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About 10,000 papers for “process control”

PROCESS CONTROL

Stanley M. Walas · Chemical Process Equipment · 1990

Cited by 0

Process Control

Engell, S., Kienle, A. · MPG.PuRe (Max Planck Society) · 2020

Cited by 0

CONTROL PROCESS

C. L. Bottasso, P. Masarati, L. Trainelli (eds et al. · 2007

Abstract. A simulational model of 6 dof Stewart platform type parallel manipulator is presented. The manipulator is driven by electrohydraulic actuators. A novel invariant error dynamics controller is used. The developed control scheme employs the dynamic and hydraulic model of the system. The manipulator model consists of the rigid body equations of motion and the hydraulic dynamics of the main elements. Friction is included in the model. Two wellknown software packages, one designed to perform control system simulation and the other dedicated to multibody simulation, are simultaneously used to conduct the study. The aim of the study is to check, what is the impact of dynamics model accuracy on the quality of control process. The simplifications of the model and the problems with finding accurate values of its parameters (especially friction parameters) are considered. The study helps to predict what are the possible results of inaccurate determination of the crucial model parameters. The simulation results show, that inverse dynamics model simplifications consisting in neglecting the mass of actuators have relatively little influence on the control quality. The effects of actuators masses neglecting can be reduced by appropriate changes in the modeled mass of the platform. The study shows that friction effects should be introduced to the inverse dynamics model and the friction parameters should be identified with possibly big accuracy. The parameters describing stictionfriction transition effects are the crucial ones.

Cited by 0

Process Control

Andreas Gadatsch · Business Process Management · 2023

Cited by 0

Process Control

Björn Sohlberg · Advances in industrial control · 1998

Cited by 0

Process Control

Richard Lakerveld, Brahim Benyahia · 2020

This chapter discusses the control aspects of continuous crystallization processes. Common control objectives for continuous crystallization are related to crystal product quality, process stabilization, economic performance, and environmental impact. Supersaturation is often used as controlled variable to obtain desirable crystal quality attributes, although direct approaches with a crystal quality attribute as controlled variable have also been developed. Sensors to measure crystal quality attributes or supersaturation in situ are readily available, which makes the application of automated feedback control loops attractive. A mixed-suspension mixed-product-removal crystallizer has limited options for process actuation unless fines dissolution is employed. Novel plug-flow crystallizers allow for the adoption of different control strategies (e.g., controlled cooling profiles with seeding). Model-based controllers in combination with state observers can handle time-varying model uncertainty, input constraints, sensor and actuator faults and asynchronous measurements, whereas model-predictive control has the unique capability to enforce multiple process constraints and is most effective when dealing with complicated interactions between multiple inputs and outputs. State observers can also be used to design effective filters for actuator fault detection. The rapid development of dynamic process models, advanced analytical techniques and improved numerical methods are main drivers of the current trend towards model-based control strategies for continuous crystallization.

Cited by 3

Process Control

Yuri Shardt · 2016

Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission. —To my family In many chemical plants, data historians store thousands of variables at fast sampling rates. Much of this collected data is routine operating data that could easily be used for system identification and forecasting, especially in the design of soft sensors. Currently, there is no framework for assessing the quality of these data sets. Therefore, this dissertation proposes a two-step method, consisting of data segmentation and data quality assessment, with application to soft sensor development.

Cited by 0

Process Control

Papers Admin, UJ Exam · University of Johannesburg · 2019

Second semester

Cited by 0Open access

Process Control

Olsson, Gustaf · Lund University Publications (Lund University) · 2010

Cited by 0

Process Control

Peter Harriott · Andalas University Repository (Andalas University) · 1964

Cited by 0

Process control

Kilian Karl Hermann Neubert, Marina Gaastra · RWTH Publications (RWTH Aachen) · 2014

Cited by 0