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Blending biomass materials of different physical or chemical properties provides an opportunity to adjust the quality of the feedstock to meet the specifications of the conversion platform. We propose a model which identifies the right mix of biomass to optimize the performance of the thermochemical conversion process at the minimum cost. This is a chance-constraint programming (CCP) model which takes into account the stochastic nature of biomass quality. The proposed CCP model ensures that process requirements, which are impacted by physical and chemical properties of biomass, are met most of the time. We consider two problem settings, a centralized and a decentralized supply chain. We propose a mixed-integer linear program to model the blending problem in the centralized setting and a bilevel program to model the blending problem in the decentralized setting. We use the sample average approximation (SAA) method to approximate the chance constraints, and propose solution algorithms to solve this approximation. We develop a case study for South Carolina using data provided by the Billion Ton Study. Based on our results, the blends identified consist mainly of pine and softwood residues. The cost of the centralized supply chain is 2 to 6% lower, which shows that the assumption of centralized decision making leads to underestimating costs in the supply chain., 33 pages, 2 figures

Operational inefficiencies due to wide variability in physical and compositional attributes characteristic of existing corn stover supply systems threaten the development of a sustainable bioeconom...

Recently, major car-sharing service providers have begun to include electric vehicles (EVs) in free-floating car-sharing fleets. For these EVs, downtime due to charging, including time spent traveling to and waiting in queues at charging stations in a sparse charging infrastructure network, is a major barrier to sustainable operation. An intuitive solution to overcome this barrier is to increase the number of fast-charging stations in the fleet service area. This paper studies the relationship between fleet vehicle downtime and the number of charging stations by modeling the fleet operations of a major car-sharing service provider. An integer programming model is developed that jointly optimizes charging station allocation, in terms of the number and location of charging stations, and the assignment of EVs to charging stations. Case study results showed that fleet vehicle charging time comprises 72–75% of the total downtime spent on charging trips. This indicates that charging time, as opposed to travel time or waiting time, is the dominant component of total fleet downtime for charging. The study also shows that by adding 5–20 new charging stations reduces total fleet downtime and travel time during charging trips by 2–4% and 26–49%, respectively. Although adding new charging stations to the fleet service area reduces total charging trip travel time significantly, it does not reduce total downtime significantly. Results also show that if the EV battery state of the charge (SOC) threshold—below which a charging trip is initiated—is less than 18%, not all EVs in the car-sharing fleet can be charged using the existing direct current fast chargers (DCFCs) in the fleet’s service area. This implies that adequate charging infrastructure coverage is required to ensure that EVs of a given range can operate in the car-sharing fleet.

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US's cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system's reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. The Sample Average Approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory's pilot biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution increase utilization of the reactor and reduce operational costs.

Many factors may globally affect the trade pattern for industrial-grade wood pellets, with one such factor being the decision strategies by potential buyers and suppliers to optimize feedstock pric...

This study develops a two-stage stochastic mixed-integer programming model to manage multi-purpose pellet processing depots under feedstock supply uncertainty. The proposed optimization model would help to minimize cost and mitigate emissions from the supply chain network. We consider three alternative Biomass Processing and Densification Depot (BPDD) technologies; namely, conventional pellet processing, high moisture pellet processing, and ammonia fiber expansion. These three technologies pre-process/pre-treat and densify different types of biomass into more highly densified intermediate products for different markets in order to improve movability and overall supply network performance in terms of costs and emissions. A hybrid decomposition algorithm was developed that combines Sample Average Approximation with an enhanced Progressive Hedging (PH) algorithm to solve this challenging NP -hard problem. Some heuristics such as Rolling Horizon (RH) heuristic, variable fixing technique were later applied to further enhance the PH algorithm. Mississippi and Alabama were selected as a testing ground and ArcGIS was employed to visualize and validate the modeling results. The results of the analysis reveal promising insights that could lead to recommendations to help decision makers achieve a more cost-effective environmentally-friendly supply chain network.

