Deep Learning (DL) models are becoming the preferred approach for process monitoring
due to their higher prediction accuracy; however, they are still viewed as black boxes …

In practical industrial processes with complex mechanisms, there are both linear and
nonlinear relationships between process variables. However, focusing solely on one of …

High-throughput catalytic reactors are useful for developing large datasets of kinetic results
and discovering faster new catalysts and models. However, these large datasets are often …

In this paper, we propose a robust method for estimating the parameters of robot
manipulators using the torque separation technique, which was developed previously by the …

Identification of the EIV system has attracted much interest due to the potential usage of the
EIV system in the engineering sciences and elsewhere. It is a more difficult problem in …

Identification of closed-loop systems from the input-output data has been studied quite
extensively for several decades. In this work, we extend the use of the dynamic iterative …

Identification of autoregressive models with exogenous input (ARX) is a classical problem in
system identification. This article considers the errors-in-variables (EIV) ARX model …

Batch distillations are extensively used worldwide to produce fruit wine spirits with distinctive
aromas. These processes are typically operated manually, based on the experience of the …

This doctoral thesis presents an integral and comprehensive investigation on the modeling,
optimization, and control of fruit wine distillation processes. The work is based on the …

In this paper, we propose a robust method for estimating the parameters of robot
manipulators using the torque separation technique, which was developed previously by the …