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Industrial Bioprocessing and Bioremediation 2014 ! Welcome to Industrial Bioprocessing and Bioremediation 2014 ! (Environmental Biotechnology) Unit coordinator background: http://profiles.murdoch.edu.au/myprofile/ralf-cord-ruwisch/ This unit is different to your other biotechnology units as it focusses on the TECHNOLOGY part (engineering). This requires being able to analyse processes, solve problems, predict outcomes, carry out mass balances, etc.

Industrial Bioprocessing and Bioremediation 2014 ! Welcome to Industrial Bioprocessing and Bioremediation 2014 ! (Environmental Biotechnology) Example processes: How to: make renewable biogas from organic wastes. remove polluting nutrients from wastewater breed microalgae for food or energy production make beer, yoghurt, mine ores by using bacteria (bioleaching) understand microbial processes in ocean, soil and bioreactors

Fundamentals taught Bioprocesses convert S to P How much S to P? Mass balance How much hydrogen gas can we make as fuel from fermenting sugars ? How much oxygen is needed for respiration? How much electricity can be formed from sugar ?

Lecture content over the next weeks Day Time Venue Topic Lecturer 1 Mon Jul 21 9.30-10.30 AMEN 2.023 Introduction, Diffusion, Bioreactor   RCR 10.30-11.30 LB 3.032 Tues Jul 22 8.30-9.30 1.2 Oxygen solubility and transfer Wed Jul 23 12.30-1.30 1.3 Oxygen mass transfer coefficient kLa 2 Jul 28 2.1 Microbial oxygen uptake 10.30-11-30 Help with BioProSim1 Jul 29 2.2 Oxygen steady state calculations Jul 30 2.3 Online OUR monitoring 3 Aug 4 3.1 Fundamentals of microbial growth Aug 5 3.2 Microbial competition for substrate Aug 6 3.3 Four growth constants determine growth

Lab schedule over the next weeks Venue Topic Time 1 LB 3.032 Small Comp Lab Virtual Lab 1- BioProSim1 1.30-5.30pm 2   BS 2.050 Oxygen Transfer - RCR 3 Oxygen Uptake - RCR 4 Penicillin Production as a secondary metabolite demonstration – RCR 5 Algal Biotechnology- NM 6 Study Break 7 Chemostat Project (or week 9) 1.30-5.30pm* 8 Chemostat Project (or week 10) All Week * 9 Chemostat Project (or week 7) 10 Chemostat Project (or week 8) All Week* 11 Study Break 12 Bioprocess Modelling 13 Off Campus Visit of Industrial Bioprocessing Site 2.00pm offsite 14 TBA 15 Study Break

Assessed activities over the next weeks Type Topic Marks Due Week Due Day CBLA1) Oxygen solubility (OTR1) 1 Fri Oxygen transfer (OTR2) Oxygen uptake (OTR3) 2 Group Instant Lab Report on oxygen transfer Thurs Instant Lab Report on oxygen uptake 3 Individ. BioProSim 1 mass transfer simulation Microbial growth principles (GRO1) Mon Microbial growth kinetics (GRO2) Wed Microbial cell cultivation (GRO3) Lab Report on Algal Biotechnology 4 5 Penicillin a secondary metabolite? 7 Bio-reaction oxidation states (OXS) BioProSim 2 chemostat simulation 8   Exam 2) Mid-semester Exam 25 Chemostat Group Report 10 10 /12 End-semester Exam 40 Total 100

Type Topic Marks Due Week Due Day CBLA1) Oxygen solubility (OTR1) 1 Fri Oxygen transfer (OTR2) Oxygen uptake (OTR3) 2 Group Instant Lab Report on oxygen transfer Thurs Instant Lab Report on oxygen uptake 3 Individ. BioProSim 1 mass transfer simulation Microbial growth principles (GRO1) Mon Microbial growth kinetics (GRO2) Wed Microbial cell cultivation (GRO3) Lab Report on Algal Biotechnology 4 5 Penicillin a secondary metabolite? 7 Bio-reaction oxidation states (OXS) BioProSim 2 chemostat simulation 8   Exam 2) Mid-semester Exam 25 Chemostat Group Report 10 10 /12 End-semester Exam 40 Total 100

Type Topic Marks Due Week Due Day Group Instant Lab Report on oxygen transfer 1 2 Thurs Instant Lab Report on oxygen uptake 3 Individ. BioProSim 1 mass transfer simulation Fri Lab Report on Algal Biotechnology 4 5 Penicillin a secondary metabolite? 7 BioProSim 2 chemostat simulation 8 Mon   Exam 2) Mid-semester Exam 25 Chemostat Group Report 10 10 /12 End-semester Exam 40 Total

