GOALI: Infrastructure Investment and Operation Decisions for Biobased Production Networks

GOALI：生物基生产网络的基础设施投资和运营决策

• 批准号: 0933392
• 负责人: Matthew Realff
• 金额: $ 29.74万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0933392&HistoricalAwards=false
• 关键词: GOALI; Infrastructure; Investment; Operation; Decisions

0933392LeeThe objective of this GOALI project is to develop an optimization model that supports the multi-stage decision-making of infrastructure investment and operation of bio-refineries. These decisions include the selection of fuel conversion technologies, design and expansion of processing network, and the logistics of transportation. In collaboration with Weyerhaeuser NR, the PIs recently developed a preliminary version of such an optimization model. Testing of the model with preliminary data provided by the company yielded some useful insights into the relationship between design structure and various cost elements. However, the current model is able to use only very basic information (e.g., transportation cost, capital cost, operating cost, etc.), is limited in the scope of technologies it considers, and does not consider synergies with existing wood processing infrastructure. In addition, it does not account for uncertainties in performance and costs of major system components and does not support dynamic decision-making needed to address various temporal aspects of the problem. All of these are inherent features of the problem. In this project, the PIs will develop a richer, broader model that considers opportunities for integration with existing forest product processing facilities, uncertainties associated with various silvicultural, technical and economic parameters of the problem, and temporal aspects such as active planning of biomass supplies through forestry management and multi-period expansion of the processing capacities. They will test the models on company-representative data and analyze the results in terms of profits and other measures of system performance, such as the percentage carbon in the forestry resource that is captured in the product, the ton miles travelled, and the percentage utilization of capacity for different market shares of new chemicals and fuels. This information will be integrated into a life cycle inventory of the system to support life cycle assessments for renewable fuel standards. Intellectual Merit: Extension of the current model to remove these limitations requires meeting several intellectual challenges: (1) Synergistic integration with existing wood processing infrastructure requires decisions regarding mass and heat integration schemes with the existing operations to be combined with process and location choices. The industrial collaborator can provide specific facility data for this purpose. (2) To address uncertainties, such as those in the biomass availability, process performance, and final product demand / price, multi-stage stochastic decision problems will be formulated and solved. The industrial collaborator is in a position to provide reasonable structures and estimates of these uncertainties. The multi-stage formulation is also needed to support dynamic decision making. The solution of multi-stage stochastic decision problems can be computationally challenging, and will require development of a tailored solution algorithm. Broader Impact: A systems model of bio-refinery investment optimization can help maximize return on investments, existing and new, made to convert forestry resources into a broader product portfolio. With one of the leading forestry product companies participating in the project, the research outcome is expected to make an immediate impact in the forest product industries. The model can serve as a tool to inform policy makers and other stakeholders what types of policies incentives for investment are needed to spur the economic development based on fuels and chemicals from forestry resources. It will promote a more quantitative, engineering-based, approach to evaluating investments in bio-based fuels and chemicals. The PIs also expect to advance the state of the art of ADP by applying it to an important problem of a scale never tried before. The success of ADP on a problem of major national interest could spur greater interest in this problem solving technique within the process systems community. On the education side, there is a paper science and technology option that could absorb many of the results on the detailed process simulation and integration and a graduate data driven modeling course, which uses a case study approach. In addition, the company will educate its executives of the need for systematic evaluation of bio-refinery options and general capabilities of advanced computational systems.

0933392Lee这个目标项目的目标是开发一个支持生物炼油厂基础设施投资和运营的多阶段决策的优化模型。这些决策包括燃料转换技术的选择、加工网络的设计和扩展以及运输的物流。在与Weyerhaeuser NR的合作下，PIS最近开发了这种优化模型的初步版本。使用该公司提供的初步数据对该模型进行了测试，对设计结构和各种成本要素之间的关系产生了一些有用的见解。然而，目前的模型只能使用非常基本的信息(例如，运输成本、资本成本、运营成本等)，它考虑的技术范围有限，没有考虑与现有木材加工基础设施的协同作用。此外，它没有考虑到主要系统组件的性能和成本方面的不确定性，也不支持解决问题的各种时间方面所需的动态决策。所有这些都是问题的内在特征。在这一项目中，项目执行机构将开发一个更丰富、更广泛的模式，考虑与现有森林产品加工设施整合的机会、与问题的各种营林、技术和经济参数相关的不确定性，以及通过林业管理积极规划生物质供应和多阶段扩大加工能力等时间方面的问题。他们将在具有公司代表性的数据上测试模型，并从利润和其他系统性能指标的角度分析结果，例如产品中捕获的林业资源中的碳百分比、行驶里程数以及不同新化学品和燃料市场份额的产能利用率百分比。这些信息将纳入该系统的生命周期清单，以支持可再生燃料标准的生命周期评估。智力优势：扩展当前模式以消除这些限制需要应对几个智力挑战：(1)与现有木材加工基础设施的协同集成要求与现有操作的质量和热集成方案的决策与工艺和位置选择相结合。行业协作者可以为此目的提供特定的设施数据。(2)为了解决生物质可获得性、工艺性能和最终产品需求/价格等方面的不确定性，将建立和求解多阶段随机决策问题。行业合作者有能力提供合理的结构和对这些不确定性的估计。为了支持动态决策，还需要多阶段的表述。多阶段随机决策问题的求解可能在计算上具有挑战性，并需要开发一种定制的求解算法。更广泛的影响：生物炼油厂投资优化的系统模型可以帮助最大限度地实现现有和新投资的回报，将林业资源转化为更广泛的产品组合。随着一家领先的林产公司参与该项目，预计研究成果将对林产行业产生立竿见影的影响。该模式可以作为一种工具，告知政策制定者和其他利益攸关方，需要哪些类型的投资激励政策来刺激以林业资源燃料和化学品为基础的经济发展。它将促进一种更加量化、以工程为基础的方法来评估生物燃料和化学品的投资。PI还希望通过将ADP应用于一个以前从未尝试过的重要问题来推动ADP的技术水平。ADP在一个重大国家利益问题上的成功，可能会在过程系统社区内激起人们对这一问题解决技术的更大兴趣。在教育方面，有一个纸质科学和技术方案，可以吸收详细流程模拟和集成的许多结果，还有一个研究生数据驱动建模课程，它使用案例研究方法。此外，该公司将对其高管进行培训，使其了解对生物精炼选项和先进计算系统的一般能力进行系统评估的必要性。