Development of a Flow Model for the Design of a Momentum Type Beet Mass Flow Sensor

Hennens, D.; Baert, Jonathan; Broos, B.; Ramón, Herman; Baerdemaeker, Josse De

doi:10.1016/s1537-5110(03)00095-3

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…The model for mass flow measurements of sugar beets reported in [16] is a successful example of such an approach. In the case of the model development undertaken for the present effort, the physical process has been divided into four stages, namely, 1. the filling stage, in which a certain mass of grain is deposited on an elevator paddle;…”

Section: Physical Modelsmentioning

confidence: 99%

Self-Calibrating Mass Flow Sensor

Reinke

Dankowicz

2012

Volume 6: 1st Biennial International Conference on Dynamics for Design; 14th International Conference on Advanced Vehicle Techn

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A comprehensive analysis was conducted for increased accuracy and self-calibration for a mass flow sensing system on a combine. This was undertaken as part of the John Deere Technology Innovation Center (JDTIC)-sponsored research program "Self-calibrating mass-flow sensor", in turn part of a John Deere Moline Technology Innovation Center (MTIC) effort toward optimization and closer integration of the components of the mass-flow sensing system in Deere harvesting combines. The long-term objective was to achieve a self-calibrating sensor system capable of adapting to varying input conditions due, for example, to changes in grain moisture content and aging of the system's elevator paddles.In analyzing the mass flow sensing system, a physics-based model was developed to describe the relationship between the rate of mass flow through the combine and the measured force imparted to the impact plate in terms of mechanical properties of the grains and the interior geometry of the combine. A computational realization of this model was constructed in Matlab. Accurate mass flow rate estimation was achieved through model-based estimation based on nonlinear regression applied to the physics-based model and data acquired through simulation and experimentation. Model-based estimation was also extended as a means for self-calibration of the sensing system. Through development of the physicsbased model, the dependence of the force imparted to the impact plate on the orientation of the impact plate was identified. By inducing known changes to the impact plate orientation and implementing model-based estimation, a means of self-calibration of the sensing system was achieved. Three methods of model-based estimation were successfully demonstrated using data generated from the physics-based model. Additionally, these were further verified using data collected from discrete element modeling simulations, and experimental data collected in two fashions: using a full-scale replica of the mass flow sensing system, and using a small-scale, benchtop testing apparatus. Furthermore, the ability of the developed algorithm to update theoretical model parameters while simultaneously estimating mass flow rate was shown to enable the system to self-calibrate. This was argued to allow the system to accommodate different operating conditions that may be encountered during combine harvesting, such as changes in crop moisture, grain variety, and aging of combine components.ii

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Section: Physical Modelsmentioning

confidence: 99%

Self-Calibrating Mass Flow Sensor

Reinke

Dankowicz

2012

Volume 6: 1st Biennial International Conference on Dynamics for Design; 14th International Conference on Advanced Vehicle Techn

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“…The results of the experiment, by contrast, denote a strongly nonlinear relation between the grain flow and the impact force at larger grain flow [9], so nonlinear models have been proposed for grain flow sensor to estimate a wide range of grain flow [10,11] and are gradually employed for mass flow sensors to minimize the effects of changes in material properties [12,13]. At this time, nonlinear regression methods are used to estimate parameter of nonlinear models with minimizing a given objective function [11,14,15].…”

Section: Introductionmentioning

confidence: 99%

Using an Improved Artificial Bee Colony Algorithm for Parameter Estimation of a Dynamic Grain Flow Model

Liang

Gao

2018

Mathematical Problems in Engineering

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An effective method is proposed to estimate the parameters of a dynamic grain flow model (DGFM). To this end, an improved artificial bee colony (IABC) algorithm is used to estimate unknown parameters of DGFM with minimizing a given objective function. A comparative study of the performance of the IABC algorithm and the other ABC variants on several benchmark functions is carried out, and the results present a significant improvement in performance over the other ABC variants. The practical application performance of the IABC is compared to that of the nonlinear least squares (NLS), particle swarm optimization (PSO), and genetic algorithm (GA). The compared results demonstrate that IABC algorithm is more accurate and effective for the parameter estimation of DGFM than the other algorithms.

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“…potato, sugar beet, etc.) yields, the following measurement systems have been reported: radiometric [2], load cells in belt conveyors [4,42,43], image analysis [14,25], pivoted table with a load cell [27], bounce plate [8,9] and weighing frame with load cells and speed sensors [37], curved impact plate [11]. For sugar cane yield monitoring the following attempts have been reported: development of hydraulic pressure and flow measurement system [5], construction of load cell-based mass measurement trailer [26], weight scale yield sensor [3], load cell transducers and speed detectors mounted on side conveyor [21] and load cells as a billet weighing instrument set up on the harvester side conveyor just before the sugar cane billets are dropped into an infield trailer [17].…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study of a Pivoted-Plate Sensor for Silage Corn Yield Monitoring

Maharlouie¹,

Loghavi²,

Kamgar³

2012

Int J of Agri Scie

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Crop yield map is a useful means to quantify yield variations within the fields and to provide a scientific basis for implementing site -specific crop management strategies. This research explores the feasibility of developing a new silage yield monitoring system by employing a new mass flow sensor based on using the momentum of chopped material impacting a pivoted plate loaded by a retractable spring installed at the end of a chopper discharge spout. A precise rotary potentiometer was used to measure the degree of pivoted plate rotation. To save and process the output voltage of the potentiometer, a programmable circuit comprising of AVR microcontroller ATMEGA series and MAX 232 microchip was used and the output data was sent to a portable computer for further processing. To calibrate the output signal of the sensor versus instantaneous chopped material flow rate, the chopper was fed artificially with predetermined mass flow rates of 6, 8 and 10 kg s -1 of silage corn at five replications for each feeding rate. The results of statistical analysis showed high correlation between degrees of the pivoted-plate rotation and instantaneous feed rates with adjusted R 2 of 0.98. To evaluate the accuracy of mass flow rate estimation, a platform scale weighing system was used during the field tests. The statistical analysis did not show any significant difference between sensor mass flow rate estimation and platform scale weighing system output. By using the resulted calibration equation, yield map of a silage cornfield was generated.

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Development of a Flow Model for the Design of a Momentum Type Beet Mass Flow Sensor

Cited by 11 publications

References 3 publications

Self-Calibrating Mass Flow Sensor

Self-Calibrating Mass Flow Sensor

Using an Improved Artificial Bee Colony Algorithm for Parameter Estimation of a Dynamic Grain Flow Model

Feasibility Study of a Pivoted-Plate Sensor for Silage Corn Yield Monitoring

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