BioMASS v2.0: A NEW TOOL FOR BIOPROCESS SIMULATION

Transcription

1 Clemson University TigerPrints All Theses Theses BioMASS v2.0: A NEW TOOL FOR BIOPROCESS SIMULATION Y Phan-thien Clemson University, nhuy2711@gmail.com Follow this and additional works at: Part of the Biochemistry Commons Recommended Citation Phan-thien, Y, "BioMASS v2.0: A NEW TOOL FOR BIOPROCESS SIMULATION" (2011). All Theses This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact kokeefe@clemson.edu.

2 BioMASS v2.0: A NEW TOOL FOR BIOPROCESS SIMULATION A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree of Master of Science Biosystems Engineering by Y N. Phan-Thien May 2011 Accepted by: Dr. Caye M. Drapcho, Committee Chair Dr. John Nghiem Dr. Terry Walker

3 ABSTRACT A simulation software (BioMASS Biological Modeling and Simulation Software) was upgraded from the previous version and provided with additional enhancements. Several new bioprocess configurations and their subroutines have been added. The additional processes included continuous stirred tank reactor (CSTR) with biomass recycle, and CSTR with additional stream in the second stage. The primary goal in the development of BioMASS v2.0 was to provide users with a ready-to-use, expressive visual modeling tools. In this new version, output from simulation can be visualized in graphics. Printing, exporting, and saving file options also are available. In summary, BioMASS v2.0 offers an effective way of better understanding bioprocessing. ii

4 ACKNOWLEDGEMENTS I wish to express my deepest gratitude to Dr. Caye Drapcho for suggesting the topic of this thesis as well as her professional guidance and constructive criticism. I am also greatly indebted to Dr. Nhuan Nghiem, whose valuable advices, and enduring motivation and support were of inestimable value. Sincere thanks are also to him for critical review of my thesis. I also would like to thank Dr. Walker for giving useful lectures which helped me a lot to complete this thesis. I would like to take this opportunity to express my deep gratitude to my parents, my brother, and my little sister for their love, patience and encouragement during the long course of my study. Finally, I would like to thank all my friends in the United States for just everything they did to make my stay here an experience for a lifetime. iii

5 TABLE OF CONTENTS TITLE PAGE... i ABSTRACT... ii ACKNOWLEDGEMENTS... iii TABLE OF CONTENTS... iv LIST OF FIGURES... vi LIST OF TABLES... ix Chapter INTRODUCTION... 1 Chapter BioMASS v Chapter BIOPROCESS MODELING AND SIMULATION USING BioMASS v Chapter CONCLUSION APPENDICES APPENDIX A COMMANDS IN BioMASS v APPENDIX B DETAILS OF NOTEBOOK COMMAND APPENDIX C VB CODE FOR ZERO-ORDER CHEMICAL REACTION APPENDIX D VB CODE FOR FIRST-ORDER CHEMICAL REACTION iv

6 APPENDIX E VB CODE FOR SECOND-ORDER CHEMICAL REACTION APPENDIX F VB CODE FOR SINGLE LIMITING NUTRIENT APPENDIX G VB CODE FOR MULTIPLE LIMITING NUTRIENTS APPENDIX H VB CODE FOR INHIBITION MODEL REFERENCES v

7 LIST OF FIGURES Figure 2.1. Start Page window Figure 2.2. Commands in File Menu Figure 2.3. Add another reactor checkbox is selected Figure 2.4. Simulation window of Irreversible Chemical Kinetics for zero-order reaction Figure 2.5. Temperature Effect window Figure 2.6. Simulation window of Single Limiting Nutrient Figure 2.7. Enter Data for Bioreactor 1 window Figure 2.8. Simulation results of Single Limiting Nutrient were displayed in Table Figure 2.9. Simulation results of Single Limiting Nutrient were displayed in Graph Figure Save Data, Results and Graphs in Excel Figure Simulation window of Multiple Limiting Nutrients Figure Enter data for Substrate window of Multiple Limiting Nutrients Figure Enter data for product window of Multiple Limiting Nutrients Figure Simulation window of Inhibition Model Figure Enter data for Substrate window of Inhibition Model Figure Enter data for product window of Inhibition Model Figure Enter data for Xenobiotic Compound of Inhibition Model Figure 3.1. Start Pageof BioMASS v Figure 3.2. Enter data for Substrate window Figure 3.3. List of supported product inhibition model in BioMASS v Figure 3.4. Notebook window in BioMASS v vi

8 Figure 3.5. Experimental (points) and simulated (lines) profiles of substrate, biomass and H 2 concentration in a batch system including impact of H 2 inhibition Figure 3.6. Studentized residual plot for glucose concentration Figure 3.7. Studentized residual plot for biomass concentration Figure 3.8. Studentized residual plot for Hydrogen concentration Figure 3.9. Experimental (points) and simulated (lines) profiles of substrate, biomass and H 2 concentration in a batch system without including impact of H 2 inhibition Figure Simulated profiles of H 2 concentration with various initial glucose concentration in a batch system including impact of H 2 inhibition Figure Simulated profiles of H 2 concentration with differentµ values in a batch system including impact of H 2 inhibition Figure Simulated profiles of H 2 concentration with different K S values in a batch system including impact of H 2 inhibition Figure Simulated profiles of H 2 concentration with different max Y X / S values in batch system including impact of H 2 inhibition Figure Simulated results of hydrogen production in a CSTR system at HRT of 6h Figure Simulated profiles of glucose, biomass and H 2 concentration at various retention time in a CSTR system Figure Simulated profiles of H 2 concentration at various initial glucose concentration in a CSTR system at HRT of 6 hours Figure Experimental (points) and simulated (lines) profiles of glucose, xylose and xylitol concentration in a batch system Figure Studentized residual plot for xylose concentration Figure Studentized residual plot for xylitol concentration Figure Simulated profiles of xylitol concentration with different µ max values in a batch system vii

9 Figure Simulated profiles of xylitol concentration with different K S values in a 1 batch system Figure Simulated profiles of xylitol concentration with different K S values in a 2 batch system Figure Simulated profiles of xylitol concentration with different YX / S values in a 1 batch system Figure Simulated profiles of xylitol concentration with different YX / S values in a 2 batch system Figure Simulated profiles of glucose, xylose, xylitol and ethanol concentration in a batch system Figure A. 1. Commands in View Menu Figure A. 2. Commands in Tools Menu Figure A. 3. Commands in Windows Menu Figure A. 4. Commands in Help Menu Figure A. 5. Notebook window Figure A. 6. About BioMASS v2.0 window Figure A. 7. Standard Toolbar and Calculation Toolbar Figure A. 8. Scroll Bar and Status Bar viii

10 LIST OF TABLES Table 3.1. Simulated results of hydrogen production at different retention time Table 3.2. Simulated results of rate of H 2 production with different initial substrate concentration in a batch system Table 3.3. Simulated results of xylitol and ethanol concentration with different initial glucose concentration in a batch system Table 3.4. Simulated results of xylitol and ethanol concentration with different initial xylose concentration in a batch system Table A. 1. List of command icons in Standard Toolbar Table A. 2. List of command icons in Calculation Toolbar Table A. 3. List of common function keys ix

11 Chapter 1 INTRODUCTION Bioprocess simulation software In the world of software development, systems often begin as simple and wellunderstood, and normally contain elements such as creation of process flow diagrams, and generation of material and energy balances. To meet the ultimate requirements, models become more complex to include calculations such as equipment sizing, and capital and operating cost estimation. Simulation is becoming a requirement for all major process designs. Using simulation, bioprocess engineers can identify potential problems ahead of time and take corrective action. A common use for bioprocess simulation is for process mapping and cost analysis. Process mapping enables investigators to analyze or predict the action of organisms in response to certain specific inputs. Cost analysis identifies the expensive process steps and other cost items that have major impacts overall process economics. A simulation program development must meet several requirements, e.g. the mathematical models must be described in the ordinary differential equations (ODE). In addition, one of the challenges for users when using such simulation tool is accumulation of the appropriate data. Simulation can only be run until all the data is collected and put into the system.

