Master’s dissertation submitted in partial fulfilment of the requirements for the joint degree of

International Master of Science in Environmental Technology and Engineering

an Erasmus Mundus Master Course

jointly organized by UGent (Belgium), ICTP (Prague) and UNESCO ‐IHE (the Netherlands)

Academic year 2013 – 2014

Real Time Optimization of Hydrogen Production in a Continuous Fermentation Bioreactor

Host University: Institute of Chemical Technology, Prague

Axue Zhang Promotor: Ing. Jan Bartáček, Ph.D Co-promotor: Dr. Germán Buitrón Méndez

This thesis was elaborated at National Autonomous University of Mexico and defended at Ghent

University, Belgium within the framework of the European Erasmus Mundus Programme “Erasmus

Mundus International Master of Science in Environmental Technology and Engineering" (Course N° 2011-0172)

© [2014] [Prague], [Axue Zhang], Ghent University, all rights reserved.

DECLARATION This thesis/dissertation was written at the Laboratory for Research on Advanced Processes for

Water Treatment, Academic Unit Juriquilla, Instituto de Ingeniería, Universidad Nacional Autónoma de México <Feb. 2014 -- Aug. 2014>. The thesis topic was promoted by Department of Water Technology and Environmental Engineering of the Institute of

Chemical Technology in Prague.

I hereby declare that this thesis is my own work. Where other sources of information have been used, they have been acknowledged and referenced in the list of literature and other

sources. I have been informed that the rights and obligations implied by Act No. 121/2000 Coll. on Copyright, Rights Related to Copyright and on the Amendment of Certain Laws (Copyright

Act) apply to my work. In particular, I am aware of the fact that the Institute of Chemical Technology in Prague has the right to sign a license agreement for use of this work as school

work under §60 paragraph 1 of the Copyright Act. I have also been informed that in the case that this work will be used by me or that a license will be granted for its usage by another entity, the Institute of Chemical Technology in Prague is entitled to require from me a

reasonable contribution to cover the costs incurred in the creation of the work, according to the circumstances up to the full amount.

I agree to the publication of my work in accordance with Act No. 111/1998 Coll. on Higher Education and the amendment of related laws (Higher Education Act).

In Queretaro on 19/08/2014

ABSTRACT

This study presents the optimization of bio-hydrogen production in a continuous stirred tank

reactor (CSTR) by considering the inlet flow rate as the optimization variable. The substrate

(glucose) concentration was estimated by a software sensor-observer (Luenberger observer

coupled to a Super-Twisting one) and delivered to the optimization strategy. A nonlinear

optimization problem (NLP) was proposed as the objective function of the relationship

between organic loading rate (OLR) and hydrogen production rate (HPR). The maximum

HPR of 22.57±1.73 lH2/lreactor-d (29.90±2.29 mmolH2/lreactor-h) was obtained when the observer

accurately estimated the input glucose concentration, about 15g/l, and the hydraulic retention

time (HRT) was adjusted as 4h by the optimizer. When the input glucose concentrations were

about 10g/l, the estimations were lower than the values of off- line analysis. The observer was

able to estimate the input glucose concentration at 20g/l with fluctuations in the beginning.

The HPR were 14.99±1.85 lH2/lreactor-d (19.86±2.45 mmolH2/lreactor-h, HRTmin: 4h) and

15.41±0.99 lH2/lreactor-d (20.42±1.31 mmolH2/lreactor-h, HRTmin: 6h), respectively.

Key words : Bio-hydrogen production, real-time optimization, CSTR, robust estimation,

Super-Twisting observer, Luenberger observer.

ACKNOWLEDGEMENT

Foremost, I would like to express my sincere gratitude to my thesis co-promoter

Dr. Germán Buitrón Méndez, from Laboratory for Research on Advanced

Processes for Water Treatment, Academic Unit Juriquilla, Instituto de Ingeniería,

Universidad Nacional Autónoma de México, for his immense knowledge,

patience and motivation. His guidance helped me during the research and

writing of this thesis.

I would like to thank for my thesis promoter, Ing. Jan Bartáček, Ph.D, from

Institute of Chemical Technology, Prague, for his instructions and insightful

comments.

My sincere thanks goes to Alberto Villa Leyva, Dr. Jesús Ixbalank Torres

Zúñiga, and Dr. Alejandro Vargas Casillas for their encouragement and

teamwork on this project.

I thank for my fellow lab mates in Laboratory for Research on Advanced

Processes for Water Treatment, Academic Unit Juriquilla, Instituto de Ingeniería

Universidad Nacional Autónoma de México for all their help, hard questions

and joyful moments. Special thanks to Jaime Pérez Trevilla for his valuable

technical assistance with this research work.

In addition, I want to express my great gratitude to DGAPA-UNAM through

project PAPIIT IN100113 for funding this research project.

Last but not least, I would like to thank my parents and all my colleagues from

the IMETE program, 2012-2014.

TABLE OF CONTENTS

1. Introduction .............................................................................................................. 1

1.1 Hydrogen as an ideal fuel......................................................................... 1

1.2 Biological process of hydrogen production ............................................. 1

1.2.1 Biophotolysis of water using algae and cyanobacteria ...................... 2

1.2.2 Photofermentation.............................................................................. 3

1.3 Dark fermentation .................................................................................... 4

1.3.1 Concept of dark fermentation ............................................................ 4

1.3.2 Types of feedstock ............................................................................. 5

1.3.3 Microorganisms ................................................................................. 6

1.3.4 Operational parameters and their influences ..................................... 6

1.3.5 Types of bio-reactors ......................................................................... 8

1.3.6 By-products and further exploitation of by-products ........................ 8

1.3.7 Current research on dark fermentation .............................................. 9

1.4 Real-time optimization strategy ............................................................. 10

1.4.1 Basic concepts and goals ................................................................. 10

1.4.2 Algorithm......................................................................................... 11

1.4.3 The concept and function of a coupled observer ............................. 14

1.5 Research goal ......................................................................................... 15

2. Materials and Methods ........................................................................................... 16

2.1 Inoculum ................................................................................................ 16

2.2 Culture medium and its composition ..................................................... 16

2.3 Experimental setup................................................................................. 16

2.4 Start-up and operation of the reactor...................................................... 17

2.5 Off- line measurements and analytical methods ..................................... 20

2.6 Online data collection system ................................................................ 21

2.7 Experimental conditions ........................................................................ 22

2.8 Project team............................................................................................ 22

3. Results and Discussion ........................................................................................... 23

3.1 Performance of the reactor during start-up ............................................ 23

3.2 Analysis of reactor performance based on data collected for the optimization strategy design ................................................................................... 24

3.3 Evaluation of the relationship between OLR and HPR ......................... 28

3.4 Validation of the observer and the optimization strategy ...................... 29

3.4.1 Validation of observer ..................................................................... 30

3.4.2 Validation of optimization strategy ................................................. 32

3.5 Complete optimization strategy performance ........................................ 34

4. Conclusion .............................................................................................................. 40

5. Reference ................................................................................................................ 42

6. Annex ..................................................................................................................... 46

1

1. INTRODUCTION

1.1 Hydrogen as an ideal fuel

Worldwide industrial progress has not only improved the quality of human life

dramatically, it has also caused environmental problems that threatened human health in

the past century. Significant changes in the atmosphere, ocean, biogeochemical cycles,

and climate have been observed. Atmospheric levels of greenhouse gases targeted by

the Kyoto Protocol have increased and, as a result, global temperatures increased and

sea levels rose. It is reported that the globally averaged combined land and ocean

surface temperature data showed a warming of 0.72°C over the period 1951—2012

(Intergovernmental Panel on Climate Change, 2013). Although the changes in sea level

vary regionally, an overall rising trend is demonstrated and it is predicted that there will

be a significant increase in future sea levels in some regions by 2100. Both marine

ecosystems and coastal low-lying areas are being affected by global warming.

Among all the environmental issues, concerns about the environmental effects of fossil

fuels have been raised due to the growth of energy consumption. Primary energy

demand by 2050 is anticipated to be in the range of 600-100EJ, compared to about 500

in 2008 (International Energy Agency, December 2009). The continued usage of fossil

fuels threatens the environment by increasing concentration of greenhouse gases and

causing serious pollution problems in the atmosphere. Moreover gasoline, diesel and

natural gas, supplying nearly 80% of global energy demand, are finite sources and are

rapidly becoming scarcer and more expensive (Evans, 2007). Therefore, the scientists

have been pressing for the development of clean renewable energy as a substitute for

fossil fuels, which is expected to play a major role in the future sustainable energy

supply on a global basis.

Hydrogen has been introduced as a potential replacement for fossil fuels due to its high

efficiency of conversion to usable power, low generation of pollutants and high energy

density.

1.2 Biological process of hydrogen production

Hydrogen production has been under research for decades. Various ways of generating

hydrogen are proposed, such as steam reforming from hydrocarbons; partial oxidation

from fossil fuels; electrolysis and thermolysis from water. These methods, which are

controversial from the standpoint of sustainable development, either consume non-

2

renewable resources or require a large amount of energy input. According to Maddy et

al. (2003), 45 billion tons (90% of world production) of hydrogen is nowadays produced

by reforming of fossil fuels, with large amount o pollutions generated, i.e., 10 tons of

carbon dioxide.

Compared to the conventional technologies of producing hydrogen, biological processes

are found to be more environmental friendly and less energy intensive. It is also

possible to utilize waste as feedstock for the biological conversion to hydrogen and

other by-products. However, due to the low yield and production rate, these

biohydrogen technologies are impractical to put into real application. Some novel

techniques and improvements to existing techniques have been studied in an attempt to

increase the hydrogen production rate and yield. A hybrid two-stage system, one of the

techniques that enable the complete conversion of substrate, will be discussed later in

this section. This breakthrough makes biological hydrogen production more feasible

from a practical standpoint.

Biological hydrogen production processes can be classified into three main categories:

biophotolysis of water using algae and cyanobacteria, photodecomposition of organic

compounds by photosynthetic bacteria (photofermentation), and fermentative hydrogen

production from organic wastes or energy crops (Das & Veziroglu, 2001).

1.2.1 Biophotolysis of water using algae and cyanobacteria

In the biophotolysis process, photosynthetic green algae or cyanobacterium capture

sunlight to split water and reduce ferredoxin to generate hydrogen gas. This process

involves the usage of solar energy by two distinct photosynthetic systems operating in

series: a water splitting and oxygen gas producing system (“photosystem II” or PS II)

and a PS I photosystem that produces the reductant used for CO2 reduction (Das &

Veziroglu, 2001). The conversion of water into hydrogen is represented by the

following reaction:

H2O→PS II→PS I→Fd→Hydrogenase→H2.

↓

O2

Microalgae, both eukaryotic (the green algae) and prokaryotes (the cyanobacteria or

blue-green algae), processes hydrogenase enzymes and are able to generate hydrogen.

In plants, only the CO2 reduction occurs, because the hydrogenase enzymes, which

catalyze hydrogen formation, are absent (Das & Veziroglu, 2001). Hydrogenase

3

enzymes can be activated and synthesized under anaerobic conditions in the dark by

green algae. During this process, a small amount of hydrogen production is observed.

However, the rate for CO2 reduction is higher than the rate of hydrogen generation.

