This thesis focuses on data-driven modeling and control of batch and batch-like processes. These processes are highly nonlinear and time-varying which, unlike continuous operations, are characterized by the finite duration of operation and absence of equilibrium conditions. This makes the modeling and control approaches available for continuous processes not readily applicable and requires appropriate adaptations of the available approaches to handle a) batch data structure for modeling and b) a control objective different than that of maintaining a steady-state operation as often encountered in a continuous process.
With these considerations, this work adapted the batch subspace identification for modeling and control of a variety of batch and batch-like processes. A particular focus of this work was on the application of the proposed ideas on real engineering systems along with simulated case studies. The applications considered in this work are batch crystallization, a hydrogen plant startup dynamics in a collaboration with Praxair Inc. and a rotational molding process in collaboration with the polymer research group at McMaster University. For the seeded batch crystallization process, subspace identification techniques are adapted to identify a linear time invariant model for the, otherwise, infinite dimensional process. The identified model is then deployed in a linear model predictive control (MPC) strategy to achieve crystal size distribution (CSD) with desired characteristics subject to both manipulated input and product quality constraints. The proposed MPC is shown to achieve superior performance and the ability to respect tighter product quality constraints as well as robustness to uncertainty in comparison to an open loop policy as well as a traditional trajectory tracking policy using classical control. In another contribution, merits of handling data variety in a subspace identification framework was demonstrated on the crystallization process. The proposed approach facilitates the specification of a desired shape of the particle size distribution required at the termination of the batch process. Further, novel model validity constraints are proposed for the subspace identification based control framework. In the collaborative work on hydrogen plant startup, it is recognized as a batch-like process due to its similarity to batch processes. Firstly, in this work a high fidelity model of the Hydrogen unit was developed with relevant startup and shutdown mechanisms. This setup is used to mimic the trends in the key process variables during the startup/shutdown operation. The simulated data is used to identify a state-space model of the process and validated on new simulated startup. Further, the approach was demonstrated on real plant data from one of the Praxair's plants. The predictive capabilities of the model provide ample handle for the plant operator for averting failures and abrupt shutdown of the entire plant. This is expected to have immense economic advantages. Finally, the subspace identification based modeling and control approach was applied to a lab-scale rotational modeling (RM) process. It is a polymer processing technique that is characterized by the placement of a polymer resin inside a mold, subsequent closure of the mold, followed by the simultaneous application of uni-axial (as is the case in the present work) or bi-axial rotation and heat. The resin is deposited on the mold wall where it forms a dense unified layer following which, the mold is cooled while still rotating the mold. Once demolding temperatures are achieved, the finished part is removed from the mold. Its potential as a manufacturing process for polymeric components is limited by a number of concerns including difficulties in process control, in particular, determining efficiently the process operation to yield the desired product consistently, and produce new products. This work has contributed by developing optimal control strategies for the process to achieve user-specified product quality and reject variability across batches. The results obtained demonstrate the merits of the proposed approach. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24065 |
| Date | January 2018 |
| Creators | Garg, Abhinav |
| Contributors | Mhaskar, Prashant, Chemical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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