1. Fermentation

2. Factors affecting Fermentation
Temperature
Most microorganisms that are mesophiles require temperatures of C for optimum growth. As temperatures increase or decrease the growth rate is adversely affected. Thermophiles require high temperatures over 50 C; they cannot survive at low temperatures. Psychrophiles require very low temperatures - 15 to 20 C for survival and growth

3. Factors affecting Fermentation
Water
Microorganisms require optimum amounts of water to maintain their metabolism and produce required products. pH
Optimum pH for bacteria is , yeasts 4-5, molds 4-7. The pH of the medium should be maintained at optimum level for the micro- organism being employed to ensure better product yield.

4. CONTROL / PROCESS VARIABLES
1. Temperature
2. Pressure
3. Vessel contents
4. Foam
5. Impeller speed
6. Gas Flow rates
7. Liquid flow
8. pH
9. Dissolved and Gas phase Oxygen
10. Dissolved and Gas phase Carbon Dioxide
11. General gas analysis

5. Optimization of the Fermenter System
Fermenter should be designed to exclude entrance of contaminating organisms as well as containing the desired organisms
Culture volume should remain constant
Dissolved oxygen level must be maintained above critical levels of aeration and culture agitation for  aerobic organisms
Parameters such as temperature of pH must be controlled, and the culture volume must be well mixed.

6. Control of Chemical and Physical Conditions
Intensive properties
temperature, concentration, pressure, specific heat‡ 
Extrinsive properties
mass, volume, entropy and energy‡ 
Mass and energy levels should balance at the start an d finish of fermentations.‡ 
Combining this with determination of thermodynamic properties and rate equations we can build computer and mathematical models to control processes.

7. Process sensors and their possible control functions
Category Sensor Possible control function
Physical Temperature Heat/cool
Foam Foam control
Weight Change flow rate
Flow rate Change flow rate
Chemical pH Acid or alkali addition
Redox Additives to change redox potential
Oxygen Change feed rate
Exit-gas analysis Change feed rate
Medium analysis Change in medium composition

8. Control systems An agitator system An oxygen delivery system
A foam control system
A temperature control system
A pH control system

9. Model of the Fermentation Process

10. Three Sub-systems of the model
Reactor – where the fermentation occurs
Heat Exchanger – used to control the temperature of the process
Feed System – controls both the flow and pH dynamics

11. Fermentation Operation
The cane juice that is mixed with acid substance to maintain the pH in a desired value feeds the reactor until it reaches the desired level defined by the operator.
The reactor is a batch unit and during each production period, a continuous recirculation of the must is performed.

12. Fermentation Operation
The must passes through a heat exchanger, which uses a coolant circulation to keep the must inside the reactor at an ideal temperature.

13. pH dynamic Model

14. pH dynamic Model very common process in sugar and alcohol plants
strong nonlinearity of this process makes it one of the hardest type of SISO (single input single output) systems to be controlled
large variations in pH can significantly impact the quality of the product and process efficiency.
Besides temperature and dissolved oxygen concentration, pH is one of the major influential factors in fermentation processes.

15. What causes the pH to change?
Overfeeding substrate can cause the cells to  produce  organic acids, such as acetate – pH drops
Lack of carbohydrate substrate causes the  cells to consume protein in the media – producing NH3-NH4OH –pH rises

16. pH Control
In real life, bioreactors actually use on-off control for pH. The two position control system is designed; so that the element controlling reagent addition is always set in one of two positions, either fully open or fully closed. The important consideration in pH control is hold time, which is required to provide time for neutralization reaction to go to completion.

17. pH Control
Regarding the model for pH neutralization process, it is known that the pH denotes the concentration of the ions [H+] through the following logarithmic function:
The dissociation equation for water is expressed as:

18. pH Control
The equation describing the pH neutralization process dynamics is
Where Fa and Fb are the flow of acid and base, ca and cb represents the concentration of acid and base.

19. A dynamic model for neutralization process is described by a first order with dead time; the approximate transfer function is;
The responses of pH electrode were analyzed to give the first order transfer function which is
-adaptive control of pH (Putrus, 2006)

20. Transfer of Heat in Bioreactors
Microbial growth is usually accompanied by the release of metabolic heat into the fermentation medium.
Metabolic activities can generate as much as BTU gal-1h-1 of thermal energy, while mechanical energy inputs of 0.5 and 2.5 HP per 100 gal can generate an additional BTU gal-1h-1.

21. Transfer of Heat in Bioreactors
To maintain a constant temperature in the fermenter, heat is either supplied or removed from the fermentation broth during the course of fermentation.
Heat transfer takes place in well stirred fermenters by forced convection.

22. Temperature control
Provision of heat – fermenter in thermostatically controlled bath or by use of internal heating coils or by a silicone heating jacket through which water is circulated
Heat jacket consists of silicone rubber mats wrapped around the vessel
Cooling surface/cooling water

23. Cooling water addition
Total heat to be removed by cooling water is due to metabolic heat generation plus heat added from the stirring
Heat is removed by running cooling water through the cooling coils in the reactor coils in the reactor

24. Cooling water addition
U is the overall heat transfer coefficient of the coils
Ac is the total area of the coils in contact with the broth
ΔT is the “log-mean temperature difference” between the broth and the cooling water

25. Temperature Heating system
In many applications such as electrical heating systems, the heating element is controlled by a contactor that has only two states (on or off)
Electric heater controls the temperature of the liquid in a tank and the aim is to control the temperature at the desired value.

26. Temperature Heating system
Most of the temperature control systems can be approximated to a single order transfer function with a transportation delay and time constant which depends on the parameters such a specific heat coefficient, temperature coefficient etc.,

27. Temperature Heating system
Transfer function of the temperature system where a liquid at temperature enters a tank which is heated with an electric heater and q is the heat flow of the heating element with flow rate constant k, specific heat coefficient P, thermal resistance of tank insulation R and thermal capacitance of water C, is given by,

28. Temperature Heating system
According to Ziegler and Nichols, an open- loop process can be approximated by the transfer function
The coefficients K, Td and Ti are found from a simple open-loop unit step response of the process.
Defines the mathematical model for heating system which controls the temperature of the bioreactors process

29. Reactor Modeling
The mathematical modeling of the alcoholic fermentation was performed based on mass balance equations with the correspondents kinetic rates of the cell, substrate and product as well as global balance of energy for the overall process.

30. Reactor Modeling
Thus, the volume variation during the fermentative process is described as:
in which F3(m3/h) is the volumetric flow rate of must at the entrance of the reactor.

31. Reactor Modeling
The cell growth rate is defined as follows

32. Sample Control Strategy
Global Control System: in this top layer, the optimizer computes the optimum pH, temperature (T) and level (H) in the reactor to maximize the ethanol production. It works as master controller and defines the set-point for the slave loops.
Local Control System: this layer is composed by three slave loops which work in cascade with the optimizer. The objective of these local controllers is to keep the fermentation process in the operating point (pH; T; H) defined by the upper layer.

33. Sample Control Strategy