Review of Technological Advances in Bioethanol Recovery and

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Review of Technological Advances in Bioethanol Recovery and Dehydration Ashish Singh, and Gade Pandu Rangaiah Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b00273 • Publication Date (Web): 11 Apr 2017 Downloaded from http://pubs.acs.org on April 15, 2017

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Industrial & Engineering Chemistry Research

Review of Technological Advances in Bioethanol Recovery and Dehydration Ashish Singh and Gade Pandu Rangaiah* Department of Chemical and Biomolecular Engineering National University of Singapore, Singapore 117576 *Corresponding author: [email protected]

Abstract Today, at the threshold of the 21st century, rising apprehensions about the instability of oil prices, energy security and adverse effects of fossil fuels on the environment, have made it imperative to search for alternative energy resources that are clean and sustainable. Among various biofuels, bioethanol is very promising. Bioethanol obtained from the fermentation of biomass is dilute and needs to undergo recovery and dehydration before its use as a fuel. This separation step is one of the energy-intensive steps in bioethanol production, which continues to motivate continual advances in bioethanol separation design. Hence, this review paper focuses on the recent advancements in the development of bioethanol recovery and dehydration processes. It is organized in the form of an annotated bibliography, whereby 54 journal papers and book chapters from the year 2008 to 2016 are summarized based on a classification according to separation technology employed. In addition, quantitative performance indicators (namely, cost and energy required for separation) in the papers/book chapters reviewed are presented on a consistent basis (per unit of bioethanol produced). All these will be useful to researchers and practitioners for technology selection and/or further advances in bioethanol separation. 1. Introduction Over the next few decades, population and income are anticipated to rise, resulting in greater demand for secure and sustainable energy. The world energy demand is expected to increase by 48% (from 549 quadrillions Btu to 815 quadrillions Btu) between 2012 and 2040.1 Most of the world’s energy growth will occur in countries outside the Organization for Economic Cooperation and Development (OECD), where relatively strong, long-term economic growth drives increasing demand for energy. The energy consumption in non-OCED Asia, including China and India, is expected to increase by 71% between 2012 and 2040 compared to an increase of 18% in OECD nations.1

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Currently, around 32% of energy used comes from oil, 30% from coal and 24% from natural gas, thus accounting for 86% from fossil fuels in total; hydroelectricity accounts for 7%, nuclear for 4% and other renewables for just 3%.2 Fossil fuels will continue to be the dominant source of energy for powering the global economy, providing around 60% of the growth in energy and accounting for almost 80% of total energy supply in 2035, but down from 86% in 2014.3 BP’s Energy Outlook 20163 envisages that global CO2 emissions from fossil fuels may be 20% higher in 2035 than they were in 2014, partly as a consequence of coal use in rapidly growing economies. Renewable energy sources, namely, solar, wind, thermal, hydroelectric and biofuels are selfsustaining and are vital to steer the energy system to the low-carbon future envisioned in the Paris climate conference.4 Measures to cut down greenhouse gas (GHG) emissions and the promotion of renewable energy sources are now cornerstones in climate and energy security agenda around the world. In the past decade, there has been a flurry of interest over biofuels as a renewable source of energy. Increased focus on the mitigation of GHG emissions can result in incentives for expanded biofuels production, particularly bioethanol, through policy measures including mandatory blending, utilization targets, and low-carbon fuel standards. Further, biofuels hold greater promise; for instance, they can be produced and used as a direct substitute for liquid fuels in transportation and machinery.5 In this context, bioethanol is very promising for blending with gasoline and diesel, mainly from the standpoint of preserving the global environment and concern about the long-term supplies of fossil fuels.6-8 Despite the profound interest in bioethanol, there are only a few large players in this field. In 2016, Brazil and the U.S. together accounted for 85% of global bioethanol production for use as a fuel.9 Brazil produces ethanol from sugar cane, and more than 20% of Brazilian cars use pure ethanol as fuel, including flex-fuel engines and ethanol alone engines.8 The baggase generated in the process is used to produce process steam and electricity. The U.S. feedstock is mostly corn and wheat.10 In developing countries, bioethanol production from edible/first generation feedstock (corn, sugarcane, and wheat) is controversial due to food versus fuel debate. Consequently, present research focusses on bioethanol production from lignocellulosic materials (second-generation feedstock) such as rice husk, corn stalk, sugarcane bagasse, softwood, switchgrass, and wheat straw because of their abundance, low cost and being non-edible. Bioethanol production from third generation feedstock (algae) is in its early stages of development.11-13


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The broth from the fermentation step contains 4-12 wt.% ethanol depending on the type of feedstock used and fermentation conditions; it is also due to the toxic effect of ethanol on microorganisms.8,14 Furthermore, lower ethanol concentration arise due to difficulty in release of sugars in the second and third generation feedstock as compared to the first generation type. Future feedstock is likely to be the second and third generation type in order to avoid competition with food sources.8, 13, 14 Then, ethanol concentration in the fermentation broth is likely to be ~5 wt.% ethanol.13, 14 Energy-intensive separation is required to produce fuel-grade ethanol (FGE) of high purity, mainly due to low ethanol concentration in the broth and presence of minimum-boiling azeotrope of ethanol and water (95.6 wt.% ethanol at 78.15°C and 1 atm). The main energy requirement is for ethanol recovery (also known as pre-concentration) from 4-12 wt.% ethanol to about 95 wt.% ethanol; this recovery step accounts for 60-80% of total separation cost of bioethanol from water.15-18 Ethanol dehydration (also known as purification) from near azeotropic composition to FGE specification (> 99.5 wt.% ethanol), is complex and has been of significant research interest. Bioethanol separation in the present paper refers to the entire separation process of recovery and dehydration together. In Brazil and United States, the two largest producers of ethanol in the world, distillation continues to be dominant in industries for the recovery section, as evident from Wooley et al.19, Humbird et al.20, Haelssig et al.21, Batista et al.22, Bessa et al.23, Ponce et al.24, PalciosBereche et al.25, and Batista and Meirelles26. For the dehydration section in Brazilian and United States industries, azeotropic distillation with cyclohexane, extractive distillation with ethylene glycol and adsorption with molecular sieves are used.27 According to reports by Wooley et al.19 and Humbird et al.20, pressure swing adsorption (PSA) with molecular sieves is used in the dehydration section. Also, Tavares et al.28 stated that most of current ethanol dehydration facilities in the United States rely on hydrophilic zeolite molecular sieves. Bioethanol as a fuel will be more attractive with improvements in its production steps, namely, pre-treatment, fermentation, ethanol recovery and dehydration. During the last three decades, numerous studies pertaining to these processing stages have been reported. The scope of the current review is on the post-fermentation separation process, specifically, bioethanol recovery from the fermentation broth and further dehydration to FGE. Although separation processes are relatively mature and in most cases proven in industries, they are generally energy intensive. Hence, improvements in them are required to increase energy efficiency and economics of the whole process. These include improvements to current



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separation methods, energy integration and development of less energy-intensive processes such as membrane technology. Cardona and Sanchez29 comprehensively presented the state of the art until the year 2007 in bioethanol production from process-engineering point of view and explored process integration as an important avenue for improvements in biofuel production. Vane30 reviewed bioethanol recovery and dehydration technology options from the year 2001 to 2008, and compared them with an emphasis on the energy footprint. He also discussed in detail hybrid technologies such as distillation-pervaporation, distillation-vapor permeation and membraneassisted vapor stripping. A comprehensive review that covers all the advances in bioethanol separation in the 20th century can be found in Huang et al.14 With focus on bioethanol dehydration, Kumar et al.31 presented a general overview of the state of the art from the year 1930 to 2008. In addition, Frolkova and Raeva32 reviewed technologies for bioethanol dehydration from the year 2000 to 2008. To the best of authors’ knowledge, there is no review of developments on bioethanol recovery and dehydration from the year 2008. This motivated the present review paper with the aim of summarizing the advancements made in bioethanol recovery and dehydration from the year 2008 to 2016. The rest of this article is organized systematically to reflect the research methodology employed. Section 2 describes the annotated bibliography and provides a comprehensive review of different recovery and dehydration technologies from fermentation broth (4-12 wt.% ethanol), intermediate concentration of ethanol (12-90 wt.%) and near azeotropic concentration of ethanol (90-95 wt.%). Section 3 outlines challenges in the comparison of results in the literature, and suggests guidelines for facilitating reproducible results and accurate comparison. Section 4 highlights process improvements and possible future trends in bioethanol separation. Lastly, Section 5 summarizes the main findings of this review. 2. Annotated Bibliography of Bioethanol Separation The present review summarizes the advancements made in the process development and design of bioethanol recovery and dehydration. In total, 54 papers published in journals and book chapters from the year 2008 to 2016 have been included in the annotated bibliography. Out of these, 42 were published since the year 2012 (5 years) as compared to 12 during the period 2008-2011 (4 years). This shows the increasing interest in bioethanol recovery and dehydration. As illustrated in Figure 1, these papers have been published in a variety of journals. About one-third of papers are in the Industrial & Engineering Chemistry Research. Journals with only one paper on bioethanol separation are grouped together in the ‘Other 4 ACS Paragon Plus Environment

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Journals’. In alphabetical sequence, these are Bioresource Technology, Brazilian Journal of Chemical Engineering, Chemical Engineering and Technology, Chemical Engineering Research and Design, Energy Conversion and Management, Energy Sustainability and Society, Korean Journal of Chemical Engineering, Procedia Engineering, and Process Safety and Environment Protection.

Figure 1: Papers on bioethanol separation in the years 2008 to 2016, by the journal of publication.

2.1. Classification and Presentation After reviewing papers covered in this annotated bibliography, we classified them based on the ethanol concentration in the feed as follows: a) Fermentation broth (4-12 wt.%); 34 papers using this feed b) Intermediate concentration (12-90 wt.%); 4 papers using this feed c) Near azeotropic concentration (90-95 wt.%); 16 papers using this feed Each of the above categories is further sub-divided based on separation technology for bioethanol recovery and dehydration (Figure 2). •

Studies employing distillation including dividing-wall column

•

Studies employing hybrid separation processes such as distillation-liquid liquid extraction, distillation-pressure swing adsorption, HiGee stripper-membrane system, distillation-pervaporation and distillation-vapor permeation.



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Bioethanol Recovery (Pre-concentration) and Dehydration (Purification

Fermentation broth (4-12 wt.% ethanol) Studies employing distillation including dividing-wall column Zhang et al.35, Errico and Rong36, Kiss and Ignat37, Errico et al.39, 42, Hussain and Pfromm43, Kiss and Ignat44, Luyben45, Segovia-Hernández et al.46, Ramírez-Márquez et al.41, Vázquez-Ojeda et al.67, 68, HipólitoValencia et al.47, Errico et al.48, Li et al.49, Loy et al. 69, Luo et al.50, Díaz and Tost34, Nhien et al.52, Torres-Ortega and Rong18, 53

Intermediate concentration (12-90 wt.% ethanol) Studies employing distillation including dividing-wall column Mulia-Soto and FloresTlacuahuac70, VázquezOjeda et al.67, 68, Shirshat et al.71, Hipólito-Valencia et al.47

Studies employing hybrid separation process Martínez et al.66, Roth et al.72, Vázquez-Ojeda et al.67, 68, HipólitoValencia et al.47

Near azeotropic mixture (90-95 wt.% ethanol) Studies employing distillation including dividing-wall column Chavez-Islas et al.76, Ravagnani et al.77, García-Herreros et al.78, Sun et al.79, Kiss and Suszwalak38, Li and Bai40, Tavan and Hosseini73, Gil et al.81, Pla-Franco et al.82, Tututi-Avila et al.83 , An et al.74, Figueirêdo et al.84, Brito et al.85, Tavan and Shahhosseini 87, Tututi-Avila et al.86, Zhu et al.88

Studies employing hybrid separation process Dimean and Bildea54, Vane and Alvarez55, Huang et al.56, Martin and Grossmann58, Quintero and Cardona59, Paulo et al.60, SanchezSegado et al.61, Brunet et al.62, Gudena et al.57, 63, Kanchanalai et al.13, Vázquez-Ojeda et al.67, 68, Lassmann et al.64, Hipólito-Valencia et al.47, Loy et al.69, Nagy et al.65

Figure 2: Classification of papers covered in the annotated bibliography. Papers underlined and in italics fall under two major and as well as their corresponding sub-categories.


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Feed to the separation step in many studies (29 out of 38 papers on bioethanol separation) consists of ethanol and water only. Exceptions to this are mentioned in the summary of relevant papers. To avoid repetitive definition in the summary of each paper, we adopt the following terminology in the present paper. Bioethanol separation refers to the entire separation process consisting of both recovery and dehydration steps. Conventional distillation sequence (CDS) for this separation comprises of three columns: pre-concentration by a distillation column (PDC) followed by dehydration using extractive distillation system (EDS) having two columns with the first column (known as extractive distillation column, EDC) producing FGE and the second column (known as solvent regeneration column, SRC) regenerating the solvent for recycle. In addition, solvent used in EDS is ethylene glycol (EG), unless otherwise stated. Similar to EDS, azeotropic distillation system (ADS) comprises azeotropic distillation column (ADC) and SRC. The solvent used in ADS is stated in the summary of related papers. The process of pre-concentration via distillation followed by dehydration using pressure swing adsorption is denoted as (D-PSA). The focus of the annotated bibliography is on development and design of bioethanol recovery and dehydration processes although some papers reviewed cover upstream sections, namely, biomass (feedstock) processing and fermentation. This review includes only journal publications and not conference papers or reports. Journal articles covering bioethanol production with very limited information on bioethanol recovery and dehydration, and journal papers discussing only ethanol recovery from the fermentation broth (thus producing concentrated ethanol and not FGE) are not included in the present review. However, some of the latter, e.g., Haelssig et al.21, Batista et al.22, Bessa et al.23, Ponce et al.24, Palcios-Bereche et al.25 and López-Plaza et al.33 are interesting and coud help achieve substantial savings in energy requirement in the separation section. Key performance indicators (namely, cost and energy required for separation alone) in the papers reviewed are presented in tables on a consistent basis (per unit of bioethanol produced). Cost of separation ($/kg of bioethanol) is calculated by dividing the total annualized cost (TAC) of the separation process by annual production of bioethanol. For a holistic economic evaluation, TAC should include costs of membrane/adsorbent replacement, solvent make-up etc., if any. However, these are not considered in the calculated separation cost due to lack of information in the cited articles. Note that cost of feed is very often not included in TAC since it is usually unknown and feed to the separation process is similar in related studies.



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Tables 1-4 summarize values of key performance indicators along with other information such as feed composition, product composition and ethanol production capacity in the papers reviewed. Many journal papers have reported values of cost of separation and total energy required, and so they are directly given and clearly identified in these tables. Caution should be exercised in comparing cost of separation in different papers since cost correlations and data used are unlikely to be same in those studies. For papers without production capacity, cost of separation and/or total energy values, plant operation of 8250 h/year and ethanol recovery of 99% are assumed for calculating key performance indicators; further, total energy requirement (i.e., MJ-fuel/kg of bioethanol) is found, where possible, by adding thermal and electrical energies with efficiency factors of 0.9 and 0.3 respectively for steam and electricity production from fuel (natural gas).34

2.2. Journal Papers using Feed of Fermentation Broth Out of the 54 papers, 34 papers belong to this category, of which 17 papers fall in the first sub-category of distillation including dividing-wall column (DWC), 13 papers in the second sub-category of hybrid separation processes and the remaining 4 papers fall in both the subcategories. In the following, we summarize the papers according to these sub-categories, and in chronological order followed by alphabetical sequence within each sub-category.

2.2.1. Studies employing Distillation including Dividing-Wall Column Zhang et al.35 investigated ethanol production from a hybrid lignocellulosic feedstock. Starting from the fermentation broth (with 2.69 wt.% ethanol), first distillation column concentrates to 40 wt.% ethanol. Subsequently, it is concentrated in a rectification column to 95 wt.% ethanol and then subjected to azeotropic distillation using cyclohexane as solvent to obtain 99.5 wt.% ethanol. Process simulation was performed using Belsim-VALI. Energy integration through pinch analysis and cogeneration is discussed but economic evaluation of the process developed is lacking. Errico and Rong36 generated new distillation sequences for bioethanol separation using a binary feed of ethanol and water, based on a step-by-step procedure and extending the concept of thermally coupled structures and column sections recombination. The new sequences generated from CDS are grouped as modified thermally coupled sequences (i.e. substitution of a heat exchanger having no-product stream by a liquid-vapor interconnection), modified thermodynamic equivalent sequences (i.e. moving a column section with a common reflux or vapor boil up between two consecutive columns in a modified thermally coupled sequence) and intensified sequences (i.e. substituting a single column section of


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thermodynamically equivalent sequences with a liquid/vapor side draw or with a thermal coupling). Further, they are simulated in Aspen Plus V7.3 and are compared using ‘energy index’ as one of the performance indicators. The modified thermodynamic equivalent sequences leads to a reduction of ~7% and ~5% in energy required and capital cost as compared to other proposed sequences. Kiss and Ignat37 proposed extensions to the conventional extractive DWC (E-DWC) studied by Kiss and Suszwalak38 for bioethanol dehydration (and so reviewed later), such that bioethanol separation is performed in a single DWC. In other words, the proposed configuration integrates all the three columns of CDS into a single DWC. Both CDS and EDWC are optimized for minimum energy requirement using sequential quadratic programming (SQP) in Aspen Plus. E-DWC leads to 17% reduction in energy requirement and 16% in TAC over CDS. However, solvent makeup cost is not included in the economic evaluation of CDS. Based on the values in the paper, makeup solvent in CDS amounts to ~0.0007 kg of EG/kg of bioethanol. Assuming plant operation of 8250 h/year and EG price of $1.43/kg, makeup cost is $106,178 or 1.5% of reported TAC of CDS. Note that data for estimating makeup solvent flowrate in E-DWC are not available in the paper. Errico et al.39 added another pre-concentration column to CDS studied by Li and Bai40 (reviewed later), thus making it a four-column sequence (referred to as the base case). Besides, they explored alternative sequences in the four-column configuration, through various combinations of partial and total condensers. Process simulation results from Aspen V7.3 indicate that the generated sequences are equally good amongst themselves and promising as compared to the base case, in terms of energy demand and capital cost savings. For instance, the alternative configuration with three partial condensers and one total condenser resulted in savings of 20% in energy and 3% in capital cost. On the other hand, CDS with vapor recycle of Ramírez-Márquez et al.41, reviewed later, showed savings of 22% and 17% in energy requirement and capital cost respectively. In second part of the study, Errico et al.42 generated different subspaces of column configurations using thermal coupling and column section recombination principles stated by Errico and Rong36, leading to complex column sequences. Introduction of thermal coupling between EDC and SRC in CDS gave marginal benefit (~2%) in capital cost and no improvement in the energy requirement. In contrast, the sequence comprising preconcentrator and DWC with liquid and vapor recycle separately lowered capital cost by 23%. Two-column configuration with vapor side stream failed to achieve complete solvent recovery, thus affecting its performance and suitability. 9 ACS Paragon Plus Environment


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Hussain and Pfromm43 suggested technological modifications in the pre-concentration section of CDS, namely, inclusion of double-effect distillation (DED) with split feed and vapor compression distillation (VCD). For VCD, a polytropic efficiency of 0.72 is assumed for the compressor. In the dehydration section of CDS, CaCl2 is used as an extracting agent instead of EG in EDC, and a novel scheme of electro-dialysis and spray drying for salt recovery is explored. Process simulations starting with a realistic feed of 10 components and economic evaluation are performed using Aspen Plus 2006.5. Compared to D-PSA, VCD with saltbased EDS gives savings of 23% and 12% in energy and TAC (which includes cost of membrane replacement in electro-dialysis) respectively, whereas DED with salt-based EDS results in 12% and 8% savings in energy and TAC (which includes cost of membrane replacement) respectively. This indicates VCD is better than DED for pre-concentration of fermentation broth. Nevertheless, salt makeup and handling costs are not considered in both the schemes involving salt-based EDS. Kiss and Ignat44 optimized the bioethanol separation process as a function of ethanol concentration in the PDC distillate of CDS using SQP and sensitivity analysis in Aspen Plus. Ethanol concentration in the distillate of PDC was varied from 75 to 93.5 wt.%. Sensitivity analysis indicates that PDC distillate of 91 wt.% ethanol leads to optimum TAC along with lower energy requirement. Further, the authors commented that over 15% energy savings is possible in existing bioethanol plants, which use distillate composition of 93.5 wt.%, closer to azeotrope condition. Solvent makeup cost is not included in the economic evaluation. Based on the values in the paper, makeup solvent amounts to ~0.0004 kg of EG/kg of bioethanol and its cost (based on assumptions stated previously) amounts to $58,988 or 1% of reported TAC of CDS. This is unlikely to affect the optimum ethanol concentration in PDC distillate. Luyben45 too analyzed the effect of ethanol concentration in PDC distillate on energy and capital investment of bioethanol separation by ADS using benzene as solvent. Process simulations are performed in Aspen Plus. On varying PDC distillate composition from 88 to 93.5 wt.% ethanol, results show that optimum TAC is at 91 wt.% ethanol. In the later part of the study, choice of solvent is discussed for the same optimized process, and use of cyclohexane over benzene is encouraged due to environmental and health hazards as well as ~13% lower energy requirement. Ramírez-Márquez et al.41 studied the dynamic behaviour of the configurations proposed by Errico and Rong36 Out of these various configurations, five sequences: CDS (with liquid/vapor recycle), intensified sequence (with side stream vapor recycle) and thermally coupled sequence (with liquid/vapor recycle) are selected, bearing in mind lower energy 10 ACS Paragon Plus Environment

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requirement and capital cost. The results from Aspen dynamic simulations indicate that the side stream does not introduce complexity from operation point of view. Intensified sequence outperformed the dynamic behavior of CDS and thermally coupled sequence for both liquid and vapor recycle. Later, Segovia-Hernández et al.46 studied dynamic behavior of the same sequences using Singular Value Decomposition (SVD) instead of Relative Gain Array (RGA) analysis employed by Ramírez-Márquez et al.41 Results using SVD were found to be similar to those using RGA analysis. For improving energy integration, Hipoĺito-Valencia et al.47 suggested the use of SYNHEAT model and Organic Rankine Cycle-Heat Exchanger Network (ORC-HEN) model in the sequences in Segovia-Hernández et al.46 On using SYNHEAT model, 7%, 11% and 11.2% savings in TAC is achieved respectively for DWC, CDS and intensified sequence with side stream vapor recycle, over those without heat integration. Further, on using ORC-HEN model, < 1% reduction in TAC is achieved for all sequences as compared with SYNHEAT model. This is due to additional equipment cost and small amount of income from electricity sales. Further, electricity price variation will affect these nominal savings. Errico et al.48 eliminated the reboiler and condenser associated with PDC and SRC, respectively, in CDS, thereby merging PDC and SRC by means of a side stream. The new generated sequence of two columns (i.e., pre-concentration column and PDC-SRC together) gives 3% and 10% savings in energy and capital cost. Analysis using Aspen Dynamics showed better control properties for the generated sequence over CDS. Li et al.49 investigated two fuel additive-based processes i.e. ADS with and without SRC. Among the various gasoline additives (methyl tert-butyl ether, MTBE, ethyl tert-butyl ether, diisopropyl ether and tert-amyl methyl ether), MTBE is screened as a potential solvent by analysing residue curve maps, both experimentally and through simulations in Aspen Plus. In both the processes, binary feed of ethanol and water is pre-concentrated to ~92 wt.% ethanol followed by ADS, whereby a mixture of ethanol and MTBE is obtained as the bottoms stream and a heterogeneous azeotrope of water-MTBE as the overhead stream. Process simulations in Aspen Plus indicate marginal savings of 0.5% in reboiler duty for ADS without SRC (in other words, direct recycle of water-rich phase from the decanter to PDC) over ADS with SRC. The former case with MTBE as the solvent gives savings of 60% in reboiler duty and 50% in total investment in comparison to using isooctane as the solvent, for a plant capacity of 8000 ton of bioethanol/year. Luo et al.50 proposed addition of a vapor recompression heat pump (HP) to the optimized EDWC of Kiss and Ignat.37 This process with suitable heat integration is simulated using 11 ACS Paragon Plus Environment


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Aspen Plus, and sensitivity analysis is performed to determine the discharge pressure of the compressor. Economic evaluation of the process is carried out but without any discussion on solvent makeup cost. For the optimal case, addition of HP to E-DWC leads to 40% and 18% savings in energy (MJ-fuel/kg ethanol) and TAC, respectively, compared to E-DWC (i.e., without HP). Based on the values in the paper, makeup solvent amounts to ~0.001 and 0.02 kg of EG/kg of bioethanol in CDS and HP assisted E-DWC, respectively. Cost of this solvent makeup (based on the assumptions stated earlier) is $148,137 and $2,945,836 for CDS and HP assisted E-DWC respectively. Hence, solvent loss in HP assisted E-DWC can make it costlier than CDS. Díaz and Tost34 explored DED with split feed and VCD, to reduce the required energy in the pre-concentration section. For both the technologies investigated, ethanol is pre-concentrated to 89 wt.% in vapor phase, before dehydration by EDS using glycerol as the solvent and SRC. For VCD involving compression from vacuum, isoentropic efficiency of 0.75 is assumed for the compressor; this is higher than isoentropic efficiency of 0.3 to 0.5 given in Ryans and Bays51 for vacuum conditions. For a binary feed of 10 wt.% of ethanol in water and with suitable heat integration, process simulations in Aspen Plus V7.3 indicate 55% and 40% savings in energy (MJ-fuel/kg bioethanol) by the use of VCD and DED, respectively, in the proposed sequences over CDS with glycerol as the solvent. In terms of TAC, the process with DED gives 13.5% savings and that with VCD costs 2.5% more (owing to high costs incurred for compressor), over CDS for a plant capacity of 14,670 ton of bioethanol/year. These results suggest DED is better than VCD for pre-concentration, which is contrary to the indication that VCD is better than DED based on results in Hussain and Pfromm.43 This could be due to differences in design configurations, operating conditions and/or cost data used. Nhien et al.52 designed and optimized a heat-integrated and intensified bioethanol separation process for a realistic feed consisting of 24 components. HP assisted distillation and DED for the pre-concentration step as well as E-DWC with glycerol as the solvent for the dehydration step are assessed. The alternatives studied are simulated in Aspen Plus V8.8. Between HP assisted distillation and DED, the latter is selected for pre-concentration, owing to 46% savings in TAC over the former. Further, DED followed by E-DWC gives 48% and 57% savings in TAC and carbon emissions respectively, compared to a process with a PDC followed by CDS using glycerol. Note that Nhien et al.52 optimized E-DWC alone and not the entire bioethanol separation process.


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Torres-Ortega and Rong18 investigated different recovery and dehydration alternatives for bioethanol separation from a feed with 25 components including very high amount (~4 wt.%) of CO2. Different recovery alternatives based on flash drums, centrifuge filters, distillation and absorption are studied. For dehydration, different purification technologies based on EDS, PSA, vacuum distillation etc. are explored. Process simulations are performed in Aspen Plus V8.0 and detailed economic evaluation (including raw material cost) is carried out. The optimal recovery sequence consists of first separating solids by a centrifuge filter, then separating stillage (impurities and water) by distillation, and finally separating gases from a 90 wt.% bioethanol stream by a series of flash drums. The optimal sequence for dehydration step is EDS with glycerol as the solvent and SRC replaced by an air-stripping column. The optimal separation scheme achieved 14% savings in TAC over the separation process of Humbird et al.20, where recovery section consists of removal of gases by flash drum, bioethanol pre-concentration using distillation followed by removal of solids and stillage by centrifuge filter, and dehydration consists of D-PSA. Torres-Ortega and Rong53 extended their previous work18 by intensifying the recovery and dehydration sections of the optimal separation scheme. They proposed intensification by replacement of flash drums by column sections, hybridizing unit operations by rearranging column sections, and relocation of column sections as novel synthesis approaches to formulate hybrid units and DWCs. The optimal intensified sequence consists of first separating the solids by a centrifuge filter, pre-concentration to 90 wt.% ethanol and dehydration to obtain 99.5 wt.% ethanol by DWC having three dividing walls. It achieves 23% and 28% savings, respectively, in TAC and total energy (thermal and electrical) over the optimal sequence obtained in Torres-Ortega and Rong18. In addition, Torres-Ortega and Rong53 compared the optimal intensified sequence with the multi-effect distillation (with feed split) followed by dehydration sequence of Torres-Ortega and Rong18 for 5 to 20 wt.% ethanol in the fermentation broth. Compared to the latter, the optimal intensified sequence gives 15-20% savings in TAC. The significant increase in energy (Table 1) for bioethanol separation in Torres-Ortega and Rong18,

53

as compared to Nhien et al.52 may be due to

difference in feed composition (for instance, high concentration of CO2, insoluble and soluble solids) in Torres-Ortega and Rong18, 53.



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Table 1: Studies employing distillation including dividing-wall column for feed of fermentation broth. S. No.

Reference

Capacity (ton of bioethanol product/year)

Ethanol in feed & product (wt. %)

Energy for reboiler(s) and compressor(s) (MJ/kg of bioethanol)

Cost of separation ($/kg of bioethanol)

Remarks

1

Zhang et al.35

600

2.7 & 99.5

-

-

Neither reported energy usage nor performed economic evaluation.

2

Errico and Rong36

79,000

11.9 & 99.9

5.3-8.1

-

Reported only capital cost. Assumed 8250 h/year for calculating production capacity.

3

Kiss and Ignat37

100,000

10 & 99.8

7.5-8.9

0.06-0.07

Solvent makeup cost not considered.

4

Errico et al.39

32,000

11.9 & 99.9

4.9-6.3

-

Reported only capital cost

5

Errico et al.

42

32,000

11.9 & 99.9

4.5-4.6

-


6

Hussain and Pfromm43

210,000

5.5 & 99.5

9-10

-


7

Kiss and Ignat44

100,000

10 & 99.8

7.6

0.06

Solvent makeup cost not considered.

8

Luyben45

19,000

11.9 & 99.8

7.5

0.08

Minimized TAC by analysing one or two variables at a time

9

RamírezMárquez et al.41

32,000

11.9 & 99.9

4.5-4.7

-

Reported only capital cost. Assumed 8250 h/year for calculating production capacity.

10

Errico et al.48

83,000

12.1 & 99.9

7.9-8.3

-


8000

11.9 & 99.2

13.12-27.1

0.17-0.34

Minimized solvent consumption and not TAC

11

Li et al.

49

12

Luo et al.50

100,000

10 & 99.9

4.9*

0.05#

*This is in fuel equivalents. #Solvent makeup cost not considered.

13

Díaz and Tost34

15,000

10 & 99.7

2.5-3.4*

0.08-0.09#

*This is in fuel equivalents. #Solvent makeup cost considered

14

Nhien et al.52

180,000

4.9 & 99.7

3.32*

0.68*

*Energy and separation cost for realistic fermentation broth.

15

TorresOrtega and Rong18

170,000

4.9 & 99.5

17.3*

0.18*

*Reported total energy (thermal and electrical) and separation cost (including solvent makeup cost) for realistic fermentation broth.

16

TorresOrtega and Rong53

170,000

4.9 & 99.5

12.2*

0.14*

*Same as for the previous reference.


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2.2.2. Studies employing Hybrid Separation Processes Dimian and Bildea54 described the design of a bioethanol plant starting with a realistic feed of 19 components and using Aspen Plus. The fermentation broth containing ~8 wt.% ethanol is pre-concentrated in two distillation columns, with the first one producing 77 wt.% ethanol and the second one producing 92.5 wt.% ethanol. Finally, the concentrated stream is dehydrated to produce 99.8 wt.% ethanol by adsorption using molecular sieves. Although a realistic feed is considered, the process lacks suitable heat integration, and economic evaluation is not reported. Vane and Alvarez55 proposed an alternative separation sequence of hybrid stripper and membrane for FGE production from a feed with 1 wt.% and 5 wt.% ethanol. For the stripper column in this hybrid system, two pressures, namely, 20 and 101.3 kPa are considered; the corresponding compressor (of 75% adiabatic efficiency, which is high for vacuum pressure) discharge pressure is 101.3 and 304 kPa. With the stripper column at 20 kPa and 55ºC, CHEMCAD simulations of the process with suitable heat integration show 2% and 32% savings in energy (MJ-fuel/kg bioethanol) compared to the stripper column operating at 101.3 kPa and 99ºC, for a feed of 1 and 5 wt.% ethanol, respectively. However, stripper column under vacuum becomes capital intensive and results in higher separation cost as compared to operating at (above) atmospheric conditions. Similar to Vane and Alvarez55, Huang et al.56 studied hybrid stripper-membrane process, but with a more concentrated (11.5 wt.% ethanol) feed and stripper column at 50 kPa. In addition, they developed composite membranes that can withstand temperatures up to 130ºC, and assumed realistic membrane permeability for the study. Ethanol in the overhead of the stripper column is set to 64.7 wt.% by considering the trade-off between steam for reboiler and membrane area. The proposed process with appropriate heat integration is simulated in CHEMCAD 5.6. It results in energy savings of 49% in comparison to heat integrated D-PSA (with both stripping and rectification columns at 50 kPa) simulated by them. However, according to the process flowsheet given, D-PSA simulated is using ternary feed of ethanol, water and CO2 contrary to binary feed employed in hybrid stripper-membrane process. Thus, D-PSA and hybrid stripper-membrane process are not compared for the same feed. Further, overall economic evaluation of the hybrid separation process is not reported in Huang et al.56 Later, Gudena et al.57 suggested slight modifications to make the hybrid process realistic and performed multi-objective optimization (MOO), discussed in detail later.



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Martın and Grossmann58 investigated the conceptual design of bioethanol production via gasification of switchgrass. Operations involved in this process are modeled using short-cut equations and empirical correlations, and then implemented in GAMS. The optimal process flowsheet consists of direct gasification of switchgrass at high pressure followed by steam reforming, removal of excess hydrogen by PSA, absorption of sour acids, catalytic synthesis and finally bioethanol separation by DED with split feed followed by PSA. Inclusion of DED and heat integration led to significant reduction in energy consumption, yielding an operating cost (for the entire production process including raw material cost and income from hydrogen sales) of $0.41/gal of anhydrous ethanol. Quintero and Cardona59 studied FGE production from a lignocellulosic biomass with corresponding fermentation broth containing 15 components. Bioethanol separation is achieved by D-PSA with pre-concentration step employing two distillation columns. Process simulations and economic analysis are performed using Aspen Plus. Energy requirement for the entire process (including pre-treatment, cellulose hydrolysis, concentration and detoxification, fermentation and separation) is reported to be 86.75 MJ/L of ethanol produced; fermentation and separation steps account for 30% of the total energy. Production cost of ethanol starting from rice hull biomass (0.192$/L of ethanol) includes energy from cogeneration and electricity sales in the analysis. Paulo et al.60 proposed model-based cost optimization for different plant capacities of bioethanol extraction-dehydration plants using supercritical propane as the solvent. The conventional scheme consists of a high-pressure extractor and a dehydration column with suitable heat integration. Two alternative process schemes based on conventional scheme; first scheme (hereby termed as Scheme 1) has HP with a steam-turbine driver and second scheme (Scheme 2) has HP with an electric motor. They are developed to have energyefficient and economic designs. The two process alternatives are formulated as non-linear programming problems in FORTRAN 90, and are optimized using SQP. Scheme 1 shows the best performance among all the alternatives and for all plant capacities analysed, with lowest energy consumption and TAC. It gives 40% savings (owing mainly to savings in operating cost) in unit separation cost of ethanol over the related conventional scheme for a plant capacity of 120,000 ton of ethanol/year. Sánchez-Segado et al.61 designed a bioethanol plant using carob pod as feedstock and simulated in CHEMCAD 6.0. Different sections of the plant, namely, storage, sugar extraction, fermentation, bioethanol separation and co-generation are studied in detail. For bioethanol separation, the technology adopted is D-PSA with pre-concentration section 16 ACS Paragon Plus Environment

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employing two distillation columns; first column concentrates ethanol to 82.3 wt.% and the second column to 87 wt.% before dehydration to FGE using PSA. Economics of the plant (including raw materials cost) with 7% internal rate of return, sensitivity analysis for plant capacity, internal rate of return and price of feedstock are studied. Brunet et al.62 presented a systematic method for the optimal design of corn-based bioethanol plant coupled with solar-assisted steam generation. Apart from downstream separation of bioethanol, upstream processing is also discussed in detail. In the downstream section, bioethanol separation is achieved using D-PSA with pre-concentration section employing two columns. Economic analysis (including raw material cost) of the entire bioethanol plant is presented. MOO for maximizing net present value (NPV) and minimizing specific energy requirement is performed in GAMS using ε-constraint method. Process simulation in SuperPro Designer provides basis for calculating objective functions in MATLAB. The optimum Pareto solution chosen resulted in NPV of ~$76 million and production cost of 0.68 $/kg of ethanol including the cost of corn. Gudena et al.57 studied MOO of a modified flowsheet of hybrid stripper-membrane process proposed by Huang et al.56 for bioethanol recovery and dehydration from a feed containing 11.5 wt.% ethanol, for two sets of objectives: bioethanol purity and operating cost, and ethanol loss and operating cost. A multi-stage compressor with realistic adiabatic efficiencies for vacuum conditions and intermediate cooling is implemented instead of single-stage compressor with 75% adiabatic efficiency as used in Huang et al.56 In another study, Gudena et al.63 investigated a process intensification technology, HiGee stripper combined with membrane process for bioethanol recovery and dehydration, starting with a relatively low ethanol content (5 wt.%) in the feed as opposed to 11.5 wt.% in Gudena et al.57 The optimal HiGee stripper with membrane provides 10% and 3% reduction in capital and operating costs respectively, and 8-fold reduction in packing volume as compared to the conventional stripper-membrane process. Kanchanalai et al.13 explored bioethanol production from a very dilute ethanol stream obtained by photosynthesis of blue-green algae (third generation feedstock) in photobioreactors. The dilute ethanol stream is pre-concentrated to 7 wt.% by reverse osmosis (RO) using polyacrylamide membrane before distillation for further concentration and finally dehydration using a pervaporation (PV) module (having polyvinyl alcohol membrane). The overall separation process is modeled as a mixed-integer non-linear programming (MINLP) problem in GAMS for minimizing separation cost. In the optimal design, inclusion of preconcentration by RO led to reduction in both energy consumption and separation cost as 17 ACS Paragon Plus Environment


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compared to the process without RO, for feed concentrations up to 3 wt.% ethanol and throughput of 30 million gallons of ethanol/year. However, for feed of more than 3 wt.% ethanol, inclusion of RO for bioethanol pre-concentration is questionable. Lassmann et al.64 investigated bioethanol separation using realistic feed (with 17 components) through two configurations: two distillation columns in one and three columns in the other, in the pre-concentration section of D-PSA. Simulation of the entire separation process with suitable heat integration and co-generation is modeled in Aspen Plus. Pinch analysis is applied to the entire production process. Owing to 6% savings in energy, the configuration utilizing three columns in the pre-concentration section is favoured over the one utilizing two columns. However, economic evaluation of the process is lacking. Nagy et al.65 emphasized the use of hybrid process of distillation followed by one or multistage PV rather than distillation alone (e.g., CDS), for a feed of 5 wt.% ethanol in water. PV is operated under different operating modes, mainly for lowering specific energy demand of separation process. Simulation results from CHEMCAD demonstrate that standalone PV can provide FGE, using consecutively switched hydrophobic and hydrophilic membranes having high separation coefficient. For instance, three-stage PV with each membrane module having a separation coefficient of at least 50 could be used to obtain FGE, with an energy requirement of 7.18 MJ/kg of bioethanol. Compared to this, two-stage PV with each membrane module having a separation coefficient of 100, lowers energy by 31% for producing FGE. This decrease in energy requirement is at the expense of higher cost of membranes, and membranes with such high separation coefficient are yet to be developed. From an overall economic perspective, standalone PV with current membrane technology is not attractive over hybrid process of distillation and membrane for bioethanol separation.

2.2.3. Studies employing both Distillation and Hybrid Separation Processes Similar to Martínez et al.66, reviewed in a later sub-section, Vázquez-Ojeda et al.67 investigated bioethanol separation by CDS and an alternative separation sequence (ASS) utilizing liquid-liquid extraction (LLE) using n-dodecane as solvent followed by CDS. Energy integration is implemented through SYNHEAT model in GAMS with an objective to reduce utility costs and energy usage. The optimization methodology adopted for the separation processes is differential evolution in MATLAB coupled with Aspen Plus simulator. Although this approach is sequential with optimization of the separation process followed by energy integration, energy integration reduces thermal energy usage for two feeds of 5 and 12 wt.% ethanol.


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In the second part of the study, Vázquez-Ojeda et al.68 performed economic evaluation of both CDS and ASS using different solvents (iso-octanol and octanoic acid) for LLE. The optimized CDS gives 48% and 23% savings in TAC over optimized ASS using octanoic acid for a feed of 5 wt.% ethanol and optimized ASS using octanoic acid for a feed of 12 wt.% ethanol respectively. Nonetheless, optimal sequences in Vázquez-Ojeda et al.68 are without heat integration, and so further improvements in them may still be possible. HipoĺitoValencia et al.47 incorporated SYNHEAT model and ORC-HEN model in the optimal sequences obtained by Vázquez-Ojeda et al.68 Less than 7% reduction in TAC for 5 wt.% ethanol feed is achieved for CDS and ASS sequences with ORC-HEN model, as compared to their corresponding sequences using SYNHEAT model. Loy et al.69 investigated bioethanol separation by two different technologies, namely, D-PSA and E-DWC using EG as solvent, starting with a ternary feed of ethanol, water and CO2. Processes with suitable heat integration are simulated in Aspen HYSYS V8.4 and are optimized using SQP within Aspen HYSYS. Unit cost of manufacture (COM) is chosen as the performance metric for holistic economic evaluation including solvent makeup and adsorbent costs. Although optimized E-DWC is better than optimized D-PSA in capital cost (~55% savings) and thermal energy demand (~ 28% savings), the latter results in 33% lower cost of manufacture (COM) as compared to the former process, owing to the cost incurred for solvent makeup in E-DWC. Further, Loy et al.69 explored economies of scale and identified the optimal production capacity of the D-PSA process to be 400,000 m3 of bioethanol/year. Table 2: Studies employing hybrid separation processes as well as employing both distillation and hybrid separation processes using feed of fermentation broth.

S. No.

Reference





Remarks

Studies employing hybrid separation processes 1

Dimean and Bildea54

200,000

8 & 99.5

7.9

-

Assumed 8250 h/year for calculating production capacity.

2

Vane and Alvarez55

3000

1-5 & 99.5

2.5-9.0*

0.05-0.12

*This is in fuel equivalents.

3

Huang et al.56

90,000

11.5 & 99.7

3-6

-

No economic evaluation.

4

Martin and Grossmann58

180,000

10 & 99.9

7.8*

0.15*

*These are total (thermal and electrical) energy and cost (including raw material cost and hydrogen sales income) for the entire



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bioethanol process. 5

Quintero and Cardona59

22,000

5.5 & 99.6

20-120*

0.24-0.56*

*These are total (thermal and electrical) energy and cost (including raw material cost) for the entire bioethanol process.

6

Paulo et al.60

120,000

10 & 99.5

3.9-4.2*

0.03-0.08

*This is in fuel equivalents.

7

SanchezSegado et al.61

15,000

9.8 & 99.5

6.1*

0.93#

*This is for separation step alone. #This is for the entire bioethanol process including raw material cost.

8

Brunet et al.62

120,000

8 & 99.6

32.3*

0.68#

*This is in fuel equivalents. #This includes raw material cost and is for the entire bioethanol process.

9

Gudena et al.57

100,000

11.5 & 99.7

6.3*

-

*This is in fuel equivalents. Evaluated only operating cost.

10

Gudena et al.63

43,000

5 & 99.7

7.8*

0.16#

*This is in fuel equivalents. #Separation cost includes membrane replacement cost.

11

Kanchanalai et al.13

89,000

0.5-2 & 99.4

11-64*

0.05-0.16#

*This is in fuel equivalents. #Separation cost includes membrane replacement cost.

12

Lassmann et al.64

100,000

4 & 99.4

10-18*

-

*This is for realistic fermentation broth.

13

Nagy et al.65

15,000

5-99.6

5-7.2

-


Studies employing both distillation including dividing-wall column and hybrid separation processes 1

VázquezOjeda et al.67

350-900

5-12 & 99.5

6.1-13

-

Reported only operating cost.

2


350-900

5-12 & 99.9

7.2-13

0.79-3.65

High separation cost owing to high operating cost.

3

Loy et al.69

160,000

10 & 99.8

6.1*

0.12#

*This is in fuel equivalents for D-PSA. #Separation cost includes adsorbent replacement cost.

2.3. Journal Papers using Feed of Intermediate Concentration Out of the 54 papers, only 4 papers fall under this category with 2 papers employing distillation including DWC and 2 papers employing hybrid separation processes. Firstly, we summarize the papers in the first sub-category followed by those, which come under the second sub-category. Apart from these, there are 3 papers that consider feed of fermentation 20 ACS Paragon Plus Environment

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broth as well as feed of intermediate concentration; they also fall under both the subcategories for feed of intermediate concentration (see Figure 2). As these journal papers have already been discussed in Section 2.2, only key findings are summarized in Sub-section 2.3.3.

2.3.1. Studies employing Distillation including Dividing-Wall Column Mulia-Soto and Flores-Tlacuahuac70 investigated an internally heat integrated pressure-swing distillation (IHIPSD) process combining pressure-swing distillation (PSD) and internal heat integration for feed with 38.9 wt.% ethanol. This process is simulated in Aspen Plus by providing side duty options. Sensitivity analysis is performed to determine the recycle stage from the high-pressure column to the low-pressure column as well as distillate to feed ratio in the low-pressure column, in order to meet the desired bioethanol purity. Owing to internal heat integration, IHIPSD brings 23% energy savings as compared to PSD alone. In the later part of the study, Mulia-Soto and Flores-Tlacuahuac70 investigated dynamic behaviour of these processes. Although IHIPSD is energy efficient, it requires further research for lower ethanol concentration in the feed, optimization and economic evaluation. Shirshat et al.71 suggested integration of pre-separator to EDS using n-butyl propionate as solvent, for feed of 72 wt.% ethanol. Simulation results from CHEMCAD for this proposed configuration after optimization (to minimize capital cost alone) show savings of 6% thermal energy and 15% capital cost from the integration of pre-separator. The proposed process lacks heat integration and detailed economic evaluation.

2.3.2. Studies employing Hybrid Separation Processes Martínez et al.66 explored two hybrid systems, namely, AS-1 (LLE using n-dodecane as solvent followed by EDS using glycerol and lastly recovery of n-dodecane) and AS-2 (LLE using n-dodecane, recovery of n-dodecane, followed by EDS using glycerol) for a feed of 22.1 wt.% ethanol in water. Simulation results in Aspen Plus indicate 30% savings in thermal energy and 50% reduction in TAC for optimal AS-2 over CDS using glycerol as solvent, owing mainly due to the recovery on n-dodecane prior to EDS. In contrast, optimal AS-1 requires 37% more thermal energy over CDS using glycerol. These results indicate potential of LLE for relatively concentrated feed streams. Nonetheless, the optimal sequences obtained are without heat integration. Roth et al.72 evaluated operating cost and energy requirement of several hybrid configurations, namely, D-PSA, distillation-vapor permeation (D-VP), VP-PSA, VP in cascade and distillation-VP-PSA, all for bioethanol dehydration from 80 wt.% to FGE. Process simulations are carried out using Aspen Plus and the process is optimized using an



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evolutionary algorithm to minimize operating cost. Operating cost of optimised membraneassisted configurations differed in a small range of -3% to 6% compared to D-PSA for dehydration. However, in comparison to D-PSA under similar process conditions, VP-PSA is the promising combination for lower operating cost (by 6%) and lower energy requirement (by ~30%) for dehydration. Cost reduction of membrane and improvements in membrane performance can further lower cost of membrane-assisted configurations. However, capital cost and overall economic evaluation are not covered in the paper.72 Table 3: Studies using feed of intermediate concentration. S. No.

Reference




Remarks


Studies employing distillation including dividing-wall column 1

Mulia-Soto and Flores Tlacuahuac70

7500

38.9 & 99.6

4.3-6.3

-


2

Shirshat et al.71

3000

72 & 99.6

8.6-9.2

0.37-0.44

Solvent makeup cost considered. Production capacity calculated assuming 8250 h/year and 99% ethanol recovery.

Studies employing hybrid separation processes 1

Martínez et al.66

1700

22.1 & 99.9

7.5-11

0.17-0.41

Minimized energy usage (and not TAC) by analysing one variable at a time.

2

Roth et al.72

20,000200,000

80 & 99.6

-

0.17-0.21#

Energy usage not given. #Only operating cost (including membrane replacement cost) evaluated.

Studies employing both distillation including dividing-wall column and hybrid separation processes 1


1700-2600

22-31 & 99.5

5.5-7.9

-

Reported only operating cost.

2


1700-2600

22-31 & 99.9

6-7.8

0.32-0.69

Heat integration not performed.

2.3.3. Studies employing both Distillation and Hybrid Separation Processes In continuation to the summary of Vázquez-Ojeda et al.67 in Section 2.2.3, the optimized CDS with energy integration leads to savings of 15% and 11% in reboiler duty than that without energy integration, for a feed of 22 and 31 wt.% ethanol respectively. Further, the


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optimized ASS (using octanol for LLE) of Vázquez-Ojeda et al.68 brings 47% and 17% savings in TAC over optimized CDS with energy integration, for feed of 22 and 31 wt.% ethanol respectively. These and results summarized in Section 2.2.3 indicate the suitability of CDS only for dilute feed of 5-12 wt.% ethanol compared to alternatives studied in VázquezOjeda et al.67,68. In continuation to the summary of Hipoĺito-Valencia et al.47 in Section 2.2.3, inclusion of ORC-HEN model reduces TAC (by less than 7.2% for 22 wt.% ethanol feed) for CDS and ASS sequences compared to the corresponding sequence using SYNHEAT model.

2.4. Journal Papers based on Near-azeotropic concentration Out of the 54 papers, 16 papers belong to this category and all of them come under the first sub-category of distillation including DWC. Among these, Tavan and Hosseini73 and An et al.74 employed reactive distillation (RD) for bioethanol dehydration. Since RD is an example of process integration of reaction and distillation, these two papers are reviewed in the first sub-category itself. Stacey et al.75 proposed an interesting possibility of replacing EDS by gasoline blending with a stream of near azeotropic mixture of ethanol and water, followed by separation using two-stage counter-current LLE to obtain the desired fuel mixture. Further study on optimization and comprehensive economic evaluation of this novel alternative is necessary.

2.4.1. Studies employing Distillation including Dividing-Wall Column Chavez-Islas et al.76 investigated ethanol dehydration by EDS using ionic liquid ([emim] [DMP]) as solvent. The proposed process is formulated as a MINLP problem and optimized using GAMS. In addition, authors performed sensitivity analysis to study the effect of varying azeotropic feed stage on TAC and reboiler energy requirement. However, comparison of ionic liquid with common solvents used in EDS is lacking.76 Ravagnani et al.77 studied ethanol dehydration by EDS using EG and tetraethylene glycol (TEG) as solvents. In addition, they emphasized on solvent selection at the preliminary design stage from process economics and energy usage standpoint. Both the proposed processes, simulated in Aspen HYSYS, start with an azeotropic feed of ethanol and water. EDS utilizing TEG as solvent shows higher EDC reboiler duty, larger EDC and higher solvent flow rate compared to EDS using EG as solvent. However, considering environment and health concerns, use of TEG over EG is encouraged. García-Herreros et al.78 studied EDS utilizing glycerol as solvent, starting with ethanol-water azeotropic feed. Process is designed and optimized in GAMS for maximization of annual profit in FGE production. Annual profit evaluation includes market value of ethanol product



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(at $30/kmol), raw materials (at $15/kmol for azeotropic mixture of ethanol-water, $75/kmol for makeup glycerol), operating and infrastructure as well as annualization factor. Under optimal design conditions, an annual profit of ~$5.5 million for producing 25,100 ton of FGE per year (i.e., ~$220/ton of FGE) could be achieved. Sun et al.79 investigated the design and optimization of a DWC for heterogeneous azeotropic distillation (A-DWC) using cyclohexane as solvent, starting from near-azeotropic feed of ethanol and water. Simulation results from Aspen Plus indicate that A-DWC has thermal energy savings of 42% and 35% lower TAC over ADS using cyclohexane as solvent. Also, A-DWC eliminates back-mixing of ethanol thus improving thermodynamic efficiency by 1.57%. Kiss and Suszwalak38 proposed novel distillation technologies for enhanced bioethanol dehydration by extending the use of DWC to ADS (using pentane as solvent) and EDS respectively. ADS/EDS and their corresponding alternatives based on DWC are optimized using SQP in Aspen Plus. The optimized E-DWC and A-DWC sequences lead to thermal energy savings of 10% and 20% over EDS and ADS (using pentane as solvent) respectively. Li and Bai40 suggested addition of a concentrator column after SRC in EDS. This additional column is justified based on pseudo-binary VLE of ethanol-water-EG system. Sensitivity analysis is performed in Aspen Plus to establish the column operating conditions for lower thermal energy use and desired ethanol purity. The authors conclude that the proposed process is easier to operate as compared to EDS, mainly because it is not necessary to withdraw ethanol completely in EDC. The proposed process resulted in ~30% savings in specific energy consumption over EDS with gasoline studied by Chianese and Zinnamosca80. Nevertheless, economic evaluation of the process proposed by Li and Bai40 is lacking. Tavan and Hosseini73 proposed a novel integrated process for bioethanol dehydration along with co-production of EG via hydration of ethylene oxide in a RD. This process is simulated in Aspen HYSYS, starting with an azeotropic feed of ethanol and water. Parametric study is performed to optimize the RD design, namely, number of reaction trays, azeotropic feed stage and ethylene oxide feed stage, to minimize the total duty (of condenser and reboiler). Compared to ADS utilizing benzene as solvent, the proposed RD process requires 83% less reboiler duty owing mainly to high exothermal heat of hydration of ethylene oxide. Further study is required on detailed economic evaluation of the integrated process, which also depends on the demand for EG production.


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Gil et al.81 investigated ethanol dehydration by EDS using EG-glycerol mixture as solvent. This process with suitable heat integration is simulated in Aspen Plus 7.2. Sensitivity analysis is performed to study the effect of important column design variables on thermal energy use and ethanol purity. The optimum composition of solvent mixture for minimum energy is found to be 60 mol% EG and 40 mol% glycerol in order to obtain a desired ethanol purity of 99.96 wt.% or higher. Moreover, pinch analysis for the dehydration section is conducted to establish operating conditions (e.g., feed preheat temperature and column reflux ratio) and minimum thermodynamic condition thus ensuring lesser thermal energy usage. Pla-Franco et al.82 assessed three solvents, namely, diisopropyl ether (DIPE), isobutyl alcohol (IPA) and benzene in ADS but they did not compare their performance with that of cyclohexane, which is commonly used in ADS. The use of IPA as solvent is discarded in the preliminary design stage based on phase equilibrium and poor solubility in ethanol-water system. Based on process simulations in Aspen HYSYS, use of DIPE is found to require 63% more thermal energy compared to using benzene as solvent. However, owing to environmental and health issues, use of benzene is discouraged. Similar to Tavan and Hosseini73, An et al.74 studied the feasibility of ethanol dehydration from near-azeotropic mixtures in RD column based on the hydration of ethylene oxide to produce EG. Sensitivity analysis of essential design variables is performed with respect to ethanol recovery, ethylene oxide conversion, EG selectivity and water conversion to obtain the optimal design parameters. The by-product (mixture of glycols) obtained in the bottom stream could be of great use in the fine chemicals industry. Further study is required for economic evaluation and holistic comparison with other separation technologies. Tututi-Avila et al.83 studied the design and control of E-DWC process with the dividing wall in the top part of the column, an alternative to EDS for bioethanol dehydration to 99.5 wt.% ethanol. The process using feed of 93 wt.% ethanol in water is simulated in Aspen Plus. The above two sequences are optimized using genetic algorithm coupled with Aspen Plus. The optimal E-DWC process results in 12.4% savings in TAC over EDS. In addition, Tututi-Avila et al.83 investigated, using Aspen Dynamics, control of both processes and concluded that control properties of complex E-DWC are comparable to those of EDS. Figueirêdo et al.84 examined the effect of solvent mole fraction on the feed stage of EDC, on the separation efficiency and energy consumption in EDS with suitable heat integration for ethanol dehydration, starting with azeotropic feed of ethanol-water. In addition, the effect of important design variables on TAC and specific energy consumption is studied via sensitivity analysis. Contrary to the general notion, a higher solvent mole fraction causes inversion in the 25 ACS Paragon Plus Environment


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behaviour of TAC as well as greater energy consumption in the recovery column. Heat integration (to pre-heat azeotropic feed using EDC bottoms) results in a reduction of 22% in TAC and 18% in specific energy consumption. Brito et al.85 implemented suitable heat integration to the dehydration sequences studied by Tututi-Avila et al.86, reviewed later. They also optimized the dehydration sequence with and without heat integration based on the procedure stated by Figueirêdo et al.84, and compared them on a consistent basis. On incorporating heat integration within EDS studied by TututiAvila et al.86, 37 and 39% savings respectively in TAC and specific energy requirement are achieved. Likewise, thermally coupled EDS with side rectifier leads to 30 and 31% reduction in TAC and specific energy respectively, compared to the process without heat integration. Further, between EDS and thermally coupled EDS with side rectifier, both with heat integration, the former case is found to be slightly expensive with 3.3 and 1.5% more TAC and specific energy consumption respectively. Tavan and Shahhosseini87 assessed splitting of azeotropic feed of ethanol and water into precooled and pre-heated streams to ADC in ADS, and optimized the process to minimize energy requirement using SQP in Aspen HYSYS. The optimal feed split ratio is determined to be 0.1. The proposed sequence with suitable heat integration results in 27.5% energy savings compared to ADS. Tututi-Avila et al.86 modified EDS with a side rectifier by substituting the vapor-liquid interconnection by a liquid side stream from EDC to SRC (referred as EDS-1), and studied another alternative where the side rectifier is complemented with a stripping section (referred as EDS-2), for bioethanol dehydration from 93.1 wt.% to 99.5 wt.%. This additional section has the same task (i.e. solvent purification) as the bottom section of the thermally coupled system in Errico and Rong36. Re-mixing problem of the conventional configuration is avoided in the modified sequences by proper location of the liquid side stream. Genetic algorithm coded in MATLAB and interfaced with Aspen Plus is used to minimize TAC of the system. The optimal design of EDS-1 resulted in 12.4% and 1.7% savings in TAC over EDS and EDS-2, respectively. Besides, EDS-1 has lower solvent to feed ratio of 0.754 compared to EDS and EDS-2. This ratio is less than half of that (1.9) in the study of Kiss and Suszwalak38, thus resulting in lower reflux ratios. Zhu et al.88 investigated the feasibility of ionic liquid based ED process (i.e., ED column and a flash tank) using 1-ethyl-3-methylimidazolium chloride [emim][BF4] and 1-butyl-3methylimidazolium chloride [bmim][BF4] as solvents for bioethanol dehydration to 99.8 26 ACS Paragon Plus Environment

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wt.% from an azeotropic feed of ethanol and water. Sensitivity analysis is performed in Aspen Plus to determine the effect of total number of stages, reflux ratio, feed stage, solvent feed stage and solvent-to-feed ratio on ethanol product purity and reboiler duty. For lower solvent-to-feed ratio and reboiler duty, [emim][BF4] is chosen as the solvent, and the ionic liquid based ED process is optimized for reboiler duty (and not TAC). The ionic liquid ([emim][BF4]) based ED process of Zhu et al.88 results in ~28% savings in specific energy required over EDS with gasoline studied by Chianese and Zinnamosca.80 Further, Zhu et al.88 compared specific energy required for ionic liquid ([emim][BF4]) based ED process with that of AS-2 sequence (described earlier in Section 2.3.2) of Martínez et al.66. However, this comparison is not valid due to difference in the feed composition (i.e., azeotropic composition vs intermediate composition of 22.1 wt.% ethanol). Table 4: Studies using feed of near-azeotropic composition and employing distillation including dividingwall column S. No .

Reference



1

Chavez-Islas et al.76

280,000

93.5 & 99.9

1.5

0.003


2

Ravagnani et al.77

32,000

93.5 & 99.6

3.6-4.6*

-

*This is for EDC only.

3

GarcíaHerreros et al.78

25,000

93.5 & 99.5

1.9

0.48*

*Solvent makeup cost considered. Feed cost is included

4

Sun et al.79

7200

90 & 99.9

7.9-14

0.05-0.08

Minimized TAC by analysing one or two variables at a time.

5

Kiss and Suszwalak38

32,000

93.5 & 99.8

1.7-5.1

-


6

Li and Bai 40

32,000

93.5 & 99.9

2.2

0.02


7

Tavan and Hosseini73

69,000

93.5 & 99.9

0.71-4.1

-


8

Gill et al.81

33,000

94.9 & 99.9

1.6

-


9

Pla-Franco et al.82

23,000

92.1 & 99.6

7.1

-

Production capacity calculated assuming 8250 h/year and 99% ethanol recovery.

Energy for Cost of reboiler(s) and separation compressor(s) ($/kg of (MJ/kg of bioethanol) bioethanol)


Remarks


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10 An et al.74

30,000

91.1 & 99.6

5.3

-


11 Tututi-Avila et al.83

17,000

93 &99.5

2-2.3

0.0190.022

Cooling water cost not considered.

12 Figueirêdo et al.84

32,000

93.5 & 99.9

1.9-2.4

0.02-0.03

Production capacity calculated assuming 8250 h/year and 99% ethanol recovery.

13 Brito et al.85

15,000

95.4 & 99.5

1.4-1.9

0.0160.021

Production capacity calculated assuming 8250 h/year.

14 Tavan and Shahhosseini87

70,000

93.5 & 99.7

3

-

Economic evaluation not performed.

15 Tututi-Avila et al.86

17,000

93.1 & 99.5

1.8-2

0.0160.019

Cooling water cost not considered.

16 Zhu et al.88

33,000

93.5 & 99.9

2.3

0.022

Minimized energy usage and not TAC.

3. Challenges in the Comparison Tables 1-4 present values of energy (for reboilers and compressors, if any, in the process) and separation cost, both per kg of bioethanol produced, from the papers reviewed. As explained in the previous sections and evident from these tables, energy requirement decreases with the introduction of advanced distillation technologies such as DWC, E-DWC, HP assisted EDWC, DED followed by EDS, VCD assisted EDS and DED followed by E-DWC as well as hybrid processes like stripper-VP, distillation-pervaporation, HiGee stripper-membrane and D-PSA. For instance, CDS studied by Errico and Rong36, Kiss and Ignat37, 44 and Errico et al.48 requires 8 to 9 MJ-fuel/kg of bioethanol. Modifications in CDS in the form of thermal coupling, vapor recycle and recombination of column sections studied by Errico and Rong36, and Errico et al.39,42,48 require 6.5 to 8 MJ-fuel/kg of bioethanol ((i.e., ~15% lower). On the other hand, energy required for E-DWC (Ramírez-Márquez et al.41), DWC and D-PSA (Loy et al.69), HP assisted E-DWC (Luo et al.50), hybrid stripper membrane (Gudena et al.57) and DED followed by E-DWC (Nhien et al.52), is substantially lower at 5 to 6.5 MJ-fuel/kg of bioethanol. In this discussion of specific energy required, range of values is given because of differences due to the process simulator used, thermodynamic models and their parameters, optimization procedure and/or some variations in feed concentration and process configurations among the various papers cited. In summary, energy for bioethanol separation using efficient technologies is 19 to 24% of LHV of ethanol (namely, 26.7 MJ-fuel/kg of bioethanol)89. Most of this energy for separation in the form of steam can be obtained from burning solid waste of bioethanol plants.


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There are some challenges in comparing energy values in different papers reviewed. Reasons for this include thermodynamic model, interaction parameter values and process simulator used, whether suitable heat integration is considered or not, whether compressor efficiency is appropriate or not, whether condenser duty is included or not, whether electrical energy is considered or not, and whether efficiency factors in generating steam and electricity are included or not. Such inconsistencies are mentioned in the summary of relevant papers in Section 2. Compared to energy values, unit cost of separation in different studies is even more difficult to compare because of additional reasons, namely, differences in cost correlations, cost index and utility prices used. Most importantly, due to lack of enough information such as operating costs (including membrane/adsorbent/solvent replacement costs, if any) and overall energy requirement (both thermal and electrical), it becomes difficult to calculate unit cost of separation on a consistent basis for comparison. Hence, while evaluating process alternatives for bioethanol separation, it is important to use the same procedure including cost correlations, utility prices and process simulator throughout for consistent results. This is more likely if the same engineer/researcher or group evaluates competing alternatives. See Feng and Rangaiah90 for the effect of different data sources on cost of equipment such as heat exchangers including reboilers, distillation columns and compressors. Based on their analysis, there is generally good agreement for purchase cost of floating-head heat exchanger among different data sources. However, there is greater deviation in both purchase and total module costs of fixed-head heat exchangers and pumps. There is also significant deviation for vessels and towers in total module cost using different data sources. Hence, while evaluating bioethanol separation alternatives, it is imperative to use only one cost estimation data source/program. Going forward, we recommend the following guidelines for facilitating reproducible results and accurate comparison, and eventually leading to industrial adoption. 1. State bioethanol production rate and its purity, process simulator used and its version, and thermodynamic models and their parameter values employed. Likewise, give values of temperature, pressure and composition for all streams in the process flow sheet. A good example of this can be found in Luo et al.50 2. In each process alternative, include appropriate heat integration for realistic assessment. Then, simulate the process as realistically as possible starting with at least a ternary feed of ethanol, water and dissolved CO2. 29 ACS Paragon Plus Environment


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3. For sizing and costing, follow the procedures in Turton et al.91, which are detailed and relatively recent. Update estimated costs to CEPCI (Chemical Engineering Plant Cost Index) of 600. Costs for adsorbent, membrane replacement, make-up solvent and others, if any, must be included; alternately, state that these costs are negligible along with justification. 4. If TAC is evaluated for economic analysis, then reasonable payback period of 3 to 5 years as stated in Douglas92, Ulrich and Vasudevan93 and Turton et al.90 should be considered (as opposed to 10 years or plant life used in many papers). Alternately, COM that includes both investment and operating costs, as described in Turton et al.90, can be employed. 5. Optimize the heat-integrated process for TAC or COM; preferably, use an optimization method whereby decision variables are optimized simultaneously and not sequentially (i.e., one or two variables at a time). Make the simulation file available as part of the supplementary material for the paper, and ensure that this file is for the results reported in the paper. 6. Present thermal energy (e.g., for reboilers, heaters and steam turbine-driven compressors) and electricity (e.g., for motor-driven compressors and pumps) clearly and separately. State whether compressors are driven by steam turbines or electric motors.