12 BioMASS v1.0 BioMASS stands for Biological Modeling and Simulation Software and was developed Bhairavi in 2006 that included: Kinetics models for three different types of irreversible chemical reactions (zero, first and second order). Biological kinetics where single limiting nutrient is used for simple Monod kinetics and for multiple limiting nutrients model, which allows users to select a maximum of four substrates and a maximum of two products. The user can also select one pair of substitutable or complementary substrates. Inhibition model includes inhibitory effects of substrates, products, or xenobiotic compounds on microbial growth. All of these modes can be carried out in either batch or continuous operation and with a single bioreactor or two bioreactors in series. The advantages that the software offers include: Many complex equations may be solved simultaneously. In other words, a large number of process variations can be synthesized and analyzed interactively. The software may be run on inexpensive workstations and laptops. Results may be presented as graphs and tables. The software may be integrated with other commonly used softwares such as Excel to export results for file saving or further data analysis. The software allows the user to : 2

13 Simulate bioreactor experiments involving microbial species and predict the products of the experiments. Study the effects of key process variables, for example, initial substrate concentration, temperature. Investigate complex or integrated biochemical operations without the need of expensive or complex experiment. Study both batch and continuous bioprocesses. Performance characteristics can be described by mathematical expressions. Therefore, potential problems can be investigated, and behavior under varying conditions can be tested to allow process optimization. The software can be further developed to satisfy other demands, and also can be used in training and education. However, the aforementioned version of the BioMASS software also has several limitations, which include: Programming interface is not very attractive. Screen resolution adjustment. Creation of charts takes too long. The application toolbar contains too few options. Lack of operations that help to increase cell concentration and improve product formation such as continuous process with cell recycle. Objectives 3

14 The ultimate objective of the research is to develop a tool for bioprocess modeling, which does not require the users to have special computer programming skills. The tool will include all important aspects of common bioprocess configurations but also will be made simple and easy to understand so that it can be used by beginners who are just starting to learn bioprocessing as well as by those who already have advanced knowledge of the field. The tool will include definitions of all necessary process parameters, and also will allow simulation by inputting values, and comparison of predicted results with actual data. Overview of the Thesis There are four chapters in this thesis, followed by a list of references and relevant appendices. This section gives a brief overview of the organization of the chapters. The current chapter (Chapter 1) gives an introduction to bioprocess modeling and the need for process simulators exclusively to model microbial growth in bioreactors. Chapter 2 gives detailed description and functions of BioMASS v2.0. Output samples of irreversible chemical kinetics and biological kinetics are given in this chapter. Chapter 3 reviews the various available process simulators including SuperPro Designer, which is one of the best commercially available simulator packages. This chapter also gives literature reviews of kinetics and mass balance of chemical and biological reactions that can be used during the simulation of BioMASS. Then the simulation results are also given. The data is obtained from published research papers. Predictive results are compared with the actual values to validate the accuracy and utility of the simulator. Finally, conclusions of this research are given in Chapter 4. 4

15 Chapter 2 BioMASS v2.0 BioMASS is a flexible, easy to use program with the characteristics as follows: Communicates with the user through a user friendly graphic surface. Simple, inexpensive tool that could be easily used by the users with minimum training. The primary goal of BioMASS is to create a software application where users may choose and specify the feed, bioreactor type, operational conditions, then predicts the substrate, product, and biomass concentration over a period of bioreactor operation. The results can be stored in files and these files are reloadable and are printable at any time. The features of BioMASS v2.0 are highlighted, and examples of outputs generated by BioMASS are also given later in this chapter. System Requirements BioMASS will run on any PC and compatible with processor that runs Windows2000/XP/7. The following is a more detailed description of hardware and software requirements: Hard Disk: The program will occupy anywhere from 40 to 50 MB of space on hard dish. Processor/RAM: 256 MB of RAM is required. 5

16 Mouse: The presence of a mouse or similar pointing device supported by Windows 2000/XP/7 is required. Video adapter/monitor: The program requires a minimum resolution of 1024x768. Software: Microsoft Excel Installation The installation is very simple. First of all, copy or download all necessary files onto the hard disk at a directory of the user s choice. Then, double-click on the setup file to install the application. Finally, start running the software by double-clicking the BioMASS icon on the desktop. Start Page When the software is launched, a Start Page window will appear as default (Fig 2.1). The Start Page displays the version number of this application along with the name of developers and corresponding address. The main BioMASS window, with its accompanying toolbars and menus, closely resembles the windows the user works in for other Window based programs. The main window for the operation consists of a menu bar, a toolbar, a status bar, and the display field for graphic. 6

17 Figure 2.1. Start Page window. 7

18 Figure 2.2. Commands in File Menu. 8

19 Irreversible Chemical Kinetics The following is the irreversible chemical reaction which is considered in the simulator: A k B Within the process, all the reactant A reacts to form product B. The reactant in a bioreaction is known as the substrate. BioMASS v2.0 supports three different kinds of kinetics models of irreversible chemical reactions (zero, first and second order). The user interface contains four main blocks (Fig 2.3). - The Chemical Kinetics Reaction block contains two textboxes for the user to enter the name of reactant and product of irreversible chemical reaction. Changing names in those textboxes is optional, and it does not affect to the calculation. - The Set-up block stores the information about the initial concentrations of reactant and product; and allows user to set the mode of operation. While the initial reactant concentration is required for the simulation, product concentration can be zero. Regular units such as mg/l, g/l, mg/m 3, g/m 3 are supported in BioMASS. The default unit of concentration is mg/l. Changing the unit in a textbox will automatically change the units of the rest of the parameters. The unit of chemical constant k will be changed with the change of reaction order. The unit of k in zero-order reaction will be 3 1 ML T (M, L, T are expressed as the units of mass, length, and time, respectively), or L M t in second-order reaction. 1 T in first-order reaction, and BioMASS provides many operation options such as batch, CSTR at Transient State, and CSTR at Steady State. By default, the batch operation is checked. Click on CSTR at Transient State to run the simulation in a given duration, or click on CSTR at Steady State to let the 9