When these “anaerobically adapted” algae are returned to light but still under anaerobic

condition, the hydrogen production rate often increases significantly, but halts once the

typical photosynthesis (O2 production, CO2 fixation) is re-established. Cyanobacteria or

blue-green algae, as so-called nitrogen fixing bacteria, are able to produce hydrogen by

splitting water using both nitrogenase and hydrogenase enzymes. In some studies, this

process is also known as indirect biophotolysis (Lo, et al., 2008). Cyanobacteria

requires more energy intensive enzymes, such as ATP-demanding nitrogenase for

hydrogen production that is probably less suitable than green algae (James &

Greenbaum, 1997).

However, both green algae and cyanobacteria are oxygen sensitive in hydrogen

production. As it is explained by researcher, in order to maintain a simultaneous H2 and

O2 production, the O2 partial pressures must be below 0.1%, which is less than one

micromolar O2 in the liquid phases (Hallenbeck & Benemann, 2002). Practically

speaking, it is impossible to keep such low O2 partial pressure. Therefore, oxygen

sensitivity of the hydrogen producing enzymes is the crucial problem and an

insurmountable barrier. Even if the oxygen inhibition could be eliminated, the intrinsic

limitation in solar energy conversion efficiency and problems of gas capture and

separation still remains.

1.2.2 Photofermentation

Photofermentation is carried out by non-sulfur photosynthetic bacteria under anaerobic

and nitrogen-deficient condition. The photosynthetic bacteria capture solar energy and

produce hydrogen in the presence of nitrogenase, however, they are not capable of

deriving electrons from water and thus use organic compounds, mostly organic acids, as

substrates. The nitrogenase enzymes normally reduce N2 to ammonia, but are also able

to evolve H2, particularly in the absence of N2 gas. This technique has been

demonstrated as the most promising biological system for hydrogen production (Das &

Veziroglu, 2001). The main advantages are potential waste utilization, high theoretical

conversion yields and lack of oxygen-evolving activity that causes oxygen inhibition.

Although the major benefits of this technique have been clearly indicated, Hallenbeck

and Benemann (2002) argued that calculating the efficiency of conversion of solar

4

energy into hydrogen (almost 100%) ignores the energy content of the organic substrate.

Also, they clearly pointed out the drawbacks of this technique: the demand of

nitrogenase enzyme with its inherent high energy requirement; the low light energy

conversion efficiency; and the expensive and large surface area photobioreators. In

conclusion, the photofermention systems fall far short of plausible economic feasibility.

It is reasonable to argue that the utilization of organic substrates, as in

photofermentation, should be simpler and more efficient than that in dark fermentation

process, which is discussed in detail as the selected hydrogen production technique in

this research.

1.3 Dark fermentation

1.3.1 Concept of dark fermentation

Dark fermentation is a process that converts organic substrates, including lipids,

carbohydrates and proteins, into methane through the processes of hydrolysis,

fermentation and methanogenesis. The general scheme is shown in Figure 1. In order to

produce hydrogen instead of methane, some pretreatment methods are required to

inhibit the activity of hydrogen consumers and harvest hydrogen-producing spore-

forming anaerobes.

Fig. 1. General scheme of fermentation process

Theoretically, the maximum hydrogen yield is 4 mol H2/mol Glu, along with acetic acid

generation, according to the chemical reactions shown in Equation 1. However the

fermentative metabolism is a complicated process so that the real yield of hydrogen

Acetate

H2, CO2

CH4

Proteins Carbohydrates Lipids

Amino acid and monosaccharides

Fatty acids

VFA(propionate, butyrate, valerate, caproate)

5

production is relatively low, around 1 or 2 mol H2/mol Glu, considering that each

molecule of glucose contains 12 moles of hydrogen. Hallenbeck and Benemann (2002)

explained that this is a natural consequence. Instead of hydrogen, cell biomass is the

target product of fermentations which have been optimized by evolution. Hydrogen

recycling is another reason for low hydrogen production yields, because of the presence

of one or more uptake hydrogenases in many organisms consume a portion of the

hydrogen produced.

1.3.2 Types of feedstock

Either energy crops rich in starches or various waste streams can be used as substrate,

which are the most frequently exploited types of feedstock. The energy crops possess

the following characteristics that are preferred for hydrogen production through dark

fermentation: (a) high hydrocarbon content and low lignin content; (b) low cost with

high resistant to the environmental stresses; (c) high biomass yield. However, due to the

rising food prices and sustainable development goals, the crops are more desirable for

supporting human dietary needs than producing energy. It is also possible to utilize the

residues from agriculture, for example corn stover (Datar, et al., 2007) and corn stalks

(Panagiotopoulos, et al., 2009), which are abundant, economical and environmentally

sustainable. However, a lack of easily degraded carbohydrates makes the dark

fermentation process less efficient. The residues must be pretreated to loosen the

structure of complex carbohydrate polymers.

It is more appealing to produce hydrogen through biotransformation of wastes and

wastewater, considering the costs of pollution treatment, renewable energy and

resources recovery. There are two criteria necessary for efficient substrate: (i) a high

concentration of easily biodegradable organic compounds such as sugars; (ii) a low

concentration of chemicals that inhibit the biological activity. The ideal candidate for

dark fermentation feedstock is the wastewater from food processing industries, such as

rice wine (Ueno, et al., 1996), olive pulp (Koutrouli, et al., 2009), tequila vinasses

(Buitron & Carvajal, 2010) and cheese wastewater (Yang, et al., 2007). These

wastewaters have been tested in the laboratory and show promising results. Hydrogen

production rate (HPR) of 158.33ml H2 L-1h-1 was achieved in continuous mode by a

mixed culture from rice winery wastewater. (Yu, et al., 2002). In the same continuous

6

mode from olive pulp, the HPR was 10.8 H2 L-1h-1 (Koutrouli, et al., 2009). Hydrogen

production does not only depend on the type of feedstock, but also is influenced by the

operation parameters and by different kinds of reactors and levels of activity of the

bacteria. The dilution of raw waste is required most of the time to reduce the organic

loading rate and avoid the inhibition of the process.

1.3.3 Microorganisms

The microorganisms suitable for the dark fermentation process can be either mixed

acidogenic cultures, which are from natural environments (wastewater sludge, compost,

etc.) or pure cultures of selected hydrogen producing microbes. They can be mesophilic,

thermophilic, extreme thermophilic or hyperthermophilic. The inoculum used in this

study is a mixed culture from wastewater treatment sludge of a brewery. Compared to

the pure cultures, the mixed ones require a relatively low cost since no medium

sterilization is required and various feedstocks can be chosen because of their resistant

to contaminants. In the mixed cultures, it is likely that the non-hydrogen producing

bacteria are predominant, resulting in dramatic failure of the process. Even though it is

not economical to apply the pure culture, it delivers higher observed hydrogen yields as

a result of reducing the undesired by-products. Genetically modified microorganisms

are another choice that already has been tried in some research(Nath & Das, 2004;

Ghosh & Hallenbeck, 2009). The microorganisms that are able to produce hydrogen via

dark fermentation are various and include strict anaerobes (Clostridia, methylotrophs,

rumen bacteria), facultative anaerobes (E. coli, Enterobacter, Citrobacter), and aerobes

(Alcaligenes, Bacillus). (Ntaikou, et al., 2010)

1.3.4 Operational parameters and their influences

The operational parameters have a crucial role on the metabolic pathway of the

microorganisms and, as a result, influence the process efficiency, product gas quality

and energy inputs. Many researchers have been focusing on optimum parameters to

obtain the maximum hydrogen yield and hydrogen production rate.

Fermentative bacteria usually produces hydrogen under acidic conditions in the pH

range from 4.5 to 6.5. When the pH of the fermentation medium is too low or too high,

the metabolic activity of the hydrogen producers is inhibited or altered to adapt the

environment and, as a consequence, biohydrogen production ceases. Various reports

have shown that the maximum hydrogen yield and hydrogen prod uction rate were

7

achieved in the pH range of 5.5 to 6.0. For instance, Azbar et al. (2008) found that the

optimum hydrogen yield (22m mol/g COD) was obtained at a pH of 5.5 from cheese

processing wastewater in a CSTR system. The same pH value was reported using

condensed molasses as fermentation soluble feedstock in a CSTR system (Lay, et al.,

2010). Moreover, the pH of 5.5 was also preferable to a batch reactor studied by

Fernandes et al. (2010).

Mesophilic (20-40°C) and themophilic (50-60°C) conditions have been demonstrated

mostly for dark fermentation process by mixed cultures. Only few studies have applied

extreme thermophilic conditions (65-75°C). Higher temperatures are supposed to

facilitate the hydrogen production by enhancing the biological activities. It is necessary

to take into account the energy efficiency and safety of maintenance in operating the

reactor at lower temperatures as well. It is reported by Buitron and Carvajal (2010) that

the maximum hydrogen production rate (50.5 ml H2 L -1h-1) was gained at 35°C using

tequila vinasses as substrate in a sequencing batch reactor. Explained by Ewyernie et al.

(2001), it is possible that at high substrate concentrations, the optimum temperature is

higher than for low substrate concentrations.

Organic loading rate (OLR) is an important parameter in hydrogen production from

dark fermentation, which is determined by substrate concentration and input flow, and

is normally presented in the units of g COD l-1d-1. With the increasing OLR, the

hydrogen production will rise; however, inhibition will occur when the OLR exceeds a

threshold dose. Furthermore, various types of feedstock have a different optimal OLR,

which is also associated with reactor type and other operational parameters. Ramirez-

Morales et al. (2014) mentioned that the maximum HPR of 25.4 ± 0.4 l H2 L-1d-1 was

obtained with an optimal OLR close to 120 gCOD l-1d-1, which is a valuable reference

for this study because it used the same environmental set-up and substrate. The OLR

can be calculated as the following,

Where:

Qin: input liquid flow rate (l/d).

Gluin : glucose concentration (g COD/l).

V: reactor useful volume (l).

8

1.3.5 Types of bio-reactors

Simple reactor technology is another advantage of the dark fermentation process. There

are several types that have been studied to produce hydrogen, batch reactors, upflow

anaerobic sludge blanket (UASB) bioreactors, fixed bed reactors, granular sludge

reactors and suspended biomass bioreactors. Each type of reactor possesses advantages

and drawbacks. Batch reactors are easy to construct and operate but are less efficient in

hydrogen production. Batch reactors also demand manual work; therefore, they are

mainly used in laboratory tests. The UASB reactors are efficient in treating organic

wastes both in laboratory and pilot scale tests. Fixed bed reactors allow low hydraulic

retention time, thus they are less affected by inhibition and biomass washout. However,

their mass transfer efficiency is improved by mixing to completely convert substrate

and the packing materials of biomass growth should be chosen carefully. In general,

bioreactors with suspended solid are not capable of operating with a low hydraulic

retention time because the biological strains of biomass wash out. Therefore, suspended

solids bioreactors have a relative low hydrogen production rate compared to the fixed

bed reactors and granular sludge reactors. On the positive side, reactors with suspended

biomass do not require long start-up periods for operation as do the fixed bed and

granular sludge reactors, which require weeks or even months for the bacteria to

successfully attach onto the supporting materials. In this study, a bioreactor with

suspended solids was used in a continuous operational mode.

