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

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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.