Cereals processing technology

Wheat, corn and coarse grains milling 41
which cannot be defined in precise terms as yet. The development of automatic
assessment of these parameters would open up the possibility of automated
responses to unexpected changes in these parameters. This could potentially
have a significant impact on the manner in which mills are operated and on the
consistency of the products produced.
A fuzzy controller typically consists of four elements:
• An automatic rule generation module, which creates rules automatically for
the fuzzy controller based on input process models.
• A database management module, which synthesises the rules generated by
the automatic rule generation module.
• A fuzzy inference engine which performs fuzzification, inferencing and
defuzzification of design variables based on the rules generated by the
automatic rule generation module.
• An evaluation module to decide when to stop program execution.
Vishnupad and Shin (1998) cited the advantages of using a fuzzy controller
over conventional controllers. Fuzzy control leads to a higher degree of
automation for complex problems. This is especially true if the knowledge
available about the process can be expressed as language-based rules. It can be
expressed in the form of if–then rules, which can be incorporated easily into a
fuzzy controller. In addition fuzzy controllers are more robust than conventional
controllers.
Considering the non-linear and multi-modal nature of many processes, fuzzy
control offers a significant advantage over traditional numerical optimisation
methods. This is because most decision-making logic is developed automatically
by the fuzzy controller and requires little input from the operator. This is in
contrast to traditional methods where models have to be developed and verified
for each aspect of the process to be controlled. Because the fuzzy logic
controller develops rules on the basis of cause and effect, many process
variables are programmed for, that could not be considered using conventional
numerical techniques. The result is fuzzy controllers offer a more robust and
versatile form of control than conventional numerical techniques.
A fuzzy expert system may also be developed with analytical and heuristic
models in mind. These features of fuzzy controllers have led to their widespread
adoption for poorly understood and difficult to model processes. Linko et al.
(1992) give an example in the form of fuzzy logic applied to extrusion control.
Adaptive control
Adaptive control is where the control algorithm or parameters are altered on-line
in real time to cope with changes in process dynamics. Adaptive control is
relevant to both conventional and advanced control strategies in that each can be
programmed to operate in an adaptive environment.
The technique is useful where a rigid control strategy fails to provide
satisfactory performance because process dynamics alter, as in the flour milling
process. Process dynamics may change because of non-linearity, drifts in plant