20 simulation run until it reaches the steady state, and then the steady time is calculated and reported. In this version, images of various operating systems are also given to help the user has a better idea about the differences between them. The bioprocess with multiple stages is also considered in BioMASS v2.0. Check on Click here to add another reactor checkbox to create the system composed of two reactors link in series (Fig. 2.3). - The last two blocks are Reactor 1 and Reactor 2, which store information about the physical characteristic such as volume, flow rate, dilution rate, chemical constant, etc. Each block represents a bioreactor. By default, block of Reactor 2 will be hidden, and it will only be shown when Click here to add another reactor checkbox is selected. In each block, there is a textbox, set with orange background, to introduce which reaction order is selected to run the simulation (Fig 2.4). This is a good way to remind the user that the reaction order should be the same in a multi-stage system. If the batch operation is chosen, the physical parameters such as flow rate, volume, dilution rate, retention time are hidden and cannot be edited by the user. If the process is set to continuous mode, those physical parameters are shown again. BioMASS v2.0 supports to simulate CSTR operation with flow rate or dilution rate, or retention time. The user must select one of those options to enable the textbox. Biological reactions just like any others are temperature sensitive because reaction rate is a function of temperature, and this dependence may be modeled using Arrhenius or modified Arrhenius equation. If the user selects Temperature correction checkbox, this will bring up a dialog box (Fig 2.5). This dialog allows the user to calculate the effect of temperature on chemical constant k. When this checkbox is selected, it means that the value of chemical 10

21 constant k has been corrected by Arrhenius equation. The user can uncheck that checkbox to restore the original value. Figure 2.3. Add another reactor checkbox is selected. 11

22 Figure 2.4. Simulation window of Irreversible Chemical Kinetics for zero-order reaction. 12

23 Figure 2.5. Temperature Effect window. 13

24 Biological Kinetics Microbial growth is a complex phenomenon, and depending on environment conditions, the model can be zero, first or second-order reaction. For example, the term of the Monod equation, which is determined experimentally, makes the model fit the system investigated. µ = ˆ µ SS K + S s S (3.10) The specific growth rate µ is not constant, and dependent on the substrate concentration. The value of half saturation constant rate µ is equal to 1/2 ofµˆ K S K S is equal to the substrate concentration at which the growth ˆ µ = S when µ =. During low substrate concentration, reactions 2 µs ˆ are first order S µ, while at high substrate concentrations, they are zero order( µ ˆµ ). K S BioMASS v2.0 supports single, multiple limiting nutrient and inhibition model. Single Limiting Nutrient The user interface contains four main blocks (Fig 2.6). - The Biological Reaction block contains two text boxes to introduce the name of substrate and product. Some common substrate names of glucose, xylose, arabinose, fructose, etc., and some common product names of hydrogen, ethanol, xylitol, etc. are listed in those boxes. The user can either select one of them or enter a different name. Inputting name of substrate and product in the bioreaction are optional, and it does not affect to the calculation. However, this step will help to make sense for the simulation results; hence, it will be useful for the user to review later. The default name of substrate or product will be the first name appears in the list. If there is not any product in the user s model, no product option must be selected or 14

25 the program will ask for it and the simulation cannot run until all necessary information is entered. - The Set-up block allows the user to set the mode of operation. Single Limiting Nutrient provides more options than Irreversible Chemical Kinetics. They are batch, CSTR at Transient State, and CSTR at Steady State, CSTR with biomass recycle, multistage CSTR with additional feed stream. The user can select one of those options by checking on its radiobutton. By default, the batch operation is checked. Whenever the user changes the selection, the images in picturebox, which is used to explain that option, will also be changed. Including images for each option is one of the improvements of BioMASS v2.0. Images help the user better understand the differences of each option. - The last two blocks are Biological Reactor 1 and Biological Reactor 2, which store the information about the physical characteristic such as volume, flow rate, dilution rate, chemical constant, etc. Click on Click here to add another reactor checkbox to create the system composed of two reactors linked in series. The Biological Reactor 2 will only be shown when this checkbox is selected. Each block represents as a bioreactor, and in each block, there is an input data button of Click here to enter your data. Clicking on this button will bring up a dialog that allows the user to specify the operation conditions such as initial concentrations, kinetics parameters, maintenance parameters, etc. (Fig 2.7). The feed for the microbial species in a bioreactor comes in the form of substrate and nutrients. Depending on the substrate type, microbial species involved and bioreactor operational conditions; different kinds of products are produced in the bioprocess. 15

26 Figure 2.6. Simulation window of Single Limiting Nutrient. 16

27 Figure 2.7. Enter Data for Bioreactor 1 window. After defining all necessary information, the user may run the simulation. As a short-cut for performing simulation, the user may hit CTRL + R or simply click on the button that looks like a calculator. The user may use the following toolbar button to go back any time to the input form to change, remove, or add any information. The View Graph and View Table buttons are enabled once the calculations have been performed. The View Table button appears as a picture of a small table. Click on this icon to view the result of simulation in table (Fig 2.8). BioMASS v2.0 calculates and reports the following variables in its table result: substrate and product concentration, growth rate, rate of substrate utilization, rate of biomass formation, rate of decay, and rate of product production (Fig 2.8). The simulation is calculated by step time of 0.1. Unlike the previous version, BioMASS 17

28 v2.0 gives the user the flexibility of changing the time interval ( t ) that is displayed in table result. The format of table result is another improvement of BioMASS v2.0. The same format of spreadsheet as Microsoft Excel 2007 is used. The result of each variable is presented in a column, and the size of each column is changeable. The header of each column is the name of calculated value and its unit. When a row in table is selected, that row will be highlighted. The View Graph button appears as a picture of a small graph. After clicking this icon, a graph popup window appears immediately and displays graphs of the calculated values with time (Fig 2.9). BioMASS also allows the user to adjust the size of the graph by dragging on its edges. In the previous version of BioMASS, the results are exported to Excel and then built a graph; therefore, it takes time whenever the user would like to view graph. BioMASS v2.0 improves this disadvantage by creating the graph by itself without the help of Excel. Since concentrations of biomass and product are usually small to compare with concentration of substrate, when plotting them in the same graph, sometimes they cannot be seen. Therefore, BioMASS v2.0 plots those two values in the secondary y-axis to have a better view. There are three different graphs created in BioMASS v2.0. The first one is the graph of the calculated values of concentration with time. The second one is the graph of the rate of substrate utilization, the rate of biomass formation, and the rate of product production with time. The first two graphs are arranged vertically to help the user follow the change of the nutrients with time. The last one is the graph of growth rate with substrate concentration. 18

29 Figure 2.8. Simulation results of Single Limiting Nutrient were displayed in Table. 19

30 Figure 2.9. Simulation results of Single Limiting Nutrient were displayed in Graph. Another improvement of BioMASS v2.0 is to allow the user save their data or results in Microsoft Excel. The user can either select Save Data or Save All. Unfortunately, until now the software only works effectively on Excel The saved file will include three sheets (Fig 2.10). Those sheets are named Data, Results, and Graph, which is used to save simulation data, calculated values and images of graphs, respectively. This is the attempts of the developer to make it convenient for the user. The saved file can be used to run another simulation in BioMASS v2.0 or do further analysis. 20

31 Figure Save Data, Results and Graphs in Excel. Multiple Limiting Nutrients When the model has more than one substrate, the user can use Multiple Limiting Nutrients. The user interface contains four main blocks (Fig 2.11). - The Biological Reaction block contains two textboxes which allow the user to specify the number of substrate and product involved in the reaction. BioMASS is able to simulate up to four substrates and two products. Since this is Multiple Limiting Nutrients model, the minimum number of substrate can be chosen is two. - The Set-up block has functions as in Single Limiting Nutrient but with fewer option. The function only allows the user to simulate the system operation of batch and CSTR. 21

