Investigation of Methods for Real Time Determination of Grain Damage
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Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1-1-2002 Investigation of methods for real time determination of grain damage Michael A. Brand Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Recommended Citation Brand, Michael A., "Investigation of methods for real time determination of grain damage" (2002). Retrospective Theses and Dissertations. 19799. https://lib.dr.iastate.edu/rtd/19799 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Investigation of methods for real time determination of grain damage by Michael A. Brand A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Agricultural Engineering (Agricultural Power and Machinery) Program of Study Committee: Graeme Quick, Co-major Professor Carl Bern, Co-major Professor Stuart Birrell Dan Nettleton Brian Steward Bruce Warman Iowa State University Ames, Iowa 2002 Copyright ©Michael A. Brand, 2002. All rights reserved. 11 Graduate College Iowa State University This is to certify that the master's thesis of Michael A. Brand has met the thesis requirements of Iowa State University Signatures have been redacted for privacy 111 TABLE OF CONTENTS LIST OF TABLES v LIST OF FIGI:TRES ACKNOWLEDGEMENTS 1X CHAPTER ONE. GENERAL INTRODUCTION 1 Introduction 1 Thesis Organization 4 Literature Review 4 References 27 CHAPTER TWO. AIRFLOW SEPARATION OF DAMAGED CORN 33 Abstract 33 Introduction 33 Materials and Methods 35 Results and Discussion 40 Summary and Conclusions 45 References 60 CHAPTER THREE. ULTRAVIOLET DETECTION OF DAMAGED CORN 62 Abstract 62 Introduction 63 Materials and Methods 64 Results and Discussion 69 Summary and Conclusions 75 References 93 CHAPTER FOUR. GENERAL CONCLUSIONS 95 Phoenix Grain Damage Sensor 95 Airflow Separation Test Stand 96 Damage Measurement Design Parameters 96 Suggestions for Further Study 99 1V Reference 100 APPENDIX A. R.AW DATA AND CALCULATIONS 101 APPENDIX B. ADDITIONAL TABLES 125 v LIST OF TABLES Table 1.1 USDA Grades and Grade Requirements for Corn 5 Table 2.1 Definitions of insert configurations and corresponding maximum airflow Values 57 Table 2.2 Mass of rewetted fines in each 3 kg corn sample for three damage levels and three moisture contents 57 Table 2.3 Mean damage quantities and Tukey comparisons of means from four common damage tests at three damage levels using pilot study data 58 Table 2.4 Mean damage measurements and Tukey comparisons of means for four common damage tests at three moisture contents and three damage levels 58 Table 2.5 Summary of significant differences in means at three moisture contents for three configurations of the airflow test stand 59 Table 2.6 Calibration regression coefficients and r2 values for predicting various common damage values using test stand response as input 59 Table 2.7 Evaluation regression coefficients and r2 values for predicting various common damage values using test stand response as input 60 Table 3.1 Mass of rewetted fines in each 3 kg corn sample for three damage levels and three moisture contents 91 Table 3.2 Mean damage measurements and Tukey comparisons of mean pairs for four common damage tests at three moisture contents and three damage levels 91 Table 3.3 Mean sensor response (pulses/kg) and Tukey comparisons of mean pairs for two flow rates of the Phoenix damage sensor at three moisture contents and three damage levels. 92 Table 3.4 Summary of regression coefficients for calibration number 1 using raw number of pulses as the sensor response 92 Table 3.5 Summary of regression coefficients for calibration number 2 using average number of pulses per second as the sensor response 92 Table 3.6 Summary of regression coefficients for calibration number 3 using number of pulses per kilogram of corn as the sensor response 93 vl Table 3.7 Summary of model descriptors for 3 regression analyses and three common damage tests using grain damage sensor data 93 Table A1 Raw data from pilot study 102 Table A2 Raw data from low moisture study 107 Table A3 Raw data from mid moisture study 109 Table A4 Raw data from high moisture study 111 Table AS Phoenix damage meter calculations for pilot study data 113 Table A6 Phoenix damage meter calculations for low moisture data 114 Table A7 Phoenix damage meter calculations for mid moisture data 115 Table A8 Phoenix damage meter calculations for high moisture data 116 Table A9 Airflow test stand calculations for pilot study data 117 Table A10 Airflow test stand calculations for low moisture data 122 Table A11 Airflow test stand calculations for mid moisture data 123 Table Al2 Airflow test stand calculations for high moisture data 124 