This paper presents the development of soft sensor empirical models using support<br /> vector machine (SVM) for continual assessment 2,3-dimethylbutane and 2-methylpentane mole percentage as important product quality indicators in refinery isomerisation process. During model development, critical steps were taken, including selection pre-processing industrial process data, which are broa...

Soft sensors for the on-line estimation of kerosene 95 % distillation end point (D95) in crude distillation unit (CDU) are developed. Experimental data are acquired from the refinery distributed control system (DCS) and include on-line available continuously measured variables and laboratory data which are consistently sampled four times a day. Additional laboratory data of kerosene D95 for the...

Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the parameters of PSRMs is one critical issues that needs be urgently solved. Therefore, a nonlinear dynamic model-based method proposed in this paper. First, improve accuracy motor dynamics model, Hammerstein–Wiener model based on BP neu...

The problem of nonlinear dynamic systems modelling by means of block-oriented models has been strongly elaborated for the last four decades, due to vast variety of applications. The concept of block-oriented models assumes that the real plant, as a whole, can be treated as a system of interconnected blocks, static nonlinearities (N) and linear dynamics (L), where the interaction signals cannot ...

This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using neural network controller based on Model Predictive Control (MPC) process. We use Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled HW maintain bioethanol purity near 99% molar fraction. work proposes Artificial Neural Network (ANN) that captures dynami...