A data driven modeling and control tool

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The increased availability of sensor data and advanced computing tools has made some very lucrative opportunities for the manufacturing sector. In particular, the new large data sets we are now collecting can now be used to build accurate models for the process, which in turn can be utilized to dramatically improve on-spec production, and enable rapid new product design. This emerging technology is well suited for batch and batch like operation (specialty chemical production, pharmaceuticals or virtually in every continuous operation in startup mode) which are by definition transient in nature, and witness significant variation over a process cycle. Most of the existing commercial software focuses on operation at a steady state. Limited tools exist for modelling and controlling batch operation, but the existing tools cannot optimally control batches of varying duration to, for instance, minimize production time. There is new work in the area of machine learning/artificial intelligence based models, but those are currently limited to classification kind of problems, and not at the commercialization stage for a full-scale industrial plant. Recent results from the PI's, Prashant Mhaskar and Michael Thompson's group, have demonstrated the capability of a subspace model based data driven modelling and control approach to handle a myriad of applications, ranging from semi-continuous distillation to steel refining to hydrogen production startup to polymer processing to wastewater treatment. The present proposal aims to commercialize this framework. Hydromantis Inc. (a wastewater treatment plant models), Praxair Inc- Now Linde Inc., Imperial Oil and Rescraft Plastic Products Inc. intend to increase their competitiveness by updating their facilities with more intelligent systems and have expressed a desire to evaluate our approach once a working prototype is available. Easy access to other industrial members is readily enabled, through the PI's involvement in the McMaster Advanced Control Consortium (an academy industry consortium with 7 annual membership paying members) and McMaster Manufacturing Research Institute which serves the community offering contract and research services.