| Summary: | This thesis investigates the problem of portion control of natural objects such as fish and meat
for packaging, and develops a new method for optimal portioning. Food portions that are
processed for packaging or canning should be optimized with respect to a specified "target"
portion, for reasons such as wastage reduction, regulatory requirements, consumer appeal, and
aesthetic considerations. The approach of optimal portion control that is developed involves
cutting a set of objects into pieces by taking into account the weight distribution of each object
and grouping the pieces into package portions according to a weight-based optimality
criterion.
First, a general optimization model is developed for the portioning control problem. Next, the
model is made specific to a practical and innovative approach for can-filling of fish. An
optimization model is developed for the portion control of fish where the objective is to
minimize the weight deviation of the canned portions from the target net weight of a can. The
portioning process is then refined by incorporating realistic assumptions and constraints that
exist in industrial fish portioning processes. The modified model is further checked again for
feasibility, and the model refinement process is continued until a feasible optimization model
that can be implemented on-line in an industrial plant, is achieved. The model that is
developed in this manner provides a computational speed for portion control that is consistent
with the typical industrial requirements of the process speed and filling accuracy.
Clearly, the weight accuracy of the processed portions can be further improved by providing
the capability of adjustable blade spacing in portion cutting. In particular, during cutting of
fish into can-filling portions, if one were to start from one end of the optimally arranged
placement of fish and integrate the overall optimal weight distribution function such that the
weight between two adjacent blades equals the target weight of portion, then the portion error
will be zero. However, there are practical constraints on portion dimensions. Also, it was
found that the portion accuracy can be further improved by specifying not just one target
weight rate, but rather an interval of lower and upper bounds. This optimization problem with
adjustable blade spacing is analyzed and evaluated through computer simulations. A comparison
is made with the optimization model that uses fixed blade spacing.
In the practical process of portion control that is developed here, the fish are placed in a linear
overlapped arrangement according to the optimality criterion and cut into canning portions
using an array of gang knives. To determine the optimal placement the weight distribution
function of each incoming fish has to be measured on-line. The accuracy of a portion weight is
greatly influenced by the accuracy of the weight distribution sensor itself. A high-level
sensing approach is developed for this purpose. It involves the off-line development of nondimensional
structural models of fish which contain correlations between the weight distribution
function and simple geometric parameters that can be measured at high speed and high
accuracy. Then the on-line sensing involves measurement of the geometric parameters for
each incoming fish and matching them to a fish model in the model-base.
To analyze the performance of the optimal portioning process of fish, a comprehensive model
of fish body is developed, based on real data on salmon. The model is used to generate large
quantities of realistic data, which are used in a computer simulation of the optimal portioning
process. The results of simulation, which incorporates such practical influences as measurement
error, show that the developed sensor technique is able to provide good accuracy in
sensing weight distribution. Results further show that the optimal portion control developed in
this research, for can-filling of fish, is quite feasible and provides adequate production speed,
and good accuracy and quality of the product. === Applied Science, Faculty of === Mechanical Engineering, Department of === Graduate
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