Process Engineering Models to Physical Input-Output Tables (PIOTs): A Novel Approach to Reproducible, Transparent and Fast Regional PIOT Development Via Collaborative PIOTHub

物理输入输出表 (PIOT) 的过程工程模型:通过协作 PIOTHub 实现可重复、透明和快速的区域 PIOT 开发的新方法

基本信息

  • 批准号:
    1805741
  • 负责人:
  • 金额:
    $ 29.24万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-08-01 至 2023-07-31
  • 项目状态:
    已结题

项目摘要

Only 30% of waste generated in the US was recycled in 2013 and globally only 14% of plastics are recycled today. Realization of the environmental consequences of waste disposal and the economic cost of lost resources has mobilized global efforts to close material loops by recycling and recovery. This system is termed the "Circular Economy". However, to build a robust and resilient circular economy, it is necessary to identify hidden vulnerabilities that can occur due to increased mutual interdependence of sectors in the economy with increasing use of waste as feedstock. To achieve this goal, a full physical map of current industrial interactions must be known, which can be captured by Physical Input-Output Tables (PIOTs) that account for inter-industry interactions in physical units. But, PIOTs are rarely developed, with only very few PIOTs developed across the world since 1990s. This is attributed both to lack of data and a standardized approach for building PIOTs. Thus, the physical structure of existing economy cannot be mapped. The aim of this research is to address this critical missing piece of transitioning to a robust circular economy. The overall goal is to develop and establish a "Process to PIOT (P2P)" approach that integrates process engineering based models with an Input-Output framework for generating PIOTs for a regional economy. Specific tasks of this project are: 1) Establish a new P2P approach for developing PIOTs; 2) Compare PIOTs developed against exisiting Monetary Input-Output Tables (MIOT) for validation; 3) Develop an Open source database of Process Models that can be adapted for developing PIOTs along with an open platform for collaborative PIOT development. This project is targeted to enable transition to a robust circular economy by developing and standardizing a Process to PIOT (P2P) approach that utilizes the strength of process modeling to develop regional PIOTs. Establishing the P2P approach will provide a standardized approach for fast, reproducible and transparent PIOT development in any region of choice. A key feature of the approach is to utilize the strength of process engineering models in a modular approach to build PIOTs, and combining with an interregional trade model for improved accuracy in capturing inter-sectoral flows in a region. This research will provide an open-source collaborative platform for developing PIOTs on a regional scale (PIOTHub) using Purdue's HubZero platform. The collaborative platform will allow multiple researchers to develop PIOTs for many regions and enhance collaboration to solve sustainable resource and consumption challenges. Further, the process engineering approach will allow interested industries to use the platform for making strategic decisions in forming alliances that will be economically and environmentally beneficial without the concern of disclosing proprietary information. This is critical for industry engagement into spurring regional environmental sustainability. Undergraduate and graduate education modules developed as case studies in this project will be disseminated for teaching the concepts of the circular economy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
2013年,美国只有30%的垃圾被回收利用,而今天全球只有14%的塑料被回收利用。认识到废物处置的环境后果和资源损失的经济代价,动员了全球努力,通过再循环和回收来封闭材料循环。这个系统被称为“循环经济”。然而,为了建立一个强大而有弹性的循环经济,有必要确定由于经济部门之间的相互依赖性增加以及废物作为原料的使用增加而可能出现的隐藏脆弱性。为了实现这一目标,必须了解当前产业相互作用的完整物理地图,这可以通过物理投入产出表(PIOT)来捕获,该表以物理单位说明产业间的相互作用。但是,PIOT很少开发,自20世纪90年代以来,全世界只有很少的PIOT开发。这是由于缺乏数据和建立PIOT的标准化方法。因此,现有经济的物理结构无法绘制。这项研究的目的是解决过渡到强大的循环经济的关键缺失部分。总体目标是开发和建立一种“过程到PIOT(P2P)”的方法,将基于过程工程的模型与输入输出框架相结合,为区域经济生成PIOT。该项目的具体任务是:1)建立一种新的P2P方法来开发PIOT; 2)将开发的PIOT与验证货币投入产出表(MIOT)进行比较以进行验证; 3)开发一个开放源代码的过程模型数据库,该数据库可以适用于开发PIOT,沿着一个开放平台用于协作PIOT开发。该项目旨在通过开发和标准化PIOT(P2P)方法,利用流程建模的优势开发区域PIOT,从而实现向强大的循环经济的过渡。建立P2P方法将为任何地区的快速,可重复和透明的PIOT开发提供标准化方法。该方法的一个主要特点是以模块化方法利用流程工程模型的优势来构建PIOT,并与区域间贸易模型相结合,以提高捕获区域内部门间流量的准确性。这项研究将提供一个开源的协作平台,用于使用普渡大学的HubZero平台在区域范围内开发PIOT(PIOTHub)。该合作平台将允许多名研究人员为许多地区开发PIOT,并加强合作,以解决可持续资源和消费挑战。此外,过程工程方法将允许感兴趣的行业使用该平台,在形成经济和环境有益的联盟时做出战略决策,而不必担心泄露专有信息。这对于行业参与促进区域环境可持续性至关重要。作为该项目的案例研究开发的本科生和研究生教育模块将被传播,用于教授循环经济的概念。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
PIOT‐Hub ‐ A collaborative cloud tool for generation of physical input–output tables using mechanistic engineering models
PIOT – Hub – 一种协作云工具,用于使用机械工程模型生成物理输入输出表
  • DOI:
    10.1111/jiec.13204
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    5.9
  • 作者:
    Vunnava, Venkata Sai;Shin, Jaewoo;Zhao, Lan;Singh, Shweta
  • 通讯作者:
    Singh, Shweta
A hybrid mechanistic machine learning approach to model industrial network dynamics for sustainable design of emerging carbon capture and utilization technologies
一种混合机械机器学习方法,用于模拟工业网络动态,以实现新兴碳捕获和利用技术的可持续设计
  • DOI:
    10.1039/d3se01032e
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    5.6
  • 作者:
    Shekhar, Abhimanyu Raj;Moar, Raghav R.;Singh, Shweta
  • 通讯作者:
    Singh, Shweta
Reduced order dynamical models for complex dynamics in manufacturing and natural systems using machine learning
使用机器学习的制造和自然系统中复杂动力学的降阶动力学模型
  • DOI:
    10.1007/s11071-022-07695-x
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    5.6
  • 作者:
    Farlessyost, William;Singh, Shweta
  • 通讯作者:
    Singh, Shweta
Integrated Mechanistic Engineering Models and Macroeconomic Input-Output approach to Model Physical Economy for Evaluating the Impact of transition to Circular Economy
综合机械工程模型和宏观经济投入产出方法来模拟实体经济,以评估向循环经济转型的影响
  • DOI:
    10.1039/d1ee00544h
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    32.5
  • 作者:
    Vunnava, Venkata Sai;Singh, Shweta
  • 通讯作者:
    Singh, Shweta
A study on end of life photovoltaics as a model for developing industrial synergistic networks
  • DOI:
    10.1007/s13243-022-00111-y
  • 发表时间:
    2022-04
  • 期刊:
  • 影响因子:
    0
  • 作者:
    N. Mathur;J. Sutherland;S. Singh
  • 通讯作者:
    N. Mathur;J. Sutherland;S. Singh
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Shweta Singh其他文献

