Lignocellulosic biomass feedstock transportation bridges biomass production, transformation, and conversion into a complete bioenergy system. Transportation and associated logistics account for a major portion of the total feedstock supply cost and energy consumption, and therefore improvements in transportation can substantially improve the cost‐competitiveness of the bioenergy sector as a whole. The biomass form, intended end use, supply and demand locations, and equipment and facility availability further affect the performance of the transportation system. The sustainability of the delivery system thus requires optimized logistic chains, cost‐effective transportation alternatives, standardized facility design and equipment configurations, efficient regulations, and environmental impact analysis. These issues have been studied rigorously in the last decade. It is therefore prudent to comprehensively review the existing literature, which can then support systematic design of a feedstock transportation system. The paper reviews the major transportation alternatives and logistics and the implementation of those for various types of energy crops such as energy grasses, short‐rotation woody coppices, and agricultural residue. It emphasizes the importance of performance‐based equipment configuration, standard regulations, and rules for calculating transport cost of delivery systems. Finally, the principles, approaches, and further direction of lignocellulosic feedstock transportation modeling are reviewed and analyzed. © 2012 Society of Chemical Industry and John Wiley & Sons, Ltd

To ensure effective biomass feedstock provision for large‐scale ethanol production, a three‐stage supply chain was proposed to include biomass supply sites, centralized storage and pre‐processing (CSP) sites, and biorefinery sites. A GIS‐enabled biomass supply chain optimization model (BioScope) was developed to minimize annual biomass‐ethanol production costs by selecting the optimal numbers, locations, and capacities of farms, CSPs, and biorefineries as well as identifying the optimal biomass flow pattern from farms to biorefineries. The model was implemented to study the Miscanthus‐ethanol supply chain in Illinois. The results of the baseline case, assuming 2% of cropland is allocated for Miscanthus production, showed that unit Miscanthus‐ethanol production costs were $220.6 Mg–1, or $0.74 L–1. Biorefinery‐related costs are the largest cost component, accounting for 48% of the total costs, followed by biomass procurement, transportation, and CSP related costs. The unit Miscanthus‐ethanol production costs could be reduced to $198 Mg–1 using 20% of cropland, primarily due to savings in transportation costs. Sensitivity analyses showed that the optimal supply chain configurations, including the numbers and locations of supply sites, CSP facilities, and biorefineries, changed significantly for different cropland usage rates, biomass demands, transportation means, and pre‐processing technologies. A supply chain composed of large biorefineries with the support of distributed CSP facilities was recommended to reduce biofuels production costs. Rail outperformed truck transportation to ship pre‐processed biomass. Ground biomass with tapping is the suggested biomass format for the case study in Illinois, while high‐density biomass formats are suggested for long distance transportation. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd

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Placement of sensors in water distribution networks helps timely detection of contamination and reduces risk to the population. Identifying the optimal locations of these sensors is important from an economic perspective and has been previously attempted using the theory of optimization. This work extends that formulation by considering uncertainty in the network and describes a stochastic programming method that is capable of determining the optimal sensor location while accounting for demand uncertainties. The problem is formulated as a two stage stochastic programming problem with recourse. The solution to the problem is achieved by using a newly proposed algorithm aimed at efficiently solving stochastic nonlinear programming problems. This makes the problem solution computationally tractable as compared to the traditional stochastic programming methods. The proposed formulation and solution methodology are tested on an example network to perform a comparative study with other formulations. The results show the importance of uncertainty consideration in decision making and highlight the advantages of the proposed stochastic programming approach.