Cereals processing technology

Wheat, corn and coarse grains milling 45
• Evolutionary approaches including genetic algorithms and simulated
annealing. 17
3.8.1 Which optimisation technique for milling?
Linear and quadratic programming techniques may only be used where models
of that nature apply to the process. However, this excludes a large body of
processes, including non-linear, discontinuous processes, processes that are not
well understood, and many other classes. Milling operations have been
optimised using these techniques by numerous researchers (Tillman et al.
1969, Niernberger and Phillips 1972, Flores 1989, Flores et al. 1991, Liu et al.
1992), but this work did not examine the process itself. The models examine
aspects such as the economic performance of the operation, or the least cost
formulation of the raw material blends used. Milling processes themselves are
highly non-linear and so this type of optimisation technique may not be used.
Gradient-based search techniques such as the back-propagation method,
Brent’s method, conjugate gradient methods or variable metric methods are the
most widely used optimisation techniques for training adaptive control systems.
The methodology of these techniques is explained in considerable detail with
programming examples in Press et al. (1988). However, it has been shown that
these techniques are limited in their ability to find global solutions where the
problem model is represented by multi-modal and non-smooth functions (Press
et al. 1988). The flour milling process is an example where discrete data may be
all that is available and many near similar solutions may exist to the optimisation
problem.
Global search techniques have been identified as a potential solution to these
problems (Chen et al. 1998, Sexton et al. 1999). Simulated Annealing and
Genetic Algorithms are two such techniques. The main advantage of these
algorithms, in comparison with exact algorithms, is that they do not suffer from
an exponential explosion in computational requirement with increase in problem
size.
The Simulated Annealing algorithm is a stochastic optimisation technique,
ideally suited to the solution of combinatorial optimisation problems. Its
application in solving problems in the global wiring of integrated circuits and
the famous ‘travelling salesman problem’ are well documented (Maier and
Whiting 1998, Reyes and Steidley 1998, Treadgold and Gedeon 1998).
3.8.2 Off-line or on-line optimisation?
Optimisers can be off- or on-line. On-line refers to systems that collect data
automatically. This is the modern approach. Optimisers may also be open loop
or closed loop. Open loop optimisation refers to systems that do not take actions
automatically. Instead they are used to advise operators how to run plant and
17 Genetic algorithms and simulated annealing are computational optimisation techniques.