The Economics of Biomass Logistics and Conversion Facility Mobility: An Oregon Case Study

2018 ◽  
Vol 34 (1) ◽  
pp. 57-72 ◽  
Author(s):  
Michael David Berry ◽  
John Sessions
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Biomass Conversion ◽  
The United States ◽  
Mixed Integer ◽  
Modular System ◽  
Base Case ◽  
Biomass Availability ◽  
The Impact

Abstract. This article presents an analysis of transportable biomass conversion facilities to evaluate the conceptual and economic viability of a highly mobile and modular biomass conversion supply chain in the Pacific Northwest of the United States. The goal of this work is to support a broader effort to more effectively and sustainably use residual biomass from commercial harvesting operations that are currently piled and burned as part of site preparation. A structural representation is first developed to include sources of biomass feedstock, distributed preprocessing hubs (centralized landings), and centralized processing facilities (biomass to product conversion sites) to produce desired products and byproducts. A facility costing model was developed to evaluate potential economics of scale, which then informed the optimization study. A mixed integer linear programming model was developed to characterize, evaluate, and optimize biomass collection, extraction, logistics, and facility placement over a regional landscape from a strategic level to evaluate the mobility concept. The objective was to minimize supply chain operational costs in order to quantify financial advantages and identify challenges of the proposed system modularity and mobility. A Lakeview, Oregon case study was evaluated with an assumed modular biochar facility servicing the region. In particular, we review economies of scale, mobility, energy costs, and biomass availability tradeoffs. This analysis points towards a modular system design of movement frequency between 1 to 2 years being most viable in the conditions evaluated. It was found that the impact of plant movement, scale, and biomass availability can increase supply chain costs by $11/BDMT ($10/BDT), $33/BDMT ($30/BDT), and $22/BDMT ($20/BDT) above the base case cost of $182/BDMT ($165/BDT) for a large-scale facility [45,000 BDMT yr-1(50,000 BDT yr-1)]in the conditions evaluated. Additionally, potential energy cost savings of a non-mobile modular stationary site as compared to one which utilizes off-grid electrical powers about $11/BDMT ($10/BDT) for a biochar facility. From the cases evaluated, a large-scale plant with limited mobility would be preferred under low availability of biomass conditions, whereas a stationary grid-connected plant would be more cost effective under higher availability conditions. Results depend greatly on the region, assumed harvest schedule, biomass composition, and governing biomass plant assumptions. Keywords: Biomass products, Biomass supply, Facility location, Mixed integer programming, Strategic planning, Transportable plants.

scholarly journals Impact of government policies on Sustainable Petroleum Supply Chain (SPSC): A case study – Part I (Models)

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Davoud Ghahremanlou ◽  
Wieslaw Kubiak
Keyword(s):  
Supply Chain ◽  
General Model ◽  
Programming Model ◽  
The United States ◽  
Policy Impact ◽  
Renewable Fuel Standard ◽  
Stochastic Programming Model ◽  
The Government ◽  
The Impact

Environmental concerns and energy security have led governments to establish legislations to convertConventional Petroleum Supply Chain (CPSC) to Sustainable Petroleum Supply Chain (SPSC). The United States(US), one of the biggest oil consumers in the world, has created regulations to manage ethanol production and con-sumption for the last half century. Though these regulations have created new opportunities, they have also added newburdens to the obligated parties. It is thus key for the government, the obligated parties, and related businesses to studythe impact of the policies on the SPSC. We develop a two-stage stochastic programming model, General Model (GM),which incorporates Renewable Fuel Standard 2 (RFS2), Tax Credits, Tariffs, and Blend Wall (BW) to study the policyimpact on the SPSC using cellulosic ethanol. The model, as any other general model available in the literature, makesit highly impractical to study the policy impact due to the model’s computational complexity. We use the GM to derivea Lean Model (LM) to study the impact by running computational experiments more efficiently and consequently byarriving at robust managerial insights much faster. We present a case study of the policy impact on the SPSC in theState of Nebraska using the LM in the accompanying part II (Ghahremanlou and Kubiak 2020).

scholarly journals Machine-Learning Methods to Select Potential Depot Locations for the Supply Chain of Biomass Co-Firing

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6554
Author(s):  
Diana Goettsch ◽  
Krystel K. Castillo-Villar ◽  
Maria Aranguren
Keyword(s):  
United States ◽  
Machine Learning ◽  
Supply Chain ◽  
Ghg Emissions ◽  
The United States ◽  
Mixed Integer ◽  
Total Cost ◽  
Computational Burden ◽  
Milp Model

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is beneficial or not) are obtained through the stochastic MILP model. Multiple ML algorithms are applied to a case study in the northeast area of the United States: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Multi-Layer Perceptron (MLP) Neural Network. After applying the hybrid methodology combining ML and optimization, it is found that the MLP outperforms the other algorithms in terms of selecting potential depots that decrease the total cost of the BSC and the computational burden of the stochastic MILP model. The LR and the DT also perform well in terms of decreasing total cost.

