Multi-Bale Handling Unit for Efficient Logistics

The southeast region of the USA has the potential to be a significant producer of biobased products; however, research is needed to demonstrate the most cost‐effective feedstock delivery system. This study considers a distributed network of five pellet depots to supply feedstock (i.e., switchgrass pellets) for the operation of a hypothetical biorefinery located near South Boston, VA, USA. The study was divided into four main categories: (1) feedstock delivery from satellite storage locations (SSLs) to the pellet depots; (2) pellet production at the depots; (3) pellet storage at the depots, and (4) pellet delivery to the biorefinery to supply a continuous operation 24 h per day, 7 days per week, and 48 weeks per year. The cost analysis begins with round bales in SSLs and ends when a load of pellets arrives at the biorefinery. This study does not include a farmgate payment to grow, harvest, and store round bales in SSLs, and it also does not include receiving facility operations at the biorefinery. The weighted average cost for annual delivery to the biorefinery from the five depots was USD 129.32 per Mg pellets. The division of this cost was 51% feedstock delivery, 38% pelleting, 2% pellet storage, and 9% pellet delivery. The cost for the smallest depot was USD 145.37 per Mg pellets, in comparison with USD 115.22 per Mg pellets for the largest depot. These results indicate an economy‐of‐scale influence; there was a 21% reduction in cost as the depot size increased from 66 450 to 140 430 Mg pellets per year.

A biorefinery purchasing feedstock (perennial grass) must offer contracts that provide the same opportunity to earn a profit for a feedstock contractor located 50 or 5 km from the biorefinery. The business plan presented here specifies that the biomass is purchased in satellite storage locations (SSLs), and the load-out and hauling costs are paid by the biorefinery. Contracts can be offered for harvest in September, October, and November, a three-month harvest window, or the harvest window can be extended to December, January, and February, a six-month harvest window. Required total storage capacity is 75% of annual consumption for the three-month window and 50% for the six-month window, a significant difference in total storage capacity (cost). The storage cost difference paid by the biorefinery is 5.27 and 3.52 USD/Mg for the three-month and six-month, respectively. Several issues must be addressed in the feedstock contracts: (1) earlier harvest, before plant senescence, means less nutrients are translocated back into the soil and more are removed at harvest; (2) harvest losses are higher for all harvests after the September harvest; and (3) storage losses increase with storage time in the SSL. Time of removal from the SSL is dictated by the biorefinery; thus, the feedstock contractor must be compensated. The contracts paid by the biorefinery, averaged across the entire annual consumption, were about the same for the three-month window, and six-month window. This result was obtained because fertilizer cost decreases and harvest losses increase as the harvest date increases; thus, the two factors tend to offset. Using a 77 USD/Mg base cost, representative feedstock payment at the SSL (no storage losses included) for contractors with various month contracts are September (84.30), October (85.54), November (86.72), December 88.63), January (89.98), and February (90.58). Subsequent compensation for storage losses depends on the amount of time the particular unit of biomass is in storage before shipment.

In this paper, we address a biomass feedstock logistics problem to supply biomass from production fields to satellite storage locations (SSLs) and from there to bioenergy plants (BePs) and then to a biorefinery. It entails a new problem feature of routing load-out equipment sets among the SSLs to perform loading/unloading of biomass and/or its pre-processing operations. The ownership of the loading equipment is a very capital-intensive link of the ethanol production supply chain, which when loaded onto trucks and routed along the logistics chain significantly brings down the ethanol production costs. This will make ethanol a cost-competitive alternative to fossil fuels, lead to sustainable use of fossil fuels and add to the overall relevance of the bioenergy sector. In this regard, the objective of our problem is to minimize the total cost incurred due to the ownership of equipment sets, fixed setups, and land rental cost, as well as the cost of transporting biomass from the fields to the BePs and biocrude oil from the BePs to the refinery. A mixed-integer mathematical model of the problem is presented, and a nested Benders decomposition-based solution approach is developed which involves decomposing this large problem into three stages. Stage 1 deals with the selection of fields, BePs, and SSLs, and assignment of fields to the SSLs. The remaining model consists of multiple Capacitated Vehicle Routing Problems (CVRPs) that are separable over individual BePs. For each BeP, the CVRP is further decomposed into Stage 2 and Stage 3 sub-problems where the Stage 2 problem is an allocation problem that assigns SSLs to tours associated to each BeP, and the Stage 3 problem is a variant of Traveling Salesman Problem (TSP) that determines the sequence in which equipment is routed over the predesignated set of SSLs for each tour. These sub-problems are integer programs rather than linear programs. First novelty of our proposed approach is to effectively handle the integrality of variables arising due to the consideration of the routing of load-out equipment. Second is solution methodology and in the use of proposed multi-cut version of optimality cuts that capture the solution value at an integer solution for the sub-problems. These cuts aid in faster convergence and are shown to be stronger than those proposed in the literature. The applicability of the proposed methodology is demonstrated by applying it to a real-life problem that utilizes available GIS data for the catchment area of regions around Gretna and Bedford in Virginia. We then solved a set of varying problem size instances using the state-of-the-art CPLEX® Branch-and-Bound and Benders Strategy methods. The CPLEX® algorithms struggled to solve instances even 10 times smaller than the real-life problem size instances; with MIP optimality gaps ranging from 5.85% to 82.79% in the allowed time limit of 10,000 s. On the other hand, our proposed nested Benders decomposition algorithm was able to achieve faster convergence and provided optimal solutions for all the considered problem instances with an average CPU run-time of around 3,700 s. This validates the efficacy and superiority of our solution approach. Lastly, we summarize our work and point out some interesting potential future research opportunities.

In the bioenergy industry, highway hauling cost is typically 30%, or more, of the average cost of feedstock delivered to a biorefinery. Thus, truck productivity, in terms of Mg/d/truck, is a key issue in the design of a logistics system. One possible solution to this problem that is being explored is the utilization of modular pellet depots. In such a logistics system, raw biomass (i.e., low-bulk-density product) is converted into pellets (i.e., high-bulk-density product) by several smaller-scale modular pellet depots instead of by a single larger-capacity pellet depot. A truckload of raw biomass (e.g., round bales) is 16 Mg and a load of pellets is 34 Mg. The distribution of depots across a feedstock production area can potentially have an impact on the total truck operating hours (i.e., raw biomass hauling to a depot + pellet hauling from the depot to the biorefinery) required to deliver feedstock for annual operation of a biorefinery. This study examined three different distributions of depots across five feedstock production areas. The numbers of depots were one, two, and four per production area for totals of five, ten, and twenty depots. Increasing the number of depots from five to ten reduced raw biomass hauling hours by 12%, and increasing from five to twenty reduced these hours by 30%. Total hauling hours (raw biomass + pellets) were reduced by less than 1% with an increase from five to ten and by about 11% with an increase from five to twenty. The modular pellet depot concept demonstrated potential for providing improvements to biorefinery logistics systems, but more research is needed to optimize this balance.

Herbaceous biomass, harvested and handled as hay, has potential for bioenergy production. In the Upper Southeast, fields that cannot be competitively managed to produce another crop can produce an acceptable yield of switchgrass. Using a delayed harvest (crop is allowed to dry standing in the field), switchgrass can be harvested from production fields over a 6-month period. This gives a significant advantage over a crop residue, like corn stover, which is collected over a 5-week harvest season in the Midwest. The Southeast has the potential to be a major bioenergy production region. A land cover database, classified with 2006 aerial photography, was used to identify potential switchgrass fields within 48-km radii of Gretna and Keysville, two small towns in South Central Virginia. The total potential switchgrass production was then calculated for each area. A possible set of satellite storage locations (SSLs) for the temporary storage of round bales was generated using a raster-based approach. Transportation analysis was used to determine the travel distance from the SSLs to a hypothetical bioenergy plant. Within a 48-km radius of Gretna, it was determined that about 6.4% of the total land area could be attracted into production using a conservative assumption for change in existing land use. Using a more optimistic assumption, as much as 10.5% of the total land area could be attracted into production. For the 48-km radius around Keysville, the percentage ranged from 5.6% to 9.5%. Assuming an average yield of 11.2 Mg/ha, the area around Gretna could supply a bioenergy plant consuming from 61 up to 98 Mg/h (continuous operation, 47 wk/y). For Keysville, the size of plant that could be supplied ranged from 48 to 80 Mg/h. The mass-distance parameters for the Gretna and Keysville production areas were almost equal, 42.7 and 41.9 km, respectively, indicating that both study areas had a similar feedstock production distribution.