1.3.6 By-products and further exploitation of by-products

As mentioned above, hydrogen production is restricted by the existing metabolic

pathways, so that the theoretical maximum hydrogen yield is 4 mol H2/mol Glu. Unlike

the anaerobic digestion processes during which methane is produced as the end product,

the dark fermentation process only allows carbohydrates to transform into more

oxidized soluble compounds, leaving various volatile fatty acids (VFAs) in the liquid

phase. The main chemical reactions of the VFAs production are shown below,

(Equations. 3-7).

9

Apart from the fact that a considerable amount of carbon from the substrate is trapped in

these by-products, it is important to note that propionic acid production consumes

hydrogen which decreases the hydrogen yield to 1-2 H2/mol Glu. Lower propionic acid

production is always preferable during bio-hydrogen generation via dark fermentation.

Besides, high concentration of by-products inhibits the biological process or even forces

the bacteria to change their metabolic pathways.

The main obstacle to applying dark fermentation into practice is the low yield due to the

incomplete conversion of the substrate. Manipulation and optimization of bioprocess

parameters or of the organisms have failed to achieve more than a 25% conversion of

substrate to H2 (Hallenbeck & Ghosh, 2009). A two-stage system has been proposed to

improve this technique and it consists of a (i) first stage for the fermentation of substrate

into hydrogen and organic acids; (ii) second stage for the extraction of methane or

hydrogen from the effluent of the first stage reactor using either photofermentation or

microbial electrohydrogenesis cells (MECs). MECs is a process that supplies energy to

convert organic acids to hydrogen from a microbial fuel cell. However, further research

on the second stage and the efficient collaboration of the two reactors still needs to be

done. Another agitated granular sludge bed reactor (AGSBR) reactor with a volume of

400L was built in the Green Energy Development Center at Feng Chia University in

Taiwan. Both the synthetic wastewater and fermentation wastewater stream (condensed

soluble molasses) were tested to evaluate the biohydrogen production efficiency. An

HPR of 15.6 lH2/lreactor-d with an OLR of 240 g COD/L-d and an HPR of 1.5 lH2/lreactor-d

with substrate concentration of 40 g COD/K were obtained with synthetic wastewater

and fermentation wastewater, respectively (Lin & Lay, 2010).

1.3.7 Current research on dark fermentation

Currently, most of the studies were carried out in the laboratory and only a few pilot

scale studies are reported in the literature. A pilot scale conducted by Ren et al. (2006)

was performed in a continuous flow anaerobic fermentative reactor with a working

volume of 1.48 m3 using molasses as feedstock. The maximum hydrogen production

rate achieved 5.57 m3/ m3reactor/d with an OLR of 3.11-85.57 kgCOD/m3.

Designing such a process in pilot scale and real applications would require applicable

data from laboratory scale studies. The fact that the optimum operation conditions also

change for various feedstocks and inoculums adds one more difficulty to the design.

10

Nontheless, a pH of 4.5-5.5 and mesophilic temperatures are suggested on the basis

biological efficiency and economics.

1.4 Real-time optimization strategy

1.4.1 Basic concepts and goals

Real time optimization (RTO) of a continuous process, with a goal of maximizing

economic benefit from productivity, has again attracted the interest of researchers.

Traditionally, this method has proven to be very beneficial in oil refining and chemical

production systems. Based on a model describing the relationship between process

inputs and outputs, it repetitively optimizes the process while updating the model by

continuously using the available measurements. The optimization target always

involves emission and cost minimization, as well as efficiency and productivity

maximization. Besides the industrial application of real time optimization, only a few

studies are available in the literature, which combine the biological process with

operational optimization. Based on the opinion of Ochoa et al. (2009), a compromise

has to be considered between bioprocesses and optimal operation. This compromise

includes: guarantee of a proper operating environment for the microorganisms and the

minimization of possible inhibition factors. The productivity of the desired metabolic

product(s) should be optimized, taking into account the biological restrictions and the

presence of unknown variables. Ochoa et al. (2009) demonstrated an optimization of

bio-ethanol production by integrating the optimization and control in real time. This

work presents the possibility of real time optimization of a bioprocess in a plant scale

control and provides promising idea of tracking the productivity of the bioprocess

instead of simply maintaining the preferred operating conditions which can be “optimal”

only from a biological point of view.

As for the bio-hydrogen production, Aceves-Lara et al. (2010) has applied an optimal

closed- loop control together with state and input variable estimations by an asymptotic

observer. This model predictive control (MPC) strategy improved hydrogen production

75% from 4.72 ml H2/L-min to 8.27 ml H2/L-min, while maintaining the hydrogen

production yield around 1.20-1.35 mol H2/mol Glu. The reactor was running at a

temerperature of 37°C, a pH of 5.5 and a working volume of 1.27 L, which used diluted

molasses as the feedstock. Aceves-Lara et al. (2008) used a constrained nonlinear

optimization technique to enhance the hydrogen production. The best hydrogen

production was 15.3 lH2/lreactor-d in a continuous stirred tank reactor (2 L and a useful

11

volume of 1.2 L) with an HRT close to 6h and a molasses concentration of 20g/l.

Conditions were constantly kept at a pH of 5.5, a temperature of 37°C and a stirrer

speed of 300 rpm.

Instead of the MPC (C-MPC), Ramirez-Morales et al. (2014) chose real time

optimization for bio-hydrogen production which is an easier model, and thus able to

provide a faster response. Ramirez-Morales et al. (2014) achieved an improvement of

64.4% in hydrogen production from 8.9 ± 0.6 lH2/lreactor-d to 25.4 ± 0.4 lH2/lreactor-d. With

a reactor‘s working volume of 0.9 L, pH 5.5 and 35°C, glucose was used as substrate to

produce hydrogen. In research of Ramirez-Morales, et al. (2014), the feasibility of real-

time optimization for hydrogen production was demonstrated; however, the glucose

concentration was kept constant during the optimization stage, which is not applicable

to the real case. In reality, the input substrate concentration, especially in the waste

water stream, varies from time to time. Thus, in this study, real time optimization

strategy was applied to the bio-hydrogen production system with varying substrate

concentrations. This work is valuable to bio-hydrogen production from both an

engineering and practical industry perspective. Inflow rate is the controlled input, while

substrate concentration is the uncontrolled input that must be measured. The problem is

that measuring the substrate concentration in real time is not practical. For an observer,

a software sensor that provides an estimation of substrate concentration as it input was

suggested. The observer is discussed in the following session.

1.4.2 Algorithm

The real-time optimization strategy manipulates the organic loading rate (OLR) to

achieve the maximum hydrogen production rate (HPR) from a certain concentration of

substrate. The relationship between OLR and HPR was conducted from the real

experimental data before applying the optimization strategy. The OLR depends on both

glucose concentration Gluin (g /l) and the inflow rate Qin (ml/min). The inflow rate was

selected as a control variable. A non- linear optimization problem (NLP) was proposed

as following:

Which is subjected to:

Where:

HRTmin and HRTmax: working interval boundaries, h;

12

The working interval boundaries set in this study were 12h for HRTmax, 4h and 6h for

HRTmin.

The general algorithm of optimization strategy is shown in Figure 2.

To explain the process:

1. Fix HRTmin and HRTmax and calculate Qin,min and Qin,max according to HRTmax and

HRTmin, respectively.

2. Approximate a 3rd order function (HPR vs OLR) using the original experimental

data, which will be the objective function to maximize.

3. Read the total biogas flow Qout,gas (l/d) and the hydrogen fraction %H2 from the Data

Acquisition System (DAQ) and calculate the current HPR as:

Where: Vr is the reactor working volume (l).

4. If it is already 10 minutes, calculate the mean of Qout,gas, %H2 and HPR and go to

step 7.

5. Else wait 10 seconds (sample period) and return to step 3.

6. Calculate the current HRT and OLR considering the current inflow rate and glucose

concentration.

7. Save Qout,gas,mean, %H2,mean, HPRmean, HRT and OLR.

8. Luenberger observer estimates the glucose concentration at the reactor output

(Gluout, g/l) and the biomass concentration (VSS, g/l) from Qout,gas,mean and %H2,mean.

9. Super-Twisting observer estimates the glucose concentration in the reactor input

(Gluin, g/l) according to the estimation of Luenberger observer.

10. If the Gluin is larger than 17g/l, set the HRTmin as 6h (to ensure more glucose could

be consumed when the concentration is high).

11. Else, set the HRTmin as 4h.

12. Update the Qmax based on HRTmin.

13. Update the 3rd order function (OLR vs HPR) using the last pair (HPRmean, OLR).

14. Solve the NLP:

Subject to:

13

15. In the end, update the optimal inflow rate Q in,opt to the feed pump and repeat from

step 4.

Start

Calculate Qin,min(HRTmax)

&Qin,max(HRTmin)

Reference data (OLR & HPR)

Compute a 3rd order function (OLR vs

HPR)

Read Qout,gas & %H2 from

DAQ

Calculate HPR

10min elapsed

No

Calculate Qout,gas,mean,

%H2,mean, HPR,mean, HRT, OLR

Yes

Update the function (OLR vs HPR) with

the last pair

Calculate Qin,

maxQinHPR(OLR(Gluin,Qin)),Subject to:

Qin,min< Qin<Qin,max

Update Qin in the feed

pump

Luenberger observer estimates

Gluout & VSS

Super-Twisting observer estimates

Gluin

Gluin>17g/l Set HRTmin 6hyes

Set HRTmin 4h

No

Update Qmax

Fig. 2. Algorithm of optimization strategy.

14

1.4.3 The concept and function of a coupled observer

In order to estimate the glucose concentration at the input of a hydrogen producing bio-

reactor, an observer can be applied. To our knowledge, it has never been applied to a

bio-hydrogen production system. The observer, a mathematical tool, consists of a

Luenberger observer coupled to a Super–Twisting one. The general scheme of a

coupled observer is shown in Figure 3. The Luenberger observer estimates the glucose

and VSS concentration in the reactor (the same value as in the reactor effluent in the

steady state) by using reactor output (hydrogen and carbon dioxide gas flow), while

passing the estimations to Super-Twisting observer. The Super-Twisting one is able to

estimate the glucose concentration at the reactor input, which is the key in the

optimization strategy.

Fig. 3. The general scheme of coupled observer

The dynamics of the continuous stirred tank reactor (CSTR) of biohydrogen production,

feeding glucose, is described by Zuniga et al. (2013). The following reaction is based on

the mass balance, where Glu, Ace, Pro, Bu, X, CO2 and H2 represent, respectively, the

concentrations of glucose, acetate, propionate, butyrate, biomass, carbon dioxide and

dissolved hydrogen in the liquid phase (g/l). qCO2,gas and qH2,gas represent the gas flow

rates of carbon dioxide and hydrogen (g/l-d). D is the dilution factor (d-1), vector r is the

kinetics of the involved biological reactions (g/l-d) and K represents the matrix of

pseudo-stoichiometric coefficients.

(8)

The Luenberger observer considers only the dynamics of glucose (Glu), biomass (X),

hydrogen (H2, qH2,gas) in the liquid and gas phase, seen in Equation 9.

Qin

Gluin Bio-reactor %H2

Qgas Gluout

VSS Luenberger

observer

Gluin Super-Twisting observer

15

(9)

By comparing the estimated hydrogen flow and the actual one, which is an online

measurement (the difference is considered as an error), the Luenberger observer is able

to estimate the dynamics (x). As it goes to infinity, this error is approaching zero, which

means that the estimated dynamics is close to the practical situation.

Similar with Luenberger observer, the Super-Twisting observer compares the estimated

Glu of itself with the obtained estimated Glu from Luenberger observer to achieve a

more accurate estimation of glucose concentration in the reactor input.

As the design of the observer is not the main purpose of this work, more details of the

mathematics will not be discussed. It is available in the article of Zuniga et al. (2013).

1.5 Research goal

This work focused on maximizing the hydrogen production with various organic

loading rates in an anaerobic continuous stirred tank reactor using a dark fermentation

process. The substrate (glucose) concentration, as the uncontrolled variable, was

estimated by coupled observer. The experimental validation of observer was studied as

well.

16

2. MATERIALS AND METHODS

2.1 Inoculum

Granular anaerobic sludge from a wastewater treatment UASB reactor used by the

brewing industry was applied as inoculum after it was thermally pretreated as described

by Buitron and Carvajal (2010). The granular sludge was heated to 104°C for 24h in

order to select the hydrogen-producing, spore-forming anaerobes and to inhibit the

activity of hydrogen consumers. The heated material was broken down in a mortar and

sieved with a #20 mesh (850μm) screen. The powdery material obtained was used as

inoculum in the bio-reactor at a concentration of 4g/l. The rest was stored in a sealed

glass container at room temperature (25°C)。

2.2 Culture medium and its composition

The carbon source applied to the culture medium is glucose (10, 15, 20, 25 g/l). The

phosphorus source (K2HPO4 50 mg/g glucose) and nitrogen source (NH4Cl 104 mg/g

glucose) were added into the culture medium as a powder. For every liter of feed

solution, the following amounts of mineral salts were added: 0.4 mg; MnCl2·4H2O, 20

mg; MgCl2·6H2O, 20 mg; FeSO4·7H2O, 2 mg; CoCl2·6H2O, 2 mg; Na2MoO4·2H2O, 2

mg; H3BO4, 2 mg; NiCl2·6H2O, 2 mg; ZnCl2, 2 mg. This composition was adapted from

the study of Ramirez-Morales et al. (2014). The mineral salts were first prepared in

solution, and then used together with a carbon source, phosphorus source and nitrogen

source. The culture medium was prepared using tap water and kept in a refrigerator at

4 °C to minimize any biodegradation before feeding it into the reactor.

2.3 Experimental setup

The experiments were carried out in a continuous stirred tank reactor (CSTR) of 1.25 L,

with an actual working volume of 0.9 L. The reactor was equipped with an EZ-Control

controller (Applikon Biotechnology, Schiedam, the Netherlands). The reactor

performed at a temperature of 35 °C and a stirrer speed of 100 rpm, while the pH was

kept constant at 5.5 by a 3M NaOH and a 2M HCl solution. The conductivity-based

on/off level sensor was set up in the reactor to control the liquid level. The feed solution

was pumped into the reactor with different inflow rates by a peristaltic pump

(Masterflex, model 77800-50). The scheme of complete experimental setup is shown in

Figure 4.

17

NaOH 3M

HCl 2M

Effluent

Feed pump

Bio-controllerTap water

H2 Sensor Gas flow meter

1. Level sensor

2. Temperature sensor

3. pH sensor

1 2 3

DQA

Fig. 4. The scheme of complete experimental setup

2.4 Start-up and operation of the reactor

The experiment consists of 4 parts: start-up period, data collection for design of the

observer and optimizer, modification of observer and optimizer separately, and

validation of complete real-time optimization strategy.

The reactor was started in batch mode with a glucose concentration of 15g/l, in order to

active and accumulate the hydrogen-producing anaerobes. The reactor was run for five

cycles, with each cycle lasting 12 hours. In every cycle, 600ml of liquid in the reactor

was extracted after stopping the stirrer for 30 min. The purpose of settling was to

prevent wash out of the biomass inside the reactor. New feedstock consisting of 13.5g

glucose (the reactor’s working volume is 0.9 L) dissolved with other mineral salts in

600 ml tap water and was prepared for every cycle. During this period, the level control

was stopped while the pH control and temperature control were functional. After the

physical transformation of suspended solids from black powder to white suspended

matter, an acceptable hydrogen yield (about 2 mol H2/mol Glu) indicated the vigorous

biological activities of hydrogen producers. The reactor was then switched to

continuous mode with a hydraulic retention time (HRT) of 12h and a glucose

18

concentration of 15g/l. The level control was switched on to maintain the amount of

liquid in the reactor at 0.9 L.

As long as a stable performance was observed, the reactor was ready to collected data

for the design of the observer and optimizer. The conditions (substrate concentration

and HRT) were randomly chosen by Latin hypercube sampling (LHS), a statistical

method for creating a combination of parameter values from a multidimensional

distribution. This method ensures the ensemble of random samples that are

representative of the real variability. The experimental conditions are shown in Table 1,

indicating the absence of extreme conditions (i.e., an HRT shorter than 4 hours and

glucose concentration higher than 30g/l). A short HRT will lead to wash out of biomass

and a high concentration of substrate will result in inhibition of the biological activity,

resulting in the failure of the bio-reactor. The number of days of operation depended on

the results from off- line analyses. In order to better design the observer and

optimization strategy, the COD balance between reactor input (glucose) and reactor

output (biogas, VFAs, glucose in the effluent and biomass) should be between 85% and

115%. Moreover, all the conditions should be in steady state. During the interval

between the two experimental conditions, the standard condition (glucose concentration

15g/l, HRT 8h) was established to avoid the influence of the previous condition. This

standard condition was selected as the medium level OLR among all the conditions. The

standard condition was applied between all experimental conditions, except for the

fourth to fifth one because of the large difference between the OLRs. Thus, there may

have been a minimal effect of fourth one on the fifth one.

Conditions Date Glucose

(g/l)

HRT

(h)

OLR

(g COD/l-d)

I 03/18/2014 15 8 48.0

II 04/01/2014 10 10 25.6

III 04/21/2014 20 8 64.0

IV 05/02/2014 15 10 38.4

V 05/9/2014 25 6 106.7

VI 05/21/2014 20 6 85.4

Table 1. Operational conditions for data collection relative to optimizing the strategy

design.

19

After completing the process of data collection, the observer and optimization strategies

were tested and modified separately in the conditions shown in Table 2. Different

glucose concentrations in the reactor input were used to evaluate whether the observer

was capable of estimating them correctly. The purpose of testing the optimization was

to prove whether the reactor can deliver the maximum hydrogen production without

taking into account the estimation from observer. (The inflow rate is a controlled

variable and the substrate concentration is an uncontrolled variable.) The minimum

HRT was set at 4 hours due to biological limitations of the reactor. If the HRT is shorter

than 4 hours, it will lead to the biomass washout.

Conditions Date Glucose

(g/l)

HRTmin

(h)

HRTmax

(h)

I 06/04/2014 20 4 12

II 06/11/2014 15 4 12

III 06/17/2014 10 4 12

Table 2. Operation conditions for testing observer and optimization strategy separately

Conditions Date Glucose

(g/l)

HRTmin

(h)

HRTmax

(h)

I 06/24/2014 15 6 12

II 06/27/2014 10 4 12

III 07/01/2014 20 4 12

Table 3. Operation conditions for testing complete optimization strategy

Finally, after tested and modified separately the observer and optimizer, complete

optimization strategy (the optimizer takes into account of input glucose concentrations

estimated by the observer) was applied into the system. The conditions used in this

experiment are presented in Table 3. In the last set of conditions, the HRTmin was

changed to 6 hours at a glucose concentration of 20g/l, because incomplete conversion

of the substrate with a high OLR was observed. The design of the observer assumes that

more than 95% of substrate is consumed. The difference between the design assumption

and the actual situation resulted in an inaccurate estimation of the observer, which

would subsequently affect the performance of optimization strategy. Moreover, a 6h

HRT with a glucose concentration of 20g/l would result in more than a 96%

20

consumption of glucose. Therefore, the HRTmin had to be modified to 6 hours when the

glucose concentration was 20g/l. The other two HRTmin remained at 4 hours in order to

achieve a higher hydrogen production. A shorter HRT is correlated with a higher OLR,

resulting in a higher hydrogen production.

2.5 Off-line measurements and analytical methods

The biogas was sampled by a water displacement method and its composition

(hydrogen, carbon dioxide and methane) was measured by a gas chromatograph (Model

6890N, Agilent Technologies Inc.) equipped with a thermal conductivity detector

(TCD). The injector and detector temperatures were 90°C and 150°C, respectively.

With an initial temperature 40°C (4 min), the column was gradually increased to 110°C

at a rate of 20°C/min. The carrier gas was nitrogen with a flow rate of 20 ml/min. The

volume of sampled biogas was about 10 ml. (Hernandez-Mendoza & Buitron, 2014).

Volatile fatty acids (VFAs), including acetic acid, butyric acid, iso-botyric acid,

propionic acid, ethanol and iso-valeric acid, were determined by a gas chromatograph

(Varian model 3300, Zebron ZB-FFAP column; FID: Flame Ionization Detector).

Temperatures of injector and detector were maintained at 190 °C and 210 °C,

respectively. After the temperature of the Zebron ZB-FFAP column reached 45°C for

1.5 min, it was increased to 130°C at a rate of 8°C/min. Nitrogen was used as ca rrier gas

at a flow rate of 9.5 ml/min. The volatile fatty acid samples were filtered by cellulose

nitrate membrane filters prior to their analysis by GC/FID.

The glucose concentration was measured by the phenol-sulfuric acid method. The

procedures are as follow (Dubois, et al., 1956).

(1) Sample Dilution: The filtered samples from the bio-reactor input and output, were

diluted by a dilution factor (D). The input glucose sample was diluted by 100 times

while the effluent glucose sample was diluted by 2, 5 and 10 times, depending on

the consumption of glucose in the reactor.

(2) Phenol/Sulfuric Method: The sample was created by adding 0.5 ml diluted glucose

sample, 0.5 ml phenol (5%) and 2.5 ml sulfuric acid (97%) to a tube. A blank was

prepared using 0.5ml distilled water instead of the diluted glucose sample. Three

duplicates of the sample were created. After 8 minutes, the samples were measured

by a HACH spectrometer (DR 2800) and the average of three duplicates was noted

as (A).

(3) Calculation:

21

Volatile suspended solids (VSS) were analyzed using standard methods. The procedures

are described as follows (APHA.American Public Health Association/American Water

Works Association/Water Enviroment Federation, 2005).

(1) Preparation of Glass Fiber Filter Disk: The glass microfiber filters (diameter 55mm)

were used for the total solids and volatile suspended solids. The filter was inserted

into the filtration apparatus by applying a vacuum and then washed with 20 ml of

distilled water. The sample was removed from the filtration apparatus and

transferred to an inert aluminum dish. The filter was dried at 103-105 °C for 60

minutes in the oven and then ignited at 550 °C for 15 minutes in the furnace. The

sample was then cooled in desiccators to reduce the temperature and, finally,

weighed. The cycle of drying, igniting, cooling, desiccating and weighing were

repeated until a constant weight was obtained or until the weight change was less

than 4%. The pretreated filters were stored in desiccators and ready for use. The

filter’s weight was measured as W1 (g) by an analytical balance.

(2) Sampling: The sample (volumes of 10 to 20 ml) was taken from the effluent of the

bio-reactor and filtered by the pretreated filters in the filtration apparatus. The filter

was then transferred to the aluminum dish as support. The sample volume was noted

as V (ml).

(3) Total Solids: The sample on the filter was heated at 103-105 °C for 60 minutes in

the oven and then cooled down in the desiccators for 15 minutes. It was then

weighed by the analytical balance and recorded as W2 (g).

(4) Volatile Suspended Solids: The sample was ignited at 550 °C for 15 minutes in a

furnace, cooled in the oven (103-105 °C) for 10 minutes and in desiccators for 15

minutes, weighed in the analytical balance, and recorded as W3 (g).

(5) Calculation:

2.6 Online data collection system

Biogas flow at the bio-reactor output was monitored online by a gas flow meter

ADM2000 (Agilent Technologies Inc.), which was connected to a serial port COM1 of

22

a personal computer (PC). This device was calibrated using a water displacement

method. The hydrogen composition was recorded on- line with an analyzer HY-

OPTIMA model 700 (H2scan, Valencia CA, USA). At the bio-reactor input, a

Masterflex pump (model 77800-50) was used to control the input flow rate. All the

devices were connected to a PC by input and output analog channels of a data

acquisition (DAQ) device NI USB-6008 (National Instruments Inc.).

2.7 Experimental conditions

The experiment was carried out in the pilot laboratory of Laboratory for Research

on Advanced Processes for Water Treatment, Academic Unit Juriquilla, Instituto de

Ingeniería, Universidad Nacional Autónoma de México. The ambient temperature was

about 30°C and the atmosphere pressure was 0.79atm. Thus, all volumes of biogas were

calculated under these conditions.

2.8 Project team

The project was carried out under the instruction of Dr. Germán Buitrón Méndez. The

optimization strategy was designed by Dr. Jesús Ixbalank Torres Zúñiga and practical

reactor operation and analyses were conducted by Axue Zhang and Alberto Villa Leyva.

23

3. RESULTS AND DISCUSSION

3.1 Performance of the reactor during start-up

The results, in the start-up period, including batch mode and CSTR, demonstrated the

activation of the hydrogen producing bacteria and showed a stable and acceptable

performance for the bio-reactor.

The hydrogen producers were activated when the reactor was operated in batch mode

with the glucose concentration at 15g/l. The germination process of the hydrogen-

producing bacterial spores was the key to the start-up period. The pretreated granular

anaerobic sludge gradually changed from black powder to white suspended solid. The

biogas began to be generated and the composition of hydrogen increased. Meanwhile

the volatile fatty acids (ethanol, acetic acid, propionic acid and butyric acid) were tested.

The reason why the reactor started in batch mode was to prevent the wash-out of solids,

which could happen in the CSTR, and to successfully activate the hydrogen producing

spore-forming anaerobes.

After five cycles of batch mode, the reactor operation was moved to the CSTR (glucose

concentration 15g/l, HRT 12h), and the activation process continued. Then the reactor

operation condition changed the conditions of glucose concentration of 15g/l and HRT

8h to prepare for the experimental data collection of optimizing strategy design. The

reactor performance, shown on Table 4, demonstrated a steady state condition for the

biological process. A stable hydrogen production rate (1.97±0.32 lH2/lreactor-d, 2.61±0.42

mmolH2/lreactor-h) and a constant hydrogen composition (59.4±2.84%) were observed.

The concentration of volatile suspended solids and volatile fatty acids did not show a

significant vacillation. The hydrogen yield, 1.97±0.32 mol H2/mol Glu, suggested a

good biological hydrogen producing activity. The ratio between butyric acid and acetic

acid was 2.13±0.36. This ratio served as a relative indicator of the efficiency of the

fermentative process. As explained by Samir (2008), when the butyric acid is the

predominant fermentative by-product, the hydrogen yield is unlikely to exceed 2.5mol

H2/mol Glu. In general, these values indicated a stabilization of the bioreactor and the

establishment of the hydrogen producing culture, because these values are in agreement

with those obtained by Ramirez-Morales et al. (2014), who conducted experiments with

the same set-up and used the inoculums from the same UASB reactor (obtained from a

brewery).

24

Gluin

(g/l)

Gluout

(g/l)

Biogas

flow (ml/min)

H2

(%)

CO2

(%)

HPR

(lH2/lreactor-d)

Qin

(ml/h)

14.78± 1.21

1.74± 0.61

10.10± 1.42

59.4±2.84 40.61±2.84 1.97±0.32 1.9

VFA (g/l)

Ethanol Acetic acid

Propionic acid

Isobutyric acid

Butyric acid

Iso-valeric acid

0.25±0.04 1.41±0.17 0.10±0.02 0 2.69±0.31 0

VSS (g/l) Butyric acid/ Acetic acid H2 yield (mol H2/mol Glu)

1.18±0.13 2.13±0.36 1.97±0.32

Table 4. Reactor performance in CSTR during the start-up period

3.2 Analysis of reactor performance based on data collected for the optimization strategy design

Glu: 15g/l

HRT: 8h

Glu: 10g/l

HRT: 10h

Glu: 20g/l

HRT: 8h

Glu: 15g/l

HRT: 10h

Glu: 25g/l

HRT: 6h

Glu: 20g/l

HRT: 6h

OLR (gCOD/

l-d) 48.02 25.61 64.02 38.41 106.70 85.36

HPR (lH2/

lreactor-d)

10.62±

0.61

5.56±

0.36

15.82±

0.78

8.43±

0.42

21.3±

0.74

22.82±

0.49

H2

(%)

62.82±

2.64

61.94±

4.31

63.43±

1.20

61.51±

1.87

63.10±

1.01

64.10±

1.29

CO2 (%)

37.18±

2.64

38.07±

4.31

36.57±

1.20

38.49±

1.87

36.90±

1.01

35.91±

1.29

CH4

(%) 0 0 0 0 0 0

VSS (g/l) 1.26±0.30 1.44±0.14 1.74±0.04 2.04±0.13 1.84±0.26 1.64±0.09

Ethanol

(g/l)

0.237±

0.060

0.217±

0.036

0.543±

0.208

0.521±

0.219

1.067±

0.298

0.437±

0.137

Acetic acid (g/l)

1.914± 0.422

1.089± 0.127

2.802± 0.341

1.841± 0.235

2.440± 0.426

1.970± 0.288

Propionic

acid (g/l)

0.110±

0.035

0.254±

0.053

0.440±

0.121

0.451±

0.212

0.162±

0.114

0.098±

0.080

Butyric acid (g/l)

3.509± 0.648

3.175± 0.250

5.837± 0.437

3.779± 0.569

4.738± 0.732

4.177± 0.677

Isobutyric

acid (g/l) 0 0 0 0 0 0

Iso-valeric

acid (g/l) 0 0 0 0 0 0

Table 5. Analytical data of different operational conditions

25

After a stable and acceptable performance of the bio-reactor was observed, experimental

data obtained during steady state were about 100% of the COD balance in six different

operational conditions (i.e., various combinations of glucose concentration and HRT).

These data were used to calculate the stoichiometric and kinetic parameters of the

observer and the optimization strategies. The analytical data are shown in Table 5 and

more specifically, in Figures 5-8. The complete data are presented in Annex Table 1 and

Annex Table 2.

It is important to note that in order to make sure the experimental data were

representative, all the six operation conditions were applied independently. In other

words, each condition was not influenced by the operational condition run before it. A

standard condition (Glu: 15g/l, HRT: 8h) was run between each operation conditions to

guarantee there was no carryover. The standard condition was chosen because its OLR

represents the midpoint of the six operational conditions.

15g/l & 8h 10g/l & 10h 20g/l & 8h 15g/l & 10h 25g/l & 6h 20g/l & 6h0

5

10

15

20

25

Operataional Conditions

HP

R (

lH2/l

rea

cto

r -d

)

Fig. 5. Hydrogen production rate (HPR) for different operational conditions in the units

of lH2/lreactor-d.

26

15g/l & 8h 10g/l & 10h 20g/l & 8h 15g/l & 10h 25g/l & 6h 20g/l & 6h0

10

20

30

40

50

60

70

80

H2 CO2 CH4

Operataional Conditions

Gas c

om

po

sit

ion

(%

)

Fig. 6. Biogas composition for different operational conditions expressed as a

percentage of gas composition.

15g/l & 8h 10g/l & 10h 20g/l & 8h 15g/l & 10h 25g/l & 6h 20g/l & 6h0.0

0.5

1.0

1.5

2.0

2.5

Operataional Conditions

VS

S (

g/l)

Fig. 7. Volatile suspended solid (VSS) for different operational conditions in the units

of g/l.

27

15g/l

& 8

h

10g/l

& 1

0h

20g/l

& 8

h

15g/l

& 1

0h

25g/l

& 6

h

20g/l

& 6

h

0

1

2

3

4

5

6

7

8

Acetic acid

Ethanol

Propionic acid

Butyric acid

Operataional Conditions

VF

A (

g/l)

Fig. 8. Volatile fatty acid (VFA) concentration for different operation conditions in the

units of g/l.

There is an upward trend of HPR with an increasing OLR from 5.56±0.36 lH2/lreactor-d

(HPR: 7.37 mmolH2/lreactor-h, OLR: 25.61 gCOD/l-d) to 22.82±0.49 lH2/lreactor-d (HPR:

30.23 mmolH2/lreactor-h, OLR: 85.36 gCOD/l-d). However, when the OLR is 106.7

gCOD/l-d, the HPR is lower than when the OLR is 85.36 gCOD/l-d, which suggests a

maximum HPR with various OLR values. The relationship between OLR and HPR will

be discussed in the following session.

The hydrogen composition in the six operational conditions remained stable,

62.82±2.64%, 61.94±4.31%, 63.43±1.20%, 61.51±1.87%, 63.10±1.01%, 64.10±1.29%

for each of the six operational conditions. No methane was detected during the entire

process. Carbon dioxide was generated as a by-product. In the research work of

Ramirez-Morales et al. (2014), the hydrogen composition was slightly higher at about

70%. One possible reason could be that no propionic acid was produced and, thus, it did

not consume hydrogen and decrease the yield of hydrogen production.

The VSS concentration is influenced by both HRT and the glucose concentration in the

reactor input. However, a clear relationship between OLR and VSS concentration

cannot be defined. The lowest value of VSS, 1.26±0.30 g/l, occurred when the OLR was

48.02 gCOD/l-d (Glu: 15g/l, HRT: 8h). When the OLR was at its lowest value (25.61

28

gCOD/l-d, Glu: 10g/l, HRT: 10h), the VSS concentration was 1.44±0.14 g/l, which was

not the minimum concentration observed. Similarly, the maximum VSS concentration

obtained was 2.04±0.13 g/l, which coincided with a relatively low OLR of 38.41

gCOD/l-d (Glu: 15g/l, HRT: 10h). When the OLR was 106.70 gCOD/l-d (Glu: 25g/l,

HRT: 6h), the VSS was 1.84±0.26 g/l. In summary, the VSS concentration did not vary

dramatically which indicates a stable adaptation of the culture to the medium.

Acetic acid, butyric acid, propionic acid and ethanol were all tested as by-products.

