Instituto Tecnológico y de Estudios Superiores de Monterrey

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Campus Monterrey

School of Engineering and Sciences

Design and modelling of novel process of continuous fermentation for bioethanol production

A thesis presented by

Raúl Cruz Castro

Submitted to the

School of Engineering and Sciences

in partial fulfillment of the requirements for the degree of Master of Science

In

Engineering Science

Monterrey Nuevo León, November 30^th, 2021

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Dedication

This work is dedicated to all the persons who always support me in the good, bad, and especially in the worst moments of my life.

This work has a special dedication for the upcoming generations which use it as reference to make the things happen even in the darkest and hardest scenarios.

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Acknowledgements

I express my heartfelt gratitude to my parents, family, friends, and the whole people who support me in this process from the beginning.

I express my gratitude to my thesis advisor, Dr. Alejandro J. Álvarez, for providing enthusiasm, motivation, experience, and support during my research, and encouraging me to present a disruptive thesis dissertation.

I express my gratitude to the thesis committee, composed by Dr. Jose Luis Lopez, Dr.

Roberto Parra, and Dr. Alejandro Montesinos, for their knowledge, experience, and contributions to enhance my thesis work.

I want to acknowledge to Tecnológico de Monterrey for the support on tuition and the facilities to carry out this research work effectively. Additionally, I acknowledge all the personal which makes this institution the best university of Mexico.

I also want to acknowledge to CONACyT for the economic support for living and maintain me focused on this research work.

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Design and modelling of novel process of continuous fermentation for bioethanol production

by

Raúl Cruz Castro

Abstract

The bioethanol is considered as alternative to swap fossil fuels in transportation, although its potential is blocked by its higher production cost compared to gasoline. The most important challenge in bioethanol research to address is reducing production cost by improving the process performance and efficiency. There are some pathways to overcome this challenge such as in-situ product removal, cell immobilization, and using disruptive reactor geometries.

Therefore, disruptive reactor designs in coil tube and CFI geometries are proposed and simulated to understand its improvements compared to conventional straight tubular reactors. To analyze the performance of the proposed reactor design, a complete simulation study is done with the intention to identify the secondary flow and flow inversion effects in the fermentation process. Furthermore, an integrated continuous fermentation systems based on the proposed reactors coupled with pervaporation membrane are proposed and analyzed to determine its technical feasibility.

The proposed coil tube and CFI reactors present improved mixing effects provoked by the presence of secondary flow and flow inversion which led to higher conversion rate, product concentration, and productivity, compared to the conventional reactors. And the integrated fermentation systems can offer an enhanced process productivity. Additionally, a statistical analysis was carried out with the simulations results with the intention of generating heuristic and design rules to be used to simplifying the design and operation of the proposed fermentation processes.

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List of Figures

Figure 1. Flowchart with fermentation production systems using different feedstocks. ... 9

Figure 2. Microbial growth rate phases. ... 13

Figure 3. Reaction mechanism representation according to Langmuir Adsorption kinetic model. ... 15

Figure 4. Hybrid fermentation systems, with 1) an external pervaporation unit, and 2) and internal pervaporation unit. ... 29

Figure 5. Dean vortex streamlines in a coil tube. ... 32

Figure 6. Map suggesting flow model to adopt for straight pipes from Gargiulo (2015). 36 Figure 7. Diffusion mechanism though pervaporation membrane proposed by Qiu et al. (2019). ... 37

Figure 8. Methodology of obtaining design or heuristic rules of bioethanol continuous fermenters. ... 40

Figure 9. Geometry representation of coil tube reactor with = 7.6. ... 42

Figure 12. Geometry representation of tubular reactor with = 7.6. ... 44

Figure 13. Geometry representation of tubular ... 44

... 45

Figure 15. Geometry representation of CFI reactor. ... 46

Figure 16. Geometry representation of effective volume of CFI reactor. ... 46

Figure 17. Integrated reaction system proposal in all geometries. ... 47

Figure 18. Representation of PV membrane coupled in the bioreactor. ... 47

Figure 19. Cell immobilization matrix used in the proposed reactor designs. ... 49

Figure 20. Plane partition of the tubular reactor in xy- ... 52

Figure 23. Plane partition of the coil tube reactor in zx-plane with ... 53

Figure 24. Plane partition of the coil tube reactor in zx- ... 53

Figure 25. Plane partition of the coil tube reactor in zx- ... 54

Figure 26. Flow direction in CFI reactor. ... 55

Figure 27. Plane partitions of CFI reactor geometry. ... 56

Figure 28. Plane partitions of CFI reactor geometry in xy-plane. ... 57

Figure 29. Statistical analysis methodology. ... 61

Figure 30. Reactor performance with = 7.6. ... 66

... 67

... 68

Figure 33. Glucose concentration axial profile in different reactor geometry and operation setup. ... 72

Figure 34. Bioethanol concentration (gL-1) axial profile in different reactor geometry and operation setup. ... 73

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Figure 35. Conversion rate axial profile in different reactor geometry and operation

setup. ... 74

Figure 36. Average velocity magnitude (mm s-1) profile in different reactor geometry and operation setup... 75

Figure 37. Reynolds number profile in different reactor geometry and operation setup. 76 Figure 38. Peclet number profile in different reactor geometry and operation setup. .... 77

Figure 39. Glucose mass concentration dispersion profile for reactor in configuration setup 1. ... 80

Figure 42. Diffusive mass transfer gradient in glucose iso-concentrations per reactor geometry in configuration setup 1. ... 83

Figure 45. Vorticity and velocity magnitude profile per reactor geometry in configuration setup 1. ... 86

Figure 48. Average velocity magnitude (mm s^-1) profile in different reactor geometry and operation setup. ... 89

Figure 49. Reynolds number profile in different reactor geometry and operation setup. 90 Figure 50. Peclet number profile in different reactor geometry and operation setup. .... 91

Figure 51. Schmidt number profile in different reactor geometry and operation setup. . 92

Figure 52. Sherwood number profile in different reactor geometry and operation setup. ... 93

Figure 53. Dean number profile in different reactor geometry and operation setup. ... 94

Figure 54. Separation factor per reaction configuration and membrane. ... 98

Figure 55. Total mass flux through pervaporation membrane per reaction configuration. ... 99

Figure 56. P-PDMS membrane permeability per reaction configuration. ... 100

Figure 57. PDMS/S-ZIF-90 membrane permeability per reaction configuration. ... 100

Figure 58. Sorption coefficients in P-PDMS membrane per reactor configuration. ... 101

Figure 59. Sorption coefficients in PDMS/S-ZIF-90 membrane per reactor configuration. ... 102

Figure 60. PCA variables plot from tubular geometry results. ... 111

Figure 61. Scree plot of PCA variables of tubular reactor results. ... 112

Figure 62. PCA variables plot from coil tube geometry results. ... 113

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Figure 63. Scree plot of PCA variables of coil tube geometry results. ... 114

Figure 64. Regression performance of fluid viscosity on tubular reactor. ... 119

Figure 65. Regression performance of fluid density on tubular reactor. ... 119

Figure 66. Regression performance of conversion rate on tubular reactor... 120

Figure 67. Regression performance of bioethanol yield on tubular reactor. ... 121

Figure 68. Regression performance of Sherwood number on tubular reactor. ... 121

Figure 69. Regression performance of fluid viscosity on coil tube reactor. ... 122

Figure 70. Regression performance of fluid density on coil tube reactor. ... 123

Figure 71. Regression performance of conversion rate on coil tube reactor. ... 123

Figure 72. Regression performance of Schmidt number on coil tube reactor. ... 124

Figure 73. Regression performance of pressure drop on coil tube reactor... 124

Figure 74. Regression performance of Sherwood number on coil tube reactor. ... 125

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List of Tables

Table 1. Comparison of properties of different common fuels. Adapted from Ragauskas

(2014). ... 8

Table 2. Reactor configuration using calcium alginate as cell immobilization bed. ... 18

Table 3. Reactor configuration using polyvinyl alcohol (PVA) as cell immobilization bed. ... 19

Table 4. Immobilized cell continuous reactor configurations. ... 21

Table 5. Coil diameter and internal tube diameter ratio used in the design of coil tube reactors. ... 42

Table 6. Design specifications of coil tube reactors used in the simulations. ... 43

Table 7. Design specifications of tubular reactors used in the simulations... 45

Table 8. Design specifications of CFI reactors used in the simulations. ... 46

Table 9. PV membrane design characteristics. ... 48

Table 10. Experiment design for step 1 of the simulation study. ... 49

Table 11. Initial values of the experiments for step 1 of the simulation study. ... 52

Table 13. Initial values of the experiments for step 2 of the simulation study ... 55

Table 15. P-PDMS membrane properties at 40°C. ... 58

Table 16. PDMS/S-ZIF-90 diffusion coefficients at 40°C. ... 58

Table 17. Partial pressure correlation constants. ... 60

Table 18. Selected reactor configuration setups based in performance variables. ... 69

Table 19. Deviation percentage of simulations results with respect of simplified theorical model on coil tube reactors. ... 70

Table 20. Deviation percentage of simulations results with respect of simplified theorical model on tubular reactors. ... 70

Table 21. Performance variables for selected design for conversion rate by geometry. 77 Table 22. Performance variables for selected design for outlet product concentration by geometry. ... 78

Table 23. Performance variables for selected design for productivity of bioethanol by geometry. ... 78

Table 24. Deviation percentage of simulations results with respect of simplified theorical model on CFI reactors. ... 95

Table 25. Deviation percentage of simulations results with respect of simplified theorical model on Coil tube reactors. ... 95

Table 26. Deviation percentage of simulations results with respect of simplified theorical model on tubular reactors. ... 96

Table 27. Performance of cell immobilized reactors with different configurations. ... 96

Table 28. Performance of integrated reaction system coupled with P-PDMS membrane per configuration. ... 102

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Table 29. Performance of integrated reaction system coupled with PDMS/S-ZIF-90

membrane per configuration. ... 103

Table 30. Required PV membrane surface area for each configuration. ... 103

Table 31. Pervaporation membranes configurations performance. ... 104

Table 32. Performance of Integrated reaction systems coupled with PV membrane. . 105

Table 33. Significative design factors for each performance variables in all reactors from study 1. ... 106

Table 34. Significative design factors for each performance variables in tubular reactors from study 1. ... 107

Table 35. Significative design factors for each performance variables in coil tube reactors from study 1. ... 108

reactors. ... 114

geometry reactors. ... 115

Table 40. Simplified correlations for tubular reactors. ... 116

Table 41. Simplified correlations for coil tube reactors. ... 116

Table 42. Support Vector Machine regression performance in tubular reactors. ... 117

Table 43. Support Vector Machine regression performance in coil tube reactors. ... 117

Table 44. Regression performance from simplified correlations of tubular reactors. ... 118 Table 45. Regression performance from simplified correlations of coil tube reactors. 122

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The concern of the climate change, global warning, air pollution, and derived health problems, caused by the use fossil fuels in the electricity generation and transportation, have incentivized the research of alternatives energy sources to fulfill the world energetic requirements (El-Dalatony et al., 2017; Fu et al., 2016; Mohd-

Wei et al., 2014). The alternatives to fossil fuels are mainly based in renewables as solar, hydro, geothermal, tidal, biomass, and wind for electricity generation, with the purpose of use electricity as a transportation fuel (Pal et al., 2018; Quiroz-Pérez et al., 2019; Wei et al., 2014; Yatmaz, 2019). Meanwhile, in transportation, the use of hydrogen has been impulse to be used as fuel for heavy vehicles, airplanes, and some applications which the electrification is not technically feasible.

The biofuels can be considered as renewable and sustainable energy source because it can mitigate the effects of global warning and climate change, and support socio- economic development of a country (El-Dalatony et al., 2017; Kim et al., 2014; Mohd-

.

In the specific case for bioethanol, which it is the most promising biofuel due to the variety of feedstocks (Ahmad-Dar et al., 2018; Ajbar & Ali, 2017; Amillastre et al., 2012; Farias bet al., 2014; Gabardo et al., 2015; Grisales-Díaz & Willis, 2019; Madhania et al., 2019;

-

Wei et al., 2014; Yi & Wan, 2017), chemical processing routes (Ali & Zulkali, 2013;

Baeyens et al., 2015; Grisales-Díaz & Willis, 2019; Wei et al., 2014), microorganisms (Nurhayati et al., 2016), and it can be easily implemented in the short place around the world, especially in developing countries. The bioethanol cannot be exclusively used as transportation fuel, it can be used as raw material to produce biobutanol, biohydrogen and ethylene, or being used as solvent in various industrial processes (Baeyens et al., 2015; Moon et al., 2012; Nelson et al., 2019; Tabah et al., 2017). The improvements of design and process optimization in the continuous processes of bioethanol fermentation are crucial to exploit its potential (Ntihuga et al., 2012, 2013b).

The continuous operation of the fermentation and the cell immobilization can reduce the production cost of bioethanol because in batch operation, the production of bioethanol is presents major downtime between runs (Mathew et al., 2014; Ntihuga et al., 2012). And, in free cell reaction systems require recycle of the fermentative cells to operate in steady state, while the immobilized cell reaction systems only require nutriment supply to maintain a steady state operation (Rattanapan et al., 2011).

The purpose of this research work was designing and modeling continuous fermentation system based in tubular, coil tube and CFI geometries, with assistance of CFD software, and the information generated were used to find heuristics and design rules. Moreover, integrated reaction system with pervaporation membranes was proposed. This work can be considered as disruptive because there are no similar works published which consider an integral study of bioethanol process with the scope of generating design rules.

Some works were published with the scope to optimize fermentation processes using CFD software by using non-conventional geometries or identified the properly equipment design. Miller et al. (2019) have used CFD software to simulate non-conventional reactor geometries which uses the egg geometry to improve the heat and liquid mixing efficiency

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in the production of wine, and they have used sensibility analysis to optimize the design and process performance. And Müller et al (2020) have use of CFD models to optimize the configuration and design of the mixing system of a biogas fermenter are described and they have considered the mesh independence on simulations. Others have been published related to

bioethanol inside of the reactor and avoid the product inhibition caused by higher bioethanol concentrations (Santos et al., 2018), and some other which proposes pervaporation units coupled next to reactor (Fan et al., 2015, 2019; Shabtai et al., 1991).

To derive reliable design rules or heuristics are required the using statistical analyses to obtain significative correlations, factors, and regression models. Some statistical methods are proposed with the objective of optimizing the fermentation process performance, the ANOVA can be used as the basis of response surface methodology to optimize a performance variables with respect of factors (Lee et al., 2017; Nelson et al., 2019;

, or to identify factorial variables which affects the performance variable (Nazli et al., 2020); and Machine Learning techniques, such as: Artificial Neural Networks (Konishi, 2020; Smuga-Kogut et al., 2021; Suresh et al., 2020; Trajer et al., 2015), Prinbcipal Component Analysis (Konishi, 2020), Partial Least Square Regression (Konishi, 2020), and Genetic Algorithm (Suresh et al., 2020), with the purpose of obtaining mathematical models to be used for optimizing operation or performance variables.

1.1. Motivation

The worldwide petroleum consumption has risen from 63 million barrels per day to 115 million barrels per day from 1980 to 2040, which it triggers the releasing of tons of greenhouse gas emissions into the atmosphere (Ahmad-Dar et al., 2018; Choi et al., 2015; Parawira & Tekere, 2011; Zabed et al., 2016). The most serious global challenge in terms of energy transition is reaching the decarbonization to reduce global carbon footprint through renewable energies (Ahmad-Dar et al., 2018; Baeyens et al., 2015;

Rodrigues et al., 2018; Zou et al., 2016).

One of the initiatives to reduce carbon footprint consists to substituting petroleum products by biofuels (Ahmad-Dar et al., 2018; Amillastre et al., 2012; Handler et al., 2016;

. Some countries have been developing plans to boost its internal market on biofuels such as United States, Brazil, India, China, Canada, Japan, and Europe (Ahmad-Dar et al., 2018; Mussatto et al., 2010), which the first two produce almost 80% of world supply (Baeyens et al., 2015; Mohd-Azhar et al., 2017; Mussatto et al., 2010; Yi & Wan, 2017). There are three reasons to the growing interests to biofuels: rising oil prices and fast exhausting of global oil reserves, global pressure to reduce carbon emissions, and development of secondary markets for crop growers (Ahmad-Dar et al., 2018; Ali & Zulkali, 2013; Esfahanian et al., 2016; Mohd-Azhar et al., 2017; Mussatto et al., 2010).

The bioethanol is considered as better alternative to replace oil for being clean and renewable fuel, reduce emissions of toxic substances, and eliminates the release of acid- rain caused by sulfur dioxide for being less toxic (Ahmad-Dar et al., 2018; Mohd-Azhar et al., 2017; Mussatto et al., 2010; Zabed et al., 2016). Further use of bioethanol as transportation fuel can reduce the carbon dioxide load in discouraging the use of fossil

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fuels, and for being carbon neutral nature (Ahmad-Dar et al., 2018; Intaramas et al., 2019;

Madhania et al., . The worldwide bioethanol

production increases annually, which in 2006 was 49.7 billion of liters, and the prediction of bioethanol production is around of 134.5 billion liters in 2024 (Ahmad-Dar et al., 2018;

.

1.2. Problem statement and context

The production cost of bioethanol is expensive comparing to the production cost of gasoline, which costs around $ 0.10 - 0.18 USD per liter ,and its production cost presents heterogeneity because it depends of the substrate and process

used to produce bioethanol 2018; Mussatto et al.,

2010; Sánchez & Cardona, 2012), which only competitive production cost is the produced from sugarcane on Brazil with $ 0.20 USD per liter

2010), considering that this price is low by government subsidies (Mathew et al., 2014).

Also, D. Kumar et al. (2018) reports that the wholesale price of bioethanol is tied to oil prices and this varies between $ 0.31 USD per liter and $ 0.93 USD per liter, which average is $ 0.52 USD per liter. The challenge to reduce the production cost of bioethanol is making its processes more productive, effective, and economically attractive (Margono et al., 2018; Moon et al., 2012; Ntihuga et al., 2012), the process optimization to select the suitable configuration which leads to high productivity with lower costs is fundamental in bioethanol production.

The high concentrations of ethanol inhibits the microorganism metabolism of fermentative cells (Fan et al., 2015, 2019; S. Kumar et al., 2015; Pal et al., 2018; Rodrigues et al., 2018; Shabtai et al., 1991), especially if the fermentation process is operated on batch by the accumulation of it in the reactor, even more if the bioethanol concentration is higher than 95 (gL^-1)(Rodrigues et al., 2018). Moreover, the fermentative microorganisms are inhibited to high substrate concentration as well (Rodrigues et al., 2018; Schneiderman et al., 2015), its maximum concentration value recommended is below than 200 (gL^-1) to achieve the fermentation process in a reasonable time. The bioethanol inhibitory effects on the fermentative microorganism is the decreasing of cell membrane fluidity which causes an increased proton flux and lower intracellular pH (Schneiderman et al., 2015), which those effects causes low productivity, low product concentration, and energy- intensive product separation (Esfahanian et al., 2016; Fu et al., 2016). Some solutions to overcome the bioethanol inhibition in the fermentation process is removing it from the reaction fluid (Esfahanian et al., 2016; Fan et al., 2019; Fu et al., 2016; D. Kumar et al., 2018; Shabtai et al., 1991).

