Design and Control of an Alternative Bioethanol Purification Process

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Design and Control of an Alternative Bioethanol Purification Process via Reactive Distillation from Fermentation Broth Devrim B. Kaymak Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b04832 • Publication Date (Web): 04 Jan 2019 Downloaded from http://pubs.acs.org on January 11, 2019

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Industrial & Engineering Chemistry Research

Paper submitted to Industrial & Engineering Chemistry Research

Design and Control of an Alternative Bioethanol Purification Process via Reactive Distillation from Fermentation Broth

Devrim B. Kaymak* Department of Chemical Engineering Istanbul Technical University 34469, Maslak, Istanbul, Turkey

*To

whom correspondence should be addressed: [email protected]; Phone: +90-212-285-3539; Fax: +90-212-285-2925

October 02, 2018 Revision: December 25, 2018 1 ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 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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Abstract Bioethanol is considered as one of the most promising renewable energy sources. However, since the concentration of bioethanol obtained from fermentation process is very low, and there is a minimum boiling point azeotrope between water and ethanol, the well-known separation techniques result in an increase in the capital and operating costs of bioethanol separation processes. Thus, alternative process configurations to purify bioethanol is in the focus of attention of industry. In this study, an enhanced process configuration is economically evaluated and dynamically controlled based on the idea of consuming the water by reacting it with ethylene oxide to produce ethylene glycol, and thus obtaining pure bioethanol by breaking the water-ethanol azeotrope without adding any separation agent. Design results show that this configuration is an attractive option, because it outperforms the base-case design given in the literature by a 19.3 % decrease in total annual cost (TAC). In addition, it generates an economic income by producing ethylene glycol as an added-value byproduct. Dynamic simulations of the enhanced process configuration show that the control structure design including inferential temperature loops ensures a stable regulatory control. Keywords: Renewable energy sources; bioethanol purification; reactive distillation; azeotropic mixture; process design; process control 1. Introduction In recent years, there is an increasing concern of industry on renewable energy sources such as biofuels related to the economic and environmental sustainability. Among several biofuels, bioethanol comes forward as a promising alternative. However, the broth produced as the result of

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fermentation process includes a significant amount of water (up to 95 mole %), which must be withdrawn to obtain bioethanol satisfying the current international standards. In addition, the minimum boiling point azeotrope of ethanol-water mixture (85 mole % ethanol and 15 mole % water) at 78.2oC and atmospheric pressure makes it impossible to attain anhydrous ethanol by using conventional distillation columns. For that reason, several improvements have been done in separation technologies and process designs for the purification of bioethanol from fermentation broth which are summarized in several review papers1-3. Recently, Singh and Rangaiah4 published a review paper which focused on the technological advances in bioethanol dehydration and purification processes starting from 2008. They first classified the studies based on the ethanol concentration of the feed streams. Although some of the feed streams consist of fermentation broth (4-12 wt %), there are also several studies where the ethanol concentration is at intermediate (12-90 wt %) and near azeotropic (90-95 wt %) levels. Each of these classifications is also splitted into two categories based on the separation methods such as processes using distillation technologies including dividing-wall columns, and processes employing hybrid technologies. Processes focusing on hybrid technologies combine distillation columns with liquid-liquid extraction, pressure swing adsorption, pervaporation and membrane systems5-8. However, distillation is still the dominant technology in the industries of Brazil and United States, which are the world’s largest ethanol producers9,10. In addition, extractive distillation is one of the most major distillation technologies used in the industries of these countries11. Garcia-Herreros et al.12 designed a process consisting of an extractive column and a recovery column where the feed is an azeotropic mixture of ethanol and water. For a feed stream at a near-azeotropic composition, Li



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and Bai13 suggested a three-column process where a concentrator column is added after the recovery column. Errico et al.14 modified this process by adding a pre-concentrator in front of the extractive column where the feed is a fermentation broth including ethanol and water, and this base-case design is given in Figure 1. In their follow-up studies, they developed alternative distillation sequences for bioethanol separation applying thermal coupling or combining column sections15. In addition, an extractive dividing-wall column is proposed by Kiss and Ignat16 for bioethanol purification. On the other hand, as recently stated by Singh and Rangaiah4, most of the papers in the literature focus on the design of alternative processes and use of alternative solvents, while the number of studies dealing with control of alternative processes are relatively few. Ramirez-Marquez et al.17 and Errico et al.18 studied the controllability of the process configurations designed by the same research group. Arslan and Kaymak19 also developed control structures for three alternative design configurations including extractive distillation columns. Control of an extractive dividing-wall column process is considered by Tututi-Avila et al.20. In addition, a heat integrated pressure-swing distillation process.is controlled by Mulia-Soto and Flores-Tlacuahuac21. Although the distillation-related technologies are relatively mature and widely used in the industry, they are still energy intensive. Thus, alternative configurations are investigated to decrease energy requirements and operating cost for bioethanol purification. It is known that there are reactive distillation column studies in the literature where high purity ethylene glycol is produced as a result of ethylene oxide-water reaction. In these papers, the distillation columns operate at total reflux, and ethylene glycol is removed through the bottoms of column22,23. Tavan and Hosseini24 and An et al.25 translated this idea into a novel process design for ethanol dehydration. In their studies, the


