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State-of-the-art in control engineering
2014
Journal of Electrical Systems and Information Technology
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The paper deals with new trends in research, development and applications of advanced control methods and structures based on the principles of optimality, robustness and intelligence. ...
Present trends in the complex process control design demand an increasing degree of integration of numerical mathematics, control engineering methods, new control structures based of distribution, embedded ...
Acknowledgment The work on this paper was supported by the Scientific Grant Agency of the Ministry of Education, Science and Sports of the Slovak Republic under grant No 1/0973/14, and by the Slovak Research ...
doi:10.1016/j.jesit.2014.03.002
fatcat:hxcl5r2325eljcguo54p5lj53i
A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes
[article]
2021
arXiv
pre-print
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The first refers to the case where a data-based ML model compliments and makes the first-principle science-based model more accurate in prediction, and the second corresponds to the case where scientific ...
For applying scientific principles to improve ML models, we discuss the sub-categories of science-guided design, learning and refinement. ...
., for their support of the Center of Excellence in Process System Engineering in the Department of Chemical Engineering at Virginia Tech. We would like to thank Dr. ...
arXiv:2112.01475v1
fatcat:qvcoyiut3redfptvpg42qc2hte
Process system engineering in wastewater treatment process
2001
Korean Journal of Chemical Engineering
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A diverse range of PSE applications have evolved in the wastewater treatment process, such as modeling, control, estimation, expert system, fault detection and monitoring system. ...
This article describes several types of PSE that have proven to be effective in WWTP. The merits and shortcoming of PSE and its detailed applications are presented. ...
It applies the knowledge to the solution of the actual problems. Stephan and Anthony [1991] designed an expert system for water treatment plants and applied it to a plant in New York. ...
doi:10.1007/bf02698284
fatcat:opdbzhup4nbsxbnjmyccqp47yu
Chapter Eleven Modelling and Monitoring Environmental Outcomes in Adaptive Management
[chapter]
2008
Developments in Integrated Environmental Assessment
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Some difficulties encountered by control engineering and complex environmental models are pointed out. ...
The method will be to compare the histories, strengths and limitations of AM, control engineering and Bayesian analysis, which have superficial similarities, significant differences and perhaps lessons ...
The complexity of a control system tends to be comparable with that of the model on which its design was based, so there is a high premium on keeping the model simple so as to generate a simple, fully ...
doi:10.1016/s1574-101x(08)00611-x
fatcat:ogtgqb4ffbdvnm3le32th7dyli
Application of Fuzzy Model Predictive Control in Multivariable Control of Distillation Column
2010
International journal of chemical Engineering and Applications
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This method is based on piecewise linear fuzzy model of the process to be controlled, which is used for predicting the outputs. ...
In this paper, a fuzzy model predictive control strategy is proposed for multivariable nonlinear control problem in a distillation column. ...
It also provides an opportunity to simplify the design of model predictive controllers. ...
doi:10.7763/ijcea.2010.v1.7
fatcat:fdkgcsnijzg6xfdu4lmq4c6tzy
Bioprocess Control: Current Progress and Future Perspectives
2021
Life
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Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized ...
Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated ...
Model based control •
Model based control is implemented
in 4 steps: plant modelling,
analysing and developing controller,
simulating plant, and controller
•
Simple and intuitive design with
better ...
doi:10.3390/life11060557
fatcat:dwre7wmd65h3pdsew7zgwk26um
MPC: Current practice and challenges
2012
Control Engineering Practice
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Linear Model Predictive Control (MPC) continues to be the technology of choice for constrained multivariable control applications in the process industry. ...
This includes the design of the regulatory controls that receive setpoints from MPC, design of the multivariable controller(s) themselves, test design for model identification, model development, and dealing ...
Broadly defined, MPC refers to a control algorithm that explicitly incorporates a process model to predict the future response of the controlled plant. ...
doi:10.1016/j.conengprac.2011.12.004
fatcat:dv7rjkvemvhctkkox5emn62cjm
MPC: Current Practice and Challenges
2009
IFAC Proceedings Volumes
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Linear Model Predictive Control (MPC) continues to be the technology of choice for constrained multivariable control applications in the process industry. ...
This includes the design of the regulatory controls that receive setpoints from MPC, design of the multivariable controller(s) themselves, test design for model identification, model development, and dealing ...
Broadly defined, MPC refers to a control algorithm that explicitly incorporates a process model to predict the future response of the controlled plant. ...
doi:10.3182/20090712-4-tr-2008.00014
fatcat:kameei5vrjbqtfk6ynde7fh5fm
Modelling of the Glass Melting Process for Real-Time Implementation
2015
International Journal of Modeling and Optimization
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Improvement of process efficiency and product quality is available through implementation of more complex control algorithms and more accurate process models. ...
This paper presents the formalisation and an empirical investigation of the hypothesis that a simplified, Finite Element Method (FEM) -based model can capture the closed-loop process dynamics over longer ...
It could also be automated by combining process FEM models and optimisation algorithms [8] . 2) Predictive and multivariable process control [9] : A controller uses the process requirements from the ...
doi:10.7763/ijmo.2015.v5.490
fatcat:ukvnyogr5rhqzeaz5bkxl6a7qq
Integration of Biological Systems Content into the Process Dynamics and Control Curriculum
2004
IFAC Proceedings Volumes
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A key requirement of future courses is the introduction of theoretical concepts and application examples relevant to emerging areas such as complex biological systems. ...
The paper concludes with a discussion of open issues which require further attention from the process control and biological systems communities. ...
While comprehensive treatment of model predictive control is beyond the scope of this course, students need to develop a working knowledge of this high performance control technology. ...
doi:10.1016/s1474-6670(17)31853-0
fatcat:77g2srrq7bf45k6ph7t5k5lghe
The State of the Art in Advanced Chemical Process Control in Japan
2009
IFAC Proceedings Volumes
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Obviously, modern advanced control plays an important role to achieve this target; but it is emphasized here that a key to success is the maximum utilization of PID control and conventional advanced control ...
This paper surveys how the three central pillars of process control -PID control, conventional advanced control, and linear/nonlinear model predictive control -have been used and how they have contributed ...
ACKNOWLEDGEMENTS The authors express their appreciation to the process control group of MCC for permitting the disclosure of many data and to the task force members of JSPS PSE 143rd committee for their ...
doi:10.3182/20090712-4-tr-2008.00005
fatcat:jvridiibrranxllo7wwgjol56q
Safety management of complex technology
2006
Neural computing & applications (Print)
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To this end, an advanced risk forecasting model was devised at Railtrack to support the safety assessment and assurance of the programme within its three key evolutionary phases. ...
Apart from wisdom and tacit knowledge, experience and awareness of the physical laws governing the universe are themselves the ingredients of success in a particular undertaking. ...
This technology has now been incorporated into a nonlinear model predictive control algorithm and implemented successfully on sites around the world. ...
doi:10.1007/s00521-006-0040-3
fatcat:mh2hgectc5flppzhnsg577rheq
Control Systems Engineering
[chapter]
2010
Controlling Uncertainty
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In the Fuzzy Hammerstein (FH) model, a static fuzzy model is connected in series with a linear dynamic model. The obtained FH model is incorporated in a model-based predictive control scheme. ...
In the second sub-project investigate the use of neural networks in the design of analytic constrained predictive controllers for linear systems that combines constraint handling with speed and is applicable ...
doi:10.1002/9781444328226.ch3
fatcat:vbg2pm3qang3ricvbfo65rlgdq
Control systems engineering
1986
Microprocessors and microsystems
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The file type is application/pdf.
In the Fuzzy Hammerstein (FH) model, a static fuzzy model is connected in series with a linear dynamic model. The obtained FH model is incorporated in a model-based predictive control scheme. ...
In the second sub-project investigate the use of neural networks in the design of analytic constrained predictive controllers for linear systems that combines constraint handling with speed and is applicable ...
doi:10.1016/0141-9331(86)90308-x
fatcat:z4rjfal2uja3dnt3ptvvmk6buq
THEORY, ALGORITHMS AND TECHNOLOGY IN THE DESIGN OF CONTROL SYSTEMS
2005
IFAC Proceedings Volumes
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Challenges for future theoretical work are modelling, analysis and design of systems in quite new applications fields. ...
Control theory deals with disciplines and methods leading to an automatic decision process in order to improve the performance of a control system. ...
It is important to use adequate system models built on the basis of physical knowledge and also using a priori knowledge. ...
doi:10.3182/20050703-6-cz-1902.00422
fatcat:3h4fskpt7fan3mbzk5bd4e4que
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