CAREER: Synergistic Design, Analysis and Learning of Intensified Process Systems

职业：强化过程系统的协同设计、分析和学习

基本信息

批准号：
1943479
负责人：
MM Faruque Hasan
金额：
$ 51.33万
依托单位：
Texas A&M Engineering Experiment Station
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943479&HistoricalAwards=false
关键词：
CAREER Synergistic Design Analysis Learning

项目摘要

In this new era of highly dispersed and unconventional feedstocks and volatile prices, the Chemical Process Industry will likely evolve to include distributed chemical manufacturing performed in small-scale, modular systems. This CAREER project is motivated by the need for developing a methodology to expose and exploit synergy in the multiple steps of a complex chemical process. The ultimate purpose is to design intensified chemical processes that combine tasks into fewer units, resulting in higher product yields, higher energy efficiency and lest waste. This new methodology will be applied to design intensified processes for unconventional methane-to-chemicals conversions in remote locations for the production of high-value liquid chemicals using local sources of shale gas, biogas, or landfill gas. The research results will be integrated into educational and outreach activities, including a citizen science project focusing on intensification of shale gas activities in Texas and recruiting of underrepresented minorities into STEM careers through K-12 demonstrations of process design concepts.The proposed research will employ theoretical analysis and computer simulations to systematically locate intensification hotspots in complex chemical processes and re-orient the underlying physical and chemical phenomena while striking a balance between modularity and intensification within the overall design. Systematic methods and algorithms will be formulated for synergistic design and operation of intensified systems that are amenable to both centralized and distributed chemical manufacturing. Identification of hidden synergies will transform many chemical processes, especially where intensification is not obvious or deemed to be disadvantageous. Scaling down/out conventional processes without intensification could lead to poor economies-of-scale and high capital intensity. To this end, synergistic analysis provides a natural framework for studying the level of intensification of process designs. A graph theoretical representation of chemical processes is proposed based on phenomena-phenomena interactions (PPI) as new way to design processes with greater synergy. The PPI-based analysis can lead to innovative pathways and drastic improvements in energy, cost, emissions and waste to achieve sustainability in a resource-constrained environment. The change from the classic unit-operation-based representation into an interaction-based building block representation of chemical processes sets will enable design and intensification of complex processes without prior postulation of all plausible flowsheets. The generic block superstructure will essentially eliminate the need for problem specific designs and allow process synthesis, integration, and intensification in a single framework. The integration of research and education involves an assessment of chemical engineering curricula using a course-course interaction (CCI) graph to enhance synergistic learning and development of a new graduate course. The proposed project also includes a citizen science project to engage volunteers with diverse backgrounds in the intensification of a statewide Material and Process (MaP) network for shale gas utilization in Texas. The MaP project will provide an informal science education opportunity with a synergistic impact on participant knowledge. Women and minority students will be recruited through the Louis Stokes Alliances for Minority Participation (LSAMP) program and a building-block-based design concept will be included in projects designed for grades 1 to 4 to attract students into STEM fields.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.

在这个高度分散、非常规原料和价格波动的新时代，化学加工工业可能会发展到包括在小规模、模块化系统中进行的分布式化学制造。这个职业项目的动机是需要开发一种方法来揭示和利用复杂化学过程的多个步骤中的协同作用。最终目的是设计强化化学工艺，将任务组合成更少的单元，从而提高产品产量、能源效率并避免浪费。这种新方法将应用于设计在偏远地区将非常规甲烷转化为化学品的强化工艺，以利用当地的页岩气、沼气或垃圾填埋气生产高价值液体化学品。研究成果将融入教育和推广活动中，包括一个公民科学项目，重点关注德克萨斯州页岩气活动的强化，以及通过 K-12 过程设计概念演示，招募代表性不足的少数族裔进入 STEM 职业。拟议的研究将采用理论通过分析和计算机模拟，系统地定位复杂化学过程中的强化热点，重新定位潜在的物理和化学现象，同时在总体设计中的模块化和强化之间取得平衡。将制定系统方法和算法，用于协同设计和操作适合集中式和分布式化学制造的强化系统。识别隐藏的协同效应将改变许多化学过程，特别是在集约化不明显或被认为不利的情况下。在不强化的情况下缩小/缩小传统流程可能会导致规模经济不佳和资本密集度高。为此，协同分析为研究工艺设计的强化程度提供了一个自然的框架。基于现象-现象相互作用（PPI）提出了化学过程的图论表示，作为设计具有更大协同作用的过程的新方法。基于 PPI 的分析可以带来创新途径并大幅改善能源、成本、排放和废物，从而在资源有限的环境中实现可持续发展。从经典的基于单元操作的表示形式转变为化学过程集的基于交互的构建块表示形式，将使复杂过程的设计和强化成为可能，而无需事先假设所有合理的流程图。通用块上层结构将基本上消除对特定问题设计的需要，并允许在单一框架中进行流程综合、集成和强化。研究和教育的整合涉及使用课程-课程互动（CCI）图对化学工程课程进行评估，以增强新研究生课程的协同学习和开发。拟议的项目还包括一个公民科学项目，旨在让具有不同背景的志愿者参与强化德克萨斯州页岩气利用的全州材料和工艺 (MaP) 网络。 MaP 项目将提供非正式的科学教育机会，对参与者的知识产生协同影响。女性和少数族裔学生将通过路易斯斯托克斯少数族裔参与联盟 (LSAMP) 计划招募，并且基于构建块的设计理念将包含在为 1 至 4 年级设计的项目中，以吸引学生进入 STEM 领域。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。