Most fermentation processes for bioethanol production around the world are operated on batch reactors, but batch processes leads to low bioethanol productivity, low cell density, and great wastewater discharge (Fan et al., 2015, 2019; López-Abelairas et al., 2013;

Shabtai et al., 1991). The low bioethanol productivity presented in batch fermentation processes is due to significative downtime between runs (Mathew et al., 2014; Ntihuga et al., 2012). Furthermore, the substrate inhibition is frequently presented in batch process due to catabolic repression which the enzymes are inactivated due to high osmotic pressure (Margono et al., 2018). Some solutions are proposed to overcome this problem

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the implementation of semi batch processes with sugar concentration control system (Margono et al., 2018) and continuous processes. Nevertheless, both processes present its specific challenges, the semi batch processes requires advanced control systems and high-skilled labor (Margono et al., 2018), while the continuous processes are affected by low cell density, complex downstream processing, difficulty to maintain high production rate, lower bioethanol yields, and hard-maintaining process sterility (T. Li et al., 2014;

López-Abelairas et al., 2013; Margono et al., 2018; Moon et al., 2012). Despite of the challenges of continuous processes for bioethanol production, these offer advantages in productivity, maintenance, operation control, operative cost, and land requirement compared to batch processes (Ali & Zulkali, 2013; Choi et al., 2015; Fu et al., 2016; T. Li et al., 2014; Margono et al., 2018; Mathew et al., 2014; Mohd-Azhar et al., 2017; Ntihuga et al., 2012; Richter et al., 2013).

The cell washout is presented frequently in continuous fermentation processes due to the difficulty of matching dilution rate to cell growth, and it influence the cell density in the reactor and its productivity (Margono et al., 2018; Wirawan et al., 2020). Additionally, the cell washout increases the production cost because the cells need to be replaced to maintain high production rates. To overcome this problem, some solutions are proposed as cell recycle (Margono et al., 2018), and cell immobilization (Rattanapan et al., 2011;

Wirawan et al., 2020; Xu et al., 2005). Nevertheless, most of the bioethanol fermentation plants are widely operated using free cells system to avoid the high capital investment for cell recycling (Xu et al., 2005). The cell immobilization in continuous fermentation improves the production rate of bioethanol, increment of biomass concentration on the process, increase biological stability, simplify downstream processes, reduce labor intensity, and reduce costs (Ariyajaroenwong et al., 2015; Choi et al., 2015; Karagoz et al., 2019; Lee et al., 2017; Mathew et al., 2014; Mohd-Azhar et al., 2017; Najafpour et al., 2004; Orrego et al., 2018; Rattanapan et al., 2011; Wirawan et al., 2020; Zheng et al., 2012). The cell immobilization technology allows to a continuous utilization of the cells on fermentation process (T. Li et al., 2014).

The mixing process is really important in the design of immobilized cell reaction systems, because a proper mixing can avoid inhibition of the fermentative cells provoked by the existence of stagnant zones which leads to the accumulation of substrate and product, and problems related with physicochemical properties of the biomass, high viscosity and partial insolubility, which affects on the process performance (Góis & Seleghim, 2011;

Unrean, 2016). Additionally, mixing process is crucial to the transport of nutrients to the fermentative cells in order to remain viable (Brányik et al., 2005). Some reactor geometries used on continuous process, such as stirred tank, tubular, and variation of these types, present poor mixing properties due to their specific characteristics, these properties influence strongly reactor performance, especially in conversion and selectivity (Jokiel et al., 2017; Maiorella et al., 1984). To enhance mixing process, it is required the adding of static mixers, even in tubular reactors, or using reactor geometries based on coil tubes (Jokiel et al., 2017; V. Kumar & . The secondary flow decreases axial back mixing, narrows residence time distributions, and improve mass and energy transfer in multiphase flows (Jha et al., 2020; Jokiel et al., 2017;

Khot et al., 2019; Mansour, Thévenin, et al., 2020; Saxena & Nigam, 1984). The advantages of using coiled geometry configurations are its higher compactness, large specific surface area, improved mixing, reduction on energy consumption and waste

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generation, and higher Reynolds number compared to straight tubular reactors (Jha et Soni et al., 2019).

Additionally, if a coil flow inverter is used, it will lead a significative enhancement in mixing, residence time distribution, heat and mass transfer (Jha et al., 2020; Khot et al., 2019;

Mansour, Thévenin, et al., 2020), also it decreases the number of turns required to get a fully developed secondary flow due to the flow inversion. However, the coil tube reactors have higher pressure drop than tubular reactors and poor residence time distribution (Jokiel et al., 2017; Khot et al., 2019; Mansour, Thévenin, et al., 2020; Singh et al., 2016).

The downstream processes for bioethanol fermentation are being problematic because these represent a great share of production cost, and the complexity of obtaining fuel- grade bioethanol due to presence of azeotropes with water (S. Kumar et al., 2015; Pal et al., 2018). The most common downstream process is distillation, but this process requires large amount of energy and it has higher operative costs (Maiorella et al., 1984; Pan et al., 2021; Wei et al., 2014). The pervaporation is the most promising approach of membrane technology applied on the bioethanol recovery from fermentation broths for non-toxicity of the membranes, lower operation costs, wide flexibility, low floor space required, low energy consumption, and capacity to separate azeotropes (Fan et al., 2019;

Lue et al., 2012; Quiroz-Pérez et al., 2019; Wei et al., 2014; Yi & Wan, 2017; Zhan et al., 2009). If the fermentation process is coupled with a pervaporation membrane will be an increase of bioethanol productivity, cell density, operation time, and a reduction on amount of wastewater treatment and system energy consumption (Esfahanian et al., 2016; Fan et al., 2019). However, pervaporation membranes are sensitive to the fermentation broth components, as inhibitors, fermentation nutrients, and by-products, which reduce separation performance of the bioethanol (Wei et al., 2014; Yi & Wan, 2017), and major obstacle is the lack of suitable membrane materials (L. Li et al., 2004).

The bioethanol processes are modeled based only in conventional technologies using CFD evidencing a huge opportunity area in process modelling (Quiroz-Pérez et al., 2019).

The number of reports related to bioethanol fermentation on CFD are low due to the difficulties of modeling multiphase flows or obtaining a reliable mixing model (Madhania et al., 2019; Mansour, Khot, et al., 2020; Quiroz-Pérez et al., 2019). The use of models is considered as a useful alternative to study reactor performance instead of experimental analysis, considering the economic and time limitations of some processes, and it will be useful to optimize operation conditions or design variables for them (Farias et al., 2014;

Smuga-Kogut et al., 2021).

1.3. Research questions

This work has been based in four research questions, which these are presented below:

1. What are the effects of the secondary flow in the performance variables for a fermentation process and how these are compared with conventional tubular reactors?

