Michael R. Hilliard

Comparing Scales of Environmental Effects from Gasoline and Ethanol Production

Understanding the environmental effects of alternative fuel production is critical to characterizing the sustainability of energy resources to inform policy and regulatory decisions. The magnitudes of these environmental effects vary according to the intensity and scale of fuel production along each step of the supply chain. We compare the spatial extent and temporal duration of ethanol and gasoline production processes and environmental effects based on a literature review and then synthesize the scale differences on space–time diagrams. Comprehensive assessment of any fuel-production system is a moving target, and our analysis shows that decisions regarding the selection of spatial and temporal boundaries of analysis have tremendous influences on the comparisons. Effects that strongly differentiate gasoline and ethanol-supply chains in terms of scale are associated with when and where energy resources are formed and how they are extracted. Although both gasoline and ethanol production may result in negative environmental effects, this study indicates that ethanol production traced through a supply chain may impact less area and result in more easily reversed effects of a shorter duration than gasoline production.

The PHEV Charging Infrastructure Planning (PCIP) Problem

Increasing debates over a gasoline independent future and the reduction of greenhouse gas (GHG) emissions has led to a surge in plug-in hybrid electric vehicles (PHEVs) being developed around the world. The majority of PHEV related research has been directed at improving engine and battery operations, studying future PHEV impacts on the grid, and projecting future PHEV charging infrastructure requirements. Due to the limited all-electric range of PHEVs, a daytime PHEV charging infrastructure will be required for most PHEV daily usage. In this paper, for the first time, we present a mixed integer mathematical programming model to solve the PHEV charging infrastructure planning (PCIP) problem for organizations with thousands of people working within a defined geographic location and parking lots well suited to charging station installations. Our case study, based on the Oak Ridge National Laboratory (ORNL) campus, produced encouraging results, indicates the viability of the modeling approach and substantiates the importance of considering both employee convenience and appropriate grid connections in the PCIP problem.

Advancing Sustainable Bioenergy: Evolving Stakeholder Interests and the Relevance of Research

The sustainability of future bioenergy production rests on more than continual improvements in its environmental, economic, and social impacts. The emergence of new biomass feedstocks, an expanding array of conversion pathways, and expected increases in overall bioenergy production are connecting diverse technical, social, and policy communities. These stakeholder groups have different—and potentially conflicting—values and cultures, and therefore different goals and decision making processes. Our aim is to discuss the implications of this diversity for bioenergy researchers. The paper begins with a discussion of bioenergy stakeholder groups and their varied interests, and illustrates how this diversity complicates efforts to define and promote “sustainable” bioenergy production. We then discuss what this diversity means for research practice. Researchers, we note, should be aware of stakeholder values, information needs, and the factors affecting stakeholder decision making if the knowledge they generate is to reach its widest potential use. We point out how stakeholder participation in research can increase the relevance of its products, and argue that stakeholder values should inform research questions and the choice of analytical assumptions. Finally, we make the case that additional natural science and technical research alone will not advance sustainable bioenergy production, and that important research gaps relate to understanding stakeholder decision making and the need, from a broader social science perspective, to develop processes to identify and accommodate different value systems. While sustainability requires more than improved scientific and technical understanding, the need to understand stakeholder values and manage diversity presents important research opportunities.

Opportunities and challenges in the design and analysis of biomass supply chains.

The biomass supply chain is one of the most critical elements of large-scale bioenergy production and in many cases a key barrier for procuring initial funding for new developments on specific energy crops. Most productions rely on complex transforming chains linked to feed and food markets. The term ‘supply chain’ covers various aspects from cultivation and harvesting of the biomass, to treatment, transportation, and storage. After energy conversion, the product must be delivered to final consumption, whether it is in the form of electricity, heat, or more tangible products, such as pellets and biofuels. Effective supply chains are of utmost importance for bioenergy production, as biomass tends to possess challenging seasonal production cycles and low mass, energy and bulk densities. Additionally, the demand for final products is often also dispersed, further complicating the supply chain. The goal of this paper is to introduce key components of biomass supply chains, examples of related modeling applications, and if/how they address aspects related to environmental metrics and management. The paper will introduce a concept of integrated supply systems for sustainable biomass trade and the factors influencing the bioenergy supply chain landscape, including models that can be used to investigate the factors. The paper will also cover various aspects of transportation logistics, ranging from alternative modal and multi-modal alternatives to introduction of support tools for transportation analysis. Finally gaps and challenges in supply chain research are identified and used to outline research recommendations for the future direction in this area of study.