scholarly journals Optimal Location of Biogas Plants in Supply Chains under Carbon Effects: Insight from a Case Study on Animal Manure in North Dakota

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yong Shin Park ◽  
Joseph Szmerekovsky ◽  
Alan Dybing
Keyword(s):  
Supply Chain ◽  
Environmental Impact ◽  
Supply Chains ◽  
North Dakota ◽  
Carbon Emissions ◽  
Animal Manure ◽  
Mixed Integer ◽  
Animal Waste ◽  
The Impact

Faced with increasing concerns over the negative environmental impact due to human and industrial activities, biomass industry practitioners and policy makers have great interest in green supply chains to reduce carbon emissions from supply chain activities. There are many studies which model the biomass supply chain and its environmental impact. However, animal waste sourced biogas supply chain has not received much attention in the literature. Biogas from animal manure not only provides energy efficiency, but also minimizes carbon emissions compared to existing biomass products. Therefore, this study proposes a mixed integer linear program that minimizes total supply costs and carbon emissions from an animal waste sourced biogas supply chain while it also incorporates carbon price in the model to see the impact of a carbon policy on tactical and strategic supply chain decisions. To validate the model proposed, a case study of North Dakota is adopted where there is a high potential for a biogas plant to be developed. The results of our optimization experiment indicate that supply chain performance in terms of both costs and emissions is very sensitive to a carbon pricing mechanism.

Integrated Multimodal Transportation Model for a Switchgrass-Based Bioethanol Supply Chain: Case Study in North Dakota

2017 ◽  
Vol 2628 (1) ◽  
pp. 32-41 ◽  
Author(s):  
Yong Shin Park ◽  
Joseph Szmerekovsky ◽  
Atif Osmani ◽  
N. Muhammad Aslaam
Keyword(s):  
Supply Chain ◽  
North Dakota ◽  
Programming Model ◽  
Single Mode ◽  
Cost Effective ◽  
Mixed Integer ◽  
Strategic Decisions ◽  
Multimodal Transport ◽  
The Impact

In this study, a mixed integer linear programming model that integrates multimodal transport—truck and rail—into the switchgrass-based bioethanol supply chain was formulated. The objective of this study was to minimize the total cost for cultivation and harvesting, infrastructure, the storage process, bioethanol production, and transportation. Strategic decisions, including the number and location of intermodal facilities and biorefineries, and tactical decisions, such as the amount of biomass shipped, processed, and converted into bioethanol, were validated by using North Dakota as a case study. It was found that the multimodal transport scenario was more cost effective than a single mode of transport (truck) and resulted in a lower cost for bioethanol. A sensitivity analysis was conducted to demonstrate the impact of key factors in the decision to use multimodal transport in a switchgrass-based bioethanol supply chain and on the cost of bioethanol.

Situational Breakdowns

2019 ◽  
Author(s):  
Anne Nassauer
Keyword(s):  
United States ◽  
Large Scale ◽  
Contextual Factors ◽  
The United States ◽  
Social Outcomes ◽  
Dynamic Processes ◽  
Collective Decisions ◽  
Different Types ◽  
The Impact

This book provides an account of how and why routine interactions break down and how such situational breakdowns lead to protest violence and other types of surprising social outcomes. It takes a close-up look at the dynamic processes of how situations unfold and compares their role to that of motivations, strategies, and other contextual factors. The book discusses factors that can draw us into violent situations and describes how and why we make uncommon individual and collective decisions. Covering different types of surprise outcomes from protest marches and uprisings turning violent to robbers failing to rob a store at gunpoint, it shows how unfolding situations can override our motivations and strategies and how emotions and culture, as well as rational thinking, still play a part in these events. The first chapters study protest violence in Germany and the United States from 1960 until 2010, taking a detailed look at what happens between the start of a protest and the eruption of violence or its peaceful conclusion. They compare the impact of such dynamics to the role of police strategies and culture, protesters’ claims and violent motivations, the black bloc and agents provocateurs. The analysis shows how violence is triggered, what determines its intensity, and which measures can avoid its outbreak. The book explores whether we find similar situational patterns leading to surprising outcomes in other types of small- and large-scale events: uprisings turning violent, such as Ferguson in 2014 and Baltimore in 2015, and failed armed store robberies.

scholarly journals Autism Tsunami: the Impact of Rising Prevalence on the Societal Cost of Autism in the United States

2021 ◽  
Author(s):  
Mark Blaxill ◽  
Toby Rogers ◽  
Cynthia Nevison
Keyword(s):  
Forecast Model ◽  
The United States ◽  
Population Projections ◽  
Base Case ◽  
Prevention Strategies ◽  
Total Base ◽  
Cost Categories ◽  
The Cost ◽  
The Impact ◽  
First Time