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feed streams consist of ethylene oxide and a near-azeotropic mixture of ethanol and water. Ethylene oxide reacts with water in the mixture to produce ethylene glycol. Since water is consumed as the result of reaction, ethanol-water azeotrope is broken and pure ethanol leaves the distillation column from the top, while ethylene glycol is taken from the bottoms as a co-product. On the other hand, these studies do not take the pre-concentration step of a bioethanol purification process into account, because a near-azeotropic mixture is used as the feed stream instead of a fermentation broth. In addition, these papers cover only parametric studies based on steady-state simulations. However, they include neither the dynamic behavior nor the controllability of the process. It is a well-known fact that the control studies of new separation processes are essential besides the steady-state design advancements, since applicability and success of a process depend on both the design and control properties. As far as we know, there is no bioethanol purification study in the open literature that investigate design and control of a reactive distillation process starting from the fermentation broth. Thus, the aim of this study is to design and control a process configuration for bioethanol purification starting from fermentation broth based on the idea given by Tavan and Hosseini24. In this study, a two-column process is developed as an alternative to the process configurations including extractive distillation columns. The enhanced configuration involves a pre-concentrator column which is followed by a reactive distillation column. The steady-state results of this process is compared with the results of the base-case configuration suggested by Errico and co-workers14 in terms of total annual cost. In addition, alternative control structures are designed for the optimum steady-state configuration, and their robustness is tested using several disturbances.



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2. Process Studied The flowsheet of the process including two distillation columns is given in Figure 2. The fresh feed flowrate of fermentation broth is 1700 kmol/h. The reason for choosing this feed flowrate is to obtain the same flowrate of the product stream (85 kmol/h) as given in Li and Bai13. Although a realistic fermentation broth includes several components26, almost all the papers in the literature used a feed stream including only ethanol and water. Thus, a binary mixture including 5 mole % ethanol is used in this study as the fermentation broth. The reason for choosing this feed composition is to make a fair comparison with the base-case design using the same feed composition. 5 mole % ethanol corresponds to 11.86 wt% ethanol. First, the fermentation broth is fed into a pre-concentrator column. This column is used to remove the excess water from the system. The excess water is taken from the bottoms of the column. The stream taken from the distillate contains an ethanol-water mixture at near-azeotropic composition. This stream including 85 mole % ethanol and 15 mole % water is fed to the reactive distillation column. The second feed of the reactive distillation column is pure ethylene oxide with a flowrate of 15 kmol/h. High purity ethylene glycol is produced as the result of ethylene oxide-water reaction. There are two side reactions where two unwanted byproducts, di- and triethylene glycol, can be produced as the result of further reaction of ethylene oxide with ethylene glycol. However, Tavan and Hosseini neglected the kinetics of side reactions in their study. This is a fair assumption, because there is a negligible effect of side reactions compared with the main reaction. This is due to very high reaction rate of the main reaction compared with the rates of side reactions. Thus, the following reaction kinetics taken from Tavan and Hosseini24 are used in this study.


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Ethylene Oxide + Water → Ethylene Glycol

(1)

r (kmol m-3 s-1) = 3.15 x 1012 exp[-9547/T] XEO XWater

(2)

These kinetics have been used in the literature for both atmospheric and higher pressures22,24. For this process, different thermodynamic models have been used in the literature such as NRTL, UNIQUAC and Wilson, which are taken part in the same group of property model selection tree22,24. Here, the NRTL method suggested by Tavan and Hosseini24 is used as the thermodynamic model. In this process, the molar feed ratio of the reactants is selected as 1.0. Specifications of the products are 0.997 mole% bioethanol in the distillate and 0.997 mole% ethylene glycol in the bottoms. 3. Process Design Steady-state simulation studies are conducted using RADFRAC, the rigorous distillation column model of the commercial process simulator Aspen Plus. Before applying an economic evaluation, the effects of design variables on distillation column behaviors are studied using the sensitivity analysis. It should be stated that theoretical stages are used in this study. 3.1.

Pre-concentrator Column

The pre-concentrator column operates at a pressure of 1 atm. Design specifications of this column are chosen as the ethanol mole recovery and purity in the distillate stream. The target specifications, which are 0.9999 and 0.85, respectively, are attained by varying the distillate rate and reflux ratio. This is accomplished by using Design Spec/Vary property of Aspen Plus. Once the operating pressure and design specifications are determined, first the effect of feed tray location



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is investigated. By keeping the total tray number constant at 40, the simulation is run using different feed trays. Figure 3A shows how the change in the feed trays affects the reboiler heat duty. Results show that the minimum reboiler energy consumption is achieved by feeding on tray 30. Then, the number of stages are changed to investigate the optimum number of theoretical trays satisfying the design specification. The ratio between feed tray location and total number of theoretical trays is fixed based on the results of previous simulation [ratio = (30-1)/(40-2) = 0.763]. The simulation is run to find the total number of theoretical trays minimizing the total annual cost. As it is seen from Figure 3B, the optimum number of theoretical trays is 48 with a feed stage location of 37. 3.2.

