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Reactive distillation for production of n-butyl acrylate from bio-based raw materials
Andrzej Kraslawski and Ilkka Turunen (Editors) Proceedings of the 23 rd European Symposium on Computer Aided Process Engineering – ESCAPE 23, June 9-12, 2013, Lappeenranta, Finland 223 © 2013 Elsevier B.V. All rights reserved.
Reactive distillation for production of n-butyl acrylate from bio-based raw materials Alexander Niesbach, Philip Lutze, Andrzej Górak Laboratory of Fluid Separations, TU Dortmund University, Germany
Abstract Due to the scarcity of oil in the future, alternative resources and production pathways for the production of chemicals need to be identified. To allow for an economic production of these chemicals, the use of innovative equipment and methods has to be investigated to intensify the processes. Reaction distillation integrates separation by distillation and reaction into one unit and is already known to be a promising concept to improve process performance, leading to more sustainable processes. However, the design of reactive distillation processes using bio-based raw materials is difficult because the wide impurity profile of the reactants may lead to a large number of additional reactions and thermodynamic non-idealties. Hence, within this work, a 4-step methodology for the design of a reactive distillation column processing bio-based resources is presented. In this study, the focus is on the impact of impurities, resulting from the use of bio-based raw material. Based on these results, the product purity in presence of the impurities is analysed and operational and design changes to overcome identified product purity limitations will be presented. Keywords: Renewable resources, reactive distillation, acrylic acid, methodology
1. Introduction A main task for future chemical industry is the changeover from an oil-based to a biobased economy and the associated development of pathways to produce new bio-based platform chemicals. For an economic production of bio-based products, new innovative apparatuses and techniques have to be developed. One option is process intensification potentially leading to higher efficiencies while decreasing energy consumption for example. Reactive distillation (RD) has emerged as an excellent technology of process intensification exploiting synergy effects of the combination of reaction and distillation at the same place and time. This allows overcoming limitations regarding thermodynamics and chemical equilibria. RD has been successfully studied for a large quantity of oil-based low carbon numbers reactions and a limited number of components. For those, a multitude of models of different scales and depths as well as design methods and tools exist. New bio-based raw materials lead to a change in the impurity profile potentially forming a large set of (new) azeotropes, immiscibilities and reactions in bio-based processes. Hence, applying existing RD design methods and tools to bio-based processes is still an open issue.Therefore, in this work a methodology to design RD columns using bio-based raw materials is developed.
2. Process analysis tool for the design of processes using bio-based raw materials The aim of the presented methodology is the design of a process for the production and purification of a desired product using bio-based raw materials. Due to the variability in
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the composition of bio-based raw materials in respect to the contained impurities, a robust process design in necessary. A 4-step approach for the design of a heterogeneously catalysed RD column using bio-based raw materials is therefore introduced (Figure 1). The heterogeneously catalysed synthesis of n-butyl acrylate (BA) from acrylic acid (AA) and n-butanol (BuOH) in an RD column is used as case study. In step-1 an optimal industrial scale RD column, not taking Figure 1. Workflow ot the proposed design methodology. impurities into account, is identified. Here, conventional tools are used and pilot-plant experiments are performed for rigorous RD model development and model validation. The sub-steps are the rigorous model development in the simulation environment Aspen Custom Modeler™ (ACM) and validation using the experimental pilot-scale experiments. The model is then used for an optimisation of the industrial-scale process. In step-2 impurities from bio-based raw materials are examined and analysed. First, all potential impurities are identified taking the biosynthetic routes for AA and BuOH production into account. Thermodynamic and physical pure component as well as binary property data are calculated using Aspen Plus™ (AP) and validated using experimental literature data. The determined impurities are categorised and clustered by analysing potential occurrence in reactions, azeotropes and immiscibilities. This clustering reduces the number of components. In step-3, RD performance using bio-based raw materials is analysed. Here, a simplified RD model is used. Starting from the optimised RD process without impurities, the concentrations of the different impurities along the column height and in the product streams are monitored and critical impurities in respect to the product quality are identified. Finally, in step 4, an optimal RD-based process using biobased raw materials is identified. Using the more detailed ACM model, feasible and optimal operating windows for the RD process including the critical impurities are identified. Based on this investigation, adapted process configurations for high impurity concentrations are proposed to satisfy the product specifications for BA.