Transgression into ‘Hidden’ Feminism: Immigrant Muslim Woman from India
侵犯“隐藏”女权主义:来自印度的穆斯林移民妇女
  • DOI:
  • 发表时间:
    2012
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Shweta Singh
  • 通讯作者:
    Shweta Singh
Raman scattering and DFT calculations used for analyzing the structural features of DMSO in water and methanol
用于分析水和甲醇中 DMSO 结构特征的拉曼散射和 DFT 计算
  • DOI:
    10.1039/c3ra22730h
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Shweta Singh;S. Srivastava;D. Singh
  • 通讯作者:
    D. Singh
Capturing Human Gestures for Sign Language Detection: An Advance Technique for Detection to Help Deprived Section of Society
捕捉人类手势进行手语检测:一种先进的检测技术来帮助社会贫困阶层
Synthesis, structural studies, and antimicrobial activity of polymeric copper(II) tetrathiocyanato diargentate(I) complexes with some acylhydrazones
聚合四硫氰酸铜(II)二银酸铜(I)与某些酰腙配合物的合成、结构研究和抗菌活性
  • DOI:
    10.1002/app.28565
  • 发表时间:
    2008
  • 期刊:
  • 影响因子:
    0
  • 作者:
    V. Singh;Archana Singh;Shweta Singh
  • 通讯作者:
    Shweta Singh
Monitoring potential molecular interactions of adenine with other amino acids using Raman spectroscopy and DFT modeling.
使用拉曼光谱和 DFT 建模监测腺嘌呤与其他氨基酸的潜在分子相互作用。

Shweta Singh的其他文献

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{{ truncateString('Shweta Singh', 18)}}的其他基金

FMRG: Eco: Cyber Enabled Transformation to Circular Supply Chains for Sustainable Pharmaceutical Manufacturing Networks
FMRG:Eco:通过网络实现循环供应链转型,实现可持续药品制造网络
  • 批准号:
    2229250
  • 财政年份:
    2022
  • 资助金额:
    $ 29.24万
  • 项目类别:
    Standard Grant
Collaborative Research: MRA: Scale, Space, and Time: A Unifying Approach to Aquatic Invasions
合作研究:MRA:规模、空间和时间:水生入侵的统一方法
  • 批准号:
    2017858
  • 财政年份:
    2021
  • 资助金额:
    $ 29.24万
  • 项目类别:
    Standard Grant

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Modern Computational Environmental Science and Engineering - Improving Simulation Predictivity by Integrating Process Models and Data
现代计算环境科学与工程 - 通过集成过程模型和数据提高模拟预测能力
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不确定性下过程系统工程的新型优化模型和方法
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集成并行工程设计,结合特征、公差和生产过程模型
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集成并行工程设计,结合特征、公差和生产过程模型
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