Now showing items 1-6 of 6
Neurocontrol of a multi-effect batch distillation pilot plant based on evolutionary reinforcement learning
The time cost of first-principles dynamic modelling and the complexity of nonlinear control strategies may limit successful implementation of advanced process control. The maximum return on fixed capital within the processing ...
Rule-based characterization of industrial flotation processes with inductive techniques and genetic algorithms
By making use of machine learning techniques, the features of flotation froths and other plant variables can be used as a basis for the development of knowledge-based systems for plant monitoring and control. Probabilistic ...
Identification of nonlinearities in dynamic process systems
Process modelling is an essential element in the development of advanced (model-based) process control systems, accounting for up to 80% of the cost of development. Often, models based on historic process data are the only ...
Biplot analysis of process systems
Plant data are collected in ever-increasing volumes in modern process industries and play an essential role in monitoring of product quality, control and continuous process improvement. In this paper a modern methodology ...
The use of neural nets to detect systematic errors in process systems
The monitoring of plants and the verification of process models depend crucially on reliable sets of steady state component and total flow rate data. These measurement data are generally subject to random noise (and possibly ...
Online monitoring and control of froth flotation systems with machine vision: A review
Research and development into the application of machine vision in froth flotation systems has continued since its introduction in the late 1980s. Machine vision is able to accurately and rapidly extract froth characteristics, ...