Type Topic Marks Due Week Due Day Group Instant Lab Report on oxygen transfer 1 2 Thurs Instant Lab Report on oxygen uptake 3 Individ. BioProSim 1 mass transfer simulation Fri Lab Report on Algal Biotechnology 4 5 Penicillin a secondary metabolite? 7 BioProSim 2 chemostat simulation 8 Mon   Exam 2) Mid-semester Exam 25 Chemostat Group Report 10 10 /12 End-semester Exam 40 Total

Fundamentals taught Bioprocesses convert S to P Sugar to energy (biogas, ethanol, hydrogen, electricity) Pollutants to harmless substances (dechlorination, degradation, dirty water to clean water Need to be able to predict (modelling) Quantify (rates) Understanding (driving force, equilibrium)

Fundamentals taught Bioprocesses questions: Why ? How ? How fast? What mechanism ? Modelling

Learning tools used Bioprocess analysis (theory) Computer simulation Bioprocess execution and analysis (Chemostat project) Spreadsheets for data processing and analysis Peer reviewed websites of scientific analysis of literature Using computer learning activities Industry trip Focussed scientific writing.

Link between Research and Teaching A number of undergraduates  honours  PhD  Senior Engineers (Water Corporation, Design companies, Bioprocess Operators) Teaching topics drawn from own research projects and publications. Input from current researchers into the project Link to industry. So, on many of the topics you talk to science experts, not just “teachters that obtained their knowledge from books”

http://sphinx.murdoch.edu.au/ units/extern/BIO301/teach/index.htm Bioprocessing – Biotechnology: Make money from bioprocesses Inputs are of lower value than outputs (products) Computer based learning activities (CBLA) are on http://sphinx.murdoch.edu.au/ units/extern/BIO301/teach/index.htm

Lecture overview L1-3 Lecture 1: Intro, study guide, what is a bioreactor, Learning by interacting Lecture 2: What is diffusion, how can we predict the behaviour of a randomly moving molecule? moving dots, entropy, driving force, equilibrium, rate of diffusion, first order kinetics, kLa Lecture 3: oxygen transfer rate, kLa value. Graphical method of determining the kLa. Mathematical (2 point) determination of kLA Calculation and prediction of oxygen transfer as function of DO. Oxygen transfer efficiency. Bacterial OUR, DO. steady state

Molecular diffusion relies on random movement resulting in uniform distribution of molecules The general principle of microbial degradation is based on taking up chemical compounds from the medium into the cell and processing it here. There is a number of exceptions that we don’t want to consider now. If a bacterial cell is suspended in still solution those chemical compounds that the bacterium needs seem to “flow” towards it, while compounds that the bacterium does not use do not. This seems to be a handy principle allowing the bacterium to obtain exactly what it needs. However, as this “apparently free delivery” of desired compounds involves shifting molecules from the bulk solution towards the cell, shouldn’t there be some energy source to do the work ? To verify whether the bacterial cell itself may be involved in this apparently directed flux of useful compounds, a test can be carried out with dead bacterial cells showing that the directed molecular flux does not occur to dead cells. Most science students have heard about the principle behind this phenomenon (diffusion). Let us try to go beyond merely learning the definition, the phenomenon or the mathematical formula of molecular diffusion and try to visualise or simulate what is happening.

Oxygen Transfer Rate (OTR) Overview Diffusion, how does it work, how can we predict it? Diffusion is random …. and yet predictable. A simple model simulation can show that although the diffusion movement is random, it can be precisely predicted for large number of molecules (e.g. Fick’s law of diffusion)

Oxygen Transfer Rate (OTR) (diffusion, convection) Low OTR High OTR Transfer by diffusion is extremely slow and depends on surface area Wind Oxygen transfer by convection (turbulences) is more efficient Air In • • Bioreactors combine maximum convection with maximum diffusion • Course bubbles cause more convection, fine bubbles more diffusion How soluble is oxygen?

Oxygen solubility (cS) The net transfer of oxygen from gas phase to solution reaches a dynamic equilibrium O2 input = O2 output equilibrium results in defined saturation concentration (cs). The saturation concentration is also the oxygen solubility How soluble is oxygen?

