Development and implementation of integrated biomass supply analysis and logistics model (IBSAL)
Introduction
Recent advances in computational tools have made it possible to build mathematical models for analysis and optimization of complex supply systems. These tools are applied successfully to manufacturing, transportation, and supply chain management of many goods and services. This paper describes the implementation of these tools for simulation of supply and transportation of agricultural biomass. The agricultural biomass supply logistic consists of multiple harvesting, storage, pre-processing, and transport operations. The entire network operates in space and time coordinates. Agricultural biomass supply logistics are characterized by a wide areal distribution of biomass; time and weather-sensitive crop maturity; variable moisture content; low bulk density of biomass material and a short time window for collection with competition from concurrent harvest operations. An optimized collection, storage and transport network can ensure timely supply of biomass with minimum cost.
Tatsiopoulos and Tolis [1] evaluated the supply of cotton gin waste to small decentralized combined heat and power plants in Greece. Hansen et al. [2] developed a simulation model of sugar cane harvest and mill delivery in South Africa. Nilsson [3] described in detail the development of a simulation model (SHAM—Straw Handling Model) for baling and transporting wheat straw to district heating plants in Sweden. The simulation demonstrated the utility of systems analysis in predicting the amount and cost of biomass supply in optimum resource allocation to minimize bottlenecks. Nilsson's published model did not include bulk handling of biomass [4], [5]. Mantovani and Gibson [6] modelled a collection system for corn stover, hay, and wood residues for ethanol production using the GASP IV simulation program. They considered historical weather data and farmers’ changing attitude towards harvesting biomass. They highlighted the impact of weather variations and late harvest on biomass availability and equipment cost. Arinze et al. [7] and Sokhansanj et al. [8] modelled the changes in quality of potash fertilizer and alfalfa cubes, respectively, during storage and transport. The models considered weather data on timeliness of transport operations for these products but did not consider the entire supply chain.
Biomass Technology Group (BTG) [9] recommended a system analysis approach for reducing the costs, energy flow, and emissions of biomass operations. Berruto and Maier [10] and Berruto et al. [11] used a discrete simulation model to investigate how queue management could help to improve the performance of a country elevator receiving multiple grain streams with a single unloading pit. Humphrey and Chu [12] analysed the procurement and processing of corn in a wet milling operation using the simulation language GASP IV. Benock et al. [13] developed a GASP IV-based simulation model to analyse harvesting, on-farm transportation, and drying of corn. The model predictions agreed well with observations. Nilsson [4] and Hansen et al. [2] used the modelling language SIMAN. Rotz et al. [14] developed the dairy forage system model (DAFOSYM) based on the FORTRAN and BASIC languages.
The overall goal of this paper is to simulate the flow of biomass from field to a biorefinery. The specific objectives of this paper are:
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Develop a framework for a dynamic Integrated Biomass Supply, Analysis and Logistics model (IBSAL).
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Model climatic and operational constraints that have significant influence on the availability of biomass to a biorefinery.
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Develop a model to quantify resource allocations (such as labour, equipment and structure) for biomass supply and transport operations, and calculate biomass delivered cost ($ Mg−1). Note that biomass delivered cost in this study do not include any payment to the farmer or farming cost.
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Show the operation of the model with a case study of corn stover supply to a biorefinery.
Section snippets
Overview
The IBSAL model is a simulation of a biomass supply chain. It consists of a network of operational modules and connectors threading the modules into a complete supply chain. Each module represents a process or an event. For example, grain combining, swathing, baling, loading a truck, truck travel, stacking, grinding, sizing, storing, each process is a module. Modules may also be processes such as drying, wetting, and chemical reactions such as breakdown of carbohydrates. Costing and energy
Implementation
Fig. 5 shows the flow of biomass through the collection network. The discrete item in our simulation is 10 ha. Attributes of the discrete item (land) are moisture content, yield, minimum and maximum distance from a stacking (or storage) location. As an item enters the network, the corresponding weather data is also read in. The item becomes an accumulator of costs as the item passes through each station (also known as activity-based costing [31]). For example, an item that is worked on by
Conclusions
The objective of the present work was to develop a dynamic simulation program for collection and transportation of large quantities of biomass and to predict the delivered costs ($ Mg−1). We compiled weather and yield data from published sources. We also developed equations representing the working rate of field machinery and transport equipment. We calculated fixed and variable costs to represent labour, equipment, and structures. A commercially available simulation package EXTEND™ was used to
Acknowledgements
This project is made possible with the financial and program support from Office of Biomass Programs, US Department of Energy and funds from Natural Sciences and Engineering Research Council of Canada.
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