AbstractThe cost of ASD in the U.S. is estimated using a forecast model that for the first time accounts for the true historical increase in ASD. Model inputs include ASD prevalence, census population projections, six cost categories, ten age brackets, inflation projections, and three future prevalence scenarios. Future ASD costs increase dramatically: total base-case costs of $223 (175–271) billion/year are estimated in 2020; $589 billion/year in 2030, $1.36 trillion/year in 2040, and $5.54 (4.29–6.78) trillion/year by 2060, with substantial potential savings through ASD prevention. Rising prevalence, the shift from child to adult-dominated costs, the transfer of costs from parents onto government, and the soaring total costs raise pressing policy questions and demand an urgent focus on prevention strategies.

scholarly journals Modeling and Optimization Sustainable Forest Supply Chain Considering Discount in Transportation System and Supplier Selection under Uncertainty

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 964
Author(s):  
Komeyl Baghizadeh ◽  
Dominik Zimon ◽  
Luay Jum’a
Keyword(s):  
Supply Chain ◽  
Working Hours ◽  
Mixed Integer ◽  
Forest Industry ◽  
Northern Iran ◽  
Suggested Approach ◽  
Tactical Decisions ◽  
Environmental Goals ◽  
Forest Supply Chain ◽  
The Impact

In recent decades, the forest industry has been growingly expanded due to economic conditions, climate changes, environmental and energy policies, and intense demand changes. Thus, appropriate planning is required to improve this industry. To achieve economic, social and environmental goals, a supply chain network is designed based on a multi-period and multi-product Mixed-Integer Non-Linear Programming (MINLP) model in which the objective is to maximize the profit, minimize detrimental environmental effects, improve social effects, and minimize the number of lost demands. In addition, to improve forest industry planning, strategic and tactical decisions have been implemented throughout the supply chain for all facilities, suppliers and machinery. These decisions significantly help to improve processes and product flows and to meet customers’ needs. In addition, because of the presence of uncertainty in some parameters, the proposed model was formulated and optimized under uncertainty using the hybrid robust possibilistic programming (HRPP-II) approach. The -constraint technique was used to solve the multi-objective model, and the Lagrangian relaxation (LR) method was utilized to solve the model of more complex dimensions. A case study in Northern Iran was conducted to assess the efficiency of the suggested approach. Finally, a sensitivity analysis was performed to determine the impact of important parameters on objective functions. The results of this study show that increasing the working hours of machines instead of increasing their number, increasing the capacity of some facilities instead of establishing new facilities and expanding the transport fleet has a significant impact on achieving predetermined goals.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

Hospitalization Rates Among Dialysis Patients During Hurricane Katrina

2012 ◽  
Vol 27 (4) ◽  
pp. 325-329 ◽  
Author(s):  
David Howard ◽  
Rebecca Zhang ◽  
Yijian Huang ◽  
Nancy Kutner
Keyword(s):  
Hurricane Katrina ◽  
Large Scale ◽  
Service Use ◽  
Hospital Admissions ◽  
Gulf Coast ◽  
The United States ◽  
Time Varying ◽  
Dialysis Patients ◽  
Hospitalization Rates ◽  
The Impact

AbstractIntroductionDialysis centers struggled to maintain continuity of care for dialysis patients during and immediately following Hurricane Katrina's landfall on the US Gulf Coast in August 2005. However, the impact on patient health and service use is unclear.ProblemThe impact of Hurricane Katrina on hospitalization rates among dialysis patients was estimated.MethodsData from the United States Renal Data System were used to identify patients receiving dialysis from January 1, 2001 through August 29, 2005 at clinics that experienced service disruptions during Hurricane Katrina. A repeated events duration model was used with a time-varying Hurricane Katrina indicator to estimate trends in hospitalization rates. Trends were estimated separately by cause: surgical hospitalizations, medical, non-renal-related hospitalizations, and renal-related hospitalizations.ResultsThe rate ratio for all-cause hospitalization associated with the time-varying Hurricane Katrina indicator was 1.16 (95% CI, 1.05-1.29; P = .004). The ratios for cause-specific hospitalization were: surgery, 0.84 (95% CI, 0.68-1.04; P = .11); renal-related admissions, 2.53 (95% CI, 2.09-3.06); P < .001), and medical non-renal related, 1.04 (95% CI, 0.89-1.20; P = .63). The estimated number of excess renal-related hospital admissions attributable to Katrina was 140, representing approximately three percent of dialysis patients at the affected clinics.ConclusionsHospitalization rates among dialysis patients increased in the month following the Hurricane Katrina landfall, suggesting that providers and patients were not adequately prepared for large-scale disasters.Howard D, Zhang R, Huang Y, Kutner N. Hospitalization rates among dialysis patients during Hurricane Katrina. Prehosp Disaster Med. 2012;27(4):1-5.

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