Oxygen solubility (cS) Oxygen is not very polar  poorly soluble in water. Oxygen Solubility is described by Henry’s Law which applies to all gases p = partial pressure of oxygen k = constant depending on gas type, solution and temperature cS = concentration of oxygen dissolved in water p = k*cS Meaning: The amount of oxygen which dissolves in water is proportional to the amount of oxygen molecules present per volume of the gas phase. Partial pressure ~ number of O2 molecules per volume of gas increases with O2 concentration in gas increases with total gas pressure How to calculate partial pressure? (refer to CBLA)

Oxygen solubility (cS) Examples of using the proportionality between partial pressure of oxygen in the atmosphere and the saturation concentration cS: p = partial pressure of oxygen k = constant depending on gas type, solution and temperature cS = concentration of oxygen dissolved in water p = k*cS If the reactor is operated under 2 times atmospheric pressure (200kPa instead of 100 kPa air pressure), the new saturation concentration will be abou 16 mg/L instead of 8 mg/L. If air (partial pressure = 0.21* 100 kPa) is replaced by pure oxygen atmosphere (partial pressure 100kPa) the oxygen saturation concentration is about 40 mg/L (more precise 8*100/21) instead of 8 mg/L. How to calculate partial pressure? (refer to CBLA)

Concentration cs (mg/L) Oxygen solubility (cS) Effect of temperature cs = 468 (31.6 + T) Concentration cs (mg/L) Oxygen Saturation Temperature (°C) Oxygen solubility decreases with increasing temperature. Overall: oxygen is poorly soluble (8mg/: at room temp.) More important than solubility is oxygen supply rate (oxygen transfer rate OTR).

Oxygen Transfer Rate (OTR) (gradient, driving force) Question: What is the driving force for oxygen dissolution? At oxygen saturation concentration (cs): dynamic equilibrium exists between oxygen transferred from the air to water and vice versa.  No driving force OTR Answer: The difference between cS and the actual dissolved oxygen concentration (cL) is the driving force. OTR is proportional to the that difference. Thus: OTR ~ (cS – cL) Need to determine the proportionality factor

OTR – depends on DO (cL) Significance of OTR: critical to know and to control for all aerobic bioreactors 1. Deoxygenation (N2, sulfite + Co catalyst) 2. Aeration and monitoring dissolved oxygen concentration (D.O. or cL) as function of time 3. OTR = slope of the aeration curve (mg/L.h or ppm/h) 5 10 8 Air On cL (ppm) Time (min)

OTR – depends on DO (cL) 4. Observation: OTR decreases over time (and with incr. cL) 5. OTR is not a good measure of aeration capacity of a bioreactor 6. OTR is highest at cL = zero (Standard OTR) 7. OTR is zero at oxygen saturation concentrations (cs) 8. OTR is negatively correlated to cL 9. OTR is correlated to the saturation deficit (cs - cL), which is the driving force for oxygen transfer 9. The factor of correlation is the volumetric mass transfer coefficient kLa OTR = kLa (cs - cL) Mg/L/h h-1 mg/L

OTR –Significance of gradient First: steep step in oxygen (top layer saturated, next layer oxygen free) Then: buildup of a gradient of many layers. Each layer is only slightly different from the next  Transfer from layer to layer has little driving force. Gradient build-up inhibits fast diffusion

10. OTR is not a useful parameter for the assessment of the aeration capacity of a bioreactor. This is because it is dependent on the oxygen concentration (cL) 11. The kLa value is a suitable parameter as it divides OTR by saturation deficit: OTR (cs - cL) kLa = 12. kLa = the key parameter oxygen transfer capacity. How to determine it?

Lec 2 summary: Oxygen is poorly soluble depending mainly on partial pressure in headspace Temperature OTR is driven and proportional to driving force (cS-cL) kLa is the proportionality factor (first order kinetics) kLa describes the performance of a bioreactor to provide Oxygen to microbes Next lecture: quantify kLA

Lec 3 outlook: Aeration curve Quantify OTR at a given point of an aeration curve Quick estimate of kLa Graphical determination of kLa Mathematical determination of kLa Run computer simulation to obtain data Oxygen transfer efficiency (OTE) OTR proportional to cs-cL OTR inverse proportional to cL

OTR – Quick estimate of kLA Example: determine OTR at 6 mg/L 5 10 8 Air On cL (mg/L) Time (min) 6 4.5 min 5 mg/L OTR is the slope of the tangent for each oxygen concentration OTR = ∆ cL/ ∆ t = 5 mg/L/ 4.5 min = 1.1 mg/L/min = 66 mg/L/h

OTR – quick estimate of kLA kLa = OTR (cs-cL) = 66 ppm / h (8 ppm – 6 ppm) = 3.3 h-1 Q: Problem with this method? A: based on one single OTR slope measurement and unreliable to obtain from real data. DO Time

