Simulation of Continuous Bio-Reactor

Transcription

1 Proceeding of he 5 th nnual International Conference Syiah Kuala Univerity (IC Unyiah) 205 In conjunction with he 8 th International Conference of Chemical Engineering on Science and pplication (ChES) 205 September 9-, 205, Banda ceh, Indoneia Simulation of Continuou Bio-Reactor Rudy gutriyanto Department of Chemical Engineering, Faculty of Engineering, Univerity of Surabaya, Surabaya, Indoneia; *Correponding uthor: rudy.agutriyanto@taff.ubaya.ac.id btract Dynamic tudy of bioproce ytem play a central role in bioproce control. It i in fact on the bai of the time required for the development of the knowledge proce that the total deign, analyi and implementation of monitoring and control method are carried out. Within the framework of bioprocee, the mot natural way to determine the model that will enable the characterization of the proce dynamic i to conider the material balance of major component of the proce. hi article will preent imulation reult of continuou bio-reactor. he mathematical model for the bio-reactor baed on the material balance had been derived (Rig g and Karim, 2006) and would be adopted in thi tudy. hoe model were olved and imulated uing Matlab. It i found that the dynamic repone of the bio-reactor due to a tep change in feedrate are firt order. Key word: Simulation, bio-reactor, biochemical, fermentation Introduction Microbial fermentation i a proce in which a population of micro-organim are grown uing certain nutrient under favorable urrounding condition (temperature, ph, agitation, aeration, etc). It chematically correpond to the tranformation of ubtance (generally carbonaceou ubtrate) into product, reulting from metabolic activitie of cell. he main component of the reaction are a follow (Dochain, 2008): Subtrate, denoted a Si, which are neceary for the growth of micro-organim, or even which are precuror of a compound to be produced. hee ubtrate generally contain a ource of carbon (glucoe, ethanol, etc) and ometime nitrogen (NO 3, NH4, etc.) and phophoru (PO4, etc). Microbial biomae, denoted a xi. End product, denoted a Pi, for agri-food (oil, cheee, beer, wine, etc), chemitry (olvent, enzyme, amino acid, etc), the pharmaceutical indutry (antibiotic, hormone, vitamin, etc) or for the production of energy (bio-ethanol, bioga, etc.). Bio-ethanol, a a clean and renewable fuel, i gaining increaing attention, motly through it major environmental benefit. It can be produced from different kind of renewable feedtock uch a e.g. ugar cane, corn, wheat, caava (firt generation), celluloe bioma (econd generation) and algal bioma (third generation). Sanchez and Cardona (2008) decribed the biotechnological production of bio-ethanol from different feedtock. he agro-indutrial wate had been explored for their feaibility a culture media for the production of bioethanol (Bocanegra et al, 205; Balat, 20). Previou reearch include kinetic tudy of batch ethanol production from ugar beet raw juice (Dodic at al, 202). Continuou bio-reactor baed on a CSR are not commonly ued in biotechnology indutry although they are good candidate for the production of high volume product, uch a, bioethanol. he deign and development of continuou 50

2 Proceeding of he 5 th nnual International Conference Syiah Kuala Univerity (IC Unyiah) 205 In conjunction with he 8 th International Conference of Chemical Engineering on Science and pplication (ChES) 205 September 9-, 205, Banda ceh, Indoneia fermentation ytem have allowed the implementation of more cot effective procee. an et.al (205) ued a flocculating yeat Saccharomyce cereviiae train KF-7 to etablih the continuou ethanol fermentation proce to convert raw juice and thick juice of ugar beet to ethanol. Steady tate and dynamic tudy in continuou bio-reactor but for gluconic acid production had been tudied previouly (Fatmawati & gutriyanto, 200). hi paper decribe the dynamic repone of continuou bio-reactor for bioethanol production. Method Figure how the CSR ytem ued in thi tudy. Feed contain ugar a a ubtrate from corn or other grain (uch a wheat, rice, barley etc) and nutritional alt to upport for cell growth. he cell conume the ubtrate and produce the product and CO2. n air blower provide oxygen to the cell. he exit ga i primarily compoed of N2 from the air, the unconumed O2, and CO2 produced by the cell from the conumption of ugar. he cell concentration i meaured by a turbidity meter, the ubtrate concentration i meaured by an on-line HPLC analyzer. In indutrial bio-proce, filter are uually ued for all tream entering and leaving the reactor to maintain terile condition although they are not hown in Figure. Exit Ga Sterile Storage of Feed F pec Variable Speed Pump ir ir Blower Figure. Schematic of the continuou bio-reactor ued in thi tudy hi bio-reactor i modeled by performing ma balance on the cell, ubtrate, and product (Rigg and Karim 2006). ume Monod kinetic for the cell growth and that mot of the ubtrate i conumed by the cell. he reulting proce model are a follow: dx FV x max x dt V ds F F V V S F S dt V V Y dp dt F V V P Y max xp max xs x x () (2) (3) 5