The uncertainty and variability of algal biomass production presents several challenges to the nascent algal biofuel industry, including equipment scaling and feedstock supply. Ideally, on-site processing equipment will be scaled to minimize overall biofuel production costs, which means at times biomass production could exceed down-stream processing capacity due to seasonal variation. Biomass produced in excess of conversion capacity during summer months must be stabilized by some method, such as drying, until needed later in the year. Because of algae's high moisture content and its cohesive nature, drying is challenging. Blending algae with terrestrial biomass may provide a cost-effective method to enable drying and stabilization of algae by reducing moisture content and improving rheological (i.e. flowability) properties. To test the technical feasibility of this approach, bench-scale rotary drum dyers were constructed and tested with blends of algae ( Scenedesmus sp.), ground pine (2 mm grind), sorghum, corn stover (6 mm), sieved sand, and dried algae. In these studies, blends up to 40% algae exhibited drying behavior similar to that of pine alone, and reached dryness (2% moisture) in half of the time it took to dry algae alone. Thermogravimetric analyses performed on blends and neat blend materials provided drying curves consistent with the bench-scale dryers. Preliminary logistics analysis for production-scale operations were performed to determine cost and availability of feedstock materials for blending as compared to drying algae directly. This analysis indicates that revenue lost due to idle processing capacity had a significant impact on the per gallon gasoline equivalent feedstock cost. The blending approach, described herein, reduced feedstock-related costs, including procurement, drying, and storage by 35% relative to drying algae directly. Our results indicate that blending algae with terrestrial biomass enables the use of rotary dryers and has the potential to improve overall algal biofuel economics.

We study capacitated dynamic facility location problem (DFLP).We propose a heuristic, a pure and a hybrid Benders algorithm to solve the DFLP.We test our techniques over the extended benchmark instances.Unlike other methods, the hybrid Benders algorithm offers high quality solutions. This study presents a well-known capacitated dynamic facility location problem (DFLP) that satisfies the customer demand at a minimum cost by determining the time period for opening, closing, or retaining an existing facility in a given location. To solve this challenging NP -hard problem, this paper develops a unique hybrid solution algorithm that combines a rolling horizon algorithm with an accelerated Benders decomposition algorithm. Extensive computational experiments are performed on benchmark test instances to evaluate the hybrid algorithm's efficiency and robustness in solving the DFLP problem. Computational results indicate that the hybrid Benders based rolling horizon algorithm consistently offers high quality feasible solutions in a much shorter computational time period than the stand-alone rolling horizon and accelerated Benders decomposition algorithms in the experimental range.

In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants relies on a hub-and-spoke structure which optimizes transportation costs of biomass. The model proposed optimizes the $$\hbox {CO}_{2}$$ emissions due to transportation-related activities in the supply chain. The model also optimizes the social impact of biofuels. The social impacts are evaluated by the number of jobs created. The multi-objective optimization model is solved using an augmented $$\epsilon $$ -constraint method. The method provides a set of Pareto optimal solutions. We develop a case study using data from the Midwest region of the USA. The numerical analyses estimates the quantity and cost of cellulosic ethanol delivered under different scenarios generated. The insights we provide will help policy makers design policies which encourage and support renewable energy production.

