Process control for intensifying biogas production. in anaerobic fermentation

Transcription

1 BiogasForum Öresund seminar Process control in biogas reactors, Copenhagen, Nov. 9, Process control for intensifying biogas production in anaerobic fermentation Jing Liu Dept. of Biotechnology, Center for Chemistry and Chemical Engineering, ering, Lund University, Sweden BiogasForum Seminar, Nov 9, DTU

2 Main degradation pathways of AD Hydrolysis Organic polymers Soluble organic monomers Acidogenesis Acetogenesis and dehydrogenation Fermentation intermediates Acetate H CO Methanogenesis CH CO BiogasForum ICA, May Seminar, 31, Nov Busan 9, DTU Modified from McCarty, 19; Gujer and Zehnder, 193)

3 Biogas production in nature Biogas production occurs spontaneously in nature Marshes Biogas Rubbish dumps Cow s digestive tract BiogasForum Seminar, Nov 9, DTU Insect guts

4 Biogas production in engineered devices Energy crops Wastewater Solid waste Biogas Treated wastewater Compost Industrial AD bioreactor Landfill BiogasForum Seminar, Nov 9, DTU 1 m 3 of Biogas (7% CH + 3% CO ) =. liter diesel fuel =.7 liter petrol =. m 3 propane =. m 3 butane =. kg coal

5 History of AD as a process for practical use Sanitation concerns Pioneer work by Buswell in microbiology Fueling street lam ps in UK Closed-tank reactor with heating and mixing Waste stabilization using AD Biogas was occasional used for heat China began using biogas for heat, light and cooking Rapid and worldwide development of sim ple AD Energy crisis (1973, 1979) m ade biogas as an energy source Millions of sm all digesters were constructed in China, India. Lim ited research in AD Non-erergy benefits of AD were recognized - sludge and odor reduction, Small-scale energy and sanitation systems were promoted in Asia Fram -based & centralized digesters were develped in Europe Energy + waste handling + pathogen reduction Municipal solid waste handling Decomposition of organic toxic and hazardous com pound Pioneer work of ICA for high rate AD Be ing a part of integrated recovery system for sustainable devel. ICA+D in AD AD + FC BiogasForum Seminar, Nov 9, DTU

6 Characteristic of AD Advantages Renewable energy Waste handling Nutrients recycle Sludge reduction Pathogen control Sustainable development A multi-step process Low cellular yield Methane production Low energy yield from acetogenic step Disadvantages Long retention time Large reactor volume Unstable process Sensitive to environmental changes Risk of system overload BiogasForum ICA, May Seminar, 31, Nov Busan 9, DTU

7 Problem-solving strategies Suitable reactor configuration Microbiological study UASB, anaerobic filter (packed-bed reactor), suspended bed reactor, baffled reactor, rotating disk,etc. Intensify the process by maintaining a high density of microorganisms. Two-stage or multi-stage design To achieve a separate optimization in different stages Properly mixing feedstock from various sources that characterize in enrichment of different nutrients optimization of C/N ratio Optimization of feedstock BiogasForum Seminar, Nov 9, DTU Disadvantages Long retention time Large reactor volume Unstable process Sensitive to environmental changes Risk of system overload Close study of microbial consortia and anaerobic ecosystem better understanding of complex degradation and gain process knowledge Often being neglected in full-scale plants Enhanced process monitoring and control

8 Full scale operation Few or no Sensor PLC Bioreactor Actuators SCADA-system and plant operator Poor information flow from the process (often only flow rate, temp, etc) Process operation in black box mode, relies on operator s experience A large safety margin for a reliable operation Control variables are uncoupled from the dynamic state of process BiogasForum Seminar, Nov 9, DTU

9 Problem-solving strategies Disadvantages Intensify the process by maintaining a high density of microorganisms Operate bioreactors far below their max. capacity Enhanced process monitoring and control - ICA Long retention time Large reactor volume Unstable process Sensitive to environmental changes Risk of system overload Smaller reactor volume Short retention time Ensure reliable and stable operation Prevent the process from failure Allow the degradation and biogas production at a higher specific rate Combine a suitable bioreactor design with enhanced process monitoring and control technologies BiogasForum ICA, May Seminar, 31, Nov Busan 9, DTU

10 ICA issues Sensors and instrumentation Control and automation Actuators Information system Decision support system Diagnosis ICA in AD Detection and early warning Plant wide and integrated control Modeling and simulation BiogasForum Seminar, Nov 9, DTU

11 Cascade contorl + extremum-seeking A cascade control structure and a rule-based system with extremum-seeking feature Rule-based syst em D value Referen ce of gas flow rate + - e Master controller reference + - e1 Slave controller feedi n g rate Bioprocess Output sensor Gas flow meter Component block diagram of the control system BiogasForum Seminar, Nov 9, DTU

12 Rule-based system with extremum-seeking feature to to add add a small small increment increment in in the the feed feed rate rate over over a short short period period of oftime, then then to to closely closely monitor monitor and and analyze analyze the the process process response response to to test test the the upper upper limit limit of of the the process process treatment treatment capacity capacity and and maximize maximize the the reactor reactor performance performance Gas flow rate (lgas lreactor ay GasFlow (setpoint) GasFlow (monitored) Time (hour) BiogasForum Seminar, Nov 9, DTU