Distributed storage and pre‐processing of biomass feedstock at satellite storage locations (storage depots) has been proposed in literature to reduce costs and improve efficiency of the supply system. The performance of such a system, however, has not yet been rigorously quantified and compared with conventional alternatives. This work presents such an analysis using the BioFeed optimization model. BioFeed is a system‐level model that optimizes the important feedstock production activities and determines the optimal system configuration on a regional basis. The BioFeed model was first modified to enable modeling of mechanical pre‐processing, such as pelletization and grinding, at the input or the output of storage facilities, which can be mandatory or optional. The model was used to study different Miscanthus production scenarios in southern Illinois. The results showed that making storage pre‐processing mandatory increased the total cost by up to 16–53% as compared to the base case. However, it reduced the farmers' share of the total cost by up to 13–39%. The exact values depended on the particular pre‐processing technology and scenario modeled. The most cost‐effective system consisted of a combination of pre‐processing on the farms as well as at the storage facilities. The study recommended that biomass output from a hammer mill should be the biorefinery delivery specification; the hammer mills should be installed at the input of the storage facilities, but pre‐processing at the storage facility should be optional. This led to the minimum total cost of 46.4 $ Mg‐1. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd

Dynamic analysis and reliability design of round baler feeding device for rice straw harvest

<abstract> <bold>Abstract.</bold> As the nation moves towards energy independence, it is critical to address the current challenges associated with large-scale biofuel production. In this paper, we study the impacts in the logistics network resulting from the selection of one of four harvest scenarios. A simulation model was developed, which simulated the harvest and filling of a Satellite Storage Location (SSL), using conventional hay harvest equipment, specifically, a round baler. The model evaluated the impacts of four harvest scenarios (ranging from short, October-December, to extended, July-March), on baler equipment requirements, baler utilization, and the storage capacity requirements of round bales, across a harvest production region. Of the four scenarios examined in the analysis, each displayed similar trends across the six tours. Variations in the baler requirements that were observed among the tours resulted from variability in field size distribution, field to baler allocations, and total production area. Beginning harvest in July and extending harvest through March resulted in reductions in the number of round balers required ranging from 50-63%, as compared to the short harvest scenario. For a 9-month harvest, harvesting approximately 50% of total switchgrass production area in July-September, as compared to harvesting approximately 50% in October-December, resulted in reductions in number of round balers required ranging from 33.3 to 43.5%.

The development of methods for automating the distribution of finished products is possible using mathematical methods. Algorithms based on these methods will improve the efficiency of the transport industry and the logistics component in the network. Transportation costs account for a large part of the total logistics costs, so optimizing a multimodal logistics system increases the productivity of vehicles. The use of a mathematical model for optimizing the multimodal logistics chain allows controlling the functioning of transportation processes and combining incoming cargo information into a single data collection and processing system, which will improve the quality and efficiency of all links in the transport and logistics chain as a whole. The simulation results showed that in order to optimize a multi-modal logistics network in an urban agglomeration, many conditions, such as cost reduction, delivery time limits and the introduction of fines for exceeding exhaust gas emissions should be taken into account. The influence of the above factors on the optimal solution of the problem is studied. To solve it, the use of a heuristic algorithm for automating the design of new logistics systems was proposed taking into account changing demand and changes in transportation tariffs.

The use of modern information technologies in transport logistics makes it possible to reduce the cost of cargo delivery and optimize the time of their transportation. Reducing inventory to the most optimal level and delivering goods exactly at a certain time is very much in demand for a modern dynamic economy. One of the most important tasks of the logistics chain is to coordinate the actions of all its participants. This is facilitated by the standardization of documents necessary for the transportation of goods, the establishment of direct contractual relations between all participants in the logistics chain, as well as the possibility of a single order for all services provided by all participants in the logistics chain. Effective development of transport logistics requires a systematic approach to solving emerging practical problems. The formation of transport and logistics clusters and the creation of multimodal transport and logic centers contributes to the development of the transport and logic sector. The transport system shows its maximum efficiency not only in the presence of modern transport infrastructure, but it also requires the presence of free competition in the market of transport and logistics services. The state policy in the field of regulation of the market of transport and logistics services should be aimed at their rationalization and reduction of costs correlated with them.