Switchgrass is a perennial herbaceous plant regarded as a biomass energy crop in the United States for its high adaptability and yield potential. Processing and handling of switchgrass particles are challenging due to the erratic mechanical and flow behavior originating from their intrinsic particulate properties. In this work, we present a bonded-sphere discrete element model designed specifically for switchgrass particles. The model simultaneously captures three key particulate features, i.e., fibrous particle shapes, a wide range of particle sizes, and particle deformability. Realistic yet computationally efficient particle shape templates are created based on the image analysis data of switchgrass specimens. A fitting procedure is proposed to ensure both the particle width and length distributions are captured, a unique requirement for fibrous particles. Two full-scale numerical models, i.e., a uniaxial compression model and a Schulze ring shear model, are developed using information from physical experiments. The model is calibrated using experimental data of chopped-small switchgrass specimens, and then, is validated using data of chopped-large specimens in both compression and ring-shear tests. Numerical results show that the numerical models capture bulk densities accurately (with an error of 3%) while slightly underestimate the bulk friction angle. Furthermore, an extensive sensitivity analysis reveals that (1) switchgrass particles with rougher edges (due to different processing techniques) exhibit a higher shear strength and a lower flowability; (2) stiffer particles yield a lower bulk density (up to 21% lower) compared to more deformable particles, indicating particle deformability should be incorporated when modeling biomass flow in a preprocessing system.

This paper evaluates supply, operational- and market-risk reduction opportunities for the case where management at a cellulosic biorefinery adopts a supply chain design based on a distributed depot concept. In contrast to the conventional feedstock-supply system, a supply chain based on a network of depots providing feedstock to a biorefinery employs geographically distributed depots where feedstock undergoes preprocessing into densified pellets, thus allowing feedstock to be transported a greater distance. This study used simulation methods to show that distributed depots may work as a risk absorber for a cellulosic biorefinery. Compared to a base case, a distributed depot supply system reduced the operational and market risk by 17.5% and 5%, respectively. Moreover, when contract management and alternatives markets were considered, a reduction of supply, operational, and market risk, by approximately 48%, 69%, and 35%, respectively, were observed. The expected return on investment (ROI) increased from −4% to 33%. However, this positive ROI largely depends on assuming commoditization and the creation of intermediate markets for alternative, merchandisable products.

An alternative to the production of fossil transportation fuels is the production of biofuels, specifically, bioethanol. One of the main opportunity areas is the reduction in the overall cost of biofuel. An approach to reduce this cost is to design and implement a supply chain (SC) that considers the quality-related properties of the biomass as well as the economies of scale to minimize the logistics and quality-related costs. This problem is formulated as a hub location problem, which has been classified as an NP-hard problem, therefore, meta-heuristics are a suitable approach to solve this problem. We propose a hybrid meta-heuristic solution procedure to solve large-scale instances of a two-stage stochastic biomass-to-biorefinery hub-and-spoke network problem. This solution procedure is proposed to support the large-scale production and distribution of bioethanol by considering the variability in its moisture and ash contents. The hybrid method utilizes a simulated annealing-simplex method to find an initial solution and a tabu search-simplex method to improve the solution. Numerical experimentation was performed on a realistic case study in Texas. The results show that the hybrid procedure outperforms the standard L-shaped (LS) method. The meta-heuristic combining simulated annealing and tabu search with the simplex method (SATS-SM) achieved 2.48% lower costs and required 96.57% less time, on average, when compared to the results from using the LS.

The literature on bioenergy has pointed out that financial risks and limited performance benchmarks are leading barriers to wide-spread industry expansion. The purpose of this paper is to document a baseline cost and risk assessment from the perspective of biorefinery management for two herbaceous feedstocks, switchgrass and corn stover, in an application of Integrated Landscape Design (ILD). Integrating energy crops into row crops has attracted considerable attention leading to a variety of ways to implement ILD. However, variation in production conditions, different methods of implementing unit operations, and technology performance variability are among many factors leading to financial risk facing biorefineries from feedstock supply based on ILD. Utilizing data from field-based trials on switchgrass and corn stover in Virginia and Iowa in the United States, we estimate feedstock logistics cost and characterize it by a distribution of potential outcomes. The results show that, across fields included in the study, logistics cost for switchgrass vary from $73 per Mg to $171 per Mg. For corn stover, the inter-field variation in cost ranges from $83 per Mg to $105 per Mg. Results for intra-field uncertainty show that the cost for switchgrass ranges from $58 per Mg to $74 per Mg, and for corn stover they range from $50 per Mg to $65 per Mg. These results inform a baseline from which improvements in ILD can be compared, and provide biorefinery management with a starting point for cost and risk estimates of feedstock from ILD.

