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Infield Biomass Bales Aggregation Logistics and Equipment Track

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Infield Biomass Bales Aggregation Logistics and Equipment Track INFIELD BIOMASS BALES AGGREGATION LOGISTICS AND EQUIPMENT TRACK IMPACTED AREA EVALUATION A Thesis Submitted to the Graduate Faculty of the North Dakota State University of Agriculture and Applied Science By Subhashree Navaneetha Srinivasagan In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE Major Department: Agricultural and Biosystems Engineering November 2017 Fargo, North Dakota NORTH DAKOTA STATE UNIVERSITY Graduate School Title INFIELD BIOMASS BALES AGGREGATION LOGISTICS AND EQUIPMENT TRACK IMPACTED AREA EVALUATION By Subhashree Navaneetha Srinivasagan The supervisory committee certifies that this thesis complies with North Dakota State University’s regulations and meets the accepted standards for the degree of MASTER OF SCIENCE SUPERVISORY COMMITTEE: Dr. Igathinathane Cannayen Chair Dr. Halis Simsek Dr. David Ripplinger Approved: 11/22/2017 Dr. Sreekala Bajwa Date Department Chair ABSTRACT Efficient bale stack location, infield bale logistics, and equipment track impacted area were conducted in three different studies using simulation in R. Even though the geometric median produced the best logistics, among the five mathematical grouping methods, the field middle was recommended as it was comparable and easily accessible in the field. Curvilinear method developed (8–259 ha), incorporating equipment turning (tractor: 1 and 2 bales/trip, automatic bale picker (ABP): 8–23 bales/trip, harvester, and baler), evaluated the aggregation distance, impacted area, and operation time. The harvester generated the most, followed by the baler, and the ABP the least impacted area and operation time. The ABP was considered as the most effective bale aggregation equipment compared to the tractor. Simple specific and generalized prediction models, developed for aggregation logistics, impacted area, and operation time, have performed 2 well (0.88 R 0.99). An ABP of 8 bales capacity, also capable of 11 bales/trip, ≤ ≤ was recommended. iii ACKNOWLEDGEMENTS I would like to take this opportunity to extend my sincere gratitude to all those who made this thesis possible. First and foremost, I would like to extend my thanks to my major advisor, Dr. Igathinathane Cannayen for providing me an opportunity to work under his guidance, teaching, constant encouragement, and continuous support throughout my Masters. His view of research, approaching a problem, and systematic method of working has created a great impression in my life. I learned to never let go of an opportunity, as he has always encouraged and made efforts for me to participate at various conferences and events to present my research, and to perform any work at its best quality, irrespective of the platform and the audience. He often quotes “Anything is possible in the world, where humans like us send rockets to the moon — so can’t we tackle this research opportunity?” which changed my perspective of looking at any problem that I encountered. I am more than glad to have been associated with a person like Dr. Cannayen in my life. I would like to thank my supervisory committee members, Dr. Halis Simsek and Dr. David Ripplinger for their valuable time, guidance, suggestions, and encouragement during this research. My heartfelt thanks to Dr. Ganesh Bora and Dr. Xinhua Jia for extending their support by providing me Teaching Assistant (TA) scholarship during my early semesters at the university. It was indeed a great pleasure working under them. My sincere thanks to Dr. Sreekala Bajwa for encouraging me to explore different avenues and for the extended support. iv This work was supported by the funds of USDA-ARS, Fund numbers: FAR0020464 and FAR0028541, and in part by the USDA, NIFA, NDSU Hatch Projects: ND01472 and ND01481, with accession numbers: 229896 and 1014700, respectively. These funding supports are thankfully acknowledged. Research and office facilities provided by the Northern Great Plains Research Laboratory (NGPRL), USDA-ARS, Mandan, ND and the excellent support extended by the NGPRL staff are also greatly appreciated. Special thanks to my friend Mr. Sunoj Shajahan, who has been a great support system throughout my years in Masters. Discussions and brainstorming sessions and his interest in exploring new ideas have always been very helpful and inspiring for me. Also, I would like to thank Dr. Saravanan Sivarajan and his family for their moral support, and my friend Ajay Bharadwaj who has been a great help. My sincere thanks to my family