

where i is the distillation column (PDC, EDC, SRC), NT is the total number of stages, NF is the feed stage, SFR is the solvent-to-feed ratio, RR is the reflux ratio, V is the boilup rate for each of the three columns, while ym and xm are vectors of the obtained and required purities for the m products.

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Table 1. Results of the sensitivity analysis: key performance indicators (KPI) as function of the composition of the pre-concentrated ethanol stream Preconcentrated EtOH (%wt) 75.0 80.0 85.0 87.0 89.0 90.0 91.0 91.5 92.0 93.0 93.5

Total investment cost (TIC) $4,299,460 $4,197,003 $4,138,478 $4,054,603 $3,983,370 $3,951,436 $3,915,109 $3,969,593 $3,994,262 $4,199,949 $4,409,534

Total operating cost (TOC) $6,003,454 $5,842,719 $5,684,488 $5,590,383 $5,506,929 $5,493,809 $5,475,770 $5,542,080 $5,624,435 $6,042,605 $6,445,864

Total annual cost (TAC) $6,433,400 $6,262,419 $6,098,336 $5,995,843 $5,905,266 $5,888,952 $5,867,281 $5,939,039 $6,023,861 $6,462,600 $6,886,817

Reboiler duties: PDC, EDC, SRC (kW) 18135 / 6658 / 4025 18427 / 6347 / 3292 18487 / 6259 / 2578 18547 / 6021 / 2315 18608 / 5833 / 2051 18680 / 5823 / 1927 18847 / 5673 / 1829 19208 / 5658 / 1793 19777 / 5589 / 1679 21885 / 5577 / 1542 23865 / 5574 / 1453

Specific energy use (kW/kg EtOH) 2.31 2.25 2.19 2.15 2.12 2.12 2.11 2.13 2.16 2.32 2.47

In order to perform a fair comparison between all process alternatives, the total investment costs (TIC), total operating costs (TOC) and total annual costs (TAC) were calculated, as described in our previous studies (Kiss and Suszwalak, 2012). The equipment costs are estimated using correlations from the Douglas textbook, updated to the level of 2010. The Marshall and Swift equipment cost index (M&S) considered in this work has a value of 1468.6. Moreover, a price of 600 US $/m2 was used for calculating the cost of the sieve trays, and the following utility costs were considered: US $ 0.03 per ton cooling water and US $ 13.0 per ton steam. For the TAC calculations, a total plant lifetime of 10 years was considered (Kiss and Ignat, 2012).

2.50

$7,000

2.45

$6,800

2.40 TAC/ [US k$]

Specific energy use / [kWh/kg]

The composition of the pre-concentrated ethanol stream was varied in the range 75-93.5 %wt (54-85 %mol) ethanol and for each value considered the process flowsheet was optimized. Table 1 shows the main results of the sensitivity analysis, including the total investment costs (TIC), total operating costs (TOC) and the total annual cost (TAC) as well as the total reboiler duty and the specific energy use per kg product. In addition, Figure 2 shows the optimal composition value of the pre-concentrated ethanol stream for minimal specific energy use and lowest total annual cost (TAC). It is worth noting that when the pre-concentrated ethanol stream has a composition below the optimal value, the duty of the PDC decreases with the ethanol concentration in the distillate, while the duties of the EDC and SRC units increases since more effort is needed to remove the higher amount of remaining water.

2.35 2.30 2.25 2.20

$6,600 $6,400 $6,200 $6,000

2.15 2.10

$5,800

74

76

78 80 82 84 86 88 90 Pre-concentrated ethanol / [wt%]

92

94

74

76

78 80 82 84 86 88 90 Pre-concentrated ethanol / [wt%]

92

Figure 2. Specific energy use per kg of ethanol product (left) and total annual cost (right), as function of the composition of the pre-concentrated ethanol stream

94

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Similarly, for pre-concentrated compositions higher than the optimal value, the duties of the EDC and SRC units is lower since less effort is needed to remove the smaller amount of remaining water. However, the duty of the PDC unit has a very steep increase due to approaching the azeotropic composition. Balancing these two effects lead to the optimal value of 91.0 %wt ethanol in the pre-concentrated stream. Remarkable, just by changing this key parameter, over 15 % energy savings are possible in existing plants that still use a pre-concentrated stream of near azeotropic composition. It is also worth mentioning that a similar value for the trade-off concentration (80 %mol ethanol in beer-still distillate) was reported for a heterogeneous azeotropic distillation process for ethanol dehydration, using a slightly more concentrated ethanol feed (5 % mol) and benzene or cyclohexane as light entrainers (Luyben, 2012). Table 2 lists the key design and process parameters of the optimized flowsheet. Note that in case of the non-optimal configurations, the number of stages varies within r 20 % more or less stages depending on the separation difficulty. The effect of the pre-concentrated ethanol composition on the equipment design can be summarized as follows: the number of stages and the diameter of the PDC column increases with the ethanol concentration in the pre-concentrated stream, due to the more difficult separation and higher reflux required. However, for the EDC and SRC columns the variation of the required number of stages is rather minor due to the insignificant change in the separation difficulty, while the column diameters are increasing at lower pre-concentrated composition since more water is present in the feed. In order to assess the controllability of the optimal design, a dynamic simulation model was built using Aspen Dynamics. For all columns, the pressure is controlled by condenser duty, while the distillate and bottoms flow rates are used to control the levels in the reflux drums and column sumps, respectively. The pre-concentration column is operated at constant reflux ratio, while the temperature in the stripping section (stage 25) is controlled by the reboiler duty. Similarly, the EDC unit is operated at constant reflux, constant solvent to feed ratio, the temperature in the lower part (stage 15) being controlled by the reboiler duty. Dual temperature control (stages 4 and 13), by means of reflux rate and reboiler duty, is employed for the solvent recovery column. Figure 3 presents results of dynamic simulation which prove the controllability of the optimal design. Starting from the steady state, the feed flow rate is increased by 10 %, from 125 to 137 ton/h (Figure 3, left). The transitory regime lasts for about 2 h, new values for the product flows being established. The water and ethanol purity remain very close to the initial value. In a second simulation (Figure 3, right), the concentration of the raw material is reduced from 10 - 8 %wt ethanol. The new values of the product flow rates are achieved in about 2 h, with minor deviations of the product purities. 0.12