The steel manufacturing process is characterized by the requirement of expeditious development of high quality products at low cost through effective and judicious use of available resources. Identifying solutions that meet the conflicting commercially imperative goals of such process chains is hard using traditional search techniques. The complexity embedded in such a problem increases due to the presence of large number of design variables, constraints and bounds, conflicting goals and the complex sequential relationships of the different stages of manufacturing. A classic example of such a manufacturing problem is the design of a rolling system for manufacturing a steel rod. This is a sequential process in which information flows from first rolling stage/pass to last rolling pass and the decisions made at first pass influence the decisions that are made at the later passes. In this paper, we present a method based on well-established empirical models and response surface models developed through simulation experiments (finite element based) along with the compromise Decision Support Problem (cDSP) construct to support integrated information flow across different stages of a multi-stage hot rod rolling system. The method is goal-oriented because the design decisions are first made based on the end requirements identified for the process at the last rolling pass and these decisions are then passed to rolling passes that precede following the sequential order in an inverse manner to design the entire rolling process chain. We illustrate the efficacy of the method by carrying out the design of a multi-stage rolling system. We formulate the cDSP for the second and fourth pass of a four pass rolling chain. The stages are designed by sequentially passing the design information obtained after exercising the cDSP for the last pass for different scenarios and identifying the best combination of design variables that satisfies the conflicting goals. The cDSP for second pass helps in integrated information flow from fourth to first pass and in meeting specified goals imposed by the fourth and third pass designed. The end goals identified for this problem for fourth pass are minimization of ovality (quality) of rod, maximization of throughput (productivity) and minimization of rolling load (performance and cost). The method can be instantiated for other multi-stage manufacturing processes such as the steel making process chain having several unit operations. In future, we plan to use the method for supporting decision workflow in steel making process by formulating cDSPs for the multiple unit operations involved and linking them as a decision network using coupled cDSPs.

E-commerce plays a predominant role in the process of buying and selling of goods and services over the Internet. It helps in establishing businesses and sales with a wide range of customers across the globe, offering the customers to shop online 24/7. This study helps to explore various aspects that contribute to the reduction of operational costs in ecommerce such as inventory management, automation of business processes, logistic and supply chain management, labor cost, warehousing, etc. Additionally, processing orders, payments, and customer services through automation, it helps business to gain more profit by reducing the costs associated with it. Through digital marketing, it can reach customers across the globe efficiently. Data analytics helps to make decisions effortlessly by providing valuable insights. The findings in the article help to understand how the adoption of e-commerce in businesses helps to achieve a significant reduction of administration costs, storage costs, shipping costs, etc by helping the business earn more profitability. The study looks at a number of important ways that e-commerce can save costs, such as through automating corporate processes, enhancing inventory control, requiring less physical infrastructure, having a worldwide reach and being scalable, streamlining communication, and producing less paperwork.

