304 W. Pennsylvania Avenue

Urbana, IL, United States

304 W. Pennsylvania Avenue

Urbana, IL, United States
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Domdouzis K.,Energy Biosciences Institute | Rodriguez L.,304 W. Pennsylvania Avenue | Hansen A.,304 W. Pennsylvania Avenue | Ting K.C.,304 W. Pennsylvania Avenue
American Society of Agricultural and Biological Engineers Annual International Meeting 2010, ASABE 2010 | Year: 2010

Biomass feedstock production is an integral element of bioenergy production. The amount of useful information distributed among various stages of biomass feedstock production is large while there is increased complexity in the relationship between the generated data. A systems informatics infrastructure is therefore considered necessary in order for the people involved in biomass feedstock production to be able to access the generated data, exchange data amongst each other, and also manipulate them. The ultimate goal of this infrastructure is the provision of effective procedures for decision making in a concurrent way. Specifically, all the stages of biomass feedstock production should be able to handle data simultaneously. Furthermore, any knowledge associated with the infrastructure must be managed efficiently and this can be achieved by the use of the appropriate software engineering techniques. The use of such techniques allows the identification of the requirements for the biomass feedstock production supply chains and the design of an efficient informatics platform based on these requirements. This paper aims to introduce the application programming interface (API) which is the core element of the informatics infrastructure developed for the biomass feedstock production. The API is connected to a database containing data related to each stage of biomass feedstock production. The API provides a number of capabilities to its users, such as the access to and visualization of both existing data and simulation results for analysis purposes, the provision of a metadata-based database search engine, the identification of hidden relationships between data through the use of data clustering algorithms, and the identification of the strength of these relationships through the use of rule-based techniques. Furthermore, the capabilities of the API will expand to the field of artificial intelligence and especially artificial neural networks and genetic algorithms for the realization of predictions and optimization. Regression analysis is another technique which will be provided by the API to facilitate the exploration of optimal values of data which affect other data related to biomass feedstock production to be explored.


Shastri Y.,Energy Biosciences Institute | Hansen A.,304 W. Pennsylvania Avenue | Rodriguez L.,304 W. Pennsylvania Avenue | Ting K.C.,304 W. Pennsylvania Avenue
American Society of Agricultural and Biological Engineers Annual International Meeting 2010, ASABE 2010 | Year: 2010

Biomass feedstock production is an important component of the biomass based energy sector. Seasonal and distributed collection of low energy density material creates unique challenges, and optimization of the complete value chain is critical for cost-competitiveness. BioFeed is a mixed integer linear programming (MILP) model that has been developed and successfully applied to optimize biomass feedstock production of bioenergy crops. It integrates the individual farm design and operating decision with the transportation logistics issues to analyze them as a single system. However, this integration leads to a model that is computationally demanding, leading to large simulation times for simplified case studies. Given these challenges, and in wake of the future model extensions, this work proposes a new computational approach that reduces computational demand, maintains result accuracy, provides modeling flexibility and enables future model enhancements. The new approach, named the Decomposition and Distributed Computing (DDC) approach, first decomposes the model into two separate optimization models: a production model, focusing on on-farm activities such as harvesting, and a provision model, incorporating the post-production activities such as transportation logistics. An iterative scheme based on the concepts from agent based modeling is adapted to solve the production and provision problems iteratively until convergence has been reached. The computational features of the approach are further enhanced by enabling distributed computing of the individual farm optimization models. Simulation studies comparing the performance of the DDC approach with the rigorous MILP solution approach illustrate an order of magnitude reduction in computational time using the proposed DDC approach. Moreover, the solution obtained using the DDC approach is within 5% of the MILP solution. This approach can be a valuable tool to solve complex supply chain optimization problems in other sectors where similar challenges are encountered.


Shastri Y.,Energy Biosciences Institute | Hansen A.,304 W. Pennsylvania Avenue | Rodriguez L.,304 W. Pennsylvania Avenue | Ting K.C.,304 W. Pennsylvania Avenue
American Society of Agricultural and Biological Engineers Annual International Meeting 2010, ASABE 2010 | Year: 2010