3. Case-Study The equilibrium-limited esterification of AA and BuOH to BA and water is the chemical system investigated in this study. The reaction scheme of this reaction is shown below.
3.1. Step-1: Design of base-case process In the first step of the proposed method, an optimal industrial scale RD column needs to be identified. In previous investigations, the production in an industrial scale single RD column was proposed and experiments in a pilot-scale RD column were performed. and used to validate a nonequilibrium-stage model implemented in the simulation environment ACM. Using this model, an RD column for the production of 20,000 t BA per year was optimised in respect to production costs using an evolutionary algorithm [1]. The final design of the optimised RD column is summarised in Table 1:
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Table 1. Configuration of optimised RD column [1]
Optimised Process Variable
Value
Optimised Process Variable
Value
Top pressure (bar) Reflux ratio (-) Distillate-to-feed mass ratio (-) Molar feed ratio (BuOH/AA) Column diameter (m)
0.58 1.67 0.41 0.81 1.68
Height of rectifying section (m) Height of reactive section (m) Height of stripping section (m) BuOH feed position (m) AA feed position (m)
1.13 15.12 1.90 1.90 17.02
The top product of the single reaction distillation column is a mixture of BuOH, BA and water due to a heterogeneous light-boiling azeotrope formed by these components and AA due to the feed position, which is located close to to the distillate stream. The bottom product is BA with a purity of 99.98 wt.-% (Tb = 420 K) [1]. 3.2. Step-2: Identification and clustering of impurities Due to new reaction pathways that are being developed for the synthesis of basic chemicals from renewable materials, reaction and downstream processes will face new challenges resulting from changes in the impurity profile of their raw materials. Within this investigation, potential impurities in bio-based AA and BuOH need to be identified. All boiling points shown in this investigation are calculated for atmospheric pressure. 3.2.1. Impurities in bio-butanol The metabolic synthesis of glucose to BuOH (Tb = 391 K) using Clostridium acetobutylicum in the so called Acetone-Butanol-Ethanol (ABE) process is divided in two phases. In the first phase, butyric acid (Tb = 436 K) and acetic acid (Tb = 391 K) are produced. These acids are then converted to BuOH, acetone (Tb = 329 K) and ethanol (Tb = 351 K) in the second phase. Besides the two acidic intermediates and the two other final products of the ABE-process, side-products of the metabolic pathways of C. acetobutylicum are 1-propanol (Tb = 370 K), isobutanol (Tb = 381 K), 2-methyl-1butanol (2M1B) (Tb = 402 K) and 3-methyl-1-butanol (3M1B) (Tb = 404 K) [2]. In the current industrial production, these components are separated by distillation [3]. Nevertheless, a complete separation of these components from the BuOH is not expected resulting from the close boiling points of the abovementioned alcohols in comparison to the boiling point of BuOH. Due to the large differences in the boiling points of acetone, BuOH and ethanol and the fact, that these components do not form any azeotropes, a complete separation of these components during the purification of bio-based BuOH is assumed. The two intermediates butyric acid and acetic acid are considered as impurities, as the boiling point of acetic acid is comparable to the boiling point of BuOH and butyric acid is a heavy boiler in this system, which will result in small amounts still present in purified BuOH, as BuOH is the bottom product of the water – butanol separation after the fermentation process. 3.2.2. Impurities in bio-acrylic acid The fermentation process using the substrate glycose to produce AA (Tb = 414 K) can be performed in three different ways, whereas the intermediate changes between lactic acid, 3-hydroxypropionic acid and 3-hydroxypropanal [4]. Of these three reaction types, the lactic acid (Tb = 490 K) pathway is currently the industrially most relevant fermentation. Besides the formation of AA, the synthesis of acetic acid and propionic acid (Tb = 414 K) from lactic acid are possible side reactions [5]. Due to similar boiling points with AA, acetic acid and propionic acid are taken into consideration in this study. Lactic acid itself is not considered, as large differences in the boiling points in comparison to AA exist and no azeotropes can be found in the lactic acid – AA system.
