Next Generation Data Driven Modeling and Control of Batch and Batch Like Processes

下一代数据驱动的批量和类批量过程的建模和控制

• 批准号: RGPIN-2022-04647
• 负责人: Mhaskar, Prashant
• 金额: $ 4.01万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=747169
• 关键词: Next; Generation; Data; Driven; Modeling

There are numerous products created using batch processing, such as pharmaceuticals and specialty chemicals. The startup of almost every process involves a batch like  operation that takes the process from shutdown mode to continuous operation. Many of these processes currently employ recipes to generate on spec product (with the product quality typically measured only at batch termination)- and this has two major drawback. First - the development of these recipes is very expensive and time consuming (think e.g, of the time required in developing production recipes for new medication), and the second is that these recipes do not work well when the raw material changes. The present research proposes to collect data from existing recipe based operation, create a model between process `recipe' and product quality to in-turn develop recipes rapidly for new products, and to create online control algorithm to maintain on-spec products. The program will leverage advances in data driven modeling from the PI's group to create tools that incorporate machine learning based approaches (only where appropriate), and availability of newer sensing technologies (such as images and acoustics). Applications to biopharmaceuticals and rotational molding will be used to both develop the approaches and demonstrate proof of concept. The impact of these program goals will be quick- the first thread on rapid product development using linear models using a rotational molding setup (with a goal to produce, for instance, recycled plastic products) will lead to a direct utilization by the rotomolding industry (e.g., Rescraft Inc.) in ways that will cut down production cost and directly impact the environment positively. The utilization of neural networks along with subspace identification methods will pave the way for use of these techniques, for instance, in the case of bioreactors. Bioreactors are known to have  nonlinear and complex dynamics, and using these techniques will make production of biopharamaceuticals less expensive (through utilization by industrial partners such as Sartorius Inc). The use of non-traditional data such as images will impact a huge range of industries, such as the steel industry, where high temperatures make use of traditional sensors difficult (along with rotational molding and bioreactors). More importantly, since the tools that will be developed will be of a general nature, they will be readily applicable not just to various industrial partners  (as part of the McMaster Advanced Control Consortium) but to several manufactureres all over Canada. It is anticipated that by year 4 of the program, the benefits of the short term goals will already start manifesting and by the end of the five year program, will save Canadian manufacturing to the tune of hundreds of thousands of dollars a year in development and operational cost, and be well set for accomplishing the longer term goal of creating an auotomated rapid product design and control tool.

有许多产品使用批量处理，如药品和特种化学品。几乎每一个过程的启动都涉及到一个类似于批量操作的过程，它将过程从关闭模式转变为连续操作。许多这些过程目前采用配方来产生规格产品（产品质量通常只在批次终止时测量）-这有两个主要缺点。首先-这些配方的开发是非常昂贵和耗时的（例如，想想开发新药生产配方所需的时间），第二是当原材料改变时，这些配方不能很好地工作。本研究提出从现有的配方为基础的操作，收集数据，创建一个模型之间的过程“配方”和产品质量反过来又迅速开发新产品的配方，并创建在线控制算法，以保持在规格的产品。该计划将利用PI团队在数据驱动建模方面的进步，创建包含基于机器学习的方法（仅在适当的情况下）以及更新的传感技术（如图像和声学）的工具。生物制药和旋转成型的应用将被用于开发方法和证明概念。这些计划目标的影响将是快速的-使用线性模型的快速产品开发的第一个线程使用滚塑设置（目标是生产，例如，回收塑料产品）将导致滚塑行业的直接利用（例如，Rescraft Inc.）以降低生产成本并直接对环境产生积极影响的方式。利用神经网络沿着子空间识别方法将为使用这些技术铺平道路，例如，在生物反应器的情况下。众所周知，生物反应器具有非线性和复杂的动力学，使用这些技术将使生物制药的生产成本更低（通过工业合作伙伴，如Sartorius Inc.的利用）。使用非传统数据（如图像）将影响大量行业，如钢铁行业，高温使传统传感器难以使用（沿着旋转成型和生物反应器）。更重要的是，由于将开发的工具将是一般性的，他们将很容易适用于不仅是各种工业合作伙伴（作为麦克马斯特先进控制联盟的一部分），但几个企业在加拿大各地。预计到该计划的第四年，短期目标的好处将开始显现，到五年计划结束时，将为加拿大制造业每年节省数十万美元的开发和运营成本，并为实现创建自动化快速产品设计和控制工具的长期目标做好准备。