OTR – Graphical determination of kLa 1. Monitor aeration curve 2. Determine graphically the OTR at various oxygen concentrations (cL) 8 2 4 6 5 10 At 6 ppm: OTR = 25 mg/L/h At 4 ppm: OTR = 50 mg/L/h cL (ppm) At 3 ppm: OTR = 60 mg/L/h At 0.5 ppm: OTR = 30 ppm/h Time (min) 3. Tabulate OTR and corresponding cL values cL (mg/L) Cs - cL (mg/L) OTR (mg/L/h) 0.5 3.0 4.0 6.0 8.0 7.5 5.0 4.0 2.0 0.0 30 60 50 25

OTR – Graphical determination of kLa 4. Plot OTR values as a function of cs - cL. Standard OTR 100 kLa = = 12 h-1 70 mg/L/h 6 mg/L OTR (mg/L/h) cs 50 2 4 6 8 cs- cL (mg/L) 5. A linear correlation exists between kLa and the saturation deficit (cs - cL) which is the driving force of the reaction. 6. The slope of the plot OTR versus cs - cL is the kLa value. 7. The standard OTR (max OTR) can be read from the intercept with the cs line. (Standard OTR = 96 ppm/h)

• ( ) ( ) Mathematical Determination of kLa 1. OTR is a change of cL over time, thus = dcL/dt 2. kLa = dcL/dt (cs- cL) Integration gives • Concentration (mg/L) Dissolved Oxygen ci = 6 co = 3 to ti Time (min) cs ( ) cs - co 3. kLa = ln cs - ci ti - to ( ) 8 - 3 ppm kLa = ln 8 - 6 ppm 10.5 - 6.1 min = ln 2.5 4.4 min = 0.21 min-1 = 12.5 h-1

4. This method should be carried out for 3 to 4 different intervals. By aver 5. Once the kLa is known it allows to calculate the OTR at any given oxygen concentration: OTR = kLa (cs - cL)

Factors Affecting the Oxygen Transfer Coefficient kLa kLa consists of: • kL = resistance or thickness of boundary film • a = surface area Bulk Liquid [Oxygen] Bubble Cell Distance Main boundary layer = steepest gradient → rate controlling, driving force

Effect of Fluid Composition on OTR The transfer across this boundary layer increases with: ↓ thickness of the film, thus ↑ degree of shearing (turbulence) ↑ surface area ↓ surface tension ↓ viscosity (best in pure water) ↓ salinity ↓ concentration of chemicals or particles detergents? ↑ emulsifiers, oils, “oxygen vectors”

Oxygen Transfer Efficiency (OTE) oxygen transferred oxygen supplied Significance of OTE: economical, evaporation Calculation of OTE (%): % OTE = oxygen transferred (mol/L.h) oxygen supplied (mol/L.h) X 100 Why do students find this type calculation difficult? Units are disregarded. Molecular weights are misused.

Oxygen Transfer Efficiency (OTE) A bioreactor ( 3 m3) is aerated with 200 L/min airflow. If the OTR is constant (100 mg/L/h) determine the %OTE. 1. Convert the airflow into an oxygen flow in mmol/L/h 200 L air /min = 12000 L air/h (x 60) = 2520 L O2/h (x 21%) = 102.9 mol O2/h (÷ 24.5 L/mol) = 34.3 mmol O2/L.h (÷ 3000 L) 2. OTR 100 mg/L.h = 3.1 mmol O2/L.h (÷ 32 g/mol) % OTE = 3.1 (mmol/L.h) 34.3 (mol/L.h) X 100 = 9%

Oxygen Transfer Efficiency (OTE) OTE is dependent upon the cL in the same way than OTR OTE decreases with increasing airflow (more oxygen is wasted) % OTE 5 10 Airflow

Engineering Parameters Influencing OTR Increase depth vessel Deeper vessel  bubbles rise a long way  ↑ OTR, OTE but more pressure required  ↑ $$ Decrease bubble size Larger surface area  ↑ OTR, OTE smaller bubbles rise slower  more gas hold up  ↑ OTR, OTE Increase air flow rate  ↑ Number of bubbles  ↑ OTR but ↓ OTE Increase stirring rate ↑ turbulence  ↓ thickness of boundary layer  ↑ OTR, OTE  ↓ Bubble size  ↑ OTR, OTE

OTR – from aeration curve to kLa summary (first order kinetics) (cs) Air on Dissolved Oxygen Aeration Curve Time max OTR Slope = kLa Rate is proportional to concentration  First order kinetics OTR (mg/L.h) Dissolved oxygen [mg/L] OTR = kLa (O2 saturation (cS) – O2 concentration (cL))

OTR – Aeration curve from CBLA The transfer of oxygen from solution to headspace is important During aeration of oxygen free water, the dissolved oxygen increases in a characteristic way

OTR – aeration curve from CBLA Highest Rate at lowest dissolved oxygen concentration Rate of zero when DO reaches saturation concentration Can the relationship between rate and DO be expressed mathematically?