2. How flow inversion caused by bends in CFI helps to reduce axial dispersion in concentration along the reactor?

3. What are the characteristics required to operate hybrid pervaporation for extracting bioethanol in the reactor?

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4. Which variables can describe heuristic rules in the design and operation of continuous fermentation system for bioethanol production?

1.4. Objectives

The main objective of this research work is to design and model continuous fermentation integrated systems for bioethanol production and find its design and heuristic rules.

Additional there are some specific objectives required to accomplish the scope of this research work and generate answer for the research questions, these are:

1. To identify the effect of secondary flow in selected design of coil tube reactors and compare it with tubular reactors.

2. To simulate a CFI reactor and find the concentration profiles.

3. To propose pervaporation membranes to intensify the reaction process.

4. To obtain factors and mathematical expressions based in the reactor variables and parameters that can be used for heuristic or design rules.

1.5. Solution overview

This research work applies for an immobilized cell continuous reaction system. The glucose decomposition into bioethanol is the only reaction analyzed in this work due to it is the reference for most of bioethanol production systems. Also, the glucose is the only substrate analyzed in this research work to simplify the computational simulations. This work will be developed with use of computational simulations to achieve the proposed objectives.

Multiphysics and MATLAB. The use of these software will be adequate to have a precise simulation of the reaction performance in the proposed reaction system designs in a steady state.

The reaction systems proposed are tubular, coil tube and coil flow inverter, with a calcium alginate-based immobilization bed located in the internal surface of the geometries, in some cases the reactors will have a PDMS composite pervaporation membrane located next to the reactor.

Later a statistical analysis will be carried out according to the obtained results from the computational simulations, which the similarities and differences will be analyzed or correlated to state heuristic rules to design these reaction systems.

Despite of the simplicity in the reaction process and the complexity of the proposed design of the reactors, this research work will increase the possibility of further studies of integrated pervaporation membranes in reactors, CFI reactors used to produce biofuels, and the generation of design rules or heuristics to simplify the industrial design of these reaction systems.

1.6. Main contributions

The main contributions of this research work are:

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1. The proposal of new geometrical designs of bioethanol fermenters which increase the process efficiency and the potential to reduce production costs.

2. The integration of different technologies to intensify the bioethanol fermentation process.

3. One of the first research works to use CFD to simulate bioethanol process using non-conventional geometries and technologies.

4. The proposition of heuristic rules to being used as empirical parameters to optimize the design and operation of continuous fermentation reaction systems for bioethanol production.

1.7. Dissertation organization

The present dissertation is organized in 6 chapters which those are introduction, literature review, methodology, results, design rules for continuous fermentation systems and conclusions, with addition of appendixes for additional information at the end of dissertation.

In the first chapter is presented the motivation, problem statement, context, and research proposal of the present dissertation. The second chapter has an extensive literature review about the bioethanol fermentation processes in continuous operation, kinetic models of the chemical reaction used in the fermentative process, cell immobilization technologies for bioethanol fermentation, design rules proposed for fermenters, pervaporation membranes and the fundamentals required for the modelling of these processes in CFD.

The third chapter describes the methodology applied in this dissertation and how the simulations studies are carried out, with addition of the statistical studies. The fourth chapter discusses the results obtained from simulation studies and statistical analysis used to find correlations between variables and determine the effect of the design or operation variable in the process performance. In the fifth chapter, there is the final product of the research work which is the identification and generation of the heuristic rules for fermentation processes. Finally, in the last chapter there are the final conclusions of the dissertation and recommendations for further studies.

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2. Literature review 2.1. Bioethanol

Bioethanol is a biofuel obtained by the fermentation of organic substances, as crops, lignocellulose, sugars, microalgae, and others (Ahmad-Dar et al., 2018; Amillastre et al., 2012; Farias et al., 2014; Gabardo et al., 2015; Grisales-Díaz & Willis, 2019; Madhania

-

et al., 2019; Wei et al., 2014; Yi & Wan, 2017). It is used mainly as transportation fuel, and its physicochemical features are different to the gasoline

Ragauskas, 2014; Zaky et al., 2018), see Table 1.

Table 1. Comparison of properties of different common fuels. Adapted from Ragauskas (2014).

Properties Gasoline Bioethanol Bio methanol Biobutanol Molecular weight (gmole^-1) 100-105* 46.07 32.04 74.12

Energy density (MJL^-1) 32.00 19.60 16.00 29.20

Air-Fuel ratio 14.60 9.00 6.50 11.20

Specific energy (MJ kgair^-1) 2.90 3.00 3.10 3.20

Heat of vaporization (MJkg^-1) 0.36 0.92 1.20 0.43

Research Octane Number

(RON) 91-99 129 136 96-105

* = Molecular weight of the gasoline is taken from Ragauskas (2014).

Compared to the gasoline, bioethanol has higher octane number, higher heat of vaporization, less toxic, readily biodegradable, lower temperature of autoignition, and produces a minor amount of airborne pollutants, such as hydrocarbon, carbon monoxide, and oxynitride (Baeyens et al., 2015; Mohd-Azhar et al., 2017; Phwan et al., 2018; Tan et al., 2015). Although, it presents some disadvantages with respect to the gasoline which those are lower energy density, lower vapor pressure, low flame luminosity, miscibility with water, and when it is mixed with gasoline, its evaporative emissions increases . The combustion of bioethanol is cleaner due to its chemical composition that contains oxygen (Mussatto et al., 2010). Nevertheless, the biofuel has similar properties than the gasoline is the biobutanol, but its production is a byproduct from the ABE fermentation process which wants to obtain acetone and bioethanol.

The bioethanol production mainly consists in three steps: obtention of the fermentable sugar solution, sugar fermentation into bioethanol and its separation and purification, which the first step of the production process is found the main difference of the bioethanol production which is related to the feedstock and determines its complexity (Mohd-Azhar et al., 2017; Mussatto et al., 2010; Sánchez & Cardona, 2012), see Figure 1.

There are three processes used in bioethanol fermentations such as: separate hydrolysis and fermentation (SHF), simultaneous saccharification and fermentation (SSF), and simultaneous saccharification and co-fermentation (SSCF). In SHF, hydrolysis of lignocellulosic is carried out in a separation unit of bioethanol fermentation; while, SSF and SSCF, the fermentation and hydrolysis processes are in the same reactor, but the difference is in the second one, the fermentation of pentoses and glucoses are not happening in different reactors as SSF (Mohd-Azhar et al., 2017; Phwan et al., 2018).

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Figure 1. Flowchart with fermentation production systems using different feedstocks.