Reactive Distillation Column

Column pressure is a dominant design variable for reactive distillation columns. Besides its effects on vapor-liquid equilibrium, reaction kinetics are also significantly affected by pressure, because operating pressure is directly related with the temperature profile through the column, and reaction kinetics are direct function of temperature. To be able to achieve the desired specifications, which are the purities of top and bottoms products, the necessary conversion should be ensured for the reaction of water and ethylene glycol. Otherwise, the unreacted reactants must to leave the column as impurities from the distillate or bottoms depending on their relative volatilities, and the purity of product streams are directly affected by this situation. First, a simulation is run for a reactive distillation column operating at 1 atm pressure with a total tray number of 41. For this case, there is a stripping section with two trays which is located below the reactive zone. The azeotropic mixture is fed from Tray 29, while ethylene oxide is fed from Tray 31. Results illustrate that the target purity specifications cannot be obtained, since the conversion of the reaction is very low at


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1 atm (conv = 0.886). Thus, the effect of operating pressure on the conversion of water and ethylene oxide is investigated. It is seen from Figure 4 that a minimum pressure of 4.5 atm should be used to achieve a 99.5 mole % conversion, if no excess of ethylene oxide is fed. Once the operating pressure is determined, than the effect of other parameters on reboiler heat duty and TAC are studied. For the reactive distillation column, ethanol and ethylene glycol mole purities at the top and bottoms of the column, respectively, are selected as design specifications. The desired purities for both products are 0.997 mole%. These specifications are achieved using the Design Spec/Vary property of Aspen Plus. Distillate rate and reflux ratio are chosen as the design variables to be varied in the simulations. One of the parameters investigated for reactive distillation column design is the feed tray locations of ethylene oxide and azeotropic mixture. Figure 5A shows the effect of ethylene oxide feed tray on the reboiler heat duty and TAC. In this case, total tray numbers is fixed to 41 and azeotropic mixture is fed from Tray 31. It is seen that the reboiler heat duty or TAC are not critically impacted by the location of ethylene oxide feed tray. Although the range of the reboiler heat duty value is very narrow, there is an optimum feed tray location. The minimum values for the reboiler heat duty and TAC are found when the ethylene oxide feed tray is located at Tray 38, which is the lowest reactive tray. Since the boiling point of ethylene oxide is very low compared to that of other components taking part in the process, it is very reasonable to feed this stream from the bottom of reactive section. Figure 5B gives the effect of azeotropic feed tray on the reboiler heat duty and TAC with fixed total theoretical trays of 41 and ethylene oxide feed tray location of Tray 38. It is apparently seen that the location of the azeotropic feed tray is a dominant variable and significantly effects the reboiler heat duty. As the feed tray is moved down through the distillation column, reboiler heat



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duty necessary to satisfy the design specifications decreases gradually. It is found that the azeotropic feed tray located at Tray 37 gives the minimum reboiler heat duty and TAC. To investigate the effect of reactive trays, total tray numbers are first decreased from 41 to 38 and 35, respectively. In this case, the reactive zone is located above a stripping section including two trays. For each case, ethylene oxide feed tray is fixed at the lowest reactive tray, while azeotropic feed tray is changed as a design variable. Since there is a change in the number of total theoretical trays, which results in a capital cost change besides the energy cost, the comparison is done based on TAC instead of reboiler heat duty reflecting only the operating costs. Figure 6A illustrates the effect of reactive trays on the total annual cost. To achieve the desired design specifications, there is an increase in the reboiler heat duty as the number of reactive trays decreases and location of azeotropic feed tray increases (trays are counted up to down). In addition, the magnitude of change in TAC decreases as the number of reactive trays increases. Then, the total tray numbers (NT = 41) are fixed and the number of reactive trays are changed to investigate the effect of reactive tray numbers. Three different cases are studied, where the number of reactive trays reduces from 37 to 34 and 31, respectively. For all cases, the upper reactive tray is fixed at the top of the column. For each case, azeotropic feed tray is changed as a design variable, while ethylene oxide feed tray is fixed at the lowest reactive tray. It is seen from Figure 6B that the increase in the reactive trays and the decrease in the location of azeotropic feed tray result in a decrease in the reboiler heat duty to achieve the desired design specifications. Besides, the increase in the number of reactive trays decreases the magnitude of change in reboiler heat duty.


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3.3.

