Optimal control of a continuous bioreactor for maximized beta-carotene production

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1 Engineering Conferences International ECI Digital Archives Integrated Continuous Biomanufacturing II Proceedings Fall Optimal control of a continuous bioreactor for maximized beta-carotene production Nazmul Karim Texas A&M University, nazkarim@mail.che.tamu.edu Jonathan Raftery Texas A&M University Xinghua Pan Texas A&M University Follow this and additional works at: Part of the Biomedical Engineering and Bioengineering Commons Recommended Citation Nazmul Karim, Jonathan Raftery, and Xinghua Pan, "Optimal control of a continuous bioreactor for maximized beta-carotene production" in "Integrated Continuous Biomanufacturing II", Chetan Goudar, Amgen Inc. Suzanne Farid, University College London Christopher Hwang, Genzyme-Sanofi Karol Lacki, Novo Nordisk Eds, ECI Symposium Series, (2015). biomanufact_ii/100 This Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion in Integrated Continuous Biomanufacturing II by an authorized administrator of ECI Digital Archives. For more information, please contact franco@bepress.com.

2 Optimal control of a continuous bioreactor for maximized carotene production Jonathan P. Raftery and Dr. M. Nazmul Karim Artie McFerrin Dept. of Chemical Engineering, Texas A&M University, College Station, Texas 77843

3 β-carotene Market An orange pigment produced by diverse organisms such as plants, fungi, and bacteria Used in many industries: Food Animal nutrition Pharmaceuticals Cosmetics Colorant Projected worldwide market value is $1.4 billion in Higher antioxidant activity found in naturally produced βcarotene when compared to the synthetic version 1 Carotenoids Market By Type (Astaxanthin, Beta-Carotene, Canthaxanthin, Lutein, Lycopene, & Zeaxanthin), Source (Synthetic And Natural), Application (Supplements, Food, Feed, And Cosmetics), & By Region - Global Trends & Forecasts To MarketsandMarkets February 2015

4 β-carotene Production via Recombinant Saccharomyces cerevisiae

5 Continuous Production of β-carotene Recovered Glucose and Off Gases Pump Fermenter Pump Only Ethanol Acetic Acid Recovery Ethanol Acetic Acid Cell Separator Carotene and Solvent Carotene Recovery Unit Pure Carotene Product Carotene Purification Unit Solvent Liquid Waste Solvent Recovery Oxygen Cellular Waste To Waste Treatment Cells with Carotene Product Cell Disruptor Fresh Solvent Recovered Solvent

6 Outline Motivation Batch Operation of a Carotene Bioreactor Extension of Batch Operation to Continuous Systems Model Predictive Control of a Continuous Bioreactor to Maximize β-carotene Production Results and Conclusions

7 Batch Process Overview The aim of this study is to: Develop a suitable and reliable kinetic model for the carotene production in batch cultures of an engineered Saccharomyces cerevisiae strain using glucose as the main substrate Apply this model to predict cell growth, substrate consumption, ethanol and acetic acid formation and later assimilation. Determine the carotene productivity of the batch system

8 Biomass, Acetic acid, Ethanol (g/l) Batch Operation of a β-carotene Bioreactor Glucose (g/l) and Beta-carotene (mg/l) Concentration Organism Strain Saccharomyces cerevisiae SM 14 1 Product Reactor Working Volume Initial Glucose Air Flow Rate Run Time Beta-carotene 3 Liters 20 g/l 6 L/min 72 hours Temperature 30 (controlled) ph 4 (controlled) Air X, G, P, E, A Volume Time (h) 0 1 Reyes, L.H., Gomez, J.M., and Kao, K.C. Improving carotenoids production in yeast via adaptive laboratory evolution. Metabololic Engineering. 21 (2014)

9 Unstructured Batch Models Growth Rate Models: μ = μ G + μ E + μ A μ G = μ E = μ max,g ξ E ξ A G K SG + G + a ge E + a ga A μ max,e E K SE + E + a eg G + a ea A Batch Models: dx dt = r X = (μ G +μ E + μ A ) X dg dt = r G = μ GX Y X G de dt = r E = k 1 μ G X μ EX Y X E μ A = μ max,a A K SA + A + a ag G + a ae E da dt = r A = k 2 μ G + k 3 μ E X μ AX Y X A Inhibition Models: ξ E = f E ξ A = f A dp dt = r P = α 1 μ G + α 2 μ E + α 3 μ A X + βx

10 Estimation Procedure and Results Biomass, Ethanol, Acetic Acid Concentration (g/l) Glucose (g/l) and Carotene (mg/l) Concentration Time (hr) Carotene Productivity 120 mg L 72 + x hrs = mg L hr assuming x = 10 hours for filling, cooling, and cleaning

11 Continuous Process Overview The aim of this study is to: Propose a novel continuous carotene production process utilizing a two tank system to allow for the independent manipulation of the inlet flow rate and inlet glucose composition Develop continuous models describing the dynamic nature of this novel fermentation system Utilize dynamic optimization techniques to develop a model predictive controller capable of maximizing carotene production to rival that of batch production processes

12 Continuous Operation of a β-carotene Bioreactor Recovered Process Models: Glucose and G f Pump F 2 F 1 Pump Only dg dt = F 2 V G f F out V Recovery dx dt = F out V X + r X dp dt = F out V Acetic P + Acid Ethanol r P Acetic Acid Ethanol G r G Carotene and Solvent P Ca P Off Gases Fermenter Bioreactor F out de dt = F out V da dt = F out V E + r E Cell Separator A + r A Carotene Recovery Unit Oxygen Cells with Carotene Product dv dt = F G+M + F M F out F out = f V Cell Disruptor

13 Optimization Algorithm Starting at the beginning of continuous operation characterized by the time t = t switch = 20 hours: 1. Discretize the process models for a given Δt = 1 hours with n 10 n 2. Solve the following optimization problem for the optimum hourly flowrate from each tank, F 1 and F 2 : min P t + α F 1 F 2 t V t V r 2 s. t. Discretized ODEs 0 F 1,2 F max 3. Repeat Step 2 for each hour after t switch, using the previous hour s end state as the new initial condition, to determine the optimum flowrate as the system moves forward in time

14 Concentration (β-carotene = mg/l, all else in g/l) Flowrate (L/hr) Steady State Analysis Concentration Profiles Optimal Control Actions Time (hours) Glucose Biomass β-carotene Time (hours) Only + Glucose Ethanol Acetic Acid

15 Batch vs Continuous Comparison Batch Operation β-carotene Concentration Fermentation Time P = 120 mg L Continuous Operation β-carotene Concentration P = mg L Inlet Flowrate (F total ) and Volume t = 72 hours F total = L hr V = 3 L Productivity Productivity P t = 1.46 mg L hr P F V mg = 1.62 L hr Continuous operation increases process productivity by 10.5% when compared to traditional batch processing.

16 Conclusions Continuous operation has the potential to increase productivity of bioreactor systems A novel two-feed continuous reactor system capable of independently varying the dilution rate and inlet glucose concentration was implemented A bi-level dynamic optimization methodology was to determine the maximum productivity of steady-state continuous β-carotene production Continuous production shows a 10.5% increase in βcarotene productivity compared to a tradition batch system

17 Acknowledgements Texas A&M Institute for Advanced Studies National Science Foundation Dwight Look College of Engineering Graduate Teaching Fellowship Karim Research Group (M. Carolina Ordoñez-Franco, Tejasvi Jaladi, Melanie DeSessa) Kao Research Group (S. cerevisiae SM14)