Some microorganisms are studied for bioethanol production, S. cerevisiae, P. stipitis and Z. mobilis are the most used microorganisms in industrial scale, which the first is the prime specie, and the last is the most promising alternative (Nurhayati et al., 2016; Yatmaz, 2019).

The fermentation process of bioethanol is affected by substrate limitation, substrate inhibition, product inhibition, and cellular death of the fermentative microorganisms (Farias et al., 2014).

2.1.1. Substrates

Exists three generation of substrates according its feedstock source (R. Kumar et al., 2019). Which the most used feedstock for producing bioethanol are from agricultural products as sugar, and starch (Mussatto et al., 2010; Tabah et al., 2017). For the first generation of substrates are obtained from sugar or starch feedstocks (El-Dalatony et al., 2017; Karagoz et al., 2019), where the substrate is obtained directly from crops (Karagoz et al., 2019; Tabah et al., 2017). The second generation of substrates are based form lignocellulosic material as corn stover, sugarcane bagasse, forest biomass, organic waste, soy molasses, wheat straw, rice straw, switchgrass (Akbas & Stark, 2016; Karagoz et al., 2019; Long & Gibbons, 2013; Shokrkar et al., 2019; Zaky et al., 2018), in which is necessary to obtain fermentable sugars from using hydrolysis processes. Finally, the third generation of substrates are the microalgae-based feedstock (Ashokkumar et al., 2019;

El-Dalatony et al., 2017; Ho et al., 2014; Kim et al., 2014; Phwan et al., 2018), which is considered to be the most promising one.

In industrial scale, the glucose is most basic fermentable sugar mainly obtained from hydrolysis pretreatment of lignocellulosic material, where the cellulose and hemicellulose are broken to pentoses and hexoses which those are glucose, xylose, arabinose and

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galactose (Karagoz et al., 2019; Tabah et al., 2017). Sometimes the glucose is obtained from SSF processes where the lignocellulosic material is converted in fermentable sugar and simultaneously the fermentable sugar is convert to ethanol, these processes are performing in the same equipment and minimizes the production and equipment costs (Sivarathnakumar et al., 2019).

The first generation substrates have the challenge that the raw materials price represents from 40 to 75 % of the total bioethanol production cost and it makes that the gasoline production will be 2 times cheaper . The production of bioethanol from sweet sorghum, faces the multiple challenges which the most important are limited harvest windows and it requires a very effective supply chain management because the fermentable juice has low time of degradation (Ahmad-Dar et al., 2018). In addition, the bioethanol production by grains has important environmental impacts such as soil erosion, biodiversity loss, and pollution generated by high volatile organic compound and NOx, also the use of these substrates requires an extensive plantation area (Mussatto et al., 2010). Additionally, most crops are used for food supply, so its fed stocks availability are season and geographic location dependent, and it has attracted critics regarding to rising of food prices and food security (Mussatto et al., 2010; N .

The study of the molasses is related to the second generation of substrates for bioethanol production, because this material is a waste from other processes, as sugar from sugarcane and soy oil (Dahiya & Nigam, 2018; Long & Gibbons, 2013). The interest of this kind of substrates is the multiple uses which it can be used, as animal, feed, bakery yeast, pharmaceuticals and substrate for processing sugar and bioethanol, because it has high concentration of fermentable sugar and it requires pretreatment as hydrolysis (Akbas & Stark, 2016; Long & Gibbons, 2013). The most common microorganism for fermenting this kind of substrates is the S. cerevisiae because it can reach high concentrations of ethanol without inhibition problems, upper than 80 (gL^-1) (Akbas &

Stark, 2016; Karagoz et al., 2019). The substrates of second generation represents a bigger challenge, because the hydrolysis of lignocellulosic biomass are more complex than the hydrolysis of starch-based (Kim et al., 2014; Tabah et al., 2017), its pretreatment process is considered as the main economic cost of overall process (Mussatto et al., , and it can provoke the generation of inhibitors caused for the thermo-chemical pre-treatment of biomass (Parawira & Tekere, 2011). Also, the bioethanol production from lignocellulosic material faces many challenges, such as the improvement of the hydrolysis process and the optimization of energy consumption (Mussatto et al., 2010). Another challenge for commercial ethanol production using lignocellulosic substrates is the low ethanol yield obtained, which is caused by incomplete biodegradation in the hydrolysis (Zabed et al., 2016).

The microalgae substrates are part of the third generation of substrates used for biofuels production, this kind of substrate do not require arable land to cultivate. So, it can be cultivate in the sea and it is easy to handle in large quantities (Phwan et al., 2018). Using microalgae is an advantage compared to other substrates, because their carbohydrate compounds and cellulose are easily broken to form fermentable sugars. The E. coli, Z.

mobilis, and S. cerevisiae are the most effective microorganisms to produce ethanol, where the most used one is the S. cerevisiae, because it is not a pathogenic microorganism and it has high ethanol and inhibitors tolerance; the Z. mobilis has a rapid conversion of glucose to ethanol, and it can achieve a high bioethanol yield than

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traditional yeast fermentation; and E. coli ferments a wider range of sugars than other microorganism but it has a lower ethanol yield due to high rate of by-products (Phwan et al., 2018). The commercial use of microalgae is not attractive because the high cost of microalgae production and microbial activity problems which provoke low productivity in the reaction process (Ho et al., 2014; Mussatto et al., 2010). And the algal biomass harvesting is a challenge because it requires large volumes of water to recover the biomass produced, this process represents the 20 30 % of the biomass production cost (Wei et al., 2014).

The production of bioethanol is the main targeted product from syngas-based substrates, which the most crucial factors studied are reaction system and configuration, also the operation variables related to the reaction process (Asimakopoulos et al., 2018). Syngas fermentation into ethanol follows a metabolic route called Wood-Ljungdahli pathway where the carbon dioxide, carbon monoxide and hydrogen molecules are metabolized and converted to acetyl-coA, which is an intermediate metabolite, that synthetizes acetic acid and ethanol (Daniell et al., 2012; Phillips et al., 2017), this pathway states that the ethanol is produced by Acetyl-CoA in two steps; via acetalhyde, where it is reduced by alcohol dehydrogenase, or via acetate, which is reduced to acetalhyde. The microorganisms which performs this pathway are called acetogens and this group has begun with this process since 3.8 billion of years ago, this kind of bacteria is anaerobic (Daniell et al., 2012). The acetogens which have preference to produce ethanol are Clostridium ljungdahli, Clostridium autoethanogenum, Clostridium ragsdalei and Alkalibaculum bacchi, which the Clostridium ljungdahli is the most studied, because it can reach higher ethanol concentration than other ones, around of 48 g/l (Daniell et al., 2012;

Phillips et al., 2017).