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3.2.3. Side-reactions Besides the impurities already introduced in section 3.2.1 and 3.2.2, side-reactions need to be considered in the RD column. As all impurities identified in AA and BuOH are alcohols or acids, esterification reactions with BuOH and AA are likely to occur in the RD column. The esters formed by the impurities together with the main components are butyl acetate (Tb = 399 K), butyl propionate (Tb = 418 K) and butyl butyrate (Tb = 438 K) from reactions with BuOH and propyl acrylate (Tb = 392 K), isobutyl acrylate (Tb = 410 K), 3-methyl-1-butyl acrylate (Tb = 437 K) and 2-methyl-1-butyl acrylate (Tb = 439 K) from reactions with AA. Due to the large excess of the main component in comparison to the impurity, no reaction kinetic is taken into account, but the impurities are added to the BuOH and AA feed streams and handled as additional impurities, that are directly fed to the RD column. 3.2.4. Thermodynamic and physical property data Reliable thermodynamic and physical data are essential to achieve a high accuracy in the simulation of chemical processes. Thermodynamic data in this work was calculated using AP. The UNIQUAC model was used to calculate the activity of all components in the liquid phase and the Hayden O’Connell equation of state to account for nonidealties in the gas phase. The pure components property data for the four main components was already validated and used for process simulations. This was published in preliminary studies [1]. The vapour pressure of the impurities and vapour-liquid equilibria of the impurities together with the main components are of high importance for an accurate process simulation of the RD column. Therefore, the vapour pressure calculated with AP was compared to literature data published by Yaws [6] for all components. Besides the pure component data, parameters for the calculation of vapour-liquid equilibria were taken from AP and compared to available literature data. After successful validation, these property data was used for further clustering and process simulation. 3.2.5. Clustering of impurities During the theoretical investigation of a chemical process, a reduction of the investigated components is useful to reduce the resulting computational effort. In the first part of step-2 of the proposed design methodology, 14 impurities are identified for the RD process when bio-based raw materials are used. The first reduction of the number of analysed components, the clustering step, is based on pure-component and mixture property data. Within this step, rules of thumb are used to group the impurities and identify one representative component for each identified group. The clustering in this section is based on pure component boiling points, the shape of the vapour-liquid equilibria and the formation of azeotropes. If components with comparable property data are identified, a representative component was chosen for further process simulation. This results in a reduction from 14 to eight components, as summarised in table 2, which will be considered in the further investigation. Table 2. Clustering of impurities from bio-based AA and BuOH
Components Propanol Isobutanol 2M1B 3M1B Acetic acid Butyric acid Propionic acid
Representative Isobutanol 3M1B Acetic acid Butyric acid Propionic acid
Components Butyl acetate Propyl acrylate Butyl propionate Isobutyl acrylate Butyl butyrate 2M1B-acrylate 3M1B-acrylate
Representative Butyl acetate Butyl propionate Butyl butyrate
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3.3. Step-3: Process simulation and process analysis 3.3.1. Aspen Plus RD model The AP RadFrac distillation model is used for the simplified process simulation in step-3 of the proposed design methodology. The process is simulated using a nonequilibrium-stage modelling approach. Within this step, at first the RadFrac model is adapted to the optimised RD column configuration resulting from step-1 and validated by a comparison of simulation results to the nonequilibrium-stage model implemented in ACM, that is used in step-1. 3.3.2. Determination of effect of impurities The investigation in step-3 is a process simulation in the rate-based model introduced in section 3.3.1. Using this model, the effects of the eight representative impurities, resulting from the clustering described in section 3.2.5, on the RD process with respect to the purity of BA are determined. Hereby, key components are identified which are investigated in more detail using the nonequilibrium-stage model implemented in ACM. Due to common industrial purity specifications for BuOH and AA, the maximum investigated amount of impurities in this work is 3000 ppm1. Figure 2 shows the mole fraction of the representative impurities in top and bottom product of the RD column for an addition of 3000 ppm of each impurity. For each shown