32 - The Operation Parameters block stores the information about the physical characteristic such as simulation time, volume, flow rate, dilution rate, etc. - The Multiple Limiting Nutrients block provides two types of multiple substrate models. Since there is more than one substrate in this model, the user needs to select either complementary substrate, which can be interactive or non-interactive model, or substitutable substrates. Clicking on Substrate button, which is the red button, will open a window where stores the information related to substrates and nutrients being fed to the bioreactor (Fig 2.11). Clicking on Product button, which is the blue button, will open a window where stores the information regarding the products of the bioprocess (Fig 2.11). Figure Simulation window of Multiple Limiting Nutrients. 22

33 When inputting data for substrate, some values are initial concentrations, biomass yield, maximum growth rateµ max, half saturation concentration K S need to be entered to enable running the simulation (Fig 2.12). Neglecting one of these important values will cause to appear an error message, and the error message will keep appearing until all necessary information is entered. The values of µ max and b for Substrate 1 will be set constant for the rest of the substrates. If the user selects Temperature correction for Decay constant or Temperature correction for Max Growth Rate, this will bring up a dialog. This dialog allows the user to calculate the effect of temperature on decay constant, b or maximum growth rate, µ by using the Arrhenius or Modified Arrhenius equation. max Figure Enter data for Substrate window of Multiple Limiting Nutrients. 23

34 When inputting data for product, some values need to be entered to enable running the simulation such as product yield, growth associated product K product K png or both those values, depends on product type (Fig 2.13). pg or non-growth associated Figure Enter data for product window of Multiple Limiting Nutrients. Inhibition Model The user interface has a same format as in the Multiple Limiting Nutrient. In the Biological Reaction block contains three textboxes which allow the user to specify the number of substrate, product, and Xenobiotic compound involved in the reaction. BioMASS is able to simulate up to four substrates, two products, and two Xenobiotic compounds. A minimum of one 24

35 substrate can be chosen, whereas for both product and Xenobiotic compound, a minimum of zero can be selected (Fig 2.14). Figure Simulation window of Inhibition Model. The user is allowed to set inhibitory for Substrate 1 and Substrate 2 (Fig 2.15). BioMASS supports a list of substrate inhibition models of Competitive Inhibition, Non-Competitive Inhibition, Andrew s Model, Edward s Model and Modified Steele s Model. By default, the model will be set as No Inhibition. In this list, there are not only names but also equations that incorporate Monod s equation and inhibition model, to help the user easily identify them. The equation of a model will be highlighted every time it s selected. 25

36 Figure Enter data for Substrate window of Inhibition Model. BioMASS v2.0 allows the user to set inhibitory for only Product 1 (Fig 2.16). BioMASS supports product inhibition models of Competitive Inhibition, Non-Competitive Inhibition, General Inhibition Term 1, General Inhibition Term 2, Modified General Inhibition Term 2. The difference in each model can be easily seen in its equation. BioMASS allows the user to select only one competitive inhibitory term. Therefore, if there is more than one of this term is chosen, an error message will appear and inhibition model is automatically reset to No Inhibition. 26

37 Figure Enter data for product window of Inhibition Model. BioMASS v2.0 also accounts for the inhibitory effect of Xenobiotic Compound (Fig 2.17). BioMASS supports inhibition models of Competitive Inhibition, Non-Competitive Inhibition, and Uncompetitive Inhibition. The user can click on the arrow in the ComboBox to choose inhibition model for each Xenobiotic compound. 27

38 Figure Enter data for Xenobiotic Compound of Inhibition Model. 28

39 Chapter 3 BIOPROCESS MODELING AND SIMULATION USING BioMASS v2.0 Y N. Phan-Thien, Caye M. Drapcho* Department of Biosystems Engineering, Clemson University Clemson, SC-29630, USA *Corresponding author: Tel./Fax: address: cdrapcho@clemson.edu ABSTRACT A simulation software (BioMASS Biological Modeling and Simulation Software) was upgraded from the previous version and provided with additional enhancements. Several new bioprocess configurations and their subroutines have been added. The additional processes included continuous stirred tank reactor (CSTR) with biomass recycle, and CSTR with additional stream in the second stage. The primary goal in the development of BioMASS v2.0 was to provide users with a ready-to-use, expressive visual modeling tools. In this new version, output from simulation can be visualized in graphics. Printing, exporting, and saving file options also are available. In summary, BioMASS v2.0 offers an effective way of better understanding bioprocessing. Keywords: bioprocess modeling; simulation; hydrogen; fermentation. 29

40 Nomenclature: S, substrate concentration (g/l); S 0, initial substrate concentration (g/l); inlet substrate concentration (g/l); S 1, glucose concentration (g/l); S 2, xylose concentration (g/l); S t, substrate concentration at t 1 1 (g/l); S t, substrate concentration at t 2 2 (g/l); X, biomass concentration (g/l); X 0, initial biomass concentration (g/l); S i, X i, inlet biomass concentration (g/l); X t, biomass concentration at t 1 1 (g/l); X t, biomass concentration at t 2 2 (g/l); P, product concentration (g/l); P 0, product concentration (g/l); P i, inlet product concentration (g/l); critical product concentration above which cells cannot grow (g/l); P t, product concentration at 1 t 1 (g/l); P t, product concentration at t 2 2 (g/l); n, dimensionless constant; constant (g/l); K S, half saturation contant of glucose (g/l); K 1 S2 P m, K S, half saturation, half saturation constant of xylose (g/l); µ, specific growth rate coefficient (h -1 ); µ max, maximum specific growth rate coefficient (h -1 ); b, decay constant (h -1 ); Y /, biomass yield (g of X / g of S); m S, substrate maintenance constant (g of S/ g of X -h); r P, rate of production formation (g/l-h); X S Y P / S, product yield (g of P/ g of S); K pg, coefficient for growth associated product (g of P/g of X); K png, coefficient for non-growth associated (g of P/g of X); P/ g of X -h); τ, retention time (h). m P, substrate maintenance constant (g of INTRODUCTION Simulation software has been used in the petroleum and chemical industries since the late 1950 s, while its practice in bioprocessing has only taken place within the last 20 to 25 years 30

41 (Shankin, 2000). There are several challenges in the application of process simulation software to bioprocessing. First, most bio-products are produced by fermentation, and the metabolic processes of the microorganisms are very complicated and often cannot be modeled precisely. Second, most biological processes are difficult to describe mathematically or may include a large number of highly nonlinear differential equations that, are impossible to solve without the help of numerical methods and suitable computer software tools. Third, reliable online sensors are lacking. Measurement devices give insight to the process, allowing operators to know what is going on inside the system; however, some key parameters are not measurable with standard devices. Moreover, because of recent development of bioprocess, there is limited published information on the subject. On the other hand, simulation can only be run until all the data is collected and put into the system. Other factors should be taken into consideration when running the simulation. First, raw materials and products are unidentified or have varying compositions (Shankin, 2000). Also, identification of the residues or by-products is often difficult. Second, a vast majority of process data are acquired online, which are measured continuously with respect to the time. However, some important parameters such as concentrations of substrate, biomass, and product are measure off-line, which are measured periodically or at single time point only. Biomass measurements are frequently subject to experimental error. Despite many challenges, new models and measurement methods are constantly being researched and developed to overcome these difficulties. Spreadsheet applications such as Microsoft Excel are very popular. Data is input in cells and results are displayed in variety of graphs. This application can be very useful for bioreactor design (Gimbun, 2004). The model is easy to operate or scale up to meet demand by changing variable values in the spreadsheet. 31

