The purpose of this study was to develop an integrated control strategy for multiscale crystallization processes. An image analysis method using a deep learning neural network is used measure the fine-scale information process, and mathematical statistical adopted obtain mean size crystal population. A feedforward subsequently trained employed in nonlinear model predictive formulation optimal p...

Developing model predictive control (MPC) schemes can be challenging for systems where an accurate is not available, or too costly to develop. With the increasing availability of data and tools treat them, learning-based MPC has late attracted wide attention. It recently been shown that adapting only model, but also its cost function conducive achieving optimal closed-loop performance when cann...

the present sought to draw a comparison between model predictive control performance and two other controllers named simple pi and selective pi in controlling large-scale natural gas transport networks. a nonlinear dynamic model of representative gas pipeline was derived from pipeline governing rules and simulated in simulink® environment of matlab®. control schemes were designed to provide a s...

This paper describes a neural predictive control toolbox developed in Matlab/Simulink environment. The application permits all phases of the system design: simulation of the plant by means of any Simulink model, loading of input/output data, definition of the neural network architecture, training, and, finally, application of the predictive control strategy based on the neural network model. Co...

In this research, modeling, simulation, and control of a methanol-to-olefins laboratory fixed-bed reactor with electrical resistance furnace have been investigated in both steady-state and dynamic conditions. The reactor was modeled as a one-dimensional pseudo-homogeneous system. Then, the reactor was simulated at steady-state conditions and the effect of different parameters including...