Fuzzy logic control for reducing drying-induced corn breakage
Zhang, Qin
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Permalink
https://hdl.handle.net/2142/19627
Description
Title
Fuzzy logic control for reducing drying-induced corn breakage
Author(s)
Zhang, Qin
Issue Date
1992
Doctoral Committee Chair(s)
Litchfield, J. Bruce
Department of Study
Agricultural and Biological Engineering
Discipline
Agricultural Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Agriculture, Food Science and Technology
Engineering, Agricultural
Language
eng
Abstract
A fuzzy logic control system for reducing drying-induced breakage of corn for a continuous crossflow grain dryer was developed. This system makes control decisions for heater setting and discharge rotor speed based on the underlying relationships between the measurable process variables and the outlet corn moisture and breakage levels as well as on the human operator's experience in dryer control.
The fuzzy controller consists of a process identifier to classify the process state, a knowledge base to represent dryer control knowledge, a computational unit to perform approximate reasoning, and a defuzzifier to pick a definite control input.
A fuzzy prediction model was developed to predict breakage and moisture levels of outlet corn in terms of a few measurable process variables. The rates of match were 76.7% for breakage prediction and 73.7% for moisture prediction when drying newly harvested corn. Including those close predictions, there were 84.2% breakage predictions within the target range or 1.0% above and below the target and 88.7% moisture predictions within the target range or 0.5% above and below the target.
A set of knowledge matrices governed by fuzzy control rules were developed to represent dryer control knowledge. This method enables the representation of a large amount of knowledge in a few matrices, and makes fuzzy logic control feasible for a complex process such as corn drying. In a computer simulation, 86.4% of control settings provided by the knowledge inference engine matched one of several preferred control settings.
Two evaluation tests of the fuzzy logic control were conducted with a laboratory grain dryer. Comparing to the results from a manually controlled drying operation, the quality of moisture control was very similar (average outlet moisture content 15.5% with a standard deviation of 0.53% for both fuzzy control and manually control while using fresh corn). The drying-induced corn breakage could be reduced by applying fuzzy logic control. The average breakage susceptibility was 18.0% with a standard deviation of 7.56% with fuzzy control, and was 34.7% with a standard deviation of 16.96% with manually control.
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