bar, one impurity is added in one of the two feed streams to investigate the results of the addition of the single impurities. As a result of the identification of impurities in section 3.2.1-3.2.3, isobutanol, 3M1B and butyric acid are added together with the BuOH feed stream, propionic acid is added with the AA feed stream and acetic Figure 2. Resulting concentration of impurities acid as well as the representative products in top and bottom product after addition of from section 3.2.3 are added in each of 3000 ppm in one feed stream. the feed streams. For the impurities that are added to the BuOH feed stream, the components 3M1B, butyric acid and acetic acid as well as butyl propionate and butyl butyrate accumulate in the bottom product. This results from the high boiling points of these components and the lack of light-boiling azeotropes with components, present at the bottom of the column. The low boiling point of isobutanol and a formation of a light-boiling isobutanol-water azeotrope with a boiling point of 363 K leads to an accumulation of isobutanol in the top product of the RD column. As the operational parameters are constant in this investigation, this results in a concentration of more than 4000 ppm BuOH in the bottom product, reducing the BA purity significantly. The last component that is investigated as an impurity in the BuOH feed stream is butyl acetate, which accumulates both in the top and in the bottom product. This results from the relatively high boiling point of 399 K and the lightboiling azeotrope formed with water with a boiling point of 364 K. For the impurities that are added together with the AA feed stream, propionic acid, which forms a heavyboiling azeotrope with BA, and butyl butyrate only accumulate in the bottom product. The higher total concentration of these impurities in the bottom product in comparison 1
See data sheets of basic butanol and acrylic monomer suppliers.
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to the impurities fed together with the BuOH feed is a result of the excess of AA, leading to a higher total amount of impurity in the column. In contrast to the results of the BuOH stream, acetic acid is removed with the top and the bottom product when fed together with AA, as it is fed closer to the top of the column. Butyl propionate shows a comparable behavior to butyl butyrate, mainly accumulating in the bottom product. Butyl acetate can only be found in the distillate stream, due to the light-boiling azeotrope with water. Based on this analysis, isobutanol and butyric acid were chosen as key components in the BuOH feed stream, whereas butyric acid is representative for acetic acid and 3M1B, due to the same behavior in the investigated RD process. In the AA feed stream, propionic acid was chosen as key component due to a significantly larger influence on the BA quality in comparison to acetic acid while showing comparable separation behaviour. Among the esters, butyl butyrate is the most critical component in respect to the product purity and is therefore chosen as key component. 3.4. Step-4: Design of bio-based process In step-4, the critical impurities identified in step-3 are investigated in more detail. The nonequilibrium-stage model (see also step-1) implemented in ACM is used to identify optimal operating windows for the RD based process for the synthesis of BA. Finally, an adapted process configuration for the bio-based process, satisfying all product specifications for BA, is proposed and optimised.
4. Conclusion The steps two to four of a process design methodology for the synthesis of n-butyl acrylate from acrylic acid and n-butanol from bio-based raw materials in an RD column were shown within this work. In a preliminary study, a nonequilibrium-stage model implemented in Aspen Custom Modeler™ was validated using experimental pilot-scale RD data. It was then used together with an evolutionary algorithm to develop an optimised RD process for the synthesis of n-butyl acrylate in a single RD column. In the second and third step of this methodology with the addition of impurities, a simplified Aspen Plus® model is used to simulate the optimised RD process. 14 impurities are identified which can occur in bio-based acrylic acid and n-butanol. These impurities are than clustered based on pure-component and mixture property data, resulting in a reduction to eight representative components. The simplified Aspen Plus® model is then used to identify four key components, which are critical in respect to the purity of nbutyl acrylate in the final RD process. Based on this investigation, a final simulation and optimisation using Aspen Custom Modeler™ is performed, to identify the optimised process for the synthesis of n-butyl acrylate using bio-based raw materials. Acknowledgement: The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 241718, EuroBioRef.