members, Mr. Subbian, Mrs. Vasuki, Mr. Ulaganathan, Mrs. Nirmala, Mr. Devaraj, Mrs. Balamani, and Mr. Prabhakaran for standing by me and supporting in every step of the way. Finally, I would like to thank all the staff of ABEN-NDSU, and friends who have been very warm and helpful, supportive and encouraging, and any of this would not have been possible without you. v DEDICATION To my father (Mr. Navaneetha Srinivasagan), mother (Mrs. Umamaheswari), brother (Mr. Ajay Guru), and grandmother (Mrs. Mangayarkarasi). vi TABLE OF CONTENTS ABSTRACT . iii ACKNOWLEDGEMENTS . iv DEDICATION . vi LIST OF TABLES . xi LIST OF FIGURES . xii LIST OF APPENDIX TABLES . xv LIST OF APPENDIX FIGURES . xvi 1. GENERAL INTRODUCTION . 1 1.1. Statement of Objectives . 4 1.2. Thesis Organization . 4 2. REVIEW OF LITERATURE . 6 2.1. Biomass Supply Chain and Logistics . 6 2.1.1. Biomass Logistics Modeling . 6 2.1.2. Biomass Transport Models . 10 2.2. Infield Biomass Bale Logistics . 11 2.3. Equipment Track Impacted Field Area . 12 2.4. Present Study on Bale Aggregation and Equipment Track Impacted Area . 13 3. PAPER 1 - OPTIMIZED LOCATION OF BIOMASS BALES STACK FOR EFFICIENT LOGISTICS * . 14 3.1. Abstract . 14 3.2. Introduction . 15 3.3. Materials and Methods . 18 3.3.1. Simulation of Bales Layout on Field . 18 3.3.2. Bales Stack Formation and Transport . 19 3.3.3. Process Simulation and Parameters Considered . 20 vii 3.3.4. Optimum Bale Stack Location Methodologies . 20 3.3.5. Aggregation to Stack Location or to Outlet . 25 3.3.6. Bale Transport to the Outlet . 26 3.3.7. Comparison of Bale Stack Location Methods . 27 3.3.8. Statistical Analysis . 27 3.3.9. Field Shape Effect on Bale Logistics . 28 3.4. Results and Discussion . 29 3.4.1. Bale Stack Location Methods Distribution . 29 3.4.2. Optimized Bale Stack Location Methods Results . 31 3.4.3. Effect of Field Area on the Bale Stack Location Methods . 35 3.4.4. Distribution of Bale Stack Location Methods . 35 3.4.5. Statistical Analysis Results . 37 3.4.6. Comparison of Geometric Median and Field Middle Methods . 40 3.4.7. Regression Models of Logistics Distances . 41 3.5. Conclusions . 43 4. PAPER 2 - EQUIPMENT TRACK IMPACTED FIELD AREAS DURING HARVESTING, BALING, AND INFIELD BALE LOGISTICS * . 45 4.1. Abstract . 45 4.2. Introduction . 46 4.3. Materials and Methods . 50 4.3.1. Software Simulation for Bale Field Layout . 51 4.3.2. Simulating Minimum Path Distance . 51 4.3.3. Equipment Turning Path Track Simulations . 52 4.3.4. Statistical Analysis and Model Development . 57 4.4. Results and Discussion . 57 4.4.1. Simulated Results of Equipment Track Paths . 57 4.4.2. Euclidean vs Curvilinear Methods of Bale Aggregation . 60 4.4.3. Impacted Area Generated by Tractor and ABP . 65 4.4.4. Harvester, Baler, and ABP Impacted Area . 70 viii 4.4.5. Regression Analysis and Models on Impacted Area . 71 4.4.6. Overall Simulation Results and Impacted Area Model . 75 4.5. Conclusions . 77 5. PAPER 3 - BIOMASS BALE INFIELD LOGISTICS SCENARIOS USING AN AUTOMATIC BALE PICKER * . 79 5.1. Abstract . 79 5.2. Introduction . 80 5.3. Materials and Methods . 83 5.3.1. Simulation of Bales Layout . 83 5.3.2. Parameters Considered for Simulation . 85 5.3.3. Simulation of the Equipment . 85 5.3.4. Equipment Turn Path Simulation . 86 5.3.5. Statistical Analysis and Developing Prediction Models . 88 5.4. Results and Discussion . 89 5.4.1. Bale Collection Using Tractor and ABP . 89 5.4.2. Effect of Field Area on Logistics Distance . 90 5.4.3. Effect of Bales/trip on Logistics Distance . 92 5.4.4. Effect of Outlet Location on Logistics Distance . 94 5.4.5. Relative Weights Analysis for Statistical Model Development . 96 5.4.6. Aggregation Logistics Operation Time . 101 5.5. Conclusions . 102 6. GENERAL CONCLUSIONS AND SUGGESTIONS FOR FUTURE WORK . 104 6.1. General Conclusions . 104 6.2. Suggestions for Future Work . 106 REFERENCES . 108 APPENDIX A. DERIVATION OF FIELD EQUIPMENT TURNING PATHS . 122 A.1. Harvester and Baler Turning Path Derivation . 122 A.2. ABP Turning Path Derivations . 125 A.2.1. Up-Right I-Quadrant Case . 126 ix A.2.2. Down-Left, III-Quadrant Case . 129 APPENDIX B. VARIOUS ABP TURNING PATH CASES, DERIVATIONS, SIMULATION RESULTS, AND SAMPLE R CODES . 134 B.1. ABP Turning Cases . 134 B.2. Turning Case Derivations and Track Impacted Area Simulation Results . 139 B.3. Sample R Codes for Simulation, Visualization, and Model Fitting . 143 x LIST OF TABLES Table Page 3.1. Methods of optimum bale stack locations and their logistics distances and methods combined distances. 31 3.2. Comparison of field middle and geometric median logistics among various field areas from 0.5 to 520 ha. ..
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