A study of real-time identification and monitoring of barge-carried hazardous commodities

In response to increased terrorist threats related to hazardous material movements on the U.S. inland waterway system, towing vessel operators and fleet area managers, at specified reporting points, are required to notify the U.S. Coast Guards Inland River Vessel Movement Center (IRVMC) of the movement of barges loaded with Certain Dangerous Cargo (CDC). The objective of this study is to develop and field test a prototype system that provides more accurate, uniform, and timely data on hazardous movements by barges, especially those certified as CDC, and to identify and report barges with potential security threats. The system being developed, namely TRACC, is expected to automatically track and monitor barges with CDC and communicate the real-time information to a data server. The event prediction and anomaly detection modules of the system will analyze the collected real-time data and other information to identify any potential security threats, and visually display locations and routes of suspicious barges. It will benefit homeland security community, first responders, local law enforcement personnel and business by providing timely and accurate barge information to make quick and right decisions in disasters involving CDC movement on the inland waterway.

Discovering and Refining Algorithms Through Machine Learning

The development of solution methods for classes of operations research problems involves formulating the problems mathematically, developing algorithms to solve the abstracted formulations and evaluating the solutions. One possible contribution of artificial intelligence to this process is the application of machine learning to algorithm discovery and refinement. This paper presents several genetic algorithm and classifier system based experiments to discover and refine algorithms for simple scheduling problems. The discovered algorithms can be considered to be rule bases that are modified and adapted through training with examples. The quality of the resultant algorithm is investigated as a function of the training.

Future Utilization and Optimal Investment Strategy for Inland Waterways: New Model from U.S. Army Corps of Engineers to Assist Policy Makers

For nearly three decades, the U.S. Army Corps of Engineers (the Corps) has been measuring incremental system navigation transportation costs for proposed infrastructure investments in search of the National Economic Development (NED) plan: local optimization in a system-level evaluation. The increasingly complex and sophisticated analysis requires the development of additional modeling modules. The traditional analysis assumed a most-likely traffic forecast and a set investment timing. Cost-benefit analyses on various alternatives were compared to determine the without-project condition and the recommended with-project NED plan. Sensitivity analyses of traffic forecasts and investment timing were done on the with-project plan. The second generation of analysis factored in the impacts of scheduled chamber closure differences between alternatives, and the third generation of analysis factored in the impacts of unscheduled ones. The goal is to be able to optimize investments simultaneously across a system (not just investments at one site) under a series of forecast scenarios while capturing structural reliability differences (scheduled and unscheduled closures). As the demands of the analysis increased, there was a need to consolidate and dynamically link the various models and techniques developed over the years and to develop new techniques to simultaneously manage investment permutations and automatically select optimal investment plans; the desire was to perform system optimization in a system-level evaluation. The innovative analysis techniques and relational database management structure of the new Ohio River Navigation Investment Model are introduced, as is a set of flexible, integrated analysis modules that move the Corps closer to these ideals.

Simulation Modeling for Reliable Biomass Supply Chain Design Under Operational Disruptions

Lignocellulosic biomass derived fuels and chemicals are a promising and sustainable supplement for petroleum-based products. Currently, the lignocellulosic biofuel industry relies on a conventional system where feedstock is harvested, baled, stored locally, and then delivered in a low-density format to the biorefinery. However, the conventional supply chain system causes operational disruptions at the biorefinery mainly due to seasonal availability, handling problems, and quality variability in biomass feedstock. Operational disruptions decrease facility uptime, production efficiencies, and increase maintenance costs. For a low-value high-volume product where margins are very tight, system disruptions are especially problematic. In this work we evaluate an advanced system strategy in which a network of biomass processing centers (depots) are utilized for storing and preprocessing biomass into stable, dense, and uniform material to reduce feedstock supply disruptions, and facility downtime in order to boost economic returns to the bioenergy industry. A database centric discrete event supply chain simulation model was developed, and the impact of operational disruptions on supply chain cost, inventory and production levels, farm metrics and facility metrics were evaluated. Three scenarios were evaluated for a seven-year time-period: 1) bale-delivery scenario with biorefinery uptime varying from 20-85%; 2) pellet-delivery scenario with depot uptime varying from 20-85% and biorefinery uptime at 85%; and 3) pellet-delivery scenario with depot and biorefinery uptime at 85%. In scenarios 1 and 2, tonnage discarded at the field edge could be reduced by increasing uptime at facility, contracting fewer farms at the beginning and subsequently increasing contracts as facility uptime increases, or determining alternative corn stover markets. Harvest cost was the biggest contributor to the average delivered costs and inventory levels were dependent on facility uptimes. We found a cascading effect of failure propagating through the system from depot to biorefinery. Therefore, mitigating risk at a facility level is not enough and conducting a system-level reliability simulation incorporating failure dependencies among subsystems is critical.