42 MATLAB is developed by the MathWorks, used widely by many researchers and universities. It is a tool for computation and visualization in an integrated environment. MATLAB can be used to investigate the effect of many important factors in bioprocesses such as ph, temperature, flow rate, etc. (Birol, 2002). Like Excel, MATLAB provides flexibility of changing initial conditions to scale up or optimize the model. However, processing of large amounts of data as well as simulating models in those applications is usually time-consuming. STELLA is developed by ISEE systems, which is easy to learn and that is the reason for its extensive use in education. STELLA creates a diagram of the interrelationships between the components of a model. However, STELLA is not designed for large complex system modeling but for small relatively simple systems. Some other applications that have the same graphical icon-based interface as STELLA such as Madonna, GoldSim and Simulink, are also becoming popular and give the user better understanding of bioprocesses (Rizzo, 2006). Madonna is a software package developed under National Science Foundation and National Institute of Health sponsorship. The program has capacity to directly import STELLA models and is very useful when evaluating the relative importance of model parameters (Nemeth, 2008). ASPENPlus and SuperPro (Intelligen, Inc.) are considered to be the most two popular tools for bioprocess industry (Shankin, 2000). They are well suited to perform basic material and energy balances, and economic analysis. They provide a flexible flow sheet where the user simply adds graphical icons that represent specific operation unit at any location and with any frequency. However, the use of ASPEN Plus and SuperPro is often restricted to built-in model with limited option for user-defined models or customization. In addition, the cost for both is high; for example, it costs $15,950 to purchase a copy of SuperPro in 2011 ( SuperPro is a very powerful and flexible tool, and provides many 32

43 options for the user to choose; however, this is also its limitation. The user needs to be trained to learn how to use it. Some other popular names such as Simulations Plus provides a list of simulation software which can be used in pharmaceutical and biotechnology industries ( and EnviroSim or Hydromatis are developers of wastewater treatment software ( Finally, BioMASS v2.0 is the result of this work, which enables modeling of batch and continuous flow biological reactors for bioprocessing (fermentation processes such as ethanol and xylitol production), or wastewater treatment. Simulation software packages come in variety of categories and prices; therefore, the users can select a suitable tool depends on their demand. MODEL DEVELOPMENT BioMASS BioMASS was written in Visual Basic language, and includes user through a userfriendly graphic surface (Fig 3.1). It is a flexible and inexpensive tool that could be easily used by the user with minimum training. The primary goal of BioMASS is to create a software application where the user can choose and specify the feed, operational conditions, to simulate the substrate, product, and biomass concentration over a period of bioreactor operation. The results can be stored in files and these files are reloadable and are printable at any time. The installation is very simple, and includes copying all necessary files onto the hard disk at a directory of the user s choice, then starting the program by double-clicking the BioMASS icon. 33

44 Figure 3.1. Start Pageof BioMASS v2.0. BioMASS simulation provides kinetics models for three different types of irreversible chemical reactions (zero, first and second order), and three main types of biological kinetics (single limiting nutrient, multiple limiting nutrient and inhibition model), all based on Monod Kinetics (Fig 3.2, 3.3). The multiple limiting nutrients model allows user to select a maximum of four substrates and a maximum of two products. The user can also select one pair of substitutable or complementary substrates. Finally, inhibition models can be selected to model the inhibitory effects of substrates, products, or xenobiotic compounds on microbial growth. All of these modes 34

45 can be carried out in either batch or continuous operation and with a single bioreactor or two bioreactors in series. Figure 3.2. Enter data for Substrate window. 35

46 Figure 3.3. List of supported product inhibition model in BioMASS v2.0. In addition, BioMASS v2.0 will be a useful tool in education and training. In Help menu, the Notebook command brings up a window that introduces frequently asked questions, listed in alphabetical order, along with short answers, images, and animations to help the user has a better understanding about bioprocess (Fig 3.4). 36

47 Figure 3.4. Notebook window in BioMASS v2.0 Kinetics Models Kinetics is an important subject in biological engineering. It is important because it allows researchers to determine production rates of their products, or how fast organisms will uptake substrates, etc. BioMASS uses the well-known Monod model because of its simplicity and capability to simulate the biological reactor. The terms of the Monod equation, which are determined experimentally, makes the model fit the system investigated. µ = ˆ µ SS K + S s S (3. 1) Once the reaction rates of interest are know, their influence on the mass balance should be evaluated. When the mass balance equation combines with reaction rate to form simple 37

48 mathematical models, it can be used to describe quantitatively all the materials that enter, leave and accumulate in a reactor. Since there is no flow into or out of the reactor during reaction time of batch operation, the mass balance equation can be express as: dx =µ X bx (3. 2) dt ds dt µ = YX / S r + Y P P / S + m S X (3. 3) dp dt = r X (3. 4) P For growth associated products: r = K µ + m (3. 5) P pg P For non-growth associated products: r = K + m (3. 6) P png P For mixed growth products: r = K µ + K + m (3. 7) P pg png P Since there is continuous flow into and out of a CSTR, the mass balance must consider not only changes that occur as a result of reactions within the reactor but also must include those changes resulting from the hydraulic characteristics of the system as well. dx X X = i + µ X bx dt τ τ (3. 8) ds dt Si S µ = τ τ YX / S r + Y P P / S + m S X (3. 9) dp dt P P = τ τ i + r P X (3. 10) 38

49 When microbes are presented, different types of kinetics may be observed due to toxicity or inhibition of the organisms or enzymes by the substrate or product. Inhibition decreases the rate of reaction because it slows down bio-catalytic activities, and then affects the whole bioprocess. One common expression for modeling product inhibition is given in Equation 11. n ˆµ P µ = K 1 P (3. 11) S m 1 + S S BioMASS v2.0 was developed using Microsoft Visual Basic 2008, which contains different input controls like text fields, option buttons, checkboxes, and command buttons. The following is a sample of Visual Basic code to allow the user to add a second bioreactor in BioMASS v2.0. If Add2ndBioReactorCheckBox.Checked = True, groupbox of bioreactor 2 will be shown, Reactor2GroupBox.Show() ; or vice versa, the groupbox will be hiden, Reactor2GroupBox.Hide().The user needs to fill all the necessary data in the text fields and make a choice from the available options to define the model. The results are then reported by performing the forward finite differential equation calculations. X P S ( b) * X ]*( t ) [ t X 2 t + 1 t 1 2 t1 = µ (3. 12) ( k * X ) + ( k * * X )]*( t ) [ t P 2 t + 1 png t1 pg t 1 2 t1 = µ (3. 13) ( * X / Y ) + ( k * X / Y )]* ( t ) S 2 t 1 1 / t 1 t = µ (3. 14) [ t X S png t P / S 2 1 MODEL VALIDATION Two case studies will be evaluated to compare simulated results with actual data. 39