Economic Evaluation

After the effects of process variables on the design are observed, the new configuration is optimized economically. The objective function of the optimization problem to be minimized is the total annual cost including capital and energy cost terms.

𝑇𝐴𝐶 =

𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐶𝑜𝑠𝑡 𝑝𝑎𝑦𝑏𝑎𝑐𝑘 𝑝𝑒𝑟𝑖𝑜𝑑

+𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑠𝑡

(3)

In this study, a 3 years payback period is assumed. Capital cost for main equipments is calculated using standard cost formulation with an M&S equipment cost index of 1536.5 in 201227.

𝐶𝐻𝐸𝑋($) =

𝑀&𝑆

𝐶𝑠ℎ𝑒𝑙𝑙($) =

𝐶𝑡𝑟𝑎𝑦𝑠($) =

280

(474.7𝑥(𝐴𝑐 + 𝐴𝑟)0.65)(2.29 + 𝐹𝑚(𝐹𝑑 + 𝐹𝑝))

𝑀&𝑆 280

(957.9𝐷1.066𝐻0.802)(2.18 + 𝐹𝑚 𝑥 𝐹𝑝)

𝑀&𝑆 280

(97.2𝑁𝑇𝐷1.55)(2.29 + 𝐹𝑠 + 𝐹𝑡 + 𝐹𝑚)

(4)

(5)

(6)

For the calculation of heat exchanger areas (Ac and Ar), overall heat transfer coefficient U is taken 0.852 kW/K.m2 and 0.568 kW/K.m2 for condensers and reboilers, respectively. In addition, differential temperature is assumed 13.9 K and 34.8 for condensers and reboilers, respectively28. Tray sizing tool of Aspen Plus is used to obtain the column diameter (D). Column height is calculated by H = 1.2*0.61*(NT-2). As mentioned before, theoretical stages are used in this study, and this could affect the cost results. Correction factors related to material, pressure, design type of heat exchanger and tray type of distillation columns are given by Fm, Fp, Fd and Ft, respectively. On the other hand, energy cost is calculated for reboilers and condensers with low-pressure steam at 7.78 $/GJ, high-pressure steam at 9.88 $/GJ and cooling water at 0.72 $/GJ28. Since the 11 ACS Paragon Plus Environment


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economic evaluation is done based on TAC, neither raw material costs (i.e. ethylene oxide) nor selling price of products (i.e. ethylene glycol) are taken into account in this study. Table 1 gives the parameter values for the final design of the enhanced bioethanol separation process. Economic evaluations of the base-case design including four columns (Process I) and alternative design studied here (Process II) are compared in Table 2. The four-column configuration is chosen as a benchmark in this study, since it is the base configuration of extractive distillation systems which has found a wide application in the industry. These results show that the new design has a 19.3 % reduction in TAC compared with the base-case configuration. It should be noticed that ethylene glycol obtained as an added-value byproduct is an additional advantage of this process. Although the cost evaluation of this study is done using the formula given above with an assumption of payback period of 3 years, Errico et al.14 used Aspen Plus Economic Analyzer to evaluate the installation cost. However, as they also mentioned in their paper, Aspen Plus Economic Evaluator is not based on bare module factors. In addition, in their study, the capital cost was annualized considering a mean operational time of 10 years. For these reasons, there is a difference between total annual costs calculated in this study and in Errico et al, which is 1,613,000 $14. If a payback period of 10 years was used in this study, TAC of the base-case design could be found 1,964,329 $ using the formula given above. 4. Process Control Dynamic simulations of the process are run using Aspen Dynamics. The main purpose of the plantwide control is to hold the bioethanol and ethylene glycol purities at their set points against disturbances. P-only controllers with a gain of 2 are used for level control in the reflux drums and column bases. Temperature control loops are controlled using PI controllers. 1-min first-order


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measurement lags are added to these feedback control loops. In the case of composition control loops, PI controllers with a 3-min measurement lag are installed. ATV test and Tyreus–Luyben tuning rules are used to calculate the controller tuning parameters. These are built-in features of Aspen Dynamics. The span of 50 K is chosen for temperature transmitter, while a span of 0.1 mole fraction is used for composition transmitters. Control valves are half open at the steady-state design flowrates. Steady-state temperature profiles of distillation columns are used to decide the tray locations for temperature control. The robustness and performance of designed control structures are tested against disturbances such as ±20% throughput change and ±1% feed composition change (4% and 6% ethanol in fed stream). Since the ethanol fraction in the broth as the result of fermentation is very small compared to the water, a small feed composition disturbance is selected to make the test more realistic. As mentioned before, theoretical trays are used in this manuscript, and this could affect the results. In case of real trays, the increase of tray numbers could slow the dynamics of the process. 4.1. Control Structure 1 Figure 7 gives the first control structure (CS1) studied. There is a flow controller on fermentation broth stream which handles the production rate. For the pre-concentrator column, distillate and bottoms flowrates are manipulated to control the reflux drum and column base levels, respectively. Reflux is maintained in ratio with distillate using a ratio controller. Cooling water flowrate is manipulated to control the condenser pressure. Temperature of tray 39 is chosen to be controlled by manipulating reboiler heat duty based on the temperature profile given in Figure 8A. For the reactive distillation column, the distillate flowrate is manipulated to control the level of reflux drum, while the level of column base is controlled by manipulating the bottoms flowrate. A ratio controller is used to keep reflux and distillate flowrates in a ratio. Operating pressure of the column