References [1] A. Niesbach et al., 2013, Chem. Eng. Sci., http://dx.doi.org/10.1016/j.ces.2013.01.035 [2] S. Atsumi et al., 2008, Nature, 451(7174), 86-89 [3] Y. Ni and Z. Sun, 2009, Appl. Microbiol. Biotechnol., 83(3), 415-423 [4] J.E. Holladay et al., 2005, Presentation at 19th North American Meeting, Philadelphia [5] R. P. John, 2007, Appl. Microbiol. Biotechnol., 74(3), 524-534 [6] Carl L. Yaws, 1999, Physical, Thermodynamic, Environmental, Transport, Safety and Health Related Properties for Organic and Inorganic Chemicals, McGraw-Hill, New York
Reactive distillation for production of n-butyl acrylate from bio-based raw materials Alexander Niesbach, Philip Lutze, Andrzej Górak Laboratory of Fluid Separations, TU Dortmund University, Germany
Abstract Due to the scarcity of oil in the future, alternative resources and production pathways for the production of chemicals need to be identified. To allow for an economic production of these chemicals, the use of innovative equipment and methods has to be investigated to intensify the processes. Reaction distillation integrates separation by distillation and reaction into one unit and is already known to be a promising concept to improve process performance, leading to more sustainable processes. However, the design of reactive distillation processes using bio-based raw materials is difficult because the wide impurity profile of the reactants may lead to a large number of additional reactions and thermodynamic non-idealties. Hence, within this work, a 4-step methodology for the design of a reactive distillation column processing bio-based resources is presented. In this study, the focus is on the impact of impurities, resulting from the use of bio-based raw material. Based on these results, the product purity in presence of the impurities is analysed and operational and design changes to overcome identified product purity limitations will be presented. Keywords: Renewable resources, reactive distillation, acrylic acid, methodology
1. Introduction A main task for future chemical industry is the changeover from an oil-based to a biobased economy and the associated development of pathways to produce new bio-based platform chemicals. For an economic production of bio-based products, new innovative apparatuses and techniques have to be developed. One option is process intensification potentially leading to higher efficiencies while decreasing energy consumption for example. Reactive distillation (RD) has emerged as an excellent technology of process intensification exploiting synergy effects of the combination of reaction and distillation at the same place and time. This allows overcoming limitations regarding thermodynamics and chemical equilibria. RD has been successfully studied for a large quantity of oil-based low carbon numbers reactions and a limited number of components. For those, a multitude of models of different scales and depths as well as design methods and tools exist. New bio-based raw materials lead to a change in the impurity profile potentially forming a large set of (new) azeotropes, immiscibilities and reactions in bio-based processes. Hence, applying existing RD design methods and tools to bio-based processes is still an open issue.Therefore, in this work a methodology to design RD columns using bio-based raw materials is developed.
2. Process analysis tool for the design of processes using bio-based raw materials The aim of the presented methodology is the design of a process for the production and purification of a desired product using bio-based raw materials. Due to the variability in
224
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the composition of bio-based raw materials in respect to the contained impurities, a robust process design in necessary. A 4-step approach for the design of a heterogeneously catalysed RD column using bio-based raw materials is therefore introduced (Figure 1). The heterogeneously catalysed synthesis of n-butyl acrylate (BA) from acrylic acid (AA) and n-butanol (BuOH) in an RD column is used as case study. In step-1 an optimal industrial scale RD column, not taking Figure 1. Workflow ot the proposed design methodology. impurities into account, is identified. Here, conventional tools are used and pilot-plant experiments are performed for rigorous RD model development and model validation. The sub-steps are the rigorous model development in the simulation environment Aspen Custom Modeler™ (ACM) and validation using the experimental pilot-scale experiments. The model is then used for an optimisation of the industrial-scale process. In step-2 impurities from bio-based raw materials are examined and analysed. First, all potential impurities are identified taking the biosynthetic routes for AA and BuOH production into account. Thermodynamic and physical pure component as well as binary property data are calculated using Aspen Plus™ (AP) and validated using experimental literature data. The determined impurities are categorised and clustered by analysing potential occurrence in reactions, azeotropes and immiscibilities. This clustering reduces the number of components. In step-3, RD performance using bio-based raw materials is analysed. Here, a simplified RD model is used. Starting from the optimised RD process without impurities, the concentrations of the different impurities along the column height and in the product streams are monitored and critical impurities in respect to the product quality are identified. Finally, in step 4, an optimal RD-based process using biobased raw materials is identified. Using the more detailed ACM model, feasible and optimal operating windows for the RD process including the critical impurities are identified. Based on this investigation, adapted process configurations for high impurity concentrations are proposed to satisfy the product specifications for BA.