PDC Water PDC Feed SRC Water

0.08

0.998

EDC Ethanol

0.06

0.997

0.04

0.996 0.995 0

2

1

0.1

4

6

Time / [h]

8

10

Mass fraction

Mass fraction

1 0.999

0.12

PDC Water

0.1

SRC Water

0.999 PDC Feed

0.08

0.998

0.06

EDC Ethanol

0.997

0.02

0.996

0

0.995

0.04 0.02 0 0

2

4

6

8

10

Time / [h]

Figure 3. Dynamic simulation results for +10 % feed flowrate disturbance (left) and a reduction from 10 to 8 %wt of ethanol concentration in the feed (right)

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Table 2. Design parameters of an optimal conventional ED sequence for bioethanol dehydration Design parameters Total number of stages Feed stage number Feed stage of extractive solvent Column diameter Operating pressure Ethanol : water (feed mass fraction) Water : solvent (feed mass fraction) Ethanol (amount in feed stream) Water (amount in feed stream) Solvent (amount in feed stream) Reflux ratio Reboiler duty Condenser duty Ethanol recovery Water recovery Solvent (EG) recovery Purity of bioethanol product Purity of water by-product Purity of ethylene glycol recycle

PDC 30 19 – 2.9 1 0.1 : 0.9 – 12500 112500 0 1.31 18847 -8600 – 99.98 – – 99.99 –

EDC 17 11 4 1.5 1 0.91 : 0.09 – 12494 1236 20793 0.24 5673 -3652 99.96 – – 99.80 – –

SRC 16 8 – 1 1 – 0.055 : 0.945 0.625 1215 20788 0.45 1829 -1112 – 99.98 99.91 – 99.99 99.99

Unit – – – m bar kg/kg kg/kg kg/hr kg/hr kg/hr kg/kg kW kW % % % %wt %wt %wt

4. Conclusions A key contribution of this study is creating awareness that the composition of the preconcentrated ethanol stream is an important design optimization variable for bioethanol dehydration, as well as calculating the optimal value of this parameter in order to obtain minimum total annual costs. Aspen Plus simulations were used to investigate how the trade-off of the distillate composition affects the energy usage and the investment costs of the complete system for ethanol dehydration by ED. Aspen Dynamics was employed as well to prove the controllability of the optimized process. The economical optimum was found at a distillate concentration of 91.0 %wt (or ~80 %mol) ethanol, where the specific energy use is 2.11 kWh (7596 kJ) per kg ethanol (Kiss and Ignat, 2013).

References M. Bartholomew-Biggs, 2008, Nonlinear optimization with engineering applications, Springer, New York, US. K. Frolkova, V. M. Raeva, 2012, Bioethanol dehydration: State of the art, Theoretical Foundations of Chemical Engineering, 44, 545-566. A. Kiss, R. M. Ignat, 2012, Innovative single step bioethanol dehydration in an extractive dividing-wall column, Separation & Purification Technology, 98, 290-297. A. Kiss, D. J-P. C. Suszwalak, 2012, Enhanced bioethanol dehydration by extractive and azeotropic distillation in dividing-wall columns, Separation & Purification Technology, 86, 70-78. A. Kiss, R. M. Ignat, 2013, Optimal economic design of a bioethanol dehydration process by extractive distillation, Energy Technology, 1, 166-170. G. Li, P. Bai, 2012, New operation strategy for separation of ethanol-water by extractive distillation, Industrial and Engineering Chemistry Research, 51, 2723-2729. W. L. Luyben, 2012, Economic optimum design of the heterogeneous azeotropic dehydration of ethanol, Industrial and Engineering Chemistry Research, 51, 16427-16432. P. J. Ryan, M. F. Doherty, 1989, Design/optimization of ternary heterogeneous azeotropic distillation sequences, AIChE Journal, 35, 1592-1601. L. M. Vane, 2008, Separation technologies for the recovery and dehydration of alcohols from fermentation broths, Biofuels, Bioproducts and Biorefining, 2, 553-588.