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is controlled by manipulating the cooling water in the condenser. Tray 40 is selected for temperature control based on the temperature profile given in Figure 8B, and the reboiler heat duty is manipulated to control this tray temperature. The feed flowrate of ethylene oxide is arranged to control the feed stage (Tray 38) composition of ethylene oxide. This is done to satisfy the feed stream stoichiometry of reactive distillation column. Controller parameters for control loops are given in Table 3. Figure 9A gives the results of ±20% throughput changes for CS1. Although there is a large transient deviation at the beginning, it takes less than 3 h for the temperature of Tray 40 to settle down to its set point. On the other hand, settling of ethylene oxide concentration on Tray 38 takes longer time, but the magnitude of its transient deviation is smaller compared to the temperature control loop. Thus, the ethylene glycol purity in the bottoms settles down in less than 3 h with small transient deviations. However, recovery of bioethanol purity takes longer time. In addition, the magnitude of transient deviations is significantly bigger compared to that of ethylene glycol. On the other hand, although the magnitude of the offset for bioethanol purity against a positive step change is bigger than that of ethylene glycol purity, it is much smaller when the step change has a negative direction as seen in Table 3. As a result, offsets for both products are acceptable, and a stable base-level control is provided by this control structure. Figure 9B shows the responses of CS1 against ±1% feed composition disturbances. The control structure show quite similar behavior for both feed composition and production rate disturbances. In this case, product purities are kept close to their set points. Although the temperature on Tray 40 settles down to its set point in less than 3 h, it takes more than 5 h for the composition of ethylene oxide on Tray 38 to recovery back in its specification. Table 3 indicates that the magnitude of the offset for bioethanol purity is bigger than that of ethylene glycol purity, whatever


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the direction of the step change is. Similarly, settling time of the bioethanol is longer than that of the ethylene glycol. In addition, the purity of bioethanol shows significantly larger transient deviation, while a small transient deviation is observed for the purity of ethylene glycol. 4.2. Control Structure 2 The second control structure, CS2, is illustrated in Figure 10. This control structure has some similarities with CS1 in terms of common control loops. The main difference is that a ratio controller is used to hold the fresh feed of ethylene oxide in ratio with distillate flowrate, which helps to balance the stoichiometry between feed streams of reactive distillation column. The closed loop responses for ±20% throughput change are shown in Figure 11A. Reactive distillation column results show that the temperature of Tray 40 settles down into its setpoint in four hours. Similarly, ethylene glycol purity in the bottoms settles down in less than 3 h with small transient deviations. On the other hand, ethylene glycol has smaller transient deviations and shorter recovery time compared with those of bioethanol. Transient deviation of bioethanol against negative step change is bigger than that of positive step change, while the positive change has a bigger transient deviation for ethylene glycol. In CS2, transient deviations of bioethanol for step changes in both directions are smaller compared to the results of CS1. On the other hand, no significant change is observed for the dynamic behavior of ethylene glycol compared to CS1. Table 4 shows that both products settle down with an acceptable offset. As the result, CS2 provides a stable base-level control. Figure 11B illustrate the dynamic responses of CS2 against feed composition disturbances. The control structure show quite similar behavior for both feed composition and production rate disturbances. It takes less than 3 h for the temperature on Tray 40 to settle down to its set point. Although the purity of bioethanol has larger transient deviation compared to ethylene glycol purity, 15 ACS Paragon Plus Environment


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both product purities are recovered in a close vicinity of their set points. Similarly, settling time of the bioethanol is longer than that of the ethylene glycol, especially in the case of +1% feed composition disturbance. In addition, CS2 has smaller transient deviations for bioethanol compared to CS1. These results show that a control structure with inferential temperature controllers only responses as good as the one including a composition control loop besides the temperature controllers. It is noticed that the transient response of bioethanol purity is similar for both positive and negative disturbances, while the ethylene glycol purity and the related manipulated variables move to opposite directions. However, no explanation could be found for this dynamic behavior. Table 5 quantifies control performances of CS1 and CS2 in terms of Integral Absolute Errors (IAE). It is seen that IAE values of control loop for ethylene glycol purity at the bottoms are similar for both control structures against any type of disturbances. However, it is apparently seen that IAE value of CS2 for bioethanol purity control loop at the distillate is significantly better than that of CS1 for all types of disturbances. 5. Heat Integration Possibilities Since the basic idea in this study is to compare this alternative process with the base-case design given in the literature, process improvements such heat integration is not been taken into account. However, it should be noticed that there is a big difference in the temperatures of bottoms streams of two distillation columns, since the columns operate at different pressures. Thus, the bottoms stream of the second column could be used instead of fresh steam in the reboiler of the first column. An extra vapor boilup could be provided using an additional auxiliary steam-heated reboiler