The success of the bioenergy sector depends significantly on ensuring efficient and sustainable biomass feedstock production and provision, which requires a comprehensive systems theory based approach. BioFeed is a system level model that has been proposed to optimize the feedstock production and provision activities, and has been applied in the past to study switchgrass production in Illinois. This work presents recent additions to the BioFeed model to enable a more accurate representation of various biomass production activities for the energy crops. While maintaining the original model framework that focused on farm level design and operational issues in addition to storage and transportation logistics, new biomass packing and size reduction operations such as peptization and grinding have been added. The selection and operation of biomass handling equipment such as loaders, unloaders and infield transportation equipment have also been incorporated. The addition of these new operations creates the challenge of ensuring the logical validity of the operational sequence during model simulation. A super-structure of all possible operational sequences is developed and the biomass form at the output of every equipment is tracked to ensure appropriate equipment selection. The model is then applied to a case study of Miscanthus production as the energy crop in southern Illinois. The results show that the optimized delivered cost based on existing technology is about 45 \$/Mg. Biomass packing and storage are important components of the total cost distribution. The potential alternatives to reduce the delivered cost include using a single-pass mowing and baling operation, increasing the packing throughput capacity, and extending the harvesting window.


Shastri Y.,Energy Biosciences Institute | Hansen A.,304 W. Pennsylvania Avenue | Rodriguez L.,304 W. Pennsylvania Avenue | Ting K.C.,304 W. Pennsylvania Avenue
American Society of Agricultural and Biological Engineers Annual International Meeting 2011, ASABE 2011 | Year: 2011

A number of different agricultural crops such as Miscanthus and switchgrass are under evaluation as potential biomass feedstock alternatives. Since agricultural operations are weather dependent, unfavorable weather can significantly impact the feedstock production as well as subsequent storage and supply activities. In particular, the systemic impact of regional weather patterns must be considered while developing and evaluating the feedstock harvest systems. This was achieved using the BioFeed modeling framework in this work. We developed the BioFeed model to simulate and optimize production and provision activities, including infrastructure requirements and operations, in regional biomass feedstock production systems. Weather effects are now incorporated in BioFeed with the inclusion of the probability of working day (pwd) parameter in the model. The pwd defines the fraction of days in a specific period such as two weeks that are suitable for field operations, and its value depends on a number of weather related parameters such as rainfall, snow depth, soil temperature, and soil moisture content. Model simulations were conducted for Miscanthus for values of pwd between 20%-100% and intended biorefinery capacities between 1000-6000 Mg d-1; and the impact on total cost and farm machinery requirements was quantified. Results indicated that if the harvest and handling system was designed assuming 100% pwd, lower pwd values exponentially increased the cost and decreased the biorefinery capacity that could be supported by the collection region. For Illinois with the average winter pwd of about 35%, ignoring weather patterns therefore led to 38% increase in total cost and 45% decrease in the supportable biorefinery capacity. BioFeed was then used to optimize the harvest system for a particular value of pwd. Simulation studies showed that this maintained the intended biorefinery capacity and led to a linear increase in total cost with decreasing pwd. The resulting optimum system required significantly larger fleet of farm machinery. For Illinois, the total cost increased by about 3.5%, but the total capital investment in farm machinery increased by almost 34%. These results emphasized that the consideration of weather impacts on farm productivity is extremely important when considering system design and operations.


Shastri Y.,Energy Biosciences Institute | Rodriguez L.,304 W. Pennsylvania Avenue | Hansen A.,304 W. Pennsylvania Avenue | Ting K.C.,304 W. Pennsylvania Avenue
American Society of Agricultural and Biological Engineers Annual International Meeting 2011, ASABE 2011 | Year: 2011

The success of the bioenergy sector based on lignocellulosic feedstock will require a sustainable and resilient transition from the current agricultural system focused on food crops to one also producing energy crops. The dynamics of this transition are not well understood. It will be driven significantly by the collective participation, behavior and interaction of various stakeholders such as farmers within the production system. The objective of this work is to study the system dynamics through the development and application of an agent-based model using the theory of complex adaptive systems. Farmers and biorefinery, two key stakeholders in the system, are modeled as independent agents. The decision making of each agent as well as its interaction with other agents is modeled using a set of rules reflecting the economic, social and personal attributes of the agent. These rules and model parameters are adapted from literature. Regulatory mechanisms such as BCAP (Biomass Crop Assistance Program) are embedded in the decision making process. The model is then used to simulate the production of Miscanthus as an energy crop in Illinois. Particular focus has been given on understanding the time dynamics of the adaptation of Miscanthus as an agricultural crop and its impact on biorefinery capacity and contractual agreements. Results showed that only 60\% of the maximum regional production capacity could be reached and it took up to 15 years to establish that capacity. 25\% reduction in the land opportunity cost led to 63\% increase in the steady state productivity. Sensitivity analysis on the model parameters was conducted to determine the factors having a significant impact on the dynamics of this system.

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