Robust optimization of nanoparticle synthesis in a supercritical CO2 process for energy applications

能源应用超临界二氧化碳工艺中纳米颗粒合成的稳健优化

• 批准号: 0933430
• 负责人: Martha Grover
• 金额: $ 40万
• 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 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0933430&HistoricalAwards=false
• 关键词: Robust; optimization; nanoparticle; synthesis; supercritical

0933430GroverSystematic methods are needed to quantify uncertainty in nanomanufacturing processes, and subsequently to design processes that are robust to these uncertainties. Nanoscale phenomena present a new challenge for manufacturing, due to the inherent stochastic dynamics, in addition to sensitivities to macroscopic process inputs like temperature and pressure. However, processes are currently being developed to take advantage of the new discoveries and advancements in nanoscience, and cost-effective engineering approaches and tools are needed to more efficiently explore the design space to develop nanotechnology-enabled products. In this work the PIs focus on the synthesis of metal nanoparticles, which are used in a wide range of applications from energy to medicine. For example, nanoparticles of controlled size and size distribution are needed to create high performance catalysts for NOx treatment in diesel engines, which produce lower CO2 emissions relative to gasoline engines. However, developing a high-throughput manufacturing process to create durable supported catalysts in a cost-effective manner has been elusive, in part due to design tradeoffs like higher performance but lower durability at smaller nanoparticle size. Moreover, significant variability exists both within a single batch of nanoparticles, due to the inherent distribution of particle nucleation times, and also between batches, due to drift in operating conditions and noise variables.This project is a comprehensive methodology for robust optimization of a batch process, using various sources of information integrated by a rigorous Bayesian method. First, mechanistic models of mean process behaviors, as is common in the engineering disciplines, will be developed. Since models of nanoscale phenomena are typically not accurate within manufacturing tolerances, mechanistic models will be supplemented with stochastic components linking within- and between-batch variations to controllable process parameters and noise variables for robust process design. Expert opinions help model trends and expected variance for upgrading the models into a stochastic-mechanistic simulation tool. The simulated data generated will be used to build a statistical-mechanistic model, which is less complex than the simulation model, suitable for efficient exploration of process recipes. Then, physical data will be collected based on optimal experimental design plans developed to validate and improve the statistical-mechanistic model. Finally, the refined model will be used to cost-effectively search for the optimized process recipe, to achieve the desired nanoparticle size with a narrow size distribution while minimizing batch-to-batch variation.Intellectual merit. The current disconnect between the fields of robust design in statistics and mechanistic modeling in engineering will be bridged by this methodology. Incorporating all sources of information on mean behavior and variance requires domain-specific knowledge and mechanistic understanding. This modeling approach for mean and variance of process variables is required to derive the recipe for a robust optimal process.Broader impact. The PI team is uniquely equipped to develop this new methodology for robust process optimization. They combine expertise in experiments, mechanistic modeling, process control, and experimental design, along with our close collaborations with industry. The diverse team of faculty and students (graduate, undergraduate, and high school) who will participate in the project will gain experience and insight that will allow them to work in interdisciplinary nanomanufacturing environments.

需要系统的方法来量化纳米制造过程中的不确定性，并随后设计出对这些不确定性具有鲁棒性的工艺。由于其固有的随机动力学特性以及对温度和压力等宏观过程输入的敏感性，纳米尺度现象对制造提出了新的挑战。然而，目前正在开发利用纳米科学的新发现和进步的工艺，并且需要成本效益高的工程方法和工具来更有效地探索开发纳米技术支持产品的设计空间。在这项工作中，pi专注于金属纳米颗粒的合成，这些纳米颗粒在从能源到医学的广泛应用中得到了应用。例如，与汽油发动机相比，柴油发动机产生的二氧化碳排放量更低，因此需要控制尺寸和尺寸分布的纳米颗粒来制造用于处理NOx的高性能催化剂。然而，开发一种高通量制造工艺，以经济有效的方式制造耐用的负载型催化剂一直是难以捉摸的，部分原因是设计上的权衡，比如在更小的纳米颗粒尺寸下，性能更高，但耐久性较低。此外，由于颗粒成核时间的固有分布，在单个批次的纳米颗粒中，由于操作条件和噪声变量的漂移，也存在显著的可变性。这个项目是一个全面的方法稳健优化的批处理，使用各种来源的信息集成严格的贝叶斯方法。首先，将开发平均过程行为的机制模型，这在工程学科中很常见。由于纳米级现象的模型通常在制造公差范围内不准确，因此机械模型将补充随机组件，将批内和批间变化与可控工艺参数和噪声变量联系起来，以进行稳健的工艺设计。专家意见有助于建模趋势和期望方差，从而将模型升级为随机机制模拟工具。生成的模拟数据将用于构建统计机制模型，该模型比仿真模型简单，适合于有效地探索工艺配方。然后，根据制定的最优实验设计方案收集物理数据，以验证和改进统计机制模型。最后，精细化模型将用于经济有效地搜索优化的工艺配方，以窄尺寸分布获得所需的纳米颗粒尺寸，同时最大限度地减少批次间的变化。知识价值。目前统计学领域的稳健设计和工程领域的机械建模之间的脱节将通过这种方法弥合。整合关于平均行为和方差的所有信息源需要特定领域的知识和机制理解。这种对过程变量的均值和方差的建模方法是推导出稳健的最优过程的配方所必需的。更广泛的影响。PI团队有独特的装备来开发这种新的方法，以实现稳健的过程优化。他们结合了实验，机械建模，过程控制和实验设计方面的专业知识，以及我们与行业的密切合作。参与该项目的教师和学生（研究生、本科生和高中生）组成的多元化团队将获得经验和洞察力，使他们能够在跨学科的纳米制造环境中工作。