3 Proceeding of he 5 th nnual International Conference Syiah Kuala Univerity (IC Unyiah) 205 In conjunction with he 8 th International Conference of Chemical Engineering on Science and pplication (ChES) 205 September 9-, 205, Banda ceh, Indoneia he actuator i fat reponding compared to the proce dynamic; therefore the actuator i aumed to repond intantaneouly. he enor for the cell, ubtrate and product concentration are modeled eparately baed on the type of the enor ued in each cae. Below are the model equation that repreent the dynamic behaviour of the actuator and enor: ctuator : F V F V, pec dx dt Senor : x x S P M t St t Pt (4) (5) (6) (7) he proce parameter and variable for thi model are given in able. able. Proce parameter and variable (Rigg and Karim, 2006) Symbol Parameter and Variable Value and Unit FV Feed rate to the reactor Initally 000 L/h FV,pec he pecified feed rate to the bioreactor 050 L/h at t=3 h K Monod aturation contant 0. g/l P Product concentration in the reactor Initially.25 g/l S Subtrate concentration in the reactor Initially 25 g/l SF Subtrate concentration in the feed to the 50 g/l reactor t ime h V Volume of the reactor 5000 L x Cell concentration in the bioreactor Initially 0.25 g/l YxP Yield factor 0.2 g-cell/gproduct YxS Yield coefficient 0.0g-cell/gubtrate max Maximum pecific growth rate 0.2/h Ɵ he enor deadtimefor HPLC analyzer 30 min τm he time contant for the turbidity meter ued to meaure the cell concentration 20 hoe model equation were then olved and imulated uing Matlab for input change. he proce tranfer function in Laplace domain a follow can be obtained immediately: x S P = G G G 2 3 F v (8) Reult and Dicuion Figure 2 how feedrate ( Fv) tep change from 000 to 050 L/h at t = 3 h for 50 h imulation time. can be een in Figure 3, the concentration of ubtrate increae linearly and the product concentration decreae linearly. hoe reult are conitent with previou finding (Rigg and Karim, 2006). 52

4 Proceeding of he 5 th nnual International Conference Syiah Kuala Univerity (IC Unyiah) 205 In conjunction with he 8 th International Conference of Chemical Engineering on Science and pplication (ChES) 205 September 9-, 205, Banda ceh, Indoneia Fv [L/h] Figure 2. Step change of bio-reactor feedrate x [g/l] S [g/l] P [g/l] Figure 3. Dynamic repone of the continuou bio-reactor to a tep increae in feed rate 0.4 x [g/l] S [g/l] P [g/l] Figure 4. Dynamic repone of bio-reactor for longer imulation time 53

5 Proceeding of he 5 th nnual International Conference Syiah Kuala Univerity (IC Unyiah) 205 In conjunction with he 8 th International Conference of Chemical Engineering on Science and pplication (ChES) 205 September 9-, 205, Banda ceh, Indoneia can be een in Figure 4, which how imulation reult for longer time (i.e. up to 500 h imulation time), the proce are actually firt order (Marlin, 2000; Seborg et al, 200). he firt order proce tranfer function are a the following: x = S F v (9) P Concluion Dynamic tudy of a continuou bio-reactor for bioethanol production ha been performed. It wa found that the proce actually follow firt order dynamic behaviour. he gain and time contant of the firt order proce are hown in Eq (9). cknowledgement he author would like to thank to he Univerity of Surabaya for upport of thi work. Reference Dochain, D. (2008). Bioproce Control, John Wiley and Son, Inc. Sanchez, O.J., Cardona, C.. (2008). rend in Biotechnological Production of Fuel Ethanol from Different Feedtock, Bioreource echnology, 99, Fatmawati,., gutriyanto, R. (200). Steady State and Dynamic Gluconic cid Production by pergillu niger, Journal of Chemitry and Chemical Engineering, 4(7), Dodic, J.M., Vucurovic, D.G., Dodic, S.N., Grahovac, J.., Popov, S.D., Nedeljkovic, N.M., (202). Kinetic Modeling of Batch Ethanol Production from Sugar Beet Raw Juice, pplied Energy, 99, Bocanegra,.R.D., Munoz, J..., Lopez, R.. (205). Production of Bioethanol from gro- Indutrial Wate, Fuel, 49, Balat, M. (20). Production of Bio-ethanol from Lignocelluloic Material via the Biochemical Pathway: Review, Energy Converion and Management, 52 (2), an, L., Sun, Z.Y., Okamoto, S., akaki, M., ang, Y.Q., Morimura, S., Kida, K. (205). Production of Ethanol from Raw Juice and hick Juice of Sugar Beet by Continuou Ethanol Fermentation with Flocculating Yeat Strain KF-7, Bioma and Bioenergy, 8, Marlin,.E. (2000). Proce Control Deigning Procee and Control Sytem for Dynamic Performance, McGraw Hill. Rigg, J.B., Karim, M.N. (2006). Chemical and Bio-Proce Control, Pearon Prentice Hall. Seborg, D.E., Mellichamp, D.., Edgar,.F. (200). Proce Dynamic and Control, John Wiley and Son, Inc. 54