Model predictive control

Quantum mechanics

Sediment

Algorithm

Control (management)

Process (computing)

Nonlinear model

Biology

Materials science

Deposition (geology)

Operating system

Computer science

Mathematical optimization

Geology

Paleontology

Physics

Nonlinear system

Mathematics

Artificial intelligence

Geometry

Optimal control

Horizon

Control theory (sociology)

Artificial neural network

Biological system

Chemical Engineering Science

The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-driven models that would enable the shrinking horizon nonlinear model predictive control of a computationally intensive stochastic multiscale system. The system of choice was a simulation of thin film formation by chemical vapour deposition. Two ANNs were trained to estimate the system’s observables. The ANNs were subsequently employed in a shrinking horizon optimization scheme to obtain the optimal time-varying profiles of the manipulated variables that would meet the desired thin film properties at the end of the batch. The resulting profiles were validated using the stochastic multiscale system and a good agreement with the predictions of the ANNs was observed. Due to their observed computational efficiency, accuracy, and the ability to reject disturbances, the ANNs seem to be a promising approach for online optimization and control of computationally demanding multiscale process systems.

Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks