Ionic liquids and Biorefineries Design: History
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A brief overview of the increasing applicability of Process Systems Engineering (PSE) tools in two research areas, which are the design of ionic liquids and the design of integrated biorefineries, is presented. The development and advances of novel computational tools and optimization approaches in recent years have enabled these applications with practical results. A general introduction to ionic liquids and their various applications is presented followed by the major challenges in the design of optimal ionic liquids. Significant improvements in computational efficiency have made it possible to provide more reliable data for optimal system design, minimize the production cost of ionic liquids, and reduce the environmental impact caused by such solvents. A review of the recent developments in PSE applications in the field of integrated biorefineries is then presented. Various value-added products could be processed by the integrated biorefinery aided with applications of PSE tools with the aim of enhancing the sustainability performance in terms of economic, environmental, and social impacts. The application of molecular design tools in the design of integrated biorefineries is also highlighted. Major developments in the application of ionic liquids in integrated biorefineries have been emphasized. This paper is concluded by highlighting the major opportunities for further research in these two research areas and the areas for possible integration of these research fields.

  • ionic liquids
  • integrated biorefineries
  • process systems engineering
  • process optimisation
  • molecular design

1. Introduction

The competitive nature of the current chemical industry constantly demands improvements in process and product quality and efficiency. To maximize profitability and to succeed in the global competition, the chemical processes need to be operated optimally and also need to look for better products and ways to produce them. Process Systems Engineering (PSE) is defined by Grossmann and Westerberg[1] as “the field that is concerned with the improvement of decision-making processes for the creation and operation of the chemical supply chain. It deals with the discovery, design, manufacture, and distribution of chemical products in the context of many conflicting goals.” In recent decades, the focus of research in PSE fields has shifted to the areas of development of new products, process networks and enterprise, supply chain optimization, and global life cycle assessment (LCA)[2].

The development of PSE tools and methodologies has enabled the applications of these tools in various research fields. While the focus of PSE tools was more process-oriented and towards specific industrial problems in the twentieth century, the effect of globalization has prompted researchers to extend the PSE tools into new areas, e.g., development of new products, process intensification, and to address sustainability challenges. Significant improvements in computational efficiency have contributed to the application of PSE tools in several areas where historically, size-related problems had made the solutions impractical.

It has been recognized in recent years that ionic liquids may be a suitable replacement for several traditional solvents that pose environmental risks. The major potential areas for their application as a solvent include carbon capture, extraction, as an entrainer in extractive distillation, and also in various chemical, biochemical, electrochemical, and pharmaceutical industries[3]. In addition, it is possible to alter the functional groups to meet the desired attributes for various applications. The two major challenges for the design of suitable ionic liquids and the application in industries are the unavailability of reliable data and the high production cost of ionic liquids. In order to make an optimal selection of ionic liquids, various PSE approaches (both insight-based and mathematical optimization approaches) have been developed in the past decades.

In addition to the design and selection of ionic liquids, the development and application of PSE tools in the field of integrated biorefineries have been immense. In recent years, biomass utilization has shown promising results in addressing society’s dependence on non-renewable energy resources and climate change caused by fossil fuel exploitation. Other than being utilized for heat generation through direct combustion, the nature of biomass enables it to be converted into other value-added products ranging from biomaterials, biofuel, bio-chemicals, biopharmaceuticals, etc. Due to the complexity and diversity of biomass in nature, a single biomass conversion system such as gasification, fermentation, digestion, etc., is typically not able to fully recover the potential of the biomass. In view of this, integrated biorefineries is a fast-developing research area that integrates a variety of technologies to convert biomass into the abovementioned value-added products[4]. As integrated biorefinery is able to integrate multiple technologies as a single integrated system, such a system provides more flexibility in product generation and generates sufficient energy to support the entire operation and reduce the overall energy consumption compared to the processes that operate independently. In addition, with the integration of multiple technologies, the waste/by-products can be used as feedstock for another process, therefore, the material recovery can be maximized as shown in Figure 1.

Figure 1. The integrated biorefinery concept.

2. Applications of PSE in the Development of New and Green Chemicals

2.1. Ionic Liquids

Ionic liquids are organic salts that comprise organic cations with inorganic or organic anions and melt at temperatures below 100 °C. The introduction of ionic liquids has attracted considerable attention from researchers, especially in replacing traditional solvents with ionic liquids due to their extraordinary characteristics. Besides, ionic liquids also possess the attributes of “green” solvents since their vapor pressure is significantly lower than the conventional solvents. The high volatility of conventional organic solvents can cause air pollution and human health problems if they leak from process equipment.

Ionic liquids are widely recognized as “designer” solvents because of the flexibility in turning their properties by altering the cations. By selecting various combinations of anions and cations, ionic liquids with an attractive set of physicochemical properties can be synthesized. In addition, ionic liquids’ capabilities, which include a wide range of intermolecular interactions such as hydrogen bonding, ionic and covalent interactions as well as π-stacking, have also contributed to their tunable properties[5]. For these reasons, they have huge potential to reduce the environmental impact caused by organic solvents. Nonetheless, the major drawback of applying ionic liquids in various chemical processes is their high purification cost. As ionic liquids consist of highly charged ions, the ionic liquid recovery and purification process becomes difficult and cost-intensive[6]. In spite of the high recovery and purification cost, the unique properties of ionic liquids are still important and attractive from an industrial point of view.

2.2. Potential Applications of Ionic Liquids

In recent years, there is a surge in the applications of ionic liquids in various industries. Table 1 shows some potential applications of ionic liquids.

Table 1. Summary of potential applications of ionic liquids discussed in this review.

Types of Ionic Liquids

Applications

1-methylimidazolium chloride

Biphasic acid scavenging[7]

1-butylpyridinium tetrafluoroborate

Geothermal fluid in organic Rankine cycle[8]

1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide

Hydrogen compressor [9]

1-Ethyl-3-methylimidazolium acetate

Sugarcane bagasse pre-treatment[10]

1-butyl-3-methylimidazolium chloride and 1-butyl-3-methylimidazolium acetate

Rubber woods pre-treatment[11]

1-butylimidazolium hydrogen sulfate (VI)

Kraft lignin activator[12]

1,3-dibutyl-2-methylimidazoliumbromide

Flavonoids extraction[13]

1-hydroxyethyl-3-methylimidazoliumbis(trifluoromethanesulfonyl)amide

Keratin extraction[14]

1-Ethyl-3-methylimidazolium methylsulfate

Ethyl acetate and ethanol azeotropic distillation[15]

1-butyl-4-methylpyridinium tricyanomethanide

Cyclohexane and benzene azeotropic distillation[16]

1-ethyl-3-methylimmidazolium-ethylsulphate

Carbon dioxide and hydrogen sulfide separation[17]

1-butyl-3-propylamineimidazolium tetrafluoroborate

Carbon dioxide capture[18]

1-butyl-3-methylimidazolium hexafluorophosphate

Carbon dioxide capture[19]

Tetraglyme-sodium salt ionic liquids

Sulfur dioxide separation[20]

1-butyl imidazolium bis(trifluoromethylsulfonyl)imide

Ammonia separation[21]

Bis(1-butyl-3-methyl imidazolium) copper tetrachloride salt and bis(1-butyl-3-methyl imidazolium) stannum tetrachloride salt

Ammonia separation[22]

2.3. Challenges in the Design of Optimal Ionic Liquids

In order to design ionic liquids that suit a certain industrial application, it is first important to know how the anion, cation, and side chains on the cation affect physicochemical properties. This can be done through two pathways, either via experimental work or simulation approaches. Considering a very huge number of possible ionic liquids, it would be a very tedious task to select an optimum candidate through experimentation. Because of this, researchers have been working on developing systematic computer simulation methods including CAMD and/or process design simulations to design optimal ionic liquids. Computer-aided methods provide alternative routes in determining potential ionic liquids as they can explore a larger number of options in a shorter time frame. However, it does not mean that computer-aided methods can replace experiments entirely as identified candidates should be further tested via experiments to verify their performance. 

The success of employing the CAMD technique to design ionic liquids depends greatly on the availability and reliability of the associated/underlying predictive models. Despite the availability of significant thermophysical property data for an extensive range of ionic liquids in the free ILThermo database[23], predictive models for most thermophysical properties with adequate accuracy are still being developed[24]. Various approaches have been proposed to develop property prediction models for thermophysical and transport properties of ionic liquids. These approaches can be categorized into two main classes which include both theoretical and empirical methods[25]. Nonetheless, most of these existing prediction models for thermodynamic properties are limited to only a relatively small number of common families of ionic liquids.

With increasing awareness of environmental detriments, it is important to also consider environmental properties during the ionic liquid design stage. Since ionic liquids are normally highly soluble in an aqueous medium, they are readily discharged into the environment through wastewater[26]. Hence, the designed ionic liquids need to be environmentally friendly before they start accumulating in the environment. For this reason, various quantitative structure-activity response (QSAR) models were developed to assess the toxicity of ionic liquids. However, most existing models for ionic liquid toxicity prediction only cover a small number of ionic liquids.

It is notable that pure ionic liquids may not always be the optimal candidates in satisfying the target properties, however, it is possible that ionic liquid mixtures could assist in addressing such problems. Unfortunately, property prediction models for ionic liquid mixtures are limited as this research area is still in its infancy. Efforts have been devoted to developing property prediction models for ionic liquid mixtures, but the models are limited to only certain classes of ionic liquids. Future research should focus on developing reliable property prediction models for different families of ionic liquid mixtures.

2.4. CAMD for Ionic Liquid Design

The development of CAMD tools for the ionic liquid design has been limited due to the lack of accurate models in estimating ionic liquid properties. Most of the earlier work in the modeling of ionic liquids was based on molecular dynamics tools and quantum chemistry study. While these tools show reasonably accurate and reliable results, the computational time needed to complete the simulation of each candidate molecule is large. So, the applicability of these tools is more appropriate towards the final selection from a limited number of promising molecules. Figure 2 shows the general framework of ionic liquid design via CAMD. The procedure starts with an ionic liquid design problem definition where the product needs are identified. These requirements are then translated into targeted properties which will be validated using a model-based approach. Next, an extensive search of ionic liquids is conducted. The information collected from a vast number of literature sources and online databases is stored in the ionic liquid library. Various property models are also collected and stored in the property model library. In Step 4, UNIFAC and/or COSMO models are used to screen and predict ionic liquid with desirable properties. Lastly, the performance of the shortlisted ionic liquid candidates is validated either by experiments or simulations. This step is crucial to ensure that the ionic liquids identified are feasible and practical.

Figure 2. The general framework of ionic liquid design via a computer-aided molecular design (CAMD) approach.

3. Applications of PSE in Integrated Biorefineries

3.1. Introduction to Integrated Biorefineries

Over the past few decades, in order to enhance the sustainability of chemical and energy production, there has been a shift from petroleum-based feedstock to biomass-based feedstock. Besides, the societal realization of limited non-renewable resources, concerning environmental issues, technological advancements, and the discovery of additional renewable energy resources have also contributed to this shift[27].

The utilization of biomass as feedstock for the production of multiple products through a biorefinery has also gained attention from both industry and the scientific community in the past decades[4]. A biorefinery was first defined as a complex system of sustainable, environmental, and resource-friendly technologies for the comprehensive utilization and the exploitation of biological raw materials (biomass) by Kamm et al.[28]. Similar to a petroleum refinery, a biorefinery utilizes different biomass resources as feedstock, combines a diversified collection of conversion pathways to produce a wide range of value-added products for example bioenergy, bulk chemicals, and fine chemicals. As biomass consists of a wide range of organic constituents, it comes in a variety of forms with different properties and characteristics. Therefore, various processing technologies can be applied to convert biomass into higher-value market products. A range of pre-treatment systems (such as size reduction, drying system, acid, and base hydrolysis) are needed to standardize biomass into a form that can be further converted into products. In order to maximize the quality and performance of the biorefinery, process integration and optimization shall be applied to synthesize a biorefinery comprised of multiple processing systems. This realization provides the foundation for the concept of integrated biorefinery which integrates various biomass conversion platforms[4].

According to Gravitis et al.[29], an integrated biorefinery represents a processing facility consisting of multiple technologies including feedstock handling, pretreatment processes, and different biomass conversion/upgrading processes. This allows the by-products and waste to be minimized while recovering the energy generated within the biorefinery. Therefore, the integrated biorefinery concept provides an opportunity to create a variety of value-added products while enhancing sustainability performance in terms of economic, environmental, and social impacts.

As shown in Figure 1, an integrated biorefinery consists of different conversion pathways that convert different types of biomass feedstock into heat, power, and value-added products through depolymerizing and deoxygenating biomass components[30]. According to the U.S. Department of Energy/National Renewable Energy Laboratory (NREL), existing conversion pathways and technologies can be generally categorized into five different platforms based on the products produced[31]. These five platforms are the sugar platform, the thermochemical/syngas platform, the biogas platform, the carbon-rich chains platform, and the plant products platform. Table 2 summarizes the foci of these five biomass processing platforms as classified by NREL. Other than classifying the biomass conversion processes into different conversion platforms, these conversion processes and technologies are more commonly categorized according to the nature of the processes. Based on the method and nature of the processes, these conversion processes and technologies can be divided into four main groups of physical/mechanical, thermochemical, chemical, and biochemical/biological processes[30].

Table 2. Comparison of different biomass conversion platforms.

Platform

Focus

Main Products

Sugar

Fermentation of sugars obtained via extraction of biomass feedstocks

Ethanol and other building block chemicals

Thermochemical syngas

Gasification of biomass feedstocks

Gaseous and liquid fuels

Biogas

Decomposition of biomass feedstocks

Cooking gas

Carbon-rich chains

Transesterification of vegetable oil or animal fat

Biodiesel (fatty acid methyl esters)

Plant products

Selective breeding and genetic engineering of biological plant

Plant strains that can be used as feedstock for further conversion into chemicals and compounds that are difficult to obtain from plant naturally

3.2. Synthesis and Design of Integrated Biorefineries

To synthesize a sustainable integrated biorefinery with maximum performance and minimal environmental impact, it is essential to integrate different conversion technologies in a systematic and efficient manner. Process synthesis methods developed for conventional chemical processes can be extended to enable the synthesis of integrated biorefineries. Process synthesis is defined as the activity to identify the optimal interconnection of unit operations involved in the overall process and the optimal design and configuration of the units within the process[32]. According to Douglas[33], process synthesis can generally be classified into seven categories, i.e., synthesis of reaction pathway, synthesis of reactor network, synthesis of separation network, synthesis of mass exchange network, synthesis of material, synthesis of heat exchanger network, and synthesis of the complete flowsheet.

According to Kokossis and Yang[34], PSE approaches have the potential to support process synthesis and design, which can be applied and utilized in the design of integrated biorefineries. PSE is the field that covers the actions and activities involved in the engineering of systems consist of physical, chemical, and/or biological processing operations[35]. Throughout the years, various PSE approaches have been developed for the synthesis and design of integrated biorefineries[36][37][38], each with its own advantages and disadvantages. Some of the commonly used approaches are presented below.

  1. Hierarchical Approaches
  2. Heuristic Searches
  3. Insight-Based Approaches
  4. Algorithmic Approaches
  5. Mathematical Optimization Approaches
  6. Hybrid Methods

3.3. Challenges in Designing Integrated Biorefineries

The unique features of integrated biorefineries make them less straightforward compared to conventional chemical processes. Due to the complex structure and varying compositions of biomass, the important thermodynamic properties (e.g., enthalpy, entropy, heat capacity, etc.) of biomass are often not well established. Besides, the rate of reaction for biomass conversion technologies such as fermentation, hydrolysis, etc. are difficult to determine accurately most of the time. Therefore, most of the available contributions in process synthesis for chemical processes cannot be directly applied for the synthesis of integrated biorefineries. Therefore, it is essential to develop systematic procedures to address the abovementioned issues. In order to synthesize and design a sustainable integrated biorefinery, the following criteria shall be fulfilled[39][40]:

  1. Able to minimize energy consumption and potential environmental impact through material and energy integrations between different conversion platforms.
  2. Able to accommodate the varying seasonal patterns on feedstock availability and quality through integration of different biomass conversion platforms.
  3. Able to depolymerize biomass components to intermediate products that match the requirements of subsequent processing technologies.
  4. Able to maximize the yield and quality of value-added products.

4. Application of Molecular Design within the Context of Integrated Biorefineries

The utilization of lignocellulosic biomass to produce biochemical products has been receiving focus and attraction as lignocellulosic biomass is mostly plant-based waste in different forms[27]. Lignocellulosic biomass can be converted into different value-added products via different conversion technologies. Moreover, lignocellulosic biomass can be converted into different intermediate products with a high content of hydroxyl groups through the liquefaction process[41].

In view of the possibility of lignocellulosic biomass conversion into different value-added chemical products, extensive effort has been invested by industrial and research sectors to identify these value-added chemical products. According to Elliot[42], the potential chemical products can be commonly classified into fermentation products, gasification products, catalytic/bioprocessing products, derivatives of carbohydrates, and derivatives of plants. Later, findings on the identification of twelve chemical building blocks that are possible to be converted from starch through different processing technologies are presented by Werpy and Peterson[43]. These twelve building blocks provide the potential to be utilized to produce different bio-based chemical products that fulfill market needs. Skibar et al.[44] emphasized the future of biomass through their discussion on the efforts of the chemical industry in utilizing biomass to produce value-added products. In addition to the traditional usage of biomass to produce polymers, the future of biomass lies in the production of specialty products as biomass has the potential to be utilized in producing specialty chemicals such as pharmaceutical, beauty, and personal care products[44]. From the works discussed above, it is obvious that the future of biomass utilization covers the production of energy products, bulk chemical products, and new and novel products such as fine and specialty chemical products. In most cases, such products are designed to satisfy product needs[45]. Thus, the product design aspects have to be taken into account while synthesizing an optimally integrated biorefinery to make sure that the products produced by integrated biorefinery meet the required product needs. This goal can be fulfilled by integrating the synthesis of integrated biorefineries with chemical product design.

Chemical product design problems are suitable to be explained as a process to search for chemical products that fulfill preferred product needs. Traditionally, the product designers first hypothesize a target molecule that has the preferred product needs while designing a chemical product. The subsequent step would be the product synthesis step, which is followed soon by the product testing step to test for the preferred product needs. Target molecule revision and redesign are required repeated if the target molecule does not satisfy the preferred product needs. Hence, it can be seen that the traditional approaches of chemical product design require an iterative process, which leads them to be inefficient, laborious, and low in cost-effectiveness[46]. In light of these limitations, CAMD techniques provide efficient and effective options for the design of chemical products. In the past decades, CAMD techniques have been gaining attraction and appeared as reliable techniques for their capability in identifying feasible molecules that possess the desired set of product needs[47]. This includes the integration of CAMD techniques with process synthesis for the consideration of product design aspects while synthesizing integrated biorefineries.

4.1. Opportunities for Further Research

4.1.1. Expanding the Optimization Scope/Parameters for Integrated Biorefinery Design

The developed approaches can address steady-state processes, batch processes, as well as process and market uncertainties. In order to incorporate a sustainable integrated biorefinery, global life cycle optimization ought to be applied during the synthesis stage. The Life Cycle Optimization approach[48] can be extended for concurrent synthesis and optimization of sustainable integrated biorefineries with consideration of economic, environmental, and societal factors/impacts. It is noted that most of the developed methods focus on technical feasibility, environmental sustainability, market uncertainties, etc. However, there is limited consideration of societal impacts during the design/development of the integrated biorefinery. Therefore, society impacts should be taken into consideration in future efforts. It should also be noted that a lot of research and development activities are moving towards human-centered design approaches; which is an approach to problem-solving that incorporates the human perspective in all steps. Human-centered design[49] is generally applied in design and management frameworks to solve problems with developed solutions. In order to consider human perspectives in the synthesis and analysis of integrated biorefineries, integration of the human-centered design methodology into PSE approaches would need to be developed.

4.1.2. Design of Novel Ionic Liquids

Future directions should focus on developing property prediction models that are able to estimate properties for a wider range of pure ionic liquids and/or ionic liquid mixtures. This shortcoming may be addressed by predicting properties of ionic liquids based on functional groups rather than ionic liquid constituents. Accurate correlation equations are key to ensuring that the results generated by process/product design methods/tools are reliable. In addition, there are several works conducted on the effect of solvents on reaction rates. However, no such studies have been conducted on the influence of ionic liquid solvents on reactions. Therefore, it is worth exploring the application of quantum mechanical calculations to predict the rate constants of reactions in various ionic liquid media. Another new class of solvents called deep eutectic solvents (DESs) have received much attention in recent years as they are relatively cheap, simple to synthesize, and have good biodegradability[50]. DESs are solvents that comprise both hydrogen bond acceptors and donors. Some researchers have validated their feasibility as entrainers in the separation of azeotropic mixtures through experiments. Despite the potential of these materials, studies on modeling extractive distillation processes using DESs as entrainers remain limited. This may be due to the fact that there is very limited thermophysical property data available, as this particular research field is still relatively new. A general model that requires only critical pressure, temperature, and one reference viscosity point has been developed to predict the viscosities of DESs[51]. However, there is still a need to improve the model accuracy and develop more property prediction models for DESs to facilitate the computational design and/or screening of solvents.

5. Conclusions

Recent advances in the development of computational PSE tools have enabled the design of ionic liquids and the design of integrated biorefineries. It is imperative that ionic liquids may serve as potential replacements for current organic solvents when considering stringent environmental regulations and the implementation of clean manufacturing practices. Nevertheless, the process of identifying an optimal ionic liquid through experimentation is a very tedious task as a very large number of possible ionic liquid combinations exist. Various PSE approaches have been developed in order to make an optimal selection of ionic liquids prior to experimental testing. Similarly, numerous research works have been performed to optimize the synthesis and design of sustainable integrated biorefineries with maximum performance and minimal environmental impact. The application of existing PSE tools to these problems had been extremely challenging because of the difficulty in incorporating accurate property prediction tools in the design of ionic liquids and the computational challenges in the design of such a large and complex processing facility. Decomposition-based approaches developed by different researchers have been instrumental in reducing the complexity of these design problems.

 

Acknowledgments: The authors greatly appreciate the support for this research project from the Ministry of Higher Education, Malaysia under Grant FRGS/1/2019/TK02/UNIM/02/1.

 

This entry is adapted from the peer-reviewed paper 10.3390/pr8121678

References

  1. Ignacio E. Grossmann; Arthur W. Westerberg; Research challenges in process systems engineering. AIChE Journal 2000, 46, 1700-1703, 10.1002/aic.690460902.
  2. Ignacio E. Grossmann; Challenges in the new millennium: product discovery and design, enterprise and supply chain optimization, global life cycle assessment. Computers & Chemical Engineering 2004, 29, 29-39, 10.1016/j.compchemeng.2004.07.016.
  3. Yuqiu Chen; Georgios M. Kontogeorgis; John M. Woodley; Group Contribution Based Estimation Method for Properties of Ionic Liquids. Industrial & Engineering Chemistry Research 2019, 58, 4277-4292, 10.1021/acs.iecr.8b05040.
  4. Sandun Fernando; Sushil Adhikari; Chauda Chandrapal; Naveen Murali; Biorefineries: Current Status, Challenges, and Future Direction. Energy & Fuels 2006, 20, 1727-1737, 10.1021/ef060097w.
  5. Helene Olivierbourbigou; L. Magna; D. Morvan; Ionic liquids and catalysis: Recent progress from knowledge to applications. Applied Catalysis A: General 2010, 373, 1-56, 10.1016/j.apcata.2009.10.008.
  6. Ali Eftekhari; Supercapacitors utilising ionic liquids. Energy Storage Materials 2017, 9, 47-69, 10.1016/j.ensm.2017.06.009.
  7. Freemantle, M; CHEMISTRY BASF'S SMART IONIC LIQUID. Chemical & Engineering News 2003, 81, 9, 10.1021/cen-v081n013.p009.
  8. Shabnam Kazemi; Mohamad Iskandr Mohamad Nor; Wen Hui Teoh; Thermodynamic and economic investigation of an ionic liquid as a new proposed geothermal fluid in different organic Rankine cycles for energy production. Energy 2020, 193, 116722, 10.1016/j.energy.2019.116722.
  9. Nasrin Arjomand Kermani; Irina Petrushina; Masoud Rokni; Evaluation of ionic liquids as replacements for the solid piston in conventional hydrogen reciprocating compressors: A review. International Journal of Hydrogen Energy 2020, 45, 16337-16354, 10.1016/j.ijhydene.2020.01.214.
  10. Jing Bian; Feng Peng; Xiao-Peng Peng; Xiao Xiao; Pai Peng; Feng Xu; Run-Cang Sun; Effect of [Emim]Ac pretreatment on the structure and enzymatic hydrolysis of sugarcane bagasse cellulose. Carbohydrate Polymers 2014, 100, 211-217, 10.1016/j.carbpol.2013.02.059.
  11. Amir Sada Khan; Zakaria Man; Mohammad Azmi Bustam; Chong Fai Kait; Zahoor Ullah; Asma Nasrullah; M. Irfan Khan; Girma Gonfa; Pervaiz Ahmad; Nawshad Muhammad; et al. Kinetics and thermodynamic parameters of ionic liquid pretreated rubber wood biomass. Journal of Molecular Liquids 2016, 223, 754-762, 10.1016/j.molliq.2016.09.012.
  12. Tadeusz J. Szalaty; Łukasz Klapiszewski; Beata Kurc; Andrzej Skrzypczak; Teofil Jesionowski; A comparison of protic and aprotic ionic liquids as effective activating agents of kraft lignin. Developing functional MnO 2 /lignin hybrid materials. Journal of Molecular Liquids 2018, 261, 456-467, 10.1016/j.molliq.2018.04.044.
  13. Lin Zuo; Xianquan Ao; Yu Guo; Study on the synthesis of dual-chain ionic liquids and their application in the extraction of flavonoids. Journal of Chromatography A 2020, 1628, 461446, 10.1016/j.chroma.2020.461446.
  14. Yun-Xian Wang; Xue-Jun Cao; Extracting keratin from chicken feathers by using a hydrophobic ionic liquid. Process Biochemistry 2012, 47, 896-899, 10.1016/j.procbio.2012.02.013.
  15. Zhaoyou Zhu; Yongsaeng Ri; Hui Jia; Xin Li; Yong Wang; Yinglong Wang; Process evaluation on the separation of ethyl acetate and ethanol using extractive distillation with ionic liquid. Separation and Purification Technology 2017, 181, 44-52, 10.1016/j.seppur.2017.03.011.
  16. Miguel Ayuso; Andrés Cañada-Barcala; Marcos Larriba; Pablo Navarro; Noemí Delgado-Mellado; Julián García; Francisco Rodríguez; Enhanced separation of benzene and cyclohexane by homogeneous extractive distillation using ionic liquids as entrainers. Separation and Purification Technology 2020, 240, 116583, 10.1016/j.seppur.2020.116583.
  17. Madhubanti Bhattacharya; Mrinal Kanti Mandal; Synthesis and characterization of ionic liquid based mixed matrix membrane for acid gas separation. Journal of Cleaner Production 2017, 156, 174-183, 10.1016/j.jclepro.2017.04.034.
  18. Zhigang Lei; Chengna Dai; Wenjing Song; Adsorptive absorption: A preliminary experimental and modeling study on CO2 solubility. Chemical Engineering Science 2015, 127, 260-268, 10.1016/j.ces.2015.01.043.
  19. Lynnette A. Blanchard; Dan Hancu; Eric J. Beckman; Joan F. Brennecke; Green processing using ionic liquids and CO2. Nature 1999, 399, 28-29, 10.1038/19887.
  20. Qiang Xu; Wei Jiang; Jianbai Xiao; Xiong-Hui Wei; Absorption of Sulfur Dioxide by Tetraglyme–Sodium Salt Ionic Liquid. Molecules 2019, 24, 436, 10.3390/molecules24030436.
  21. Dawei Shang; Suojiang Zhang; Shaojuan Zeng; Kun Jiang; Hongshuai Gao; Haifeng Dong; Qingyuan Yang; Suojiang Zhang; Protic ionic liquid [Bim][NTf2] with strong hydrogen bond donating ability for highly efficient ammonia absorption. Green Chemistry 2017, 19, 937-945, 10.1039/c6gc03026b.
  22. Junli Wang; Shaojuan Zeng; Feng Huo; Dawei Shang; Hong-Yan He; Lu Bai; Xiangping Zhang; Jianwei Li; Metal chloride anion-based ionic liquids for efficient separation of NH3. Journal of Cleaner Production 2019, 206, 661-669, 10.1016/j.jclepro.2018.09.192.
  23. Qian Dong; Chris D. Muzny; Andrei Kazakov; Vladimir Diky; Joseph W. Magee; Jason A. Widegren; Robert D. Chirico; Kenneth N. Marsh; Michael Frenkel; ILThermo: A Free-Access Web Database for Thermodynamic Properties of Ionic Liquids†. Journal of Chemical & Engineering Data 2007, 52, 1151-1159, 10.1021/je700171f.
  24. Sayed Mostafa Hosseini; A. Mulero; Mohammad Mehdi Alavianmehr; Predictive methods and semi-classical Equations of State for pure ionic liquids: A review. The Journal of Chemical Thermodynamics 2019, 130, 47-94, 10.1016/j.jct.2018.09.022.
  25. João A.P. Coutinho; Pedro J. Carvalho; Nuno M.C. Oliveira; Predictive methods for the estimation of thermophysical properties of ionic liquids. RSC Advances 2012, 2, 7322-7346, 10.1039/c2ra20141k.
  26. José S. Torrecilla; Julián García; Ester Rojo; Francisco Rodríguez; Estimation of toxicity of ionic liquids in Leukemia Rat Cell Line and Acetylcholinesterase enzyme by principal component analysis, neural networks and multiple lineal regressions. Journal of Hazardous Materials 2009, 164, 182-194, 10.1016/j.jhazmat.2008.08.022.
  27. Yixiang Xu; Milford Hanna; Loren Isom; “Green” Chemicals from Renewable Agricultural Biomass - A Mini Review. The Open Agriculture Journal 2008, 2, 54-61, 10.2174/1874331500802010054.
  28. Kamm, B.; Kamm, M.; Soyez, K. The Green Biorefinery, Concept of Technology. In Proceeding of the first International Symposium on Green Biorefinery, Neuruppin, Germany, October 1997; Society of Ecological Technology and System Analysis: Berlin, Germany, 1998.
  29. Janis Gravitis; Janis Zandersons; Nikolai Vedernikov; Irena Kruma; Valery Ozols-Kalnins; Clustering of bio-products technologies for zero emissions and eco-efficiency. Industrial Crops and Products 2004, 20, 169-180, 10.1016/j.indcrop.2004.04.019.
  30. Francesco Cherubini; The biorefinery concept: Using biomass instead of oil for producing energy and chemicals. Energy Conversion and Management 2010, 51, 1412-1421, 10.1016/j.enconman.2010.01.015.
  31. Biomass Research . National Renewable Energy Laboratory. Retrieved 2021-1-7
  32. Naonori Nishida; George Stephanopoulos; A. W. Westerberg; A review of process synthesis. AIChE Journal 1981, 27, 321-351, 10.1002/aic.690270302.
  33. James M. Douglas; Process synthesis for waste minimization. Industrial & Engineering Chemistry Research 1992, 31, 238-243, 10.1021/ie00001a034.
  34. Antonis C. Kokossis; Aidong Yang; On the use of systems technologies and a systematic approach for the synthesis and the design of future biorefineries. Computers & Chemical Engineering 2010, 34, 1397-1405, 10.1016/j.compchemeng.2010.02.021.
  35. George Stephanopoulos; Gintaras V. Reklaitis; Process systems engineering: From Solvay to modern bio- and nanotechnology.. Chemical Engineering Science 2011, 66, 4272-4306, 10.1016/j.ces.2011.05.049.
  36. N.E. Sammons; W. Yuan; Mario R. Eden; B. Aksoy; H.T. Cullinan; Optimal biorefinery product allocation by combining process and economic modeling. Chemical Engineering Research and Design 2008, 86, 800-808, 10.1016/j.cherd.2008.03.004.
  37. Douglas H. S. Tay; Denny K.S. Ng; Norman E. Sammons; Mario R. Eden; Fuzzy Optimization Approach for the Synthesis of a Sustainable Integrated Biorefinery. Industrial & Engineering Chemistry Research 2011, 50, 1652-1665, 10.1021/ie1011239.
  38. Denny K.S. Ng; Rex T. L. Ng; Applications of process system engineering in palm-based biomass processing industry. Current Opinion in Chemical Engineering 2013, 2, 448-454, 10.1016/j.coche.2013.09.005.
  39. Emmanuel Koukios; Dimitrios Koullas; Irene Daouti Koukios; Emmanouil Avgerinos; Critical parameters for optimal biomass refineries: the case of biohydrogen. Clean Technologies and Environmental Policy 2009, 12, 147-151, 10.1007/s10098-009-0239-y.
  40. Ana Luisa Fernando; Maria Paula Duarte; Joana Almeida; Sara Boléo; Benilde Mendes; Environmental impact assessment of energy crops cultivation in Europe. Biofuels, Bioproducts and Biorefining 2010, 4, 594-604, 10.1002/bbb.249.
  41. Lingyun Liang; Zhihuai Mao; Yebo Li; Caixia Wan; Tipeng Wang; Lianhui Zhang; Lingyan Zhang; Liquefaction of crop residues for polyol production. BioResources 2006, 1, 248-256, 10.15376/biores.1.2.248-256.
  42. Elliott, Douglas C. Biomass, Chemicals from; Elsevier B.V.: Amsterdam, The Netherlands, 2014; pp. 163-174.
  43. Todd A Werpy; G. Petersen; Top Value Added Chemicals from Biomass: Volume I -- Results of Screening for Potential Candidates from Sugars and Synthesis Gas. Top Value Added Chemicals from Biomass: Volume I -- Results of Screening for Potential Candidates from Sugars and Synthesis Gas 2004, , , 10.2172/15008859.
  44. Skibar, W.; Grogan, G.; McDonald, J.; Pitts, M. UK Expertise for Exploitation of Biomass-Based Platform Chemicals; The FROPTOP Group: Runcorn, UK, 2009
  45. Achenie, L.E.K.; Gani, R.; Venkatasubramanian, V.. Computer Aided Molecular Design: Theory and Practice; Elsevier, B.V.: Amsterdam, The Netherlands, 2003; pp. 404.
  46. V. Venkatasubramanian; K. Chan; J.M. Caruthers; Computer-aided molecular design using genetic algorithms. Computers & Chemical Engineering 1994, 18, 833-844, 10.1016/0098-1354(93)e0023-3.
  47. Apurva P. Samudra; Nicolas V Sahinidis; Optimization-based framework for computer-aided molecular design. AIChE Journal 2013, 59, 3686-3701, 10.1002/aic.14112.
  48. Nathan J. Ramadhan; Yoke Kin Wan; Rex T.L. Ng; Denny K.S. Ng; Mimi Haryani Hassim; Kathleen B. Aviso; Raymond R. Tan; Life cycle optimisation (LCO) of product systems with consideration of occupational fatalities. Process Safety and Environmental Protection 2014, 92, 390-405, 10.1016/j.psep.2014.04.003.
  49. Mike Cooley; Human-centred Systems. Artifical Intelligence, Culture and Language: On Education and Work 1989, 1, 133-143, 10.1007/978-1-4471-1717-9_10.
  50. María Francisco; A Adriaan Van Den Bruinhorst; Maaike C. Kroon; Low-Transition-Temperature Mixtures (LTTMs): A New Generation of Designer Solvents. Angewandte Chemie International Edition 2013, 52, 3074-3085, 10.1002/anie.201207548.
  51. Ali Bakhtyari; Reza Haghbakhsh; Ana Rita C. Duarte; Sona Raeissi; A simple model for the viscosities of deep eutectic solvents. Fluid Phase Equilibria 2020, 521, 112662, 10.1016/j.fluid.2020.112662.
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