Butyric acid was the dominant by-product and its highest value was 5.837±0.437 g/l

when the OLR was 64.02 gCOD/l-d (Glu: 20g/l, HRT: 8h). The acetic acid, as a

desirable by-product due to its higher hydrogen yield (4mol H2/ mol acetic acid), was

present at lower concentrations than butyric acid. Propionic acid is an undesired by-

product and was detected under all operational conditions. During the HRT of 6h, the

concentrations of propionic acid were relatively low, 0.098±0.080 g/l, which could be

explained by the wash out of propionic acid-producing bacterial. The wash out of

propionic acid-producing bacterial at low HRT (HRT: 6h) was also observed in the

experiment of Ramirez-Morales et al. (2014), which reportedly increased the hydrogen

yield. Ethanol was detected in low concentrations compared to the other volatile fatty

acids. Neither isobutyric acid nor isovaleric acid were detected during the experiments.

Glucose consumption was above 95% under the operational condition of Glu: 15g/l &

HRT: 8h; Glu: 10g/l & HRT: 10h; Glu: 20g/l & HRT: 8h; Glu: 15g/l & HRT: 10h; Glu:

20g/l & HRT: 6h. This consumption data were also required by the observer design

because it was based on a model that considered a glucose consumption of almost 100%.

However, when the glucose concentration was 25g/l and the HRT was 6h, the system

was slightly overloaded, resulting in a relatively low glucose consumption of 77.0% to

86.2%.

3.3 Evaluation of the relationship between OLR and HPR

As is shown in Figure 9, the HPR increases with the OLR until it reaches an optimal

level, where the HPR value is the highest. After the system is overloaded, the HPR

declines with an increasing OLR. Thus, there is an OLR value close to where the

substrate overloading happens that optimizes the hydrogen production rate. This

maximum HPR will be considered as the optimization goal and the corresponding OLR

will be used as control variable, specifically inflow rate in this research study.

29

In order to clearly present the relationship between OLR and HPR, a third-order

polynomial function was interpolated. This was an objective function in the

optimization problem and was calculated by the main and standard deviation of HPR

and by the OLR of the six operational conditions. As shown in Equation 10, the third-

order polynomial was chosen because it was the simplest one to fit the curve. The

resulting plotted curve was also compared with those of other studies. ((Ramirez-

Morales, et al., 2014; Hafez, et al., 2010; Shen, et al., 2009; Luo, et al., 2008)

(10)

0 10 20 30 40 50 60 70 80 90 100 110 120 1300

5

10

15

20

25

y= -4.53e-5x3+0.006437x2+0.02904x+0.2961

R2=0.9889

OLR (gCOD/l-d)

HP

R (

lH2/l

rea

cto

r -d

)

Fig. 9. The relationship between HPR and OLR, a third-order polynomial, in the units

of gCOD/l-d and lH2/lreactor-d

3.4 Validation of the observer and the optimization strategy

The observer and the optimization strategy were tested separately after completing the ir

design using the experimental data collected. It was important to evaluate whether the

observer was able to deliver the correct concentration of glucose to the reactor before

combining the observer and optimization strategy (meaning that the optimization

strategy utilized the concentration of glucose that was estimated by the observer).

Therefore, the adjustment of observer gain and stability region in the mathematical

model and the operation of it were necessary. A comparison of glucose concentrations

in the reactor input and the reactor, as well as VSS in the reactor between the online

estimation of the observer and the off- line analysis, were recorded to assess the observer

performance. In addition, the optimization strategy was tested during the same time.

The concentrations of glucose at the reactor input (20g/l, 15g/l and 10g/l) were set in the

30

model. The maximum hydrogen production rate, about 22 lH2/lreactor-d(HPR: 29.15

mmolH2/lreactor-h), was the optimization target. The gas composition was also recorded

online in order to monitor the reactor performance.

3.4.1 Validation of observer

To validate the observer, the online-recorded glucose concentrations at the reactor input

and the glucose and VSS concentrations in the reactor according to the observer were

compared with the off- line analyses.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170

5

10

15

20

25

30

Gluin online observer (g/L)

Gluin off-line analysis (g/L)

Time (d)

Glu

co

se

co

nce

ntr

ati

on

(g

/l)

Fig. 10. A comparison of glucose concentration at the reactor input from the observer

estimation and the off- line analyses in units of g/l.

Figure 10 shows a comparison of the input glucose concentrations estimated by

observer with the off- line analyses. When the input glucose concentrations were about

10g/l and about 20g/l, the observer’s estimates were close to the results of the off- line

analyses. The mean absolute differences of glucose concentrations about 10g/l and

about 20g/l were 1.11g/l and 1.08g/l, respectively (The mean was calculated by

eliminating the first comparison pair.). There was an initial lag period (25.7h from the

initial set-up at a concentration of 0 to 20.5 g/l) required for the observer to detect the

actual input glucose concentration. A shorter lag time was expected when the real input

glucose concentration was lower than 20g/l and the initial set-up value was closer to the

real one. On the sixth day, there was a dramatic drop in the glucose input that was

recorded online because the Matlab was stopped and restarted due to a power outage in

the laboratory. The initial input glucose concentration was set as 15g/l when the

experiment was restarted; however, the observer was not able to accurately estimate the

input glucose concentration of 15g/l. The estimated values were lower than the real

31

concentration and the average difference was as much as 5.45g/l. Thus, the operating

point was adjusted in order to achieve more accurate estimations. The operating point is

the point around which the linearized model is equivalent to the original non- linear

model.

A comparison of VSS concentrations in the reactor between observer estimations and

off- line analyses is presented in Figure 11. A similar trend of input glucose

concentrations can be observed for the two measurements. When the concentrations of

glucose at the reactor input were about 20g/l and 10g/l, the estimations were considered

to be accurate. The absolute average differences were 0.17g/l and 0.37g/l respectively.

The VSS concentration of observer estimation was significantly lower than that of the

off- line analysis when the concentration of glucose input was about 15g/l. The average

difference was 0.74g/l. It is logical to conclude that the inaccurate estimates of input

glucose concentration were the result of inaccurate determinations of VSS

concentrations in the reactor because the glucose concentration at the reactor input was

estimated by Super-Twisting observer on the basis of VSS and glucose concentrations

measured in the reactor by a Luenberger observer.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170.0

0.5

1.0

1.5

2.0

2.5

VSS online observer

VSS off-line analysis

Time (d)

VS

S (

g/l)

Fig. 11. The comparison of VSS concentrations in the reactor between observer

estimation and off- line analyses in units of g/l.

The off- line glucose concentration analysis in the reactor (identical to the reactor output)

is plotted along with the online record of the observer on Figure 12. It is necessary to

note that a substrate consumption of almost 100% was assumed by the Luenberger

observer model. In the beginning when the system was overloaded (Glu in : 20g/l, HRT:

5.57h, OLR: 91.95 gCOD/l-d), the Luenberger observer failed to estimate the correct

value of glucose concentration in the reactor. Therefore, the HRTmin was changed after

32

the second day from 4h to 6h to prevent an overload of substrate (Gluin : 20g/l, HRT: 6h,

OLR: 85.36 gCOD/l-d). Subsequently, accurate estimates of outflow glucose

concentrations were produced by the Luenberger observer. For input glucose

concentrations of 15g/l and 10g/l, the HRTmin remained set to 4h for purposes of

maintaining a higher OLR and achieving the maximum HPR.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170.0

0.5

1.0

1.5

2.0

2.5

Gluout online observer (g/L)

Gluout off-line analysis (g/L)

Time (d)

Glu

co

se c

on

cen

trati

on

(g

/l)

Fig. 12 The comparison of glucose concentrations in the reactor between observer

estimation and off- line analyses, expressed in units of g/l.

3.4.2 Validation of optimization strategy

According to the third-order polynomial, the maximum HPR was about 22 lH2/lreactor-d

(HPR: 29.15 mmolH2/lreactor-h) when the OLR was about 96 gCOD/l-d. The exact values

of the maximum HPR and the corresponding OLR values were not fixed because the

polynomial was updated in Matlab during the entire experimental period. Thus, the goal

of optimization was to keep the HPR at the maximum; however, this optimization goal

could be realistically achieved only if the substrate concentration was relatively high.

The reason is that the minimum HRT had to be set at 4h to prevent the wash-out of

biomass. When the OLR was 96 gCOD/l-d and HRT was 4h, the corresponding input

glucose concentration was 15.00 g/l. Therefore, for any input glucose concentrations

lower than 15.00 g/l, the minimum HRT 4h would be set by the optimizer to maintain a

higher OLR. For any input glucose concentrations higher than 15.00 g/l, the HRT was

expected to be adjusted higher than the minimum by the optimizer in order to maintain

the optimal OLR. This was also the reason why three glucose concentrations (20g/l,

15g/l and 10g/l) were chosen to test the optimization strategy. However, during the

33

experiment, we found that under conditions of 20 g/l glucose and 5.57h HRT, the

biological system was overloaded. The reactor could not consume all the glucose,

leading, to an inaccurate estimation of the glucose concentration in the reactor output by

Luenberger observer. The overload corresponding to a glucose concentration of 20g/l

and HRT of 4h was observed in the research of Ramirez-Morales et al. (2014) as well.

As a result, a conservative value of input glucose concentration, 17 g/l, was selected

when changing the minimum HRT from 4h to 6h.

I concluded that the overloading system is not only affected by the value of OLR, but

also by the substrate concentration and HRT. The OLR of 91.95 gCOD/l-d, at a glucose

concentration of 20 g/l and a HRT of 5.57h, is lower than the OLR of 96.03 gCOD/l-d,

when the glucose concentration was 15 g/l and HRT was 4 g/l. The former condition

caused an overload of the system while the latter one did not because the bacteria could

consume almost all the input glucose.

The experimental results of HPR, HRT and the input glucose concentrations (based on

the online recorded values and off- line analyses of COD) are shown in Figure 13. The

results match the expectations based on the prior testing of the optimizer. When the

OLR was around 91.95 gCOD/l (Glu: 20 g/l, HRT: 5.57h), the HPR was 16.04±1.82

lH2/lreactor-d(21.25±2.41 mmolH2/lreactor-h). After changing the minimum HRT to 6h (OLR:

85.36 gCOD/l-d, Glu: 20g/l), the HPR was 19.22±1.23 lH2/lreactor-d (15.46±1.63

mmolH2/lreactor-h), which was higher due to the more efficient consumption of the

substrate. There were two episodes of increasing HPR in the period from day 0 to day 1

and again during day 2. A possible explanation may be the unsteady (non-steady) state

of the bio-process. The HPR increased to 20.05±3.29 lH2/lreactor-d (26.56±4.36

mmolH2/lreactor-h) after changing the input glucose concentration to about 15g/l. The

minimum HRT was subsequently set back to 4h The HPR reached a higher value but

the variation was also larger. The HPR decreased when the input glucose concentration

decreased to about 10g/l. The HPR at this time was 17.65±1.90 lH2/lreactor-d (23.38±2.52

mmolH2/lreactor-h). These values also fitted in the third-order polynomial. To conclude,

the optimizer was able to maintain the optimal OLR by adjusting the inflow rate, and

thus achieving the maximum HPR with different glucose concentrations in the reactor

input.