50 Case study 1: Growth-associated product formation in batch reactor using single Monod s model The bioprocess data and operating conditions required for simulation were gathered from literature (Yu, 2007). In this case, fermentation used glucose as a single limiting nutrient, and hydrogen was identified as primary (growth-associated) product. The bacterium of Thermotoga neapolitana was cultured at 77 o C, and the batch growth was carried out in 20 hours. The following input variables were used to run the simulation. The maintenance requirement for limiting substrate is assumed to be small enough to be neglected. S 0 = 5 g/l, X 0 = g/l, P 0 = 0, µ max = 0.94 h -1, K S = 0.57 g/l, b = 0.01 g/l, S, k pg = g H 2 /g X B. Y X / S = g X B /g S, P S Y / = g H 2 /g Batch fermentation Simulation was carried out to determine the suitability of Monod s model for hydrogen production in a batch system. The experimental results reported in literature were used to verify the models developed (Yu, 2007). The time course of predicted concentrations was compared with experimental values (Fig 3.5). The effect of product inhibition was taken into consideration with P m = 27 mmol/l and n =1 (Drapcho, 2008). The Monod model was incorporated with product inhibition kinetics to describe the batch growth of Thermotoga neapolitana. The comparisons demonstrated a good agreement between data from simulations with experimental data. After 20 hours of fermentation, biomass concentration of 0.5 g/l and hydrogen concentration of 26.7 mmol/l were observed. 40

51 Figure 3.5. Experimental (points) and simulated (lines) profiles of substrate, biomass and H 2 concentration in a batch system including impact of H 2 inhibition. Figure 3.7 shows a plot of studentized residual against simulated biomass concentration. A pattern of overprediction values at low concentration and underprediction at high concentration is evident, indicating some factor impacting biomass concentration is not being considered. However, studentized residual values range within ± 2 standard deviations, indicating good fit of predicted values to actual observations. 41

52 Figure 3.6. Studentized residual plot for glucose concentration. Figure 3.7. Studentized residual plot for biomass concentration. Figure 3.8 indicates that BioMASS accurately predicts H 2 concentration, but with the same pattern as Figure

53 Figure 3.8. Studentized residual plot for Hydrogen concentration. In Figure 3.9, significant differences between the experimental and simulated results were observed when the impact of inhibition not included. The growth models that incorporate the production inhibition parameter gave better fits to the experimental data compared to the models with only growth parameters. The predicted concentrations of biomass and hydrogen were much greater than those obtained from experiment, and glucose was depleted rapidly. After 10 hours of fermentation, no further glucose consumption or hydrogen production occurred. This was batch operation, which means no material was either added or removed from the bioreactor; therefore, there was potential of H 2 inhibition due to increasing H 2 concentration in bioreactor. If headspace gas was removed during incubation to avoid production prohibition, the hydrogen simulated could be doubled (71.3 mmol/l). 43

54 Figure 3.9. Experimental (points) and simulated (lines) profiles of substrate, biomass and H 2 concentration in a batch system without including impact of H 2 inhibition. When glucose was used as the carbon source, hydrogen production increased with an increase of glucose concentration. In Figure 3.10, there was no significant difference in simulated H 2 production between initial glucose concentration of 5 g/l, 10 g/l and 20 g/l. Due to the effect of product inhibition, there was no potential to increase hydrogen production in this system, and 5 g/l is practical upper limit for initial glucose concentration if H 2 gas is not removed. 44

55 Figure Simulated profiles of H 2 concentration with various initial glucose concentration in a batch system including impact of H 2 inhibition. A sensitivity analysis was conducted and compare with experimental data to determine the impact of µ max, K S, and X S Y / (Fig 3.11, 3.12, 3.13). Due to impact of product inhibition, there is no change in final H 2 concentration. However, the rate of hydrogen production was -1 increased with the increase ofµ. Atµ = 4.7 h, 26.5 mmol/l of H 2 was achieved only after max max 4 hours of fermentation (Fig 3.11). On the other hand, the rate of H 2 production was decreased when increased K S, and it took 20 hours of fermentation to reach 26.5 mmol/l of H 2 at K S = 2.85g/L (Fig 3.12). In Figure 3.13, H 2 concentration is increased with the increase of Y X / S ; however, there is no significant difference between Y X / S = g X/ g S and Y X / S = g X/ g S. The results of sensitivity analysis demonstrated a good agreement between estimated values and experimental data. 45

56 Figure Simulated profiles of H 2 concentration with differentµ values in a batch system including impact of H 2 inhibition. max Figure Simulated profiles of H 2 concentration with different including impact of H 2 inhibition. K S values in a batch system 46

57 Figure Simulated profiles of H 2 concentration with different including impact of H 2 inhibition. Y X / S values in batch system CSTR fermentation One of the advantages of BioMASS development is to investigate the behavior of bioprocess under varying conditions. The results can be analyzed to allow process optimization. In this case, in order to improve the production of hydrogen, CSTR operation was suggested. In a batch system, the cumulative hydrogen production ceases once the cells reached their stationary phase. Using a continuous system, it was possible to maintain the cell concentration by maintaining a specific favorable retention time. Consumption of substrate was found to increase with increase in retention time and the percent of glucose conversion was about 0.002% at a retention time of 1 hour (Table 3.1). 47

58 Table 3.1. Simulated results of hydrogen production at different retention time. Retention time (h) Glucose conversion (%) Biomass concentration (g/l) Rate of H 2 production (mmol/l-h) Figure 3.14 shows time course of substrate utilization, biomass growth and hydrogen production in a CSTR system at HRT of 6 hours. After 12 hours, cell growth and product formation entered a steady state. Two graphs were arranged vertically so that the user can easily follow the changing of nutrient concentrations with time. Hydrogen production increased with fermentation time. After 20 hours of fermentation, hydrogen concentration of mmol/l was achieved, corresponding to more 2.5 times than that of batch operation (27 mmol/l). The final cell density climbed up to 1.14 g/l to compare with 0.43 g/l of batch operation. 48

59 Figure Simulated results of hydrogen production in a CSTR system at HRT of 6h. Figure 3.15 shows the simulation results of steady state concentration of glucose, biomass and hydrogen. It was evident that steady state concentration of hydrogen increased when the HRT was increased from 1 to 3 hours. After 3 hours, the concentrations remained almost constant throughout the run. There was no siginificant difference in concentration of glucose, biomass, and H 2 between HRT of 3 hours and 10 hours. The minimum retention time should be more than 1 hour. Low retention time may result in washout of slow growing cell. 49

60 Figure Simulated profiles of glucose, biomass and H 2 concentration at various retention time in a CSTR system. To investigate the effect of initial substrate concentration, simulation of various concentration of glucose were performed (Fig. 3.16). As a result, 69.45, and mmol/l of hydrogen concentration were obtained from 5, 10, and 20 g/l of glucose concentration, respectively. The highest rate of hydrogen production for an initial substrate concentration of 5 g/l was mmol/l-h, whereas with an initial substrate concentration of 20 g/l the maximum rate of hydrogen production was mmol/l-h (Table 3.2). The results suggested that high production of hydrogen might be obtained if continuous operation was employed. 50

61 Table 3.2. Simulated results of rate of H 2 production with different initial substrate concentration in a batch system. Initial substrate concentration (g/l) H 2 concentration (mmol/l) Rate of H 2 production (mmol/l-h) Figure Simulated profiles of H 2 concentration at various initial glucose concentration in a CSTR system at HRT of 6 hours. Case study 2: Xylitol production from xylose and glucose as multiple substitutable nutrients Fermentation used glucose and xylose as a multiple substitutable nutrients. The microorganism prefers glucose over the other and will inhibit the use of the xylose. Since glucose (S 1 ) is preferred, then the growth rate of the microorganism using xylose (S 2 ) will be modeled as a function of S 2 with inhibition by S 1 as follows: 51

