Siam Aumi

Data-Based Modeling and Control of Nylon-6, 6 Batch Polymerization

This work addresses the problem of modeling the complex nonlinear behavior of the nylon-6, 6 batch polymerization process and then subsequently tracking trajectories of important process variables, namely the reaction medium temperature and reactor pressure, using model predictive control. To this end, a data-based multi-model approach is proposed in which multiple local linear models are identified from previous batch data using latent variable regression and then combined using an appropriate (continuous) weighting function that arises from fuzzy c-means clustering. The proposed approach unifies the concepts of auto-regressive exogenous (ARX) modeling, latent variable regression techniques, fuzzy c-means clustering, and multiple local linear models in an integrated framework capable of capturing the nonlinearities and multivariate nature of batch data. The resulting data-based model is then used to formulate a trajectory tracking predictive controller. Through simulation studies, the modeling approach is shown to capture the major nonlinearities in the nylon-6, 6 polymerization process and closed-loop simulation results demonstrate the efficacy of the proposed predictive controller and illustrate its advantages over existing trajectory tracking approaches such as conventional proportional-integral control and latent variable model predictive control.

Model predictive quality control of batch processes

This work addresses the problem of driving a batch process to a specified product quality using model predictive control (MPC) with data-driven models. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required for this problem. At a given sampling instant, the accuracy of this type of quality model, however, is sensitive to the prediction of the future (unknown) batch behavior. That is, errors in the predicted future data are propagated to the quality prediction, adding uncertainty to any control action based on the predicted quality. To address this “missing data” problem, we integrate a previously developed data-driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a predictive control framework. The key benefit of this approach is that the causality and nonlinear relationship between the future inputs and outputs are accounted for in predicting the final quality, resulting in more effective control action. The efficacy of the proposed predictive control design is illustrated via closed-loop simulations of an industrially relevant nylon-6,6 batch polymerization process.

Adaptive data-based model predictive control of batch systems

In this work, we generalize a previously developed multi-model, data-based modeling approach for batch processes to account for time-varying dynamics by incorporating online learning ability into the model. The application of the standard recursive least squares (RLS) algorithm with a forgetting factor for the model form leads to unnecessary updates for some of the models. We address this issue by developing a probabilistic RLS (PRLS) estimator (also with a forgetting factor) for each model that takes the probability of the model being representative of the current plant dynamics into account in the update. The main advantage of adopting this local update approach is adaptation tuning flexibility. Specifically, the model adaptations can be made more aggressive while maintaining better parameter precision compared to the the standard RLS algorithm. The benefits from using the PRLS algorithm for model adaptation are demonstrated via simulations of a nylon-6,6 batch polymerization reactor. The model adaptation is shown to be crucial for achieving acceptable control performance when encountering large disturbances in the initial conditions.

Data-based modeling and control of nylon-6,6 batch polymerization

This work addresses the problem of modeling the complex nonlinear behavior of a nylon-6,6 batch polymerization process and subsequently tracking trajectories of the important process variables, namely the reaction medium temperature and reactor pressure, using model predictive control (MPC). To this end, a data-based multi-model approach is proposed in which local linear models are identified from previous batch data using latent variable regression and then combined using a continuous weighting function that arises from fuzzy c-means clustering. The resulting data-based model is used to formulate a trajectory tracking predictive controller. Through simulation studies, the modeling approach is shown to capture the major nonlinearities of the process, and closed-loop simulation results demonstrate the efficacy of the proposed predictive controller and its advantages over conventional proportional-integral (PI) trajectory tracking.

Robust model predictive control & fault-handling of batch processes

This work presents a robust MPC design and a fault tolerant control framework for batch processes subject to input constraints and model uncertainty with the objective of achieving a desired product quality.