The aim of this study is to present
a stochastic back-off methodology
for integration of design and control using probabilistic-based descriptions
in the uncertainty. A challenging task in simultaneous design and
control is the specification of process designs that can accommodate
stochastic descriptions of uncertainty parameters and disturbances.
The key idea is to represent the confidence interval of process constraints
using power series expansion (PSE)-based functions. The proposed back-off
approach has the flexibility to assign different confidence levels
to each constraint depending on their relevance or significance. Monte
Carlo (MC) sampling is employed to design the PSE-based functions.
A wastewater treatment plant was used to test the proposed approach.
The results have shown that this method has the potential to determine
an optimal process design that can maintain the dynamic operability
of the system in the presence of stochastic realizations in the uncertainties
and disturbances.
A trust‐region approach is presented to address the simultaneous design and control of large‐scale systems under uncertainty. The key idea is to represent the system using power series expansions (PSEs) as piecewise models in an iterative manner while the validity of those expansions is certified in a trusted region. The mean of squared errors is used as a metric to quantify the accuracy of the PSE approximations. Identified search regions specify the boundaries of the decision variables for the PSE‐based optimization problems. The proposed algorithm shows a significant accomplishment in locating dynamically feasible and near‐optimal design and operating conditions. The proposed approach was tested in a wastewater treatment plant and the Tennessee Eastman process. The results indicate that the proposed methodology leads to more economically attractive and reliable designs while maintaining the dynamic operability of the system in the presence of disturbances and uncertainty.
The biopharmaceutical industry continuously seeks to optimize the critical quality attributes to maintain the reliability and cost‐effectiveness of its products. Such optimization demands a scalable and optimal control strategy to meet the process constraints and objectives. This work uses a model predictive controller (MPC) to compute an optimal feeding strategy leading to maximized cell growth and metabolite production in fed‐batch cell culture processes. The lack of high‐fidelity physics‐based models and the high complexity of cell culture processes motivated us to use machine learning algorithms in the forecast model to aid our development. We took advantage of linear regression, the Gaussian process and neural network models in the MPC design to maximize the daily protein production for each batch. The control scheme of the cell culture process solves an optimization problem while maintaining all metabolites and cell culture process variables within the specification. The linear and nonlinear models are developed based on real cell culture process data, and the performance of the designed controllers is evaluated by running several real‐time experiments.
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