34

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170

5

10

15

20

25

30

0

5

10

15

20

25

30

35

40

45

50

HRT

HPR (lH2/l-d)

CODin (gCOD/l) Observer

Gluin off-line analysis (gCOD/l)

Time (d)

Hyd

rog

en

pro

du

cti

on

rate

(lH

2/l

rea

cto

r-d

)

Hyd

rau

lic r

ete

nti

on

tim

e (

h)

Glu

co

se c

on

cen

tratio

n (g

CO

D/l)

Fig. 13. Hydrogen production rate (HPR), in the unit of lH2/lreactor-d; Input glucose

concentration of online observer estimation and off- line analysis, in the unit of gCOD/l;

Hydraulic retention time (HRT), in the unit of h

The biogas composition was monitored online during the entire process, as it is shown

in Figure 14. The hydrogen percentage attained during the experiment was

64.43±2.06%, which is an acceptable performance and indicates the stability of the bio-

reactor. No methane was detected.

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 170

10

20

30

40

50

60

70

80

H2 (%)

CO2 (%)

CH4 (%)

Time (d)

Bio

gas c

om

po

sit

ion

(%

)

Fig. 14. Percent biogas composition during the period of evaluating the observer and the

optimization strategy separately

3.5 Complete optimization strategy performance

The complete optimization strategy was applied to the bio-reactor after testing the

observer and the optimizer separately. The HPR, along with the HRT and the input

35

glucose concentrations, are shown on Figure 15. When the input glucose concentration

was about 15g/l during the first two days, the HPR was 22.57±1.73 lH2/lreactor-d

(29.90±2.29 mmolH2/lreactor-h). In response, the HRT was set by the controller as 4h and

the observer was able to accurately estimate the glucose concentration in the reactor

input. The absolute average difference between the measured and estimated glucose

input concentrations was 1.68g/l, which was much lower than that recorded when the

observer was tested individually (5.45g/l).

When the input glucose concentration was changed to 10g/l at day 2, there was a

decrease which could be observed in the HPR on days 2 and 3. This was the process of

establishing the new steady state for the bio-reactor. From day 3 to day 7, when the

input glucose concentration was about 10g/l, the HPR was 14.99±1.85 lH2/lreactor-d

(19.86±2.45 mmolH2/lreactor-h). The HRT was still set as the minimum 4h; however, the

estimations of input glucose concentration were not as accurate as when the observer

was tested individually under the glucose concentration of 10g/l. The absolute average

difference was 4.45 g/l. This is high compared to the 1.08g/l value when the observer

and controller were not combined. The observer gain and stability region in the

mathematic model needed to be adjusted in order to achieve more accurate estimations.

Hence, the online record was stopped and restarted on the day 3. These incorrect

estimates did not influence the hydrogen production because for all concentrations

lower than 15g/l, the HRT was set as 4h by optimizer.

The input glucose concentration of 15g/l was used as a transitive concentration before it

was changed to 20g/l on day 7 to prevent a shock to the biological process. The HPR

increased with the higher input glucose concentration, while the HRT was maintained as

4h. On the day 8, the input glucose concentration was changed to about 20g/l. The

longer HRT was adjusted by the optimizer until it received an input glucose

concentration higher than 17g/l from the observer. In addition, the HPR was lower due

to the lower OLR as a result of a longer HRT. The estimations of the input glucose

concentration (24.36±2.27g/l) by the observer under 20 g/l were less stable than under

the previous concentrations. As a consequence, The HRT was also changed every time

the optimizer received new data regarding input glucose concentrations from the

observer. It should be noted that, due to the lag phase of observer, the minimum HRT

was set manually as 6h from day 8 to day 10. The HPR was 15.41±0.99 lH2/lreactor-d

(20.42±1.31 mmolH2/lreactor-h) during these two days. The comparison of input glucose

concentrations between the online record from the observer and off- line analyses is

36

presented in Figure 16.

0 1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30

0

5

10

15

20

25

30

35

40

HPR (LH2/L-d)

HRT (h)

Gluin (gCOD/l) Observer

Gluin off-line analysis (gCOD/l)

Time (d)

Hyd

rog

en

pro

du

cti

on

rate

(lH

2/l

rea

cto

r-d

)

Hyd

rau

lic r

ete

nti

on

tim

e (

h)

Glu

co

se c

on

cen

tratio

n (g

CO

D/l)

Fig. 15. Hydrogen production rate (HPR), in the unit of lH2/lreactor-d; Input glucose

concentration of online observer estimation and off- line analysis, in the unit of gCOD/l;

Hydraulic retention time (HRT), in the unit of h during complete optimization strategy

validation

0 1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30

Gluin online observer (g/L)

Gluin off-line analysis (g/L)

Time (d)

Glu

co

se c

on

cen

trati

on

(g

/l)

Fig. 16 A comparison of glucose concentrations (g/l) at the reactor input between

observer estimation and off- line analysis of complete optimization strategy application

A comparison of glucose concentration at the reactor output between estimations by the

observer and off- line analysis is shown on Figure 17. The estimated concentrations were

generally higher than the measured ones. The absolute average difference was 0.29g/l.

A comparison of VSS between off- line measurement and the online record, as shown on

Figure 18, indicates the same trend as the data for input glucose concentrations. This

demonstrates that incorrect estimates of the input glucose concentrations were the result

37

of erroneous values of VSS from Luenberger observer. An error in the output flow rate

was discovered on day 9 and is the main reason why there are inaccurate estimates of

VSS in the bio-reactor. The units of l/d for output biogas flow were considered in the

observer design model; however, the values in the units of ml/min were used when

designing the observer model. The values of biogas flow in ml/min were lower than

those in l/d so that all estimates of VSS were lower than the real values from the off- line

measurements.

0 1 2 3 4 5 6 7 8 9 100.0

0.5

1.0

1.5

Gluout online observer (g/L)

Gluout off-line analysis (g/L)

Time (d)

Glu

co

se c

on

cen

trati

on

(g

/l)

Fig. 17. A comparison of glucose concentrations (g/l) in the reactor between observer

estimation and off- line analysis of complete optimization strategy application.

0 1 2 3 4 5 6 7 8 9 100

1

2

3

VSS online observer

VSS off-line analysis

Time (d)

VS

S (

g/l)

Fig. 18 A comparison of VSS concentrations (g/l) in the reactor between observer

estimation and off- line analysis of complete optimization strategy application

In order to define and correct this mistake of the unit of the biogas outflow, the

mathematical model of the observer was simulated using the correct unit (l/d). The

process simulation method was described in the previous research (Zuniga, et al., 2013).

The results are shown on Figures 19-21. The estimations of input glucose

38

concentrations by the observer were comparable with the ones from off- line analyses. In

addition, the estimations of glucose concentration and VSS concentration in the reactor

were accurate by process simulation. Therefore, the problem of the inaccurate

estimations by the observer was defined and solved.

0 1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30

Gluin online observer (g/L)

Gluin off-line analysis (g/L)

Time (d)

Glu

co

se c

on

cen

trati

on

(g

/l)

Fig. 19. A comparison of glucose concentrations (g/l) at the reactor input between

observer estimation by process simulation and off- line analysis

0 1 2 3 4 5 6 7 8 9 100

1

2

3 VSS online observer

VSS off-line analysis

Time (d)

VS

S (

g/l)

Fig. 20. A comparison of VSS concentrations (g/l) in the reactor between observer

estimation by process simulation and off- line analysis

39

0 1 2 3 4 5 6 7 8 9 100.0

0.5

1.0

1.5

Gluout off-line analysis (g/L)

Gluout off-line analysis (g/L)

Time (d)

Glu

co

se c

on

cen

trati

on

(g

/l)

Fig. 21. A comparison of glucose concentrations (g/l) in the reactor between observer

estimation by process simulation and off- line analysis

After correcting the mistake, the estimates of input glucose concentrations approached

the values of the measured ones. However, this change in the mathematical model lead

to the new errors in the function required to solve the Ordinary Differential Equation

governing the Super-Twisting observer. Therefore, from day 9 to day 10, the Matlab

program was stopped automatically, as can be seen on Figure 16. These errors were

caused by the discontinuous sign function. Thus, a continuous hyperbolic tangent

function was substituted because it is functionally comparable. Therefore, the problem

of discontinuous operation of the program of the observer was fixed.

The biogas concentration during this period is shown on Figure 21. The hydrogen was

measured at 63.74±2.11%, indicating that a stable metabolism pathway was established

by the hydrogen-producing bacteria throughout the experimental period.

0 1 2 3 4 5 6 7 8 9 100

10

20

30

40

50

60

70

80

H2 (%)

CO2 (%)

CH4 (%)

Time (d)

Bio

gas c

om

po

sit

ion

(%

)

Fig. 21. Percent biogas composition during the period of complete optimization strategy

application.

40

4. CONCLUSION

In this study, a simple real-time optimization strategy combined with an observer (i.e., a

Luenberger observer coupled to a Super-Twisting one) was applied to a CSTR reactor

to improve the bio-hydrogen production from glucose. The research consisted of four

experimental periods or phases: a start-up period, a data collection phase to design the

observer and optimizer, a modification phase to test the observer and the optimizer

separately, and a validation phase to complete the real-time optimization strategy.

In the start-up period, a stable hydrogen production rate (1.97±0.32 lH2/lreactor-d,

2.61±0.42 mmolH2/lreactor-h) and a constant hydrogen composition (59.4±2.84%) were

obtained. In addition, a sufficient biological hydrogen producing activity was

demonstrated by a hydrogen yield of 1.97±0.32 mol H2/mol Glu. The concentration of

volatile suspended solids and volatile fatty acids did not show a significant vacillation.

The observer and optimizer were designed from experimental data collected under six

different operational conditions (Glu: 15g/l & HRT: 8h, Glu: 10g/l & HRT: 10h, Glu:

20g/l & HRT: 8h, Glu: 15g/l & HRT: 10h, Glu: 25g/l & HRT: 6h, Glu: 20g/l & HRT:

6h). Most importantly, a third-order polynomial was established to describe the

relationship between HPR and OLR from these experimental data. The optimization

goal was developed according to the polynomial: the maximum HPR was about 22

lH2/lreactor-d (HPR: 29.15 mmolH2/lreactor-h) when the OLR was about 96 gCOD/l-d.

When the observer was tested individually, the accurate estimation of input glucose

concentrations was observed about 10g/l and about 20g/l. There was a significant

difference between estimates of input glucose concentration and off- line analyses at

concentrations of about 15g/l. The mathmatical model of the observer was modified in

order to achieve a more accurate estimate. The estimates of glucose concentrations in

the reactor output were comparable to those of off- line measurements made when the

glucose consumption was almost 100%. The optimizer was able to improve the

hydrogen production by adjusting the inflow rate. The maximum HPR, 20.05±3.29

lH2/lreactor-d (26.56±4.36 mmolH2/lreactor-h), was obtained when input glucose

concentrations were approximately 15g/l and HRT was 4h.

After combining the observer and optimizer, the maximum HPR of 22.57±1.73

lH2/lreactor-d (29.90±2.29 mmolH2/lreactor-h) was obtained when the glucose concentration

at the reactor input was about 15g/l (the observer produced an accurate estimate at this

point in time). When the input glucose concentration was about 10g/l, the HPR was

41

14.99±1.85 lH2/lreactor-d (19.86±2.45 mmolH2/lreactor-h). Estimates of the input glucose

concentrations were lower than the measurements according to the off- line analyses.