3. Case-Study The equilibrium-limited esterification of AA and BuOH to BA and water is the chemical system investigated in this study. The reaction scheme of this reaction is shown below.
3.1. Step-1: Design of base-case process In the first step of the proposed method, an optimal industrial scale RD column needs to be identified. In previous investigations, the production in an industrial scale single RD column was proposed and experiments in a pilot-scale RD column were performed. and used to validate a nonequilibrium-stage model implemented in the simulation environment ACM. Using this model, an RD column for the production of 20,000 t BA per year was optimised in respect to production costs using an evolutionary algorithm [1]. The final design of the optimised RD column is summarised in Table 1:
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Table 1. Configuration of optimised RD column [1]
Optimised Process Variable
Value
Optimised Process Variable
Value
Top pressure (bar) Reflux ratio (-) Distillate-to-feed mass ratio (-) Molar feed ratio (BuOH/AA) Column diameter (m)
0.58 1.67 0.41 0.81 1.68
Height of rectifying section (m) Height of reactive section (m) Height of stripping section (m) BuOH feed position (m) AA feed position (m)
1.13 15.12 1.90 1.90 17.02
The top product of the single reaction distillation column is a mixture of BuOH, BA and water due to a heterogeneous light-boiling azeotrope formed by these components and AA due to the feed position, which is located close to to the distillate stream. The bottom product is BA with a purity of 99.98 wt.-% (Tb = 420 K) [1]. 3.2. Step-2: Identification and clustering of impurities Due to new reaction pathways that are being developed for the synthesis of basic chemicals from renewable materials, reaction and downstream processes will face new challenges resulting from changes in the impurity profile of their raw materials. Within this investigation, potential impurities in bio-based AA and BuOH need to be identified. All boiling points shown in this investigation are calculated for atmospheric pressure. 3.2.1. Impurities in bio-butanol The metabolic synthesis of glucose to BuOH (Tb = 391 K) using Clostridium acetobutylicum in the so called Acetone-Butanol-Ethanol (ABE) process is divided in two phases. In the first phase, butyric acid (Tb = 436 K) and acetic acid (Tb = 391 K) are produced. These acids are then converted to BuOH, acetone (Tb = 329 K) and ethanol (Tb = 351 K) in the second phase. Besides the two acidic intermediates and the two other final products of the ABE-process, side-products of the metabolic pathways of C. acetobutylicum are 1-propanol (Tb = 370 K), isobutanol (Tb = 381 K), 2-methyl-1butanol (2M1B) (Tb = 402 K) and 3-methyl-1-butanol (3M1B) (Tb = 404 K) [2]. In the current industrial production, these components are separated by distillation [3]. Nevertheless, a complete separation of these components from the BuOH is not expected resulting from the close boiling points of the abovementioned alcohols in comparison to the boiling point of BuOH. Due to the large differences in the boiling points of acetone, BuOH and ethanol and the fact, that these components do not form any azeotropes, a complete separation of these components during the purification of bio-based BuOH is assumed. The two intermediates butyric acid and acetic acid are considered as impurities, as the boiling point of acetic acid is comparable to the boiling point of BuOH and butyric acid is a heavy boiler in this system, which will result in small amounts still present in purified BuOH, as BuOH is the bottom product of the water – butanol separation after the fermentation process. 3.2.2. Impurities in bio-acrylic acid The fermentation process using the substrate glycose to produce AA (Tb = 414 K) can be performed in three different ways, whereas the intermediate changes between lactic acid, 3-hydroxypropionic acid and 3-hydroxypropanal [4]. Of these three reaction types, the lactic acid (Tb = 490 K) pathway is currently the industrially most relevant fermentation. Besides the formation of AA, the synthesis of acetic acid and propionic acid (Tb = 414 K) from lactic acid are possible side reactions [5]. Due to similar boiling points with AA, acetic acid and propionic acid are taken into consideration in this study. Lactic acid itself is not considered, as large differences in the boiling points in comparison to AA exist and no azeotropes can be found in the lactic acid – AA system.