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and elsewhere (such as the High-Tonnage Feedstock Logistics projects (Webb et al. 2013a, Webb et al. 2013b, Webb et al. 2013c, Webb and Sokhansanj 2014, Sokhansanj et al. 2014) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D improvements to individual supply system unit operations, and present the status of feedstock logistics technology development for converting biomass into biofuels. These analyses are supported by experimental data where possible, and help to align the SOT relative to the cost goals defined in the Multi-Year Program Plan. The 2018 Herbaceous SOT aligned feedstock logistic design with current biorefinery’s design capacity utilized by biochemical conversion platform. Currently biochemical conversion platform utilizes a 725,000 dry ton/year biorefiney design for the techno economic analysis. Hence, feedstock delivered cost in the 2018 Herbaceous SOT is calculated based on biorefinery’s 725,000 dry ton design capacity instead of 800, 000 dry ton capacity utilized in the 2017 Herbaceous SOT. Biomass availabilities in this SOT were updated to year 2018 data from the 2016 Billion-Ton Report (BT16) (DOE 2016a), with the exception of switchgrass, for which the 2018 Herbaceous SOT utilized the 2019 switchgrass availability data from BT16. The BT16 report (DOE 2016a) does not project switchgrass availability in 2018; the soonest switchgrass is available in the BT16 report is 2019. Therefore, availability of switchgrass for this analysis was that projected for 2019. The 2018 Herbaceous SOT incorporates same technologies utilized in the 2017 Herbaceous SOT. However, a sensitivity analysis is performed to understand the impact of variation of process parameters on those technologies on feedstock logistic cost. New R&D data that shows the variations of process parameters affecting process performance is incorporated in the 2018 SOT to measure the variations in delivered feedstock cost. The 2018 Herbaceous SOT has also provided projected delivered feedstock of 2022 design case based on near term technical target under BETO funded R&D project. Finally, updated biorefinery size of 725,000 dry ton/year was incorporated within least-cost formulation model to select optimal siting and depot scales during optimization of the least cost blend. This modification to the optimization algorithm allows the trade-off between the cost of increased supply radius and the savings from selecting biomass from higher producing counties to be assessed. Such optimization has also showed the economic benefit of decentralized depots in comparison to centralized preprocessing co-located with the biorefinery by decoupling the biorefinery and feedstock locations. The 2018 Herbaceous SOT report documents the current modeled cost of a herbaceous feedstock supply system (from harvest to the pretreatment reactor throat, including grower payment) for hydrocarbon fuel production via biochemical conversion, based on equipment and processes now available or potentially available in the near term. The modeled cost also considers both the required quality and the availability of the biomass resources. The 2018 Herbaceous SOT predicts a modeled delivered feedstock cost of $83.67/dry ton (2016$); this is a $0.23/dry ton (2016$) decrease from the 2017 Herbaceous SOT. The modification of biorefinery’s designed capacity and increased projected biomass availability in the same supply shed contributed to this modeled cost reduction. The least-cost formulation model to optimally site and scale local distributed preprocessing depots also contributed to the cost reduction by considering county-level grower payment and distance from the biorefinery as variables in the optimization algorithm. Sensitivity analysis on various process parameters that affect delivered feedstock cost in the 2018 Herbaceous SOT shows that the delivered cost could varies from $80.45-$88.83/dry ton. The top factors that causes such variations are: effective baling rate, bale density, hammer mill throughput, interest rate and storage dry matter loss.

SUMMARY– Existing ultrafiltration/reverse osmosis technology provides a means of fractionating and concentrating cheese whey into liquid fractions containing a variety of protein: lactose ratios. These ratios may range from about 1:8 (raw whey) through 3:5 (a “skim milk equivalent”) to 2:1 or higher. If a two‐ or three‐stage ultrafiltration system were used with water injection between stages, a product with a protein:lactose ratio of 20:1 could be obtained. The exact protein:lactose ratio in the concentrate stream is a function of the permeability and selectivity characteristics of the membrane, and the system design and operating conditions. Some of the sanitation problems associated with the introduction of these new unit operations in the dairy and food processing industries are also treated at length.

Miniature, durable, and fast-responding temperature sensors are needed for proton exchange membrane fuel cells (PEMFCs). When embedded in a single cell or in a cell stack, they can provide useful information both at the design stage for optimizing the cell efficiency and during operation for monitoring the working conditions and thus preventing failures. Optical fiber sensors are especially promising in this field because they are small, rugged, and inexpensive. In addition, they can provide safe temperature measurements in an electrically hostile environment. A four-channel optical fiber thermometer, based on intensity-independent fluorescence lifetime thermometry was developed at INRIM. It consists of a photonic unit for the excitation/detection of the fluorescence signals and a set of custom optical fiber probes based on a temperature-sensitive fluorescent material attached to the distal end of an optical fiber. The system was characterized in the range from room temperature to about 100 °C in order to point out its metrological features. A temperature repeatability to within 0.06 °C with a response time lower than 1 s to a step temperature change was obtained. A preliminary investigation inside a PEMFC stack using the optical fiber fluorescence thermometer was also performed. In order to check the temperature uniformity along the stack, temperatures within an adjacent membrane electrode assembly (MEA) of a six-fuel-cell stack were measured during the unit operation. The system design, the probe construction, and its laboratory testing are presented in this article together with an assessment of the overall system performance. The application of such a system in a fuel-cell test rig is also described. The experimental results demonstrate the suitability of the system in real-time temperature mapping in operating fuel cells.