These incorrect estimates did not affect the hydrogen production because for all the

concentrations lower than 15g/l, the HRT was reset at 4h by optimizer. A mistake in the

unit for outflow biogas volumes in the observer design was detected and corrected in

the last operational condition of the experiment (Glu: 20g/l). The observer was able to

estimate the input glucose concentration accurately and the HPR was 15.41±0.99

lH2/lreactor-d (20.42±1.31 mmolH2/lreactor-h) at this time.

In future studies, I suggested that, due to the initial lag phase of the observer, the

observer should be started before it delivers the results of estimated glucose input

concentrations to the optimizer. The period of the lag phase is about 24h, which was

observed in this study. Furthermore, the extreme operational conditions (Glu >25g/l)

should be tested in order to better assess the bio-reactor’s behavior. These extreme

operational conditions were not selected during this study because it may have caused

the collapse of the bio-reactor. Finally, utilizing of waste stream, instead of glucose, and

estimating the COD concentration of the substrate (waste stream) by the observer

should be considered in future research.

42

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46

6. ANNEX

Annex Table 1. Glucose concentration in the reactor input, in the unit of g/l; input flow

rate, in the unit of ml/min; organic loading rate (OLR), in the unit of g/l; glucose

concentration in the reactor, in the unit of g/l; and concentration of ethanol, acetic acid

and propionic acid in the reactor, in the unit of g/l during six operational conditions

Conditi

on

Date Day

(d)

Gluin

(g/l)

Qin (ml/

min)

OLR

(g/l)

Gluout

(g/l)

Ethanol

(g/l)

Acetic acid

(g/l)

Propionic

acid (g/l)

1 2014/3/27 0.5 12.78 1.90 41.44 2.29 0.218 1.461 0.083

1 2014/3/27 1.0 11.53 1.90 37.41 0.63 0.268 1.561 0.088

1 2014/3/28 1.5 14.80 1.80 45.48 2.08 0.158 1.61 0.096

1 2014/3/28 2.0 16.60 1.80 51.01 2.31 0.184 2.091 0.114

1 2014/3/31 5.0 14.16 1.90 45.92 0.05 0.283 2.334 0.17

2 2014/4/3 8.0 10.46 1.50 26.77 0.03 0.181 1.105 0.213

2 2014/4/3 8.5 10.32 1.50 26.41 0.04 0.202 0.898 0.192

2 2014/4/4 9.0 10.38 1.50 26.57 0.09 0.208 1.108 0.218

2 2014/4/8 13.0 10.32 1.50 26.44 0.05 0.22 1.047 0.291

2 2014/4/8 13.5 10.54 1.50 26.99 0.06 0.207 1.081 0.324

2 2014/4/9 14.0 9.91 1.50 25.39 0.04 0.285 1.293 0.283

1 2014/4/21 26.0 15.06 1.80 46.28 0.07 0.311 2.426 0.813

3 2014/4/25 30.0 20.94 1.80 64.33 0.31 0.131 2.604 0.405

3 2014/4/28 33.0 20.58 1.90 66.75 0.43 0.297 3.137 0.658

3 2014/4/28 33.5 20.58 1.90 66.75 0.51 0.274 3.465 0.388

3 2014/4/30 35.0 20.21 1.90 66.55 0.26 0.779 2.746 0.383

3 2014/4/30 35.3 20.21 1.90 66.55 0.26 0.715 2.803 0.553

3 2014/4/30 35.7 20.01 1.90 64.91 0.22 0.572 2.656 0.496

3 2014/5/1 37.0 20.28 1.90 65.78 0.22 0.720 2.407 0.283

3 2014/5/1 37.5 20.79 1.90 67.43 0.21 0.447 2.597 0.352

4 2014/5/5 41.0 15.91 1.60 43.45 0.1 0.346 1.723 0.725

4 2014/5/5 41.5 14.96 1.60 40.85 0.16 0.323 1.419 0.445

4 2014/5/6 42.0 16.63 1.40 39.74 0.08 0.433 1.778 0.693

4 2014/5/6 42.5 15.60 1.40 37.27 0.09 0.355 2.045 0.745

4 2014/5/7 43.0 15.75 1.50 40.32 0.07 0.339 2.042 0.583

4 2014/5/7 43.5 15.70 1.50 40.21 0.06 0.238 1.683 0.493

4 2014/5/8 44.0 16.72 1.40 39.95 0.06 0.730 2.049 0.275

4 2014/5/8 44.3 16.41 1.40 39.23 0.08 0.704 1.794 0.198

4 2014/5/8 44.7 16.54 1.40 39.53 0.09 0.800 2.264 0.351

4 2014/5/9 45.0 16.49 1.40 39.4 0.07 0.672 1.735 0.208

4 2014/5/9 45.5 15.26 1.40 36.47 0.08 0.808 1.724 0.239

5 2014/5/12 48.0 22.30 2.46 93.7 3.15 1.278 2.107 0.109

5 2014/5/12 48.5 22.57 2.46 94.8 3.12 1.264 2.637 0.223

47

5 2014/5/13 49.0 23.16 2.46 97.2 4.62 1.106 2.446 0.207

5 2014/5/13 49.5 23.31 2.46 97.9 4.90 1.359 2.103 0.069

5 2014/5/14 50.5 23.16 2.46 97.3 5.19 1.134 3.203 0.207

5 2014/5/16 51.5 23.57 2.46 99.0 4.21 1.183 2.808 0.377

5 2014/5/20 55.0 24.18 2.46 101.5 4.76 0.653 1.921 0.027

5 2014/5/20 55.5 23.24 2.46 97.6 4.51 0.556 2.296 0.078

6 2014/5/26 61 19.73 2.60 87.6 0.31 0.656 2.030 0.124

6 2014/5/26 61.5 20.85 2.60 92.6 0.65 0.358 1.724 0.008

6 2014/5/27 62 19.14 2.60 84.9 0.27 0.459 2.505 0.098

6 2014/5/27 62.5 19.08 2.60 84.7 0.19 0.489 1.978 0.000

6 2014/5/28 63 18.75 2.60 83.2 0.32 0.246 1.820 0.188

6 2014/5/28 63.5 18.52 2.60 82.2 0.38 0.415 1.763 0.170

Annex Table 2. Concentration of Isobutyric acid, butyric acid, iso-valeric acid, and

volatile suspended solid (VSS) in the reactor, in the unit of g/l; percent biogas

composition; biogas out flow rate, in the unit of ml/h; and the chemical oxygen demand

(COD) balance during six operational conditions

Date Isobutyric acid

(g/l)

Butyric acid

(g/l)

Iso-valeric

acid (g/l)

VSS

(g/l)

CO2

(%)

H2

(%)

qCO2

(ml/h)

qH2

(ml/h)

COD balanc

e (%)

2014/3/27 0.052 2.633 0.041 0.936 36.32 63.68 222.28 389.72 97.74

2014/3/27 0.058 3.113 0.039 0.936 39.19 60.81 246.87 383.13 102.42

2014/3/28 0.007 3.292 0.027 1.241 35.64 64.36 228.18 412.02 94.7

2014/3/28 0.004 4.268 0.022 1.241 41.57 58.43 259.39 364.61 97.33

2014/3/31 0.101 3.536 0.142 1.521 34.72 65.28 222.89 419.11 99.89

2014/4/3 0.012 3.371 0.024 1.436 42.23 57.77 139.37 190.63 104.58

2014/4/3 0.008 2.976 0.017 1.436 41.43 58.57 138.22 195.37 97.78

2014/4/4 0.019 3.331 0.001 1.712 40.56 59.44 146.00 214 110.76

2014/4/8 0.065 3.167 0.071 1.365 32.71 67.29 107.95 222.05 107.04

2014/4/8 0.058 3.418 0.124 1.365 32.71 67.29 107.95 222.05 110.37

2014/4/9 0.056 2.786 0.077 1.312 38.75 61.25 130.21 205.79 106.57

2014/4/21 0.039 4.209 0.115 1.668 35.62 64.38 232.95 421 107.99

2014/4/25 0.000 5.683 0.000 1.756 39.28 60.72 348.81 539.19 91.29

2014/4/28 0.000 6.682 0.000 1.757 35.84 64.16 322.58 577.42 107.91

2014/4/28 0.000 5.448 0.000 1.758 35.84 64.16 318.28 569.72 92.48

2014/4/30 0.000 6.224 0.000 1.694 36.00 64.00 343.44 610.56 106.53

2014/4/30 0.000 5.896 0.000 1.694 36.00 64.00 349.92 622.08 104.96

2014/4/30 0.000 5.823 0.000 1.694 36.00 64.00 352.08 625.92 103.08

2014/5/1 0.000 5.361 0.000 1.795 36.63 63.70 344.12 603.88 96.19

2014/5/1 0.000 5.575 0.000 1.795 37.30 62.70 355.84 598.16 94.2

2014/5/5 0.000 3.092 0.000 2.100 40.16 59.84 195.17 290.83 85.61

48

2014/5/5 0.000 3.151 0.000 2.100 40.16 59.84 197.58 294.42 87.31

2014/5/6 0.000 3.444 0.000 2.239 36.37 63.63 187.67 328.33 91.17

2014/5/6 0.000 3.513 0.000 2.239 37.39 62.61 192.91 323.09 98.95

2014/5/7 0.000 3.811 0.000 1.860 38.47 61.53 203.11 324.89 95.22

2014/5/7 0.000 3.629 0.000 1.860 37.52 62.48 184.62 307.38 88.30

2014/5/8 0.000 4.461 0.000 1.950 34.91 65.09 178.03 331.97 100.50

2014/5/8 0.000 3.740 0.000 1.950 38.74 61.26 213.87 338.13 92.67

2014/5/8 0.000 5.073 0.000 1.950 39.98 60.06 213.49 320.51 109.65

2014/5/9 0.000 3.740 0.000 2.088 38.47 61.53 198.53 317.47 91.62

2014/5/9 0.000 3.911 0.000 2.088 41.24 58.76 210.34 299.66 101.98

2014/5/12 0.000 4.012 0.000 1.538 38.00 62.00 467.34 762.66 90.87

2014/5/12 0.000 5.118 0.000 1.538 37.31 62.69 472.36 793.64 101.58

2014/5/13 0.000 4.949 0.000 1.811 35.71 64.29 471.43 848.57 104.59

2014/5/13 0.000 3.594 0.000 1.811 38.42 61.58 514.11 823.89 94.50

2014/5/14 0.000 4.669 0.000 1.650 37.29 62.71 476.62 801.38 103.93

2014/5/16 0.000 5.809 0.000 2.055 35.92 64.08 431.03 768.97 110.00

2014/5/20 0.000 4.357 0.000 2.150 36.43 63.57 459.03 800.97 90.67

2014/5/20 0.000 5.395 0.000 2.150 36.10 63.90 446.20 789.80 101.72

2014/5/26 0.000 4.105 0.000 1.745 34.30 65.70 448.64 859.36 85.21

2014/5/26 0.000 3.723 0.000 1.745 35.60 64.40 484.87 877.13 74.41

2014/5/27 0.000 5.446 0.000 1.560 35.10 64.90 473.85 876.15 98.94

2014/5/27 0.000 4.366 0.000 1.560 36.90 63.10 493.72 844.28 85.25

2014/5/28 0.000 3.708 0.000 1.600 35.63 64.37 461.77 834.23 79.55

2014/5/28 0.000 3.713 0.000 1.600 37.90 62.10 513.92 842.08 82.49