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3.2.3. Side-reactions Besides the impurities already introduced in section 3.2.1 and 3.2.2, side-reactions need to be considered in the RD column. As all impurities identified in AA and BuOH are alcohols or acids, esterification reactions with BuOH and AA are likely to occur in the RD column. The esters formed by the impurities together with the main components are butyl acetate (Tb = 399 K), butyl propionate (Tb = 418 K) and butyl butyrate (Tb = 438 K) from reactions with BuOH and propyl acrylate (Tb = 392 K), isobutyl acrylate (Tb = 410 K), 3-methyl-1-butyl acrylate (Tb = 437 K) and 2-methyl-1-butyl acrylate (Tb = 439 K) from reactions with AA. Due to the large excess of the main component in comparison to the impurity, no reaction kinetic is taken into account, but the impurities are added to the BuOH and AA feed streams and handled as additional impurities, that are directly fed to the RD column. 3.2.4. Thermodynamic and physical property data Reliable thermodynamic and physical data are essential to achieve a high accuracy in the simulation of chemical processes. Thermodynamic data in this work was calculated using AP. The UNIQUAC model was used to calculate the activity of all components in the liquid phase and the Hayden O’Connell equation of state to account for nonidealties in the gas phase. The pure components property data for the four main components was already validated and used for process simulations. This was published in preliminary studies [1]. The vapour pressure of the impurities and vapour-liquid equilibria of the impurities together with the main components are of high importance for an accurate process simulation of the RD column. Therefore, the vapour pressure calculated with AP was compared to literature data published by Yaws [6] for all components. Besides the pure component data, parameters for the calculation of vapour-liquid equilibria were taken from AP and compared to available literature data. After successful validation, these property data was used for further clustering and process simulation. 3.2.5. Clustering of impurities During the theoretical investigation of a chemical process, a reduction of the investigated components is useful to reduce the resulting computational effort. In the first part of step-2 of the proposed design methodology, 14 impurities are identified for the RD process when bio-based raw materials are used. The first reduction of the number of analysed components, the clustering step, is based on pure-component and mixture property data. Within this step, rules of thumb are used to group the impurities and identify one representative component for each identified group. The clustering in this section is based on pure component boiling points, the shape of the vapour-liquid equilibria and the formation of azeotropes. If components with comparable property data are identified, a representative component was chosen for further process simulation. This results in a reduction from 14 to eight components, as summarised in table 2, which will be considered in the further investigation. Table 2. Clustering of impurities from bio-based AA and BuOH
Components Propanol Isobutanol 2M1B 3M1B Acetic acid Butyric acid Propionic acid
Representative Isobutanol 3M1B Acetic acid Butyric acid Propionic acid
Components Butyl acetate Propyl acrylate Butyl propionate Isobutyl acrylate Butyl butyrate 2M1B-acrylate 3M1B-acrylate
Representative Butyl acetate Butyl propionate Butyl butyrate
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3.3. Step-3: Process simulation and process analysis 3.3.1. Aspen Plus RD model The AP RadFrac distillation model is used for the simplified process simulation in step-3 of the proposed design methodology. The process is simulated using a nonequilibrium-stage modelling approach. Within this step, at first the RadFrac model is adapted to the optimised RD column configuration resulting from step-1 and validated by a comparison of simulation results to the nonequilibrium-stage model implemented in ACM, that is used in step-1. 3.3.2. Determination of effect of impurities The investigation in step-3 is a process simulation in the rate-based model introduced in section 3.3.1. Using this model, the effects of the eight representative impurities, resulting from the clustering described in section 3.2.5, on the RD process with respect to the purity of BA are determined. Hereby, key components are identified which are investigated in more detail using the nonequilibrium-stage model implemented in ACM. Due to common industrial purity specifications for BuOH and AA, the maximum investigated amount of impurities in this work is 3000 ppm1. Figure 2 shows the mole fraction of the representative impurities in top and bottom product of the RD column for an addition of 3000 ppm of each impurity. For each shown bar, one impurity is added