Development of sensing and processing technologies for optimal portion control in an automated can filling system
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, f...
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2009
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Online Access: | http://hdl.handle.net/2429/13872 |
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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. |
author |
Omar, Farag |
spellingShingle |
Omar, Farag Development of sensing and processing technologies for optimal portion control in an automated can filling system |
author_facet |
Omar, Farag |
author_sort |
Omar, Farag |
title |
Development of sensing and processing technologies for optimal portion control in an automated can filling system |
title_short |
Development of sensing and processing technologies for optimal portion control in an automated can filling system |
title_full |
Development of sensing and processing technologies for optimal portion control in an automated can filling system |
title_fullStr |
Development of sensing and processing technologies for optimal portion control in an automated can filling system |
title_full_unstemmed |
Development of sensing and processing technologies for optimal portion control in an automated can filling system |
title_sort |
development of sensing and processing technologies for optimal portion control in an automated can filling system |
publishDate |
2009 |
url |
http://hdl.handle.net/2429/13872 |
work_keys_str_mv |
AT omarfarag developmentofsensingandprocessingtechnologiesforoptimalportioncontrolinanautomatedcanfillingsystem |
_version_ |
1716652849606164480 |
spelling |
ndltd-LACETR-oai-collectionscanada.gc.ca-BVAU.2429-138722014-03-14T15:47:16Z Development of sensing and processing technologies for optimal portion control in an automated can filling system Omar, Farag 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. 2009-10-09T23:58:21Z 2009-10-09T23:58:21Z 2000 2009-10-09T23:58:21Z 2001-11 Electronic Thesis or Dissertation http://hdl.handle.net/2429/13872 eng UBC Retrospective Theses Digitization Project [http://www.library.ubc.ca/archives/retro_theses/] |