Conventional practices of siting all biomass preprocessing operations at the biorefinery is widely believed to be the most cost-effective solution for feedstock supply because of economies of scale. However, biomass preprocessing operations could be decentralized by moving the preprocessing operations to distributed biomass preprocessing centers, also known as “depots” located near biomass sources. This study presents a comparative case study with multiple biomass resources to analyze biorefinery feedstock supply logistics designs having distributed depots and a primary depot co-located with the biorefinery. A mixed-integer linear programming model was developed to simultaneously optimize feedstock sourcing decisions, and optimal preprocessing depot locations and size, utilizing biomass resources from agricultural residue, energy and municipal solid waste to meet carbohydrate specifications and feedstock demand for a biochemical conversion process. Results from a case study in the US showed that a biorefinery could increase its feedstock supply draw area and supply volume by 57.3%, 177.4% respectively without increasing the feedstock delivered cost by adopting distributed depot-in the feedstock supply chain design. A distributed-depot-based supply chain can be more economical by selecting optimal mix of biomass resource, optimal siting and depot scales during feedstock supply chain design. The findings from this study indicate that a biorefinery can utilize dynamic blending to meet the feedstock quality specifications as well as larger supply radius in the distributed depot-based supply chain design to access more available biomass to handle potential feedstock supply uncertainty.

Decentralized biomass processing facilities, known as biomass depots, may be necessary to achieve feedstock cost, quantity, and quality required to grow the future U.S. bioeconomy. In this paper, we assess three distinct depot configurations for technical difference and economic performance. The depot designs were chosen to compare and contrast a suite of capabilities that a depot could perform ranging from conventional pelleting to sophisticated pretreatment technologies. Our economic analyses indicate that depot processing costs are likely to range from ∼US$30 to US$63 per dry metric tonne (Mg), depending upon the specific technology implemented and the energy consumption for processing equipment such as grinders and dryers. We conclude that the benefits of integrating depots into the overall biomass feedstock supply chain will outweigh depot processing costs and that incorporation of this technology should be aggressively pursued.

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.

This article analyzes rail transportation costs of products that have similar physical properties as densified biomass and biofuel. The results of this cost analysis are useful to understand the relationship and quantify the impact of a number of factors on rail transportation costs of denisfied biomass and biofuel. These results will be beneficial and help evaluate the economic feasibility of high-volume and long-haul transportation of biomass and biofuel. High-volume and long-haul rail transportation of biomass is a viable transportation option for biofuel plants, and for coal plants which consider biomass co-firing. Using rail optimizes costs, and optimizes greenhouse gas (GHG) emissions due to transportation. Increasing bioenergy production would consequently result in lower GHG emissions due to displacing fossil fuels. To estimate rail transportation costs we use the carload waybill data, provided by Department of Transportation’s Surface Transportation Board for products such as grain and liquid type commodities for 2009 and 2011. We used regression analysis to quantify the relationship between variable transportation unit cost ($/ton) and car type, shipment size, rail movement type, commodity type, etc. The results indicate that: (a) transportation costs for liquid is $2.26/ton–$5.45/ton higher than grain type commodity; (b) transportation costs in 2011 were $1.68/ton–$5.59/ton higher than 2009; (c) transportation costs for single car shipments are $3.6/ton–$6.68/ton higher than transportation costs for multiple car shipments of grains; (d) transportation costs for multiple car shipments are $8.9/ton and $17.15/ton higher than transportation costs for unit train shipments of grains.

We propose a framework for designing the supply chain network for biomass co-firing in coal-fired power plants. This framework is inspired by existing practices with products with similar physical characteristics to biomass. We present a hub-and-spoke supply chain network design model for long-haul delivery of biomass. This model is a mixed integer linear program solved using benders decomposition algorithm. Numerical analysis indicates that 100 million tons of biomass are located within 75 miles from a coal plant and could be delivered at $8.53/dry-ton; 60 million tons of biomass are located beyond 75 miles and could be delivered at $36/dry-ton.