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depending on the energy need in the low-pressure column. This heat integration might also help to decrease the temperature of the bottoms stream of the second column without using an extra heat exchanger. On the other hand, heat integration could also affect the control performance besides the steady-state process design. Thus, control of the heat integrated process should be also investigated. 6. Conclusions In this study, an alternative process configuration is evaluated to purify bioethanol from fermentation broth including water and bioethanol. This configuration mainly differs from the well-known process configurations including extractive distillation columns by consuming water in a reactive distillation column and thus breaking the water-bioethanol azeotrope to obtain pure bioethanol. This configuration helps to decrease the capital and energy costs due to the smaller reboiler heat duty and diameter of the reactive distillation column. As a result, a 19.3 % decrease in TAC is obtained compared with the base-case configuration given in the literature. In addition, ethylene glycol, an added-value byproduct, is obtained as the result of the reaction. For this configuration, two control structures are designed using Aspen Dynamics. They are tested against two different load disturbances. Dynamic simulation responses show that the suggested structures are robust, and the new process configuration is easily controlled by a control structure without any composition controller. Acknowledgement This research was financially supported by Research Fund of the Istanbul Technical University (Project Number: 39126).



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References [1] Cardona, C. A.; Sanchez, O. J. Fuel ethanol production: Process design trends and integration opportunities. Bioresour. Technol. 2007, 98, 2415-2457. [2] Vane, L. M. Separation technologies for the recovery and dehydration of alcohols from fermentation broth. Biofuel, Bioprod. Biorefin. 2008, 2, 553-588. [3] Frolkova, A. K.; Raeva, V. M. Bioethanol dehydration: State of the art. Theor. Found. Chem. Eng. 2010, 44, 545-556. [4] Singh, A.; Rangaiah, G. P. Review of technological advances in bioethanol recovery and dehydration. Ind. Eng. Chem. Res., 2017, 56, 5147-5163. [5] Aviles Martínez, A.; Saucedo-Luna, J.; Segovia-Hernandez, J. G.; Hernandez, S.; GomezCastro, F. I.; Castro-Montoya, A. J. Dehydration of bioethanol by hybrid process liquidliquid extraction/extractive distillation. Ind. Eng. Chem. Res. 2012, 51, 5847−5855. [6] Loy, Y. Y.; Lee, X. L.; Rangaiah, G. P. Bioethanol recovery and purification using extractive dividing-wall column and pressure swing adsorption: An economic comparison after heat integration and optimization. Sep. Purif. Technol. 2015, 149, 413−427. [7] Nagy, E.; Mizsey, P.; Hancsok, J.; Boldyryev, S.; Varbanov, P. Analysis of energy saving by combination of distillation and pervaporation for biofuel production. Chem. Eng. Process. 2015, 98, 86−94. [8] Huang, Y.; Baker, R.; Vane, L. M. Low-energy distillation membrane separation process. Ind. Eng. Chem. Res. 2010, 49, 3760−3768. [9] Wooley, R.; Ruth, M.; Sheehan, J.; Ibsen, K.; Majdeski, D.; Galvez, A. Lignocellulosic biomass to ethanol process design and economics utilizing co-current dilute acid


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prehydrolysis and enzymatic hydrolysis current and futuristic scenarios. NREL Technol. Rep. 2002, No. NREL/TP-580-26157. [10] Humbird, D.; Davis, R.; Tao, L.; Kinchin, C.; Hsu, D.; Aden, A.; Schoen, P.; Lukas, J.; Olthof, B.; Worley, M.; Sexton, D.; Dudgeon, D. Process design and economics for biochemical conversion of lignocellulosic biomass to ethanol. NREL Technol. Rep. 2011, DOI. 10.2172/1013269. [11] Bastidas, P. A.; Gil, I. D.; Rodrigues, G. Comparison of the main ethanol dehydration technologies through process simulation. In 20th European Symposium on Computer Aided Process Engineering, Elsevier: Amsterdam, 2010. [12] Garcia-Herreros, P.; Gomez, J. M.; Gil, I. D.; Rodriguez, G. Optimization of the design and operation of an extractive distillation system for the production of fuel grade ethanol using glycerol as entrainer. Ind. Eng. Chem. Res. 2011, 50, 3977-3985. [13] Li, G.; Bai, P. New operation strategy for separation of ethanol−water by extractive distillation, Ind. Eng. Chem. Res. 2012, 51, 2723−2729. [14] Errico, M.; Rong, B. G.; Tola, G.; Spano, M. Optimal synthesis of distillation systems for bioethanol separation: Part 1. Extractive distillation with simple columns, Ind. Eng. Chem. Res. 2013, 52, 1612-1619. [15] Errico, M.; Rong, B. G.; Tola, G.; Spano, M. Optimal synthesis of distillation systems for bioethanol separation: Part 2. Extractive distillation with complex columns, Ind. Eng. Chem. Res. 2013, 52, 1620-1626. [16] Kiss, A. A.; Ignat; R. M. Innovative single step bioethanol dehydration in an extractive dividing-wall column. Sep. Purif. Technol. 2012, 98, 290-297.



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[17] Ramirez-Marquez, C.; Segovia-Hernandez, J. G.; Hernandez, S.; Errico, M.; Rong, B. G. Dynamic behavior of alternative separation processes for ethanol dehydration by extractive distillation, Ind. Eng. Chem. Res. 2013, 52, 17554-17561. [18] Errico, M.; Ramirez-Marquez, C.; Torres-Ortega, C. E.; Rong, B. G.; Segovia-Hernandez, J. G. Design and control of an alternative distillation sequence for bioethanol purificaiton, J. Chem. Technol. Biotechnol. 2015, 90, 2180-2185. [19] Arslan, D. G.; Kaymak, D. B. Control analysis of alternative design configurations for bioethanol purification, Ind. Eng. Chem. Res. 2017, 56, 3008-3016. [20] Tututi-Avila, S.; Jimenez-Gutierrez, A.; Hahn, J. Control analysis of an extractive dividingwall column used for ethanol dehydration, Chem, Eng. Process., 2014, 82,88-100. [21] Mulia-Soto, J. F.; Flores-Tlacuahuac, A. Modeling, simulation and control of an internally heat integrated pressure-swing distillation process for bioethanol separation. Comput. Chem. Eng. 2011, 35, 1532-1546. [22] Al-Arfaj, M. A.; Luyben, W. L. Control of ethylene glycol reactive distillation column, AICHE J., 2002, 48, 905-908. [23] Zhu, F.; Huang, K.; Wang, S.; Shan, L.; Zhu Q. Towards further internal heat integration in design of reactive distillation columns – Part IV: Application to a high-purity etylene glycol reactive distillation column, Chem. Eng. Sci. 2009, 64, 3498-3509. [24] Tavan, Y.; Hosseini, S. H. A novel integrated process to break the ethanol/water azeotrope using reactive distillation – Part I: Parametric study, Sep. Purif. Technol. 2013, 118, 455462.


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[25] An, W.; Lin, Z.; Chen, J.; Zhu, J. Simulation and analysis of a reactive distillaiton column for removal of water from ethanol-water mixtures, Ind. Eng. Chem. Res. 2014, 53, 60566064. [26] Dimian, A. C.; Bildea, C. S. Chemical Process Design: Computer-Aided Case Studies; Wiley-VCH Verlag, 2008, pp 429-460. [27] Dimian, A. C. Integrated Design and Simulation of Chemical Process, Elsevier, 2003. [28] Luyben, W. L. Distillation Desing and Control Using Aspen Simulation, New Jersey, 2013.



Table 1. Design parameter values Parameters C1 Column pressure (atm) 1.0 Number of theoretical trays 48 Feed tray location 37 Reflux ratio 2.29 Reboiler heat duty (kW) 4794.58 Condenser heat duty (kW) 3613.94 Diameter (m) 1.33 Table 2. Economic evaluation C1 Process I Shell [103$] 361.44 3 Trays [10 $] 36.01 3 Reboiler [10 $] 339.67 3 Condenser [10 $] 340.07 Heating [103$/year] 1,095.16 Cooling [103$/year] 78.27 3 TAC [10 $/year] 1,532.49 Process II Shell [103$] 380.21 3 Trays [10 $] 38.17 Reboiler [103$] 336.54 Condenser [103$] 331.66 3 Heating [10 $/year] 1,079.66 Cooling [103$/year] 75.31 3 TAC [10 $/year] 1,517.17

C2 4.5 41 37/38 0.21 943.06 1023.70 0.54

C3

C2

126.42 41.79 1.98 8.64 169.69 89.99 156.35 72.45 376.53 141.91 7.25 23.69 553.91 217.90

C4 46.73 2.03 44.64 51.66 48.26 4.31 100.92

KC 0.3 2 2 2 2 0.42 0.042 0.16

Total 576.38 48.66 643.99 620.52 1,661.86 113.51 2,405.23 507.62 46.18 453.48 477.75 1,349.35 96.65 1,941.01

127.42 8.00 116.95 146.09 269.68 21.33 423.84

Table 3. Controller tuning parameters Controller Set Point Action FC1 1700 kmol/h Reverse LC1 1.56 m Direct LC2 2.03 m Direct LC3 1.14 m Direct LC4 1.00 m Direct TC1 372.43 K Reverse TC2 478.02 K Reverse CC1 0.05 Reverse