The most common glycerol-based substrate is the glycerol produced from biodiesel, which the quantity of crude glycerol produced represents 10% of the biodiesel produced in terms of mass (Lee et al., 2017; Mattam et al., 2013). The use of crude glycerol for bioethanol production creates economic advantages compared to the traditional processes of bioethanol production, because crude glycerol can decrease by 37% the cost of bioethanol produced from starch-based substrates (Jitrwung & Yargeau, 2015;

Mattam et al., 2013). The bioethanol produced from crude glycerol is obtained from multiple steps reactions pathway, which the most important intermediates are pyruvate and acetyl CoA, that the last one is reduced to ethanol (Mattam et al., 2013). The using of crude glycerol as substrates for biochemical transformations can produce high-value products, additionally than ethanol, as hydrogen, butanol and 1,3-propanediol (Jitrwung

& Yargeau, 2015; Lee et al., 2012; Mattam et al., 2013). Some microorganism can perform the fermentation reaction as Escherichia coli, Kluyvera cryocrescens S26, Pachysolen tannophilus and Enterobacter aerogenes, which the last one is the most promising one because it can reach high levels of bioethanol production, and it can grow and in aerobic or anaerobic conditions (Lee et al., 2012, 2017).

2.1.2. Kinetic models

The most used reaction is the chemical decomposition of glucose, which it is main fermentative sugar, in bioethanol and carbon dioxide molecules, the chemical reaction is described below (1).

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(1)

Alternative chemical reactions can be obtained using different substrates and microorganisms to perform the bioethanol fermentation, the other chemical reactions that can model the fermentative process. The following chemical reaction are derived from Wood-Ljungdahli pathway (Esquivel-Elizondo et al., 2017; Phillips et al., 2017), that the most relevant chemical reactions are shown below (2-4). But this process is affected by the generation of acetic acid that is an inhibitor of this process.

(2)

(3)

(4) However, the kinetic model of the bio-catalyzed chemical reactions is more difficult to find, compared to elemental chemical reactions, because this kind of models adds another variable called cell concentration and this variable has a direct impact in the reaction performance.

The microbial growth is mainly described in 6 phases (Amagu-Echiegu, 2015), see Figure 2.

For the bioethanol fermentation, the cell growth rate follows a first-order kinetic equation which it is represented below (5), and the kinetic models wants to find a value for the specific growth rate.

(5) Where is the growth cell rate, is the specific growth rate, and is the cell concentration.

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Figure 2. Microbial growth rate phases.

The Monod kinetic model describes specific growth rate is dependent of the substrate concentration, see (6), and it describes the growth rate of the cell in the exponential or logarithmic phase (Zentou et al., 2019). This model describes fermentation processes where the inhibition depends on the concentration of total substrate, and the advantage of this model is the easier evaluation of the kinetic parameters, although this model does not describe the reaction mechanism and inhibitors behavior (Lerkkasemsan & Lee, 2018).

(6)

Where is maximum specific growth rate, K is the

substrate concentration.

The logistic model describes the microorganism growth behavior, and it assumes that the microorganism has enough substrate to subsist, but this model cannot predict the microorganism behavior after stationary phase (Fan et al., 2015; Moodley & Gueguim-

Kana, 2019; Sivarathnakumar et al., 2019) -

reaction system, in this case, fermentation with more than one microorganism (L. Liu et al., 2019).

This model is represented in the differential form (7) and the integrated form (8).

(7)

Lag phase

Required time for the microbial to acclimatizs the new environment Long time of generation Zero growth rate

Acceleration phase

Microbial growth rate increases

Cells had been adapted in the environment

Exponential phase

Constant generation time Maximum substrate consumption

Declining growth rate phase

Microbial growth rate decreases

Substrate concentration decreases

Accumulation of toxic metabolites

Stationary phase

Microbial population remains constant Net microbial growth rate is zero

Endogeneuous decay phase

Negative net microbial growth rate

Inverse of the exponential phase

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(8)

Where is the maximum cell concentration and t is the fermentation time and is the cell concentration at initial time.

Another kind of kinetic model based in mechanism which is the Langmuir adsorption model, it states that the ethanol production by yeast consists in three stages: glucose transportation into the cell, glucose metabolism by the cell, and the ethanol formation and its desorption (Z. Liu & Li, 2014), see (9) for adsorption-desorption model.

(9)

Where is the transporter to the cell, is the adsorption kinetic constant and is the desorption kinetic constant. Despite of the glucose is free permeate biological membrane substance, this substance requires transporters to be absorbed in the yeast membrane;

the requirement of free transporters for glucose transportation makes the adsorption, the controller rate of the reaction mechanism (Z. Liu & Li, 2014). Finally, the cell growth model proposed is represented in (10).

(10)

Where is the substrate adsorption equilibrium constant, is the product adsorption equilibrium constant, and is the product concentration.

Also, Liu & Li (2014) states that the ethanol production rate and substrate consumption rate is proportional to the growth cell rate, as seen in (11) and (12).

(11)

(12)

Where is the biomass yield with respect of the substrate and is the biomass yield with respect of the bioethanol.

This model could be fit in cell immobilized reaction systems because the mechanism proposed is consistent according to Figure 3. Which the free cell system is the original system proposed by Liu & Li (2014) and the immobilized cell system is the representation of the reaction mechanism and mass transference between fluid stream and the cell immobilized bed.

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Figure 3. Reaction mechanism representation according to Langmuir Adsorption kinetic model.

Also, the Hinshelwood model offers an accurate prediction in the substrate consumption and product fermentation, especially in immobilized cell system with calcium alginate (Birol et al., 1998; Jin et al., 2012). This model takes account the influence of substrate concentration and production concentration in the specific cell growth rate, substrate consumption rate and production rate of bioethanol (Shuang-Qi & Zhi-Cheng, 2016), and it is represented in 3 equation set described as follows, see equations (13), (14) and (15).

(13)

(14) (15) Where and are the substrate inhibition constants; and are the product inhibition constants; is the bioethanol yield; and is the maximum specific bioethanol productivity (Jin et al., 2012).

In this research work, the kinetic model used in the modelling and simulation of the continuous fermenters is the Langmuir adsorption kinetic model because the model represents the process of adsorption of the glucose into the cells and the desorption of products from the cells, as stated from Liu & Li (2014). Also, this model integrates the mass transference of the reactants and products, and this mass transfer process could have a higher impact in the reaction rate than the reaction kinetics itself according to Turton et al. (2012).