Table B1 Data summary and Tukey significance comparisons between means for pilot study data 126 Table B2 Data summary and Tukey significance comparisons between means for low moisture data 128 Table B3 Data summary and Tukey significance comparisons between means for mid moisture data 129 Table B4 Data summary and Tukey significance comparisons between means for high moisture data 130 Table BS Correlation coefficients for pilot study data 131 Table B6 Correlation coefficients for low moisture data 133 Table B7 Correlation coefficients for mid moisture data 134 Table B8 Correlation coefficients for high moisture data 135 Vll LIST OF FIGURES Figure 2.1 Test stand used for airflow separation of damaged grain 47 Figure 2.2 Wire screen used to close the top of the secondary chute 48 Figure 2.3 Two solid inserts and one grate insert between primary and secondary drop chutes 49 Figure 2.4 Calibration data for visual damage level using test stand data as input 50 Figure 2.5 Calibration data for BCFM measurement using test stand data as input 51 Figure 2.6 Calibration data for green dye damage level using test stand data as input 52 Figure 2.7 Validation data and prediction equation line for visual damage calibration 53 Figure 2.8 Validation data and prediction equation line for BCFM content calibration 54 Figure 2.9 Validation data and prediction equation line for green dye damage Calibration 55 Figure 2.10 Material stuck to grate with angled duct and insert configuration 1 at the completion of a fast cylinder damage test before and after the airflow was removed 56 Figure 3.1 Phoenix grain damage sensor test stand 77 Figure 3.2 Side by side comparison of funnels used to establish high and low flow rates 78 Figure 3.3 Calibration 1 data for BCFM test using raw number of pulses as damage sensor response 79 Figure 3.4 Calibration 2 data for BCFM test using average number of pulses per second as damage sensor response 80 Figure 3.5 Calibration 3 data for BCFM test using number of pulses per kilogram of corn a damage sensor response 81 Figure 3.6 Calibration 1 data for visual damage test using raw number of pulses as damage sensor response 82 Figure 3.7 Calibration 2 data for visual damage test using average number of pulses per second as damage sensor response 83 Vlll Figure 3.8 Calibration 3 data for visual damage test using number of pulses per kilogram of corn as damage sensor response 84 Figure 3.9 Calibration 1 data for green dye damage test using raw number of pulses as damage sensor response 85 Figure 3.10 Calibration 2 data for green dye damage test using average number of pulses per second as damage sensor response 86 Figure 3.11. Calibration 3 data for green dye damage test using number of pulses per kilogram of corn as damage sensor response 87 Figure 3.12 BCFM verification data using three calibration techniques 88 Figure 3.13 Visual damage verification data using three calibration techniques 89 Figure 3.14 Green dye verification data using three calibration techniques 90 1X ACKNOWLEDGEMENTS I would like to thank Dr. Graeme Quick, Dr. Carl Bern, Dr. Brian Steward, Dr. Stuart Birrell, professors of Agricultural Engineering, Dr. Dan Nettleton, professor of Statistics, and Bruce Warman, John Deere Engineering, for serving as the program of study committee for my research. The supervision and guidance provided by the committee has been incredibly valuable in enhancing the quality of my research. In addition to serving on my program of study committee, I would also like to thank Bruce Warman and the John Deere World Wide Combine Product Development Center for providing the funding for this graduate study. I would also like to thank William Cooper of Phoenix International for providing background information on the characteristics and operation of the ultraviolet grain damage sensor provided for evaluation. Additionally, David Kmoch and Kevin Ehrecke of John Deere Engineering provided valuable advice relating to the practical application of a grain damage sensor. Additional thanks go to Leslie Anderson, Sarah Anderson, Brian Brand, and Jared Zumbach for their help in preparing corn samples for testing and carrying out the tedious process of screening samples for visible damage. Finally, I would like to thank my wife, Angela, for her understanding, patience, and support while I conducted my research and wrote this thesis. Your support has been much appreciated! 1 CHAPTER ONE. GENERAL INTRODUCTION Introduction Mechanical grain damage can be defined as the damage inflicted upon grain by harvesting and handling equipment. This damage is often characterized by broken, cracked, split, and scuffed kernels, as well as by an increase in broken corn.