Page 22 of 35

τI (min) 0.5 13.11 8.74 14.5


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


Table 4. Steady-state offset CS1 CS2

+20% in Throughput Ethanol - EG 0.294x10-3-0.129x10-3 0.199x10-3-0.141x10-3

-20% in Throughput Ethanol - EG 0.050x10-3-0.126x10-3 0.141x10-3-0.133x10-3

-1% in Feed Comp. Ethanol - EG 0.143x10-3-0.116x10-3 0.197x10-3-0.130x10-3

+1% in Feed Comp. Ethanol - EG 0.315x10-3-0.132x10-3 0.185x10-3-0.141x10-3

-1% in Feed Comp. Ethanol - EG 0.0228 - 0.0028 0.0099 - 0.0028

+1% in Feed Comp. Ethanol - EG 0.0181 - 0.0035 0.0129 - 0.0035

Table 5. Integral Absolute Error values CS1 CS2

+20% in Throughput Ethanol - EG 0.0183 - 0.0035 0.0134 - 0.0035

-20% in Throughput Ethanol - EG 0.0227 - 0.0028 0.0098 - 0.0030

Figure Caption Figure 1 – Flowsheet of the base-case design Figure 2 – Flowsheet of the alternative process Figure 3 – Effects of design parameters on pre-concentrator column Figure 4 – Effect of operating pressure on the conversion of reactants in reactive column Figure 5 – Effect of feed trays on QR and TAC in reactive column Figure 6 – Effect of reactive trays in reactive column Figure 7 – Control structure 1 (CS1) Figure 8 – Temperature profiles: (A) Pre-concentrator, (B) Reactive columns Figure 9 – Results of CS1: (A) ±20% throughput change, (B) ±1% feed composition change Figure 10 – Control structure 2 (CS2) Figure 11 – Results of CS2: (A) ±20% throughput change, (B) ±1% feed composition change



Page 24 of 35

3

TAC [10 $/year] 1,941.01

2,405.23 Solvent Recycle Solvent Makeup

Ethanol C2

C1

Azeotropic Feed

Fermentation Broth

C1

C4

Azeotropic Feed

Fermentation Broth

C3

Ethylene Oxide

Water Water

Ethanol

C2

Ethylene Glycol

Water


Page 25 of 35 Figure 1 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


Solvent Makeup

3.76 MW 78.18 oC 1 atm C1

Fermentation Broth 77 oC 1700 kmol/h 0.05 EtOH 0.95 Water

Azeotropic Feed 100 kmol/h 0.85 EtOH 0.15 Water

4.86 MW 107.12 oC

1.14 MW 79.11 oC 1 atm C2

0.21 MW 79.02 oC 1 atm

Ethanol 85 kmol/h 0.9998 EtOH

C4

0.35 MW 82.73 oC 1 atm C3

137.03 oC 1.67 MW 120 kmol/h 0.0349 EtOH 0.1315 Water 0.8336 EG

Water 1600 kmol/h 0.9999 Water

20 kmol/h 0.21 EtOH 0.79 Water 0.21 MW 104.29 oC

0.63 MW 199.47 oC Solvent Recycle 100 kmol/h 0.9999 EG

ACS Paragon Plus Environment

Distillate Recycle 5 kmol/h 0.84 EtOH 0.16 Water


Figure 2 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


Page 26 of 35

1.02 MW 122.18 oC 4.5 atm

3.61 MW 78.18 oC 1 atm C1

RR=2.29 Fermentation Broth 77 oC 1700 kmol/h 0.05 EtOH 0.95 Water

C2

Azeotropic Feed 100 kmol/h 0.85 EtOH 0.15 Water

4.79 MW 107.98 oC

RR=0.21

Ethylene Oxide 15 kmol/h 1.00 EO



Ethanol 85.2 kmol/h 0.997 EtOH 0.0005 Water 0.0005 EO 0.002 EG 0.94 MW 254.78 oC Ethylene Glycol 14.8 kmol/h 0.002 EtOH 0.0004 Water 0.0006 EO 0.997 EG

Page 27 of 35 Figure 3 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



Figure 4 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



Page 28 of 35

Page 29 of 35 Figure 5 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


(A)

(B)


Figure 6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41



Page 30 of 35

Page 31 of 35 Figure 7 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


PC

PC

R

R

LC FT

FC

LC FT

FT

FC

F

FT

FC

ratio CC

TC

LC

VS

FEO

TC

LC


VS

ratio

Figure 8 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



Page 32 of 35

Page 33 of 35 Figure 9 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


(A)

(B)


Figure 10 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


Page 34 of 35

PC

PC

R

R

LC FT

FC

LC FT

FT

FC

F

FC

ratio

FEO TC

LC

VS

FT

TC

FC

LC


VS

FT

FT

ratio

ratio

Page 35 of 35 Figure 11 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


(A)

(B)


Design and Control of an Alternative Bioethanol Purification Process

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