2.2. Continuous fermentation

The concept of fermentation used in this research work is described as the biocatalytic reaction of sugars or carbon sources to alcoholic compounds without accumulation in the

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reaction environment. And, it is added that the substrate is constantly feed to the reactor and the product is discharged in order to achieve steady state operations (Brethauer &

Wyman, 2010). The continuous fermentation reaction systems have the particularity when they reach steady state, its operation conditions remain constant (Paz-Astudillo &

Cardona-Alzate, 2011).

The continuous fermentation systems present advantages and disadvantages compared to the other reaction systems. The main advantages of using continuous fermentation are improving of the production rate (Montealegre et al., 2012); the broth of fermentable microorganisms remains viable for a long time and for multiple runs (T. Li et al., 2014;

Richter et al., 2013); avoid repeating fermentation preparation, cleaning and sterilization;

and remove the inhibition provoked by substrates or byproducts because the final products are formed continuously (T. Li et al., 2014). Despite of the important advantages of the continuous fermentation systems in terms of automatic operation and economics, these reaction systems present disadvantages related to operation and product quality, which those are the increase of the contamination system; challenging stability maintenance of the fermentation and the biological system, caused by the nutrients diffusion limitations, metabolites and oxygen to the microorganism cells; a complicated process flow sheet and equipment are required; and the product concentration is lower than batch system, also expensive downstream process are required (T. Li et al., 2014).

The design of a continuous fermentation process is mainly affected by the substrate, reaction system, kinetic model and production volume required, which can be obtained by the performance variables due to the process will be structured according by the selected design parameters, technology required, and used fermentative microorganism.

The operation conditions, such as substrate concentration, dilution rate, cell concentration, pH value, and temperature, will be determined by the environment conditions that the microorganism can reach the maximum bioethanol productivity (Grzywacz & Ciesielski, 2019; Shabtai et al., 1991), which substrate concentration and dilution rate can control the local reactor stability when it is operated on continuous regime (Skupin & Metzger, 2015).

The substrate concentration is an important parameter of the process because affects directly in the product concentration (Skupin & Metzger, 2015), because a high substrate concentration can allow to obtain a high product concentration by stoichiometry. Even though, if the inlet substrate concentration different is lower, the fermentation tends to favorize the lower substrate concentration (Rattanapan et al., 2011).

The dilution rate is the inverse of retention time, which represents the time which the fluid takes from the inlet to the outlet, and its mathematical expression is the quotient of working reactor volume and volumetric flow. The effect of this variable can be represented in the performance variables, mainly in productivity, yield, quality, and substrate concentration, generally low flow rates favorizes the complete substrate consumption by the fermentative microorganism (Govindaswamy & Vane, 2010; T. Li et al., 2014; Z. Liu

& Li, 2014; Yatmaz, 2019). Higher dilution rates provokes greater shear stress in the cells and it forces to cell consume less glucose, then it cannot produce high concentration levels of bioethanol and high concentration of residual sugars are expected (Harde et al., 2014; Thani et al., 2016), although the bioethanol productivity of the process is higher (Rattanapan et al., 2011; Tan et al., 2015).

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The effect of temperature in fermentation process is an important consideration for operation due to each microorganism has its proper optimum operation temperature, when the fermentative cells are operating in suboptimal temperatures decreases the production rate and yield (Ali & Zulkali, 2013; Amillastre et al., 2012; Tan et al., 2015). In fermentation processes using first generation substrates are affected by temperature oscillations in continuous processes, producing significant variations in the yield and productivity, which these variables are hard to control (Violaro et al., 2018).

The challenges of the continuous fermentation process are finding the suitable operation conditions and parameters to improve the performance of bioethanol production for different substrates, improving the process stability to avoid multiple state states and oscillations during the transition between steady states, minimize the inhibitors generations to avoid cell inhibition during reaction, and scale up process without changing process performance or incrementing production costs (Baeyens et al., 2015).

2.3. Cell immobilization

The reactors with immobilized cells have the capacity of use the fermentative cells for multiple times, confer higher bioethanol tolerance and cell concentration, shorter time of fermentation, enhanced bioethanol productivity from the fermentation process, easier separation of the cells from the product, offers better process stability, and lower costs of recovery and recycling (Choi et al., 2015; Karagoz et al., 2019; T. Li et al., 2014; Mathew et al., 2014; Rattanapan et al., 2011; Zheng et al., 2012). Because, the cell immobilization avoids the effect of substrate inhibition, as happens in free cell reactors (Karagoz et al., 2019; Vaso et al., 2016).

The fermentative microorganism can be immobilized using different methods or techniques such as immobilization on solid carrier surfaces, entrapment within a porous matrix, mechanical containment behind barriers, and self-flocculation of the cells (Karagoz et al., 2019). In this research work, the cell immobilization based on entrapment using porous gel matrixes is studied because this technology allows to the reaction systems obtain high cell load in the fermentation process (Birol et al., 1998; Karagoz et al., 2019; Mohd-Azhar et al., 2017), especially if the cell immobilization matrix is made by calcium alginate.

The most common cell immobilization carrier is the calcium alginate because it has good biocompatibility, easily available, simple preparation, and low cost material (Mohd-Azhar et al., 2017; Orrego et al., 2018). The use of alginate is preferred over polysaccharides due to the high porous gel structure which allows a high substrate and product diffusion (Birol et al., 1998).

The preparation of calcium alginate immobilization cell matrix mainly consists in mixing the cells with sodium alginate solution 2% (M/W) and sprayed in with calcium chloride solution, approximately 2% (M/W) to form the gel beds (Birol et al., 1998; Choi et al., 2015; Jin et al., 2012; Shuang-Qi & Zhi-Cheng, 2016), which the bead diameters is adjusted to 2-3 (mm) or thickness is case of beds. Also, the immobilized yeast is activated in 30 (°C) prior 2 hours of being used in the fermentation process (Birol et al., 1998; Jin et al., 2012).

According to Table 2, the fermentation processes carried out in bioreactors with immobilized cells in calcium alginate bed are recommended to operate in a temperature

Instituto Tecnológico y de Estudios Superiores de Monterrey

School of Engineering and Sciences

Design and modelling of novel process of continuous fermentation for bioethanol production

Raúl Cruz Castro

Dedication

Acknowledgements

Design and modelling of novel process of continuous fermentation for bioethanol production

by

Raúl Cruz Castro

Abstract

List of Figures

List of Tables

Contents

1. Introduction

1.1. Motivation

1.2. Problem statement and context

1.3. Research questions

1.4. Objectives

1.5. Solution overview

1.6. Main contributions

1.7. Dissertation organization

2. Literature review 2.1. Bioethanol

Lag phase

Acceleration phase

Exponential phase

Stationary phase

Endogeneuous decay phase

2.2. Continuous fermentation

2.3. Cell immobilization