62 S1 µ 1 = ˆ µ K S1+ S1 (3. 15) S 2 K S1 µ 2 = f * ˆ µ K S + S2 K 1+ S 2 S 1 (3. 16) Yeast strain of Candida was cultured at 30 o C, and the batch growth was carried out in 35 hours (Pfeifer, 1996). Xylitol production was modeled as nongrowth-associated product. The following input variables were used to run the simulation. S Glu = 7 g/l, S Xyl = 28 g/l, X 0 = 0.01 g/l, P 0 = 0, µ max = 0.8 h -1, f = 0.4, K S1= 1 g/l, K S 2 = 7 g/l, b = 0.1 h -1, Y X / S = 0.16 g X/g S Glu Glu, Y X / S Xyl = 0.45 g X/g S Xyl, Y P / S Xyl = 0.55 g xylitol/g xylose, k png = 0.3 g xylitol/g X. The time course of predicted concentrations was compared with experimental values (Fig 3.17). After 10 hours of fermentation, glucose was utilized completely. After 20 hours of fermentation, complete xylose utilization was observed and g/l of xylitol concentration was obtained. The comparisons demonstrated a good agreement between data from simulations with experimental data (Pfeifer, 2000). 52

63 Figure Experimental (points) and simulated (lines) profiles of glucose, xylose and xylitol concentration in a batch system. A same pattern of studentized residual was observed in Figure 3.19, indicating some factor impacting xylitol concentration was not being considered during the simulation. Figure Studentized residual plot for xylose concentration. 53

64 Figure Studentized residual plot for xylitol concentration. A sensitivity analysis was conducted and compare with experimental data to determine the impact of µ max, K S, and Y X / S (Fig 3.20, 3.21, 3.22, 3.23, 3.24). The results indicate that the model is not sensitive to small changes in data. However, when those values were increased 20% or 50%, they had significant effect on xylitol production. 54

65 Figure Simulated profiles of xylitol concentration with different system. µ max values in a batch Figure Simulated profiles of xylitol concentration with different K S values in a batch 1 system. 55

66 Figure Simulated profiles of xylitol concentration with different K S2 system. values in a batch Figure Simulated profiles of xylitol concentration with different YX / S1 system. values in a batch 56

67 Figure Simulated profiles of xylitol concentration with different YX / S2 system. values in a batch To investigage the effect of ethanol on the xylitol production, simulation of glucose and xylose fermentation was carried out to produce xylitol as non-growth associated product and ethanol as growth associated product ( k pg = 4 g ethanol/g X and Y P / S = 0.51 g ethanol/g Glu glucose) (Krishnan, 1999). As shown in Figure 3.25, all the glucose in the medium was converted to ethanol before xylose utilization started. Ethanol production was very rapid and occurred within 10 hours after the start of fermentation and remained almost constant throughout the run. After 35 hours of fermentation, lower xylitol concentration of g/l was achieved, compared with g/l in the absence of ethanol. It s evident that the ethanol produced from utilization of the glucose partitially inhibits xylitol formation; therefore, the presence of ethanol could account for the decrease in the xylitol concentration. Although the effect is not significant, 57

68 if xylitol is the desired product, it would be necessary to remove the ethanol formed from the fermentation in order to attain high xylitol concentration. Figure Simulated profiles of glucose, xylose, xylitol and ethanol concentration in a batch system. The initial glucose concentration was varied from 0 g/l to 112 g/l to evaluate its effect on the fermentation, while the initial xylose concentration of 28 g/l remained the same (Table 3.3). A higher concentration of glucose produced a higher concentration of ethanol, while during the increase of xylose concentration, ethanol concentration was approximately the same (Table 3.4). This was because ethanol mostly produced by the fermentation of glucose. The effect of different ratio of glucose to xylose on xylitol production rate was investigated. At the ratio of 0:1, there is no glucose and the fermentation process uses xylose as single limiting nutrient, only 3.91 g/l of xylitol concentration was observed, which is lower than value obtained at the feeding ratio of 0.25:1 (14.73 g/l) or 0.5:1 (16.52 g/l). The initial glucose concentration is an important factor to obtain a high xylitol production. However, at high concentration of glucose, xylose 58

69 utilization was repressed and ethanol produced from glucose caused reduction in xylitol concentration. Therefore, the xylitol production rate at the ratio of 2:1 (1.74 g/l-h) was lower than the value obtained at the ratio of 1:2 (2.52 g/l-h). In this study, the xylitol concentration was maximum at a glucose/xylose feeding ratio of 1:4 (70.13 g/l). Table 3.3. Simulated results of xylitol and ethanol concentration with different initial glucose concentration in a batch system. Ratio Xylitol (g/l) Ethanol (g/l) Rate of xylitol production (g/l-h) Rate of ethanol production (g/l-h) 0:1 (0 g/l Glu + 28 g/l Xyl) :1 (7 g/l Glu + 28 g/l Xyl) :1 (14 g/l Glu + 28 g/l Xyl) :1 (28 g/l Glu + 28 g/l Xyl) :1 (56 g/l Glu +28g/L Xyl) :1 (112 g/l Glu + 28 g/l Xyl) Table 3.4. Simulated results of xylitol and ethanol concentration with different initial xylose concentration in a batch system. Ratio Xylitol (g/l) Ethanol (g/l) Rate of xylitol production (g/l-h) Rate of ethanol production (g/l-h) 1:0.25 (28 g/l Glu + 7 g/l Xyl) :0.5 (28 g/l Glu + 14 g/l Xyl) :1 (28 g/l Glu + 28 g/l Xyl) :2 (28 g/l Glu + 56 g/l Xyl)

70 1:4 (28 g/l Glu g/l Xyl) CONCLUSION Experimental data was used to verify the model established in this work, and satisfactory simulation results were obtained. With the verification of two cases for production of hydrogen and xylitol, the model in the present work were demonstrated to be suitable for describing the kinetics of substrate utilization, biomass growth and product formation, which can thus be used for optimizing the process. By using BioMASS v2.0, the users can perform a series of what if simulations assuming different process scenarios. In addition, the program provides a cheaper alternative for the user compared with costly commercial software. SuperPro or Aspen can provide sophisticated modeling for bioprocess. However, such simplified program as BioMASS is still very useful, which can model typical biological systems with satisfactory results. ACKNOWLEDMENTS I wish to express my deepest gratitude to Dr. Caye Drapcho for suggesting the topic of this thesis as well as her professional guidance and constructive criticism. I am also greatly indebted to Dr. Nhuan Nghiem, whose valuable advices, and enduring motivation and support were of inestimable value. Sincere thanks are also to him for critical review of my thesis. I also would like to thank Dr. Walker for giving useful lectures which helped me a lot to complete this thesis. 60

