Anaerobic digestion system control via

fuzzy logicTan Chern Yee

1000922192

Objective

To formulate dynamic mathematical model for anaerobic digestion system

Identification of important process control variable

Development of fuzzy logic controller for corresponding processes

Comparison of fuzzy logic controller against PID control in controlling the corresponding process.

Problem statement

Missing parameters

Variable not fully quoted

Difficult to code if not proficient enough

Ode model may not be suitable for all industry

Anaerobic Digestion

Anaerobic digestion is the conversion of organic matter into methane and carbon dioxide

Application: biochemical process such as production of antibiotics & alcohol but more commonly used for the treatment of wastewater when incoming COD is too high.

Eg. Dairy industry, fertilizer industry, typically for F & B industry

Oil based industry is difficult to do so, incoming COD too high and influent too thick to be process properly difficulty to maintain.

Types of Anaerobic digesters

CSTR/CHEMOSTAT

• the most basic type of anaerobic digester

• Consist of a agitator

• Biomass is removed continuously, causing long retention time

• UASB is develop to overcome

Upflow Anaerobic Sludge Blanket Reactor

• Active microorganism are kept in the reactor due to the production of highly flocculated, well settling, compact, sludge granules which the system is able to produce.

• It is considered as a CSTR/CHEMOSTAT which retains biomass.

• Advantages:

Low RTD

Simple design

Small reactor volume required for proper effectiveness

Biogas generation easily achievable by having good mixing.

• Other digester available such as anaerobic fluidized bed (AFB)

• Operating conditions can be set as thermophilic or mesophilic

Problems that affects anaerobic digestion process

pH shock(sudden drop in pH)

Volatile acid concentration too high(inhibition)

Feed overload or feed under load

Feed to Microorganism ratio too low.

Kinetic model

Monod’s Kinetic

• U = specific growth rate• Umax = max growth rate• Ks = half velocity constant• S = concentration of growth-

limiting substrate• Ki = inhibition constant• I = inhibitor concentration

Literature Review

1. Modelling of Anaerobic digestion – A review by G.Lyberatos & I.V. SKIADAS ;12/6/99

• The literature consist of a review of most anaerobic digestion model until year 1999.

• Shows how each model is develop via kinetic model.

• The literature reviews each models inhibition when applied on monads equation.

• The literature has identify similar control and start up condition for each model

2. Dynamic modelling and simulation of anaerobic digestor for high organic strength waste by Pooja Sharma, U.K. Ghosh & A.K. Ray ; Department of Polymer & Process Engineering Indian Institute of Technology, Roorkee, Saharanpur Campus, Saharanpur 247001, UP (India); Monday, November 4, 2013: 6:00 PM

• The literature shows the development of anaerobic digestion model for high organic strength waste.

• Inhibition model is based on monads equation

• Kinetic model is based on Andrews (1969), Hill et al. (1971) & Bello-Mendoza et al. (1998)

• Which is inhibition by total volatile fatty acid

• Simulated result shows ideal digester operating conditions and bad operating conditions

• Can be used to simulate operating condition of a digester in F & B industry

• Simulated key parameters : CH4,acidogenic biomass, methanogenic biomass, VFA, particulate substrate.

3. Extension of the anaerobic digestion model No. 1 (ADM1) to include phenolic compounds biodegradation processes for the simulation of anaerobic co-digestion; by Boubaker Fezzani∗, Ridha Ben Cheikh Biogas Laboratory, Industrial Engineering Department, URSAM - Ecole Nationale d’Ingénieurs de Tunis, Université Tunis El Manar, BP. 37 Le Belvédère 1002 Tunis, Tunisia of olive mill wastes at thermophilic temperature; Journal of Hazardous Materials 162 (2009) 1563–1570

• The literature shows the development of existing anaerobic digestion model 1 to include phenolic compound biodegradation process.

• The general structure of the ADM1 was not changed except for the modifications related to the introduction of phenolic compounds degradation processes into acetate and further into methane and CO2.

• Phenolic compounds are also taken into consideration into pH simulation equations.

• Result of simulation from this literature shows reasonable accuracy when compared to the real data obtain from the digester.

• Model can be used to simulate phenol waste industry : steel, biodiesel, palm oil and etc.

• Simulated parameters: pH,CH4,phenol.

• Simulated result shows reasonable accuracy due to error in estimation of anions and cation concentrations.