This paper analyzes the rural Chinese biomass supply system and models supply chain operations according to U.S. concepts of logistical unit operations: harvest and collection, storage, transportation, preprocessing, and handling and queuing. In this paper, we quantify the logistics cost of corn stover and sweet sorghum in China under different scenarios. We analyze three scenarios of corn stover logistics from northeast China and three scenarios of sweet sorghum stalks logistics from Inner Mongolia in China. The case study estimates that the logistics cost of corn stover and sweet sorghum stalk to be $52.95/dry metric ton and $52.64/dry metric ton, respectively, for the current labor-based biomass logistics system. However, if the feedstock logistics operation is mechanized, the cost of corn stover and sweet sorghum stalk decreases to $36.01/dry metric ton and $35.76/dry metric ton, respectively. The study also includes a sensitivity analysis to identify the cost factors that cause logistics cost variation. Results of the sensitivity analysis show that labor price has the most influence on the logistics cost of corn stover and sweet sorghum stalk, with a variation of $6 to $12/dry metric ton.

Logistical barrier are tied to feedstock harvesting, collection, storage and distribution. Current crop harvesting machinery is unable to selectively harvest preferred components of cellulosic biomass while maintaining acceptable levels of soil carbon and minimizing erosion. Actively managing biomass variability imposes additional functional requirements on biomass harvesting equipment. A physiological variation in biomass arises from differences in genetics, degree of crop maturity, geographical location, climatic events, and harvest methods. This variability presents significant cost and performance risks for bioenergy systems. Currently, processing standards and specifications for cellulosic feedstocks are not as well-developed as for mature commodities. Biomass that is stored with high moisture content or exposed to moisture during storage is susceptible to spoilage, rotting, spontaneous combustion, and odor problems. Appropriate storage methods and strategies are needed to better define storage requirements to preserve the volume and quality of harvested biomass over time and maintain its conversion yield. Raw herbaceous biomass is costly to collect, handle, and transport because of its low density and fibrous nature. Existing conventional, bale-based handling equipment and facilities cannot cost-effectively deliver and store high volumes of biomass, even with improved handling techniques. Current handling and transportation systems designed for moving woodchips can be inefficient for bioenergy more » processes due to the costs and challenges of transporting, storing, and drying high-moisture biomass. The infrastructure for feedstock logistics has not been defined for the potential variety of locations, climates, feedstocks, storage methods, processing alternatives, etc., which will occur at a national scale. When setting up biomass fuel supply chains, for large-scale biomass systems, logistics are a pivotal part in the system. Various studies have shown that long-distance international transport by ship is feasible in terms of energy use and transportation costs, but availability of suitable vessels and meteorological conditions (e.g., winter time in Scandinavia and Russia) need to be considered. However, local transportation by truck (both in biomass exporting and importing countries) may be a high-cost factor, which can influence the overall energy balance and total biomass costs. « less