13 AD reactor can be operated close to its maximum capacity Gas outlet Gas flow meter & infrared sensor for methane Develop a fast reacting control system based on existing simple sensors Schedule control tasks according to different time scales Protect process from overload Effluent outlet Reject disturbances Gas-liquid separation unit meter DAQ hardware Data Up-flow anaerobic fixed bed reactor Computer Feeding tank Anaerobic bioreactor can be operated close to the maximum capacity, still having enough safety margins for reliable operation P Recirculation pump P Feeding pump BiogasForum Seminar, Nov 9, DTU

14 Startup operation with control 7 7. Gas flow rate (l gas l reactor Organic degraded Gas flow monitored Gas flow setpoint Organic degraded (g COD l reactor Gas composition (%) CH (infrared sensor) CH (GC) monitored setpoint CO (GC) CO (infrared sensor) Organic load/degraded (g COD l reactor BiogasForum Seminar, Nov 9, DTU 1 COD re mova l ra t e Organic load Organic degraded COD removal rate (%) Gas yield (l gas g COD removed Gas yield CH yield Only days were needed to increase the OLR from to 7 g COD l reactor -1.

15 Short term under- & overload Gas flow (l gas l reactor OLR Gas flow setpoint Ga s flow i d 3 1 OLR (g COD l reactor Gas flow (l gas l reactor OLR Gas flow monitored Gas flow setpoint 3 3 OLR (g COD l reactor Influent COD (g l Influe nt COD Influent flow rate Influent flow rate (l d Influent COD (g l Influent COD Influent flow Influent flow rate (l d Gas compsition (%) 3 CH (infrared sensor) monitored setpoint CO (infrared sensor) Gas composition (%) monitored CH (infrared sensor) CO (infrared sensor) setpoint BiogasForum Seminar, Nov 9, DTU

16 Long term underload Gas flow rate (l gas l reactor OLR 9 Gas flow setpoint 3 Gas flow monitored OLR (g COD l reactor 9 7 Influent COD (g l 7 Influent flow rate Influent COD 3 1 Influent flow rate (l d Gas composition (%) 3 CH (infrare sensor) setpoint monitored CO (infrare sensor) BiogasForum Seminar, Nov 9, DTU

17 Long term overload & shock load Gas flow rate (l gas l reactor OLR Gas flow setpoint Gas flow monitored OLR (g COD l reactor Gas flow rate (l gas l reactor 1 Gas flow monitored 1 3 Gas flow setpoint Injection of concerned feeding 1 OLR OLR (g COD l reactor Influent COD (g l Influent flow rate Influent COD Influent flow rate (l d Influent COD (g l Influent COD Influent flow rate Influent flow rate (l d CH (infrared sensor) 7. Gas composition (%) CH (infrared sensor) setpoint CO (infrared sensor) Gas composition (%) setpoint CO (infrared sensor) monitored monitored. 3. BiogasForum Seminar, Nov 9, DTU

18 Temperature variation Temperature ( o C) Te mpe ra t ure Influent flow rate Influent flow rate (l d Gas flow rate (l gas l reactor 3 1 Gas flow monitored Gas flow setpoint OLR OLR (g COD l reactor BiogasForum Seminar, Nov 9, DTU Gas composition (%) CH (infrared sensor).9 setpoint.9. monitored CO (infrared sensor)

19 Process control stability problem at high load Gas flow rate (lgas lreactor ay Time (hour) Organic degraded (g COD lreactor ay BiogasForum Seminar, Nov 9, DTU

20 Selection of parameter for variable gain delta delta ±.1 ±.1 Variation of Δ during a start-up operation. delta delta Variation of Δ during a steady-state operation. BiogasForum Seminar, Nov 9, DTU delta Time (hr) ±.1 Variation of Δ for short-term underload, shortterm overload, long-term underload, long-term overload, long-term overload + shock load, sudden changes of reactor temperature. Δ = setpoint reactor

21 State dependent variable gain control system Rule-based model for variable gain control estimate the process gain via observation of the Δ ( setpoint - reactor ) Rule-based system D value Reference of gas flow rate + - e Master controller reference + - e1 Slave controller feeding rate Bioprocess Output sensor Gas flow meter BiogasForum Seminar, Nov 9, DTU

22 Rule-based model for variable gain control S 3 19 S3 S7 1 S 17 S S 1 S1 S 13 State machine representation of the rule-based model for variable gain control in the inner feedback loop. Each circle represents an individual state. The arrow with fine line indicates the direction of state shift. BiogasForum Seminar, Nov 9, DTU

23 Pathways of state shifting BiogasForum Seminar, Nov 9, DTU

24 1 1 State dependent variable gain control -1 Gas flow rate (l gas l reactor ) Gas flow monitored Gas flow setpoint Organic loading rate 3-1 OLR (g COD l reactor ) -1 Gas flow rate (l gas l reactor ) Control gain (Pk) Δ Gas flow setpoint Gas flow monitored Organic loading rate reactor Δ.3.. setpoint setpoint reactor Control gain (Pk) Control gain Δ OLR (g COD l reactor ) control gain..3 Δ BiogasForum Seminar, Nov 9, DTU

25 Conclusions Steer the process for a fast start-up according to the state dynamic Considerable improvement of the process stability at high-rate operation Improved performance is achieved by varying the proportional gain of inner feedback loop according to the process dynamic Controller and control structure are still quite simple allow to be easily implemented Further improvement of operational stability is possible by scheduling the pushing authority of extremum-seeking control. BiogasForum Seminar, Nov 9, DTU