Date of Award
Master of Science
Agricultural and Biosystems Engineering
Agricultural and Biological Systems Engineering
Kurt A. Rosentrater
Mathematical and statistical modeling has been used extensively in fields relating to bio-renewables and biological systems. Modeling of this nature helps predict a variety of effects, such as environmental and economic impacts, that are incurred during the manufacturing of various bio-based products. Typical modeling methodologies include: techno-economic analysis (TEA); life-cycle impact assessment (LCIA); and statistical correlation matrix analysis. The use of these methodologies can potentially be harnessed to assess the environmental, economic, and indirect impacts related to the overall stages of a product's cradle-to-grave life cycle, which includes the extraction of raw materials to pre-processing, fabrication, transportation to consumer, and end-of-life treatment. Therefore, TEAs and LCAs can project the outlook of these impact parameters and highlight which unit operation(s) produce the largest impact throughout the entire life cycle. Using these projections, producers may potentially change their materials, fabrication methods, or any production parameter to round their operation to fit the needs, standards, and constraints of their environment.
This thesis is comprised of three separate research endeavors. The first study focused on a TEA of a hypothetical commercial conversion system which converts chicken blood to bio-based flocculant. A TEA was utilized to test the economic viability of commercializing the conversion process which was analytically successful during lab based scale. The study revealed that waste water surcharges, relative to specific pollutant characteristics (BOD, COD, TSS, and NH3) found prevalent in chicken blood, were shown to have an especially high economic impact on the overall process. Additionally, the overall results determined that the hypothesized conversion plant would be highly economic feasible.
The second study utilized both TEA and LCA methodology to model the processing and overall cost(s) of poly(lactic acid) (PLA) composite production for both in-organic and organic filler material, which was compared over three product part weights and five end of life treatment options. The analysis discovered a high amount of variance in economic and environmental impacts produced, which resulted from the inclusion of organic or inorganic filler, different product part weight, and dissimilar end of life treatment selections. The inclusion of inorganic filler(s) (glass and talc) were shown to produce the largest volume of environmental burden, while organic filler(s) (wood, rice husks, and DDGS) were shown to maintain the least amount of environmental burden and economic impact. Therefore, it was suggested that when paired with PLA composite production organic fillers should be utilized over inorganic substitutes.
The third study utilized non-linear growth analysis and a linear correlation coefficient matrix to analyze and compare corn growth effects when three different nitrogen applications (low, medium, and high) and three dissimilar rotation applications (Corn-Corn (C-C), Corn-Soybean (C-S), and Corn-Soybean-Grass-Legume (C-S-G-L)) were applied. This study was focused as a continuation of a previous research endeavor (Riedell, 2011) which analyzed the same data by different methodologies. The non-linear growth modeling was shown to confirm the data suggested in the previous study, which documented significant growth variances due to interactions between rotation treatment and nitrogen application under the C-C and C-S-G-L rotations. It was speculated that the inclusion (or lack of inclusion) of legumes with in rotation treatment played a significant role in how the corn grew the following year. The linear correlation mapping highlighted interesting interactions between soil nutrient elements (NO3 and P) and grain yield and starch content, this was previously un-documented.
Randall A. Haylock
Haylock, Randall A., "Life-cycle assessment, techno-economic analysis, and statistical modeling of bio-based materials and processes" (2016). Graduate Theses and Dissertations. 14976.