4. Physical and mathematical modelling of anaerobic digestion of organic waste by G.Kiely, G. Tayfur, C. Dolan and K. Tanji; Civil and Enviromental engineering department, University College Cork, Ireland and Hydrologic science department, University of California, Davis, CA 95616. USA ; Water Research vol 31, No. 3 pp 534-540; may 1996

• Show the development of a mock reactor(CSTR) to compare simulated result with actual result

• Mock wastewater is created from household/food fraction to simulate typical waste in Europe. Which is fed with pig slurry to allow acclimatize for 13days.

• Uses monod inhibition model

• inhibition by unionized acetic acid.

• Simulated parameters : CH4, pH, NH4

• Simulated results are accurate.

5. Dynamic modelling of anaerobic digestion by R.Moletta, D. Verrier and G. Albagnac; Station de Technologie Alimentaure, institute National De la Recherche Agronomique, 369 rue J Guesde 59650 Villeneuve D’ Ascq, France; Water Research Vol 20. No.5 pp427-434; Dec 1984

• Model accuracy is tested with pea bleaching and synthetic substrate containing sucrose and organic acid.

• Inhibition by VFA(acetic acid)

• Death rate is considered as zero as experiment time is short

• Takes digestion model to be a two step process.

• Acidogenic bacteria -> Glucose -> acetate

• Methanogenic bacteria -> Methane and CO2

• Result show accurate production of CH4 with experimental data

7. A Dynamic model for simulation of animal waste digestion by D.T. Hill, University of floride, Gainesville and C.L. Barth, Clemson University, Clemson, South Carolina; Water Pollution Control Federation) Vol. 49, No. 10 (Oct., 1977), pp. 2129-2143.

• Inhibition by unionized volatile fatty acid and unionized NH3

• pH simulation is done via mass balance of CO2 system

• Simulation result is then compared with 12 reactors

• Simulation can be adjusted to change with temperature using henry’s gas law

• Result shown are satisfactory when compared to actual small scale reactor

My model

• dXa/dt=Ua.Xa-Kd.Xa• Ua=Uamax/(1 + kxa/s + Ah/Kiax)• dS/dt=D.(Sinf-S)-(Ua.Xa)/Ya+

(Ua.Xa)/Yso• Ah= AH+/Kc• dA/dt=D(Ainf-A) + Ua.Xa/Yva –

Um.Xm/Ym• dXm/dt = Um.Xm-Kdm.Xm• Um = Ummax/1+ (Kxm/Ah) +

(Ah/Kixm) + (NH3.Mnh3/Kiam)• dCH4/dt=Vmmax.Xm(Ah/

Ah+Km)• dCO2/dt=D(CO2in-CO2+HCO3in-

HCO3)+Rm+Rac+Raf-Rz-Rnh4+Rt

• Rm=UmXmYco2/Mx• Rac=Da/dt(1/Mx)• Raf=Ua.Xa.Yco2/Mx• H = Kco2Co2/HCO3-

• Rz=DZ/dt• dZ/dt=D(Zin-Z) + Ua.Xa.Ycat• Rnh4=D(NH4in-NH4)+Ua.Xa.Ynh4+Rnh4.Mnh4• dPnh4/dt=-Tp.Sv.Vrec.Rnh4/Vgsv – Pnh3Q/Vgsv• Q=Qnh3+QCh4+Qco2• Qnh3=-Sv.Vrec.Rnh3• Qch4=(SV.Vrec)(Um.Xm.Ych4)(1/Mch4)• Qco2=-Sv.Vrec.Rt• Rt=KLA(Khco2.Pco2-CO2)• dPco2/dt=-(Tp.Sv.Vrec.Rt/Vgsc)-(Pco2Q/Vgsc)• HCO3-=Z+ + NH4/Mnh4 –Ah/Mo• NH3=NH4.Knh4/H+Mnh4

• 9 ODE’s• 19 supporting equations

Subsystem

Subsystem

Result

Legend shows flow rate adjusted without a controller

The ControllerFuzzy Logic

Fuzzy logic AI

Which simulates human like responses in order to control the process

Fuzzy logic is divided into 3 parts

Fuzzification –

• which categorizes the parameter monitored as both small & big at a different degree. (fuzzy sets & rules)

Inference-

• The controller then applies if and then rules using the fuzzy set to adjust the manipulated variable

Defuzzification-

• The AI here then will balance set rules to come out with proper result

My next step

To remodel the process using matlab code instead of using Simulink

To able to simulate pH and NH3 concentration graph

Q & A