71 REFERENCES Birol, G., Undey, C., Cinar, A., A modular simulation package for fed-batch fermentation: penicillin production, Computers and Chemical Engineering, Drapcho, C. M., Nghiem, P. N., Walker, T. W., Biofuel engnieering process technology, GimBun, J., Radiah, A. B. D., Chuah, T. G., Bioreactor design via spreadsheet - a study on the monosodium glutamate (MSG) process, Journal of Food Engineering, Krishnan, M. S., Fermentation kinetics of ethanol production from glucose and xylose by Recombinant Saccharomyces 1400 (plnh33), Applied Biochemistry and Biotechnology, Nemeth, A., Sevella, B., Development of a new bioprocess for production of 1,3 propandiol I.: Modeling of Glycerol bioconversion to 1,3 propanediol with Klebsiella pneumoniae enzymes, Appl Biochem Biotechnol, Rizzo, D. M., The comparison of four dynamic systems-based software packages: Translation and sensitivity analysis, Environmental Modeling and Software, Pfeifer, M. J., Effect of culture conditions on Xylitol production by Candida guilliermondii FTI 20037, Appl Biochem Biotechnol, Shanklin, T., Selection of bioprocess simulation software for industrial applications, Biotechnol Bioeng, Shi, J., Functional Food Ingredients and Nutraceuticals: Processing Technologies (Functional Foods and Nutraceuticals), Yu, X., Biohydrogen production by the hyperthermophilic bacterium Thermotoga Neapolitana,

72 Chapter 4 CONCLUSION Experimental data was used to verify the model established in this work, and satisfactory simulation results were obtained. With the verification of two cases for production of hydrogen and xylitol, the model in the present work were demonstrated to be suitable for describing the kinetics of substrate utilization, biomass growth and product formation, which can thus be used for optimizing the process. By using BioMASS v2.0, the users can perform a series of what if simulations assuming different process scenarios. In addition, the program provides a cheaper alternative for the user compared with costly commercial software. SuperPro or Aspen can provide sophisticated modeling for bioprocess. However, such simplified program as BioMASS is still very useful, which can model typical biological systems with satisfactory results. Future Scope ph is an important factor in biological system; therefore, in the future, BioMASS should be designed to allow the user to investigate the effect of ph on final concentrations. Moreover, besides batch and CSTR operation, fed-batch approach is popular for using in fermentation process; therefore, BioMASS application should also include it. BioMASS v1.0 and v2.0 were only designed to work on Window platform, future working system of BioMASS can be Mac and Iphone application. 62

73 APPENDICES 63

74 APPENDIX A COMMANDS IN BioMASS v2.0 Menu Bar The menu bar lists the available menus. A menu contains a list of commands. Some commands carry out an action immediately; others display a dialog box so that the users can select options. The user will know that a command will display a dialog box if it is followed by three periods (...). The user can choose commands from a menu or toolbar, or the user can use shortcut keys. The keyboard shortcut keys are listed on the menu to the right of the command. The menu bar at the top of the screen containing the following headings: File, View, Tools, Windows and Help. - File Menu: Irreversible Chemical Kinetics: This command has three sub choices of Zero Order, First Order, and Second Order reaction. Biological Kinetics: This command has three sub choices of Single Limiting Nutrient, Multiple Limiting Nutrients, and Inhibition Model. Open File: This command brings up a dialog box to open a previous saved file. BioMASS v2.0 supports file format of Microsoft Excel (*.xls) Save Data: Choosing this option will raise a dialog that allows you to determine the location in which you want the current simulation to be saved and the name 64

75 under which you want to save it. Since BioMASS only supports file format of Excel, the filename will always end in.xls. To use this option, the user should have the corresponding Microsoft Excel object, which is usually supplied with the Microsoft Office package, registered on the computer. Save All: It is the same as Save Data, but this command allows the user to save both data and calculated results in Microsoft Excel. Print Preview. Print: The user can choose File > Print or Ctrl + P keyboard shortcut to print the current window. Exit: Use this command to exit the program. If any test results remain open, a dialog will appear asking whether or not the user wishes to save the results. Figure A. 1. Commands in View Menu. - View Menu: 65

76 Toolbar: Select the command when the user wants to show or hide the toolbar. Check mark is display in the menu when the corresponding option is selected. Status Bar: This command displays or hides the status bar at the bottom of the main window, which shows information about the current state of the program. Check mark is display in the menu when the corresponding option is selected. Full Screen/ Exit Full Screen: In full screen view, the application window is maximized and status bar as well as toolbar is removed. The users can restore these by clicking Exit Full Screen. Show Start Page: Click this command to show the Start Page again. Figure A. 2. Commands in Tools Menu. - Tools Menu: 66

77 Zoom in: The size of image can be changed by using the Zoom-in (+) commands on the View menu. This function lets the users get a closer view of bioreactors. Clear Form: BioMASS v1.0 does not allow the users to view and compare the results of zero, first, and second order of irreversible chemical kinetics. Fortunately, BioMASS v2.0 can handle that. When the users click on a button of Zero Order or First Order or Second Order on standard toolbar, the current project is opened, not a new project. Therefore, if the users want to start a new project, they need to click on Clear All to clear all data and results of the current project. In other words, choosing this option will erase all existing variables from the workplace, and start a new project. Figure A. 3. Commands in Windows Menu. - Windows Menu: When the user has multiple windows open, they can be arranges that the user can see all or part of each window. 67

78 Cascade: An arrangement of windows such that they overlap one another. Typically, the title bar remains visible so that the user can always see which windows are open. Tile Vertical: Every window is completely visible, and they are arranged vertically. Tile Horizontal: Every window is completely visible, and they are arranged horizontally. Close All: Close all the opening windows. Figure A. 4. Commands in Help Menu. - Help Menu: 68

79 Notebook: This command brings up a window that introduces frequently asked questions, listed in alphabetical order, along with short answers, images, and animations to help the user has a better understanding about bioprocess. About Biomass v2.0: Displays the version number of this application and introduces information related to the software version. Figure A. 5. Notebook window. 69

80 Figure A. 6. About BioMASS v2.0 window. Standard Toolbar Figure A. 7. Standard Toolbar and Calculation Toolbar. 70

81 This toolbar includes commands that are used more often. They are arranged in a toolbar to faster access with a mouse click. Tooltips explaining the functions of each button are displayed if you hold the mouse pointer over the desire button. Icon is a considerably important part in user interface. It means that users can understand the function represented by the icon and further learn how to use the function. Icon interface can reduce the trouble of memorizing functions for users. The list of command icons and their description are summarized in Table. Table A. 1. List of command icons in Standard Toolbar. Icon Description Open saved file. Save data in Microsoft Excel. Save both data and simulation results in Microsoft Excel. Open Irreversible Chemical Kinetic Project (Zero Order) Open Irreversible Chemical Kinetic Project (First Order) Open Irreversible Chemical Kinetic Project (Second Order) Open Single Limiting Nutrient Project. Open Multiple Limiting Nutrients Project. Open Inhibition Model Project. 71

82 Zoom In selected images. Clear All data and results from current running project. Answer frequently asked questions (FAQs) about bioprocess. Calculation Toolbar Table A. 2. List of command icons in Calculation Toolbar. Icon Description Come back to input data form. Run the simulation Results can be presented in suitable graphs. Click this button to view them. Click to view table. Scroll Bars When the window is zoomed-in or zoomed-out, Scroll Bars appear that you can use to view information that exists beyond the borders of the window. When you can view all the contents of the window without scrolling, the Scroll Bars are absent. Drag a Scroll Box or click on the Scroll Arrows to scroll the window. 72

83 Figure A. 8. Scroll Bar and Status Bar Status Bar The status bar is displayed at the bottom of BioMASS window. The left side of the status bar is Progress Bar. When Progress Bar is full, it indicates that the program is ready; otherwise, it indicates that the program is running. The right side of the status bar shows the current date and time and indicators of the following keys when they are latched down: Table A. 3. List of common function keys. Indicator Description 73