4. Process Improvements and Future Directions in Bioethanol Separation Bioethanol separation is one of the steps with major contribution to the operating cost and energy requirement of a bioethanol plant. It is therefore crucial to have an efficient separation technology to make bioethanol production sustainable. Over the years, researchers have proposed many alternative schemes for bioethanol separation. Out of 54 papers reviewed, 35 are on distillation technologies including the use of thermally coupled, HP, DWC and RD, and 19 are on hybrid processes. Clearly, many studies have focussed on improving distillation-related technologies for bioethanol separation. Most of the studies as discussed in Section 2 have incorporated suitable heat integration in order to reduce specific energy consumption. Improvements in distillation technologies include HP assisted DED and HP assisted E-DWC. A few of these configurations involving DWC need to be evaluated and enhanced to reduce solvent loss. Future developments of CDS and ADS should be on exploring effective and eco-friendly solvents for bioethanol dehydration. For instance, use of glycerol, a by-product in the biodiesel process, is promising over EG in CDS as it results in lower energy demand. In addition, predictive thermodynamic models (e.g., COSMO-RS and UNIFAC Dortmund model) should be developed to screen suitable separating agents, 30 ACS Paragon Plus Environment

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especially ionic liquids in order to reduce the amount of experimental work, for use in bioethanol separation. Hybrid distillation-membrane separation processes are promising to be more energy-efficient than distillation alone. Notable studies on these processes are Vane Huang et al.56, Gudena et al.57,

30

, Vane and Alvarez55,

63

, Kanchanalai et al.13 and Triana et al.94; they utilize

membrane-based separations to decrease energy requirement in the recovery or dehydration step. However, currently, cost of membranes makes separation cost high. Developing membranes that can sustain higher temperatures and provide high permeance, selectivity and/or operating life would drive down the capital and operating costs of hybrid distillationmembrane processes to the point that the technology is commercially viable and competitive with other technologies. Hybrid processes of distillation and membrane separation are not yet employed in the bioethanol industry, primarily due to membrane fouling concerns, shorter lifetime and low separation factor of polymeric membranes besides high cost of membranes. However, they are likely to gain industrial acceptance in the future because of continuing developments on membranes. For instance, composite membranes ranging from cross-linked hydrophilic polymers (polyvinyl alcohol and cellulose esters) to hydrophobic perfluoropolymers (Teflon AF and Hyflon AD) and hydrophilic membranes, which can withstand temperatures up to 130°C, have been fabricated by Membrane Technology and Research and U.S. Environmental Protection Agency Cincinnati Laboratories. Lately, research has shifted towards fabrication of mixed-matrix membranes (MMMs) consisting of Si-based zeolite particles dispersed in a polymeric matrix such as polydimethylsiloxane (Vane et al.95; Wei et al.96). The idea is to combine the advantages of both inorganic and polymeric membranes to obtain high membrane performance at low cost. Although D-PSA seems to be economical19 and commonly employed for bioethanol separation in the United States industries, there are only a few studies since the year 2008 on this hybrid process. There is potential to enhance D-PSA via studying improvements in both recovery and dehydration sections in the context of the entire separation process. Further work is needed on development of reliable models to predict equilibrium and kinetic data, development of cheaper and long-life adsorbents with good mechanical properties as those of zeolites, and optimizing D-PSA process. Computer-aided molecular design can be employed to develop adsorbents with desired properties for bioethanol dehydration.



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Academicians, researchers and engineers must consider realistic feed after the fermentation step rather than the simplified binary feed, as is the case in many papers reviewed, for the process development of bioethanol separation in order to avoid missing out phenomena that occur only with realistic feed. Amongst the minor components present in the fermentation broth, dissolved CO2 and higher alcohols affect reboiler duty.23, 26 Considering binary feed of ethanol and water alone leads to ~7% lower steam requirement compared to when realistic feed is considered.23 Presence of CO2 causes a sharp decrease in condenser temperature, and so cold utility at a lower temperature will be required. Hence, CO2 and higher alcohols must be considered in addition to ethanol and water in the fermentation broth. In case some particular components are not available in a process simulator, proxy components with similar chemical composition, structure and falling in the same class of compounds could be employed. Studies reported in journal papers have mainly focused on design of processes for bioethanol separation. There are relatively fewer studies on control of proposed separation processes. This is understandable since process design precedes control studies. Complex design of a process may be better from the steady-state design point of view but control properties can limit its applicability. On the other hand, process design may appear to be complex (e.g. introduction of a vapor side stream) but it can enhance control properties, as shown in some studies.41,

46, 48

Therefore, studies on dynamic simulation and control of promising new

separation processes are necessary for facilitating their industrial implementation.

5. Conclusions This review paper summarizes the recent advancements in bioethanol recovery and dehydration in the form of an annotated bibliography. In total, 54 journal papers and book chapters from the year 2008 to 2016 are identified and reviewed. Key performance indicators, namely, cost of separation and energy required are presented for one kg of bioethanol produced. Technological advances made in bioethanol separation have led to decreasing energy required from 8-9 MJ-fuel/kg of bioethanol by CDS to 5-6.5 MJ-fuel/kg of bioethanol by the latest technologies. Heat integration and process intensification contributed to this decrease. In spite of significant progress made in bioethanol separation technologies in the last decade, there is scope to improve them further. Distillation continues to be the dominant technology in most of the papers reviewed and relatively fewer studies have investigated hybrid processes. Many of the papers reviewed have used simplified binary feed of ethanol and water instead of a realistic feed with more components, particularly, CO2 and higher alcohols. Only half of the papers reviewed have feed flow rates similar to industrial scale. In 32 ACS Paragon Plus Environment

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general, new alternatives for bioethanol separation should be studied on a consistent and comprehensive basis for accurate comparison; guidelines for this are given. Hybrid processes involving distillation and membrane separations are promising with potential to reduce energy and operating costs of bioethanol separation. Developing these processes and also intensified technologies will help pave sustainable path for bioethanol production. One area is the development of membranes with higher ethanol-water selectivity, permeability, greater resistance to fouling, longer life and lower cost.

Acknowledgements The authors acknowledge Dr. Lakshminarayanan Samavedham and Loy Yoke Yuan of the National University of Singapore (NUS) for their inputs. The first author is grateful for the financial support provided by NUS under Graduate Student Researcher scheme.

References (1) U.S. Energy Information Administration, (EIA). International Energy Outlook 2016. http://www.eia.gov/outlooks/ieo/pdf/0484(2016).pdf; accessed in June 2016. (2) British Petroleum, (BP). BP Energy Outlook 2014. https://www.bp.com/content/dam/bp/pdf/energy-economics/energy-outlook-2014/bp-energy-outlook2014.pdf; accessed in June 2016. (3) British Petroleum, (BP). BP Energy Outlook 2016. https://www.bp.com/content/dam/bp/pdf/energy-economics/energy-outlook-2016/bp-energy-outlook2016.pdf; accessed in June 2016. (4) COP21. Adoption of the Paris agreement, United Nations Framework Convention on Climate Change: 2015. https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf; accessed in June 2016. (5)

Lee, S. Encyclopedia of Chemical Processing. Taylor & Francis: New York, 2006.

(6) Balat, M.; Balat, H.; Oz, C. Progress in bioethanol processing. Prog. Energy Combust. Sci. 2008, 34 (5), 551-573. (7) Dias, M.; Junqueira, T.; Rossell, C.; Filho, R.; Bonomia, A. Evaluation of Process Configurations for Second Generation Integrated with First Generation Bioethanol Production from Sugarcane. Fuel Process. Technol. 2013, 109, 84-89. (8) Baeyens, J.; Kang, Q.; Appels, L.; Dewil, R.; Lv, Y; Tan, T. Challenges and opportunities in improving the production of bio-ethanol. Prog. Energy Combust. Sci. 2015, 47, 60-88. (9) Renewable Fuels Association (RFA). Leading the U.S. Ethanol Industry: 2016. http://www.ethanolrfa.org/resources/industry/statistics/; accessed in June 2016. (10) Renewable Fuels Association (RFA). World Fuel Ethanol production: http://ethanolrfa.org/pages/World-Fuel-Ethanol-Production; 2013; accessed in June 2016.

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Graphical Abstract


Review of Technological Advances in Bioethanol Recovery and

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