in one of the two feed streams to investigate the results of the addition of the single impurities. As a result of the identification of impurities in section 3.2.1-3.2.3, isobutanol, 3M1B and butyric acid are added together with the BuOH feed stream, propionic acid is added with the AA feed stream and acetic Figure 2. Resulting concentration of impurities acid as well as the representative products in top and bottom product after addition of from section 3.2.3 are added in each of 3000 ppm in one feed stream. the feed streams. For the impurities that are added to the BuOH feed stream, the components 3M1B, butyric acid and acetic acid as well as butyl propionate and butyl butyrate accumulate in the bottom product. This results from the high boiling points of these components and the lack of light-boiling azeotropes with components, present at the bottom of the column. The low boiling point of isobutanol and a formation of a light-boiling isobutanol-water azeotrope with a boiling point of 363 K leads to an accumulation of isobutanol in the top product of the RD column. As the operational parameters are constant in this investigation, this results in a concentration of more than 4000 ppm BuOH in the bottom product, reducing the BA purity significantly. The last component that is investigated as an impurity in the BuOH feed stream is butyl acetate, which accumulates both in the top and in the bottom product. This results from the relatively high boiling point of 399 K and the lightboiling azeotrope formed with water with a boiling point of 364 K. For the impurities that are added together with the AA feed stream, propionic acid, which forms a heavyboiling azeotrope with BA, and butyl butyrate only accumulate in the bottom product. The higher total concentration of these impurities in the bottom product in comparison 1
See data sheets of basic butanol and acrylic monomer suppliers.
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to the impurities fed together with the BuOH feed is a result of the excess of AA, leading to a higher total amount of impurity in the column. In contrast to the results of the BuOH stream, acetic acid is removed with the top and the bottom product when fed together with AA, as it is fed closer to the top of the column. Butyl propionate shows a comparable behavior to butyl butyrate, mainly accumulating in the bottom product. Butyl acetate can only be found in the distillate stream, due to the light-boiling azeotrope with water. Based on this analysis, isobutanol and butyric acid were chosen as key components in the BuOH feed stream, whereas butyric acid is representative for acetic acid and 3M1B, due to the same behavior in the investigated RD process. In the AA feed stream, propionic acid was chosen as key component due to a significantly larger influence on the BA quality in comparison to acetic acid while showing comparable separation behaviour. Among the esters, butyl butyrate is the most critical component in respect to the product purity and is therefore chosen as key component. 3.4. Step-4: Design of bio-based process In step-4, the critical impurities identified in step-3 are investigated in more detail. The nonequilibrium-stage model (see also step-1) implemented in ACM is used to identify optimal operating windows for the RD based process for the synthesis of BA. Finally, an adapted process configuration for the bio-based process, satisfying all product specifications for BA, is proposed and optimised.
4. Conclusion The steps two to four of a process design methodology for the synthesis of n-butyl acrylate from acrylic acid and n-butanol from bio-based raw materials in an RD column were shown within this work. In a preliminary study, a nonequilibrium-stage model implemented in Aspen Custom Modeler™ was validated using experimental pilot-scale RD data. It was then used together with an evolutionary algorithm to develop an optimised RD process for the synthesis of n-butyl acrylate in a single RD column. In the second and third step of this methodology with the addition of impurities, a simplified Aspen Plus® model is used to simulate the optimised RD process. 14 impurities are identified which can occur in bio-based acrylic acid and n-butanol. These impurities are than clustered based on pure-component and mixture property data, resulting in a reduction to eight representative components. The simplified Aspen Plus® model is then used to identify four key components, which are critical in respect to the purity of nbutyl acrylate in the final RD process. Based on this investigation, a final simulation and optimisation using Aspen Custom Modeler™ is performed, to identify the optimised process for the synthesis of n-butyl acrylate using bio-based raw materials. Acknowledgement: The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 241718, EuroBioRef.
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