Idaho National Laboratory (INL) supports the U.S. Department of Energy’s bioenergy research program. As part of the research program INL investigates the feedstock logistics economics and sustainability of these fuels. A series of reports were published between 2000 and 2013 to demonstrate the feedstock logistics cost. Those reports were tailored to specific feedstock and conversion process. Although those reports are different in terms of conversion, some of the process in the feedstock logistic are same for each conversion process. As a result, each report has similar information. A single report can be designed that could bring all commonality occurred in the feedstock logistics process while discussing the feedstock logistics cost for different conversion process. Therefore, this report is designed in such a way that it can capture different feedstock logistics cost while eliminating the need of writing a conversion specific design report. Previous work established the current costs based on conventional equipment and processes. The 2012 programmatic target was to demonstrate a delivered biomass logistics cost of $55/dry ton for woody biomass delivered to fast pyrolysis conversion facility. The goal was achieved by applying field and process demonstration unit-scale data from harvest, collection, storage, preprocessing, handling, and transportation operations into more » INL’s biomass logistics model. The goal of the 2017 Design Case is to enable expansion of biofuels production beyond highly productive resource areas by breaking the reliance of cost-competitive biofuel production on a single, low-cost feedstock. The 2017 programmatic target is to supply feedstock to the conversion facility that meets the in-feed conversion process quality specifications at a total logistics cost of $80/dry T. The $80/dry T. target encompasses total delivered feedstock cost, including both grower payment and logistics costs, while meeting all conversion in-feed quality targets. The 2012 $55/dry T. programmatic target included only logistics costs with a limited focus on biomass quantity, quality and did not include a grower payment. The 2017 Design Case explores two approaches to addressing the logistics challenge: one is an agronomic solution based on blending and integrated landscape management and the second is a logistics solution based on distributed biomass preprocessing depots. The concept behind blended feedstocks and integrated landscape management is to gain access to more regional feedstock at lower access fees (i.e., grower payment) and to reduce preprocessing costs by blending high quality feedstocks with marginal quality feedstocks. Blending has been used in the grain industry for a long time; however, the concept of blended feedstocks in the biofuel industry is a relatively new concept. The blended feedstock strategy relies on the availability of multiple feedstock sources that are blended using a least-cost formulation within an economical supply radius, which, in turn, decreases the grower payment by reducing the amount of any single biomass. This report will introduce the concepts of blending and integrated landscape management and justify their importance in meeting the 2017 programmatic goals. « less

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. Fil: Lautala, Pasi T. Michigan Technological University. Department of Civil and Environmental Engineering. Estados Unidos Fil: Hilliard, Michael R. Oak Ridge National Laboratory; Estados Unidos Fil: Webb, Erin. Oak Ridge National Laboratory; Estados Unidos Fil: Busch, Ingrid. Oak Ridge National Laboratory; Estados Unidos Fil: Hess, J. Richard. Idaho National Laboratory; Estados Unidos Fil: Roni, Mohammad S. Idaho National Laboratory; Estados Unidos Fil: Hilbert, Jorge Antonio. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro de Investigaciones de Agroindustria. Instituto de Ingeniería Rural; Argentina Fil: Bittencourt, Roger. Universidade Federal de Santa Catarina. LabTrans;Brasil Fil: Valente, Amir. Universidade Federal de Santa Catarina. LabTrans;Brasil Fil: Laitinen, Tuuli. Lappeenranta University of Technology. LUT Savo Sustainable Technologies;Finlandia

The urgency of reducing Greenhouse gas (GHG) emission is increasing and countries around the world have begun investing a substantial amount of resources into renewable energy sources. Biomass co-firing can have a very influential role in achieving this new energy target as it can reduce the potential environmental impacts associated with the combustion of fossil fuels. Greenhouse gases such as CO2, NOx, and SOx emissions can be reduced by replacing a portion of coal with biomass during co-firing. The purpose of this article is to review major policies that have promoted biomass co-firing around the world. Furthermore, this article investigates the existing co-firing plants with technologies and availability of biomass resources in different countries of the world. Finally, this paper summarizes the major global biomass co-firing initiatives and the prospects of biomass co-firing in securing renewable energy target.

This paper proposes a hybrid inventory policy with split delivery under regular and surge demand. The combination of regular and surge demand can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The arrival rate of regular demand is typically higher than the arrival rate of surge demand, whereas the volume of regular demand is typically lower than the volume of surge demand. This paper proposes an inventory management model that considers both emergency and regular replenishments corresponding to both demand patterns. The equilibrium equations developed for this model are based on the level crossing theory. These equations are used to develop a search-based heuristics to identify near optimal inventory management policies. Numerical results show that the proposed hybrid inventory policy with split delivery outperforms similar hybrid inventory policy without split delivery when holding and shortage costs are relatively low.