SYSTEMS ANALYSIS FOR SUSTAINABILITY ASSESSMENT OF BIOGAS AND BIO-CH4 PRODUCTION FROM FOOD WASTE AND DAIRY MANURE MIXTURES IN THE US

The purpose of the dissertation is to relate systems analysis for bioenergy to identify optimum configurations for improved scenarios and to make better decisions in a systems perspective. Chapter 1 is a review of the literature to identify the state of the knowledge in terms of systems analysis for anaerobic digestion (AD) bioenergy systems. The key outcomes from this review showed that anaerobic digestion of mixtures of food waste and animal manure has great potential to achieve economic and environmental benefits compared to other treatments of organic waste materials, such as landfilling or conventional manure management. Chapter 2 focuses on carbon footprint of one particular bio-CH4 production facility. This study developed consequential methodology to address the environmental impacts of system parameters such as avoiding landfilling and manure management. The key results from this LCA show that the AD Bio-CH4 pathway has 15.5% lower greenhouse gas (GHG) emissions compared to the prior practice of composting of food waste and manure in Denver, CO. Then chapter 3 provides opportunities for additional studies in bioenergy environmental and economic performance. The objective of this chapter is to gain a system level understanding of the integration of bioenergy crops into rotations with food grains. This study combines both environmental and economic impacts into a single decision assessment. The key results in this chapter show the system parameter yield is the deciding parameter in finding the most optimum crop rotations with integration of bioenergy crops. In Chapter 4 the objective is to understand enzyme accessibility inside woody biomass and its role in controlling the rate of conversion of cellulose to glucose. The goal of this study was to measure the cellulose accessibility due to the effect of dilute acid pretreatment (DAP) and enzymatic hydrolysis (EH) time of Populus biomass. The last chapter of the dissertation presents a novel systems sustainability analysis framework that evaluates the optimum locations, sizes, and the number of plants for AD biogas power production in Wisconsin accounting for both the profits from the biopower supply chain and carbon credits. This dissertation ends with overall conclusions and recommendations for future research

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp