Abstract
Process industries with complex operations require comprehensive training for plant operators/process engineers to understand the dynamics and to achieve process control efficiency. Cost effective and highly customizable simulator is the need for many industries. This paper elaborates the design, prototype, and features of a generic process simulator framework, components, and interfaces to represent intelligent model formats, knowledge, facts, behaviors, and rules. The benefits of fully functional simulator range from theoretical learning of processes to the synthesis of modeled and tuned controllers to mimic various stages/sections of a continuous process industry.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chiang LH, Russell EL, Braatz RD (2001) Fault detection & diagnosis in industrial systems. Springer, London
Sanz V (2012) Modeling of hybrid control systems using the Modelica library. Control Eng Pract 10:24–34
Li N, Wenqing W (2012) Development & application of intelligent system modeling. Simul Model Pract Theory 29:149–162
Venkatasubramanian V, Rengaswamy R (1992) A framework for process monitoring, diagnosis, & control using knowledge-based systems & NN. IFAC, Delaware, pp 49–54
Uraikul V, Chan CW (2007) Artificial intelligence for monitoring & supervisory control of process systems. Eng Appl Artif Intell 20(2):115–131
Vlas R (2011) Rule-based natural language techniques. In: 44th Hawaii international conference on system sciences
Li N, Deng (2010) A novel optimization algorithm: particle back propagation NN optimization, CACS
Holkar KS, Waghmare LM (2010) Overview of MPC. Int J Control Autom 3
Konakom K, Kittisupakorn P (2010) Neural network-based model predictive control (NNMPC) strategy. In: World congress on engineering and computer science 2010, vol II
Perros H (2009) Computer simulation techniques: definitive introduction. NC State University
Chandwani V, Agarwal V (2014) Applications of ANN in strength of concrete: a state of the art review. Int J Curr Eng Technol 4(4):2949–2956
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sridevi, S., Sakthivel, P. (2017). Real-Time Simulation Design for Continuous Process Industries. In: Nath, V. (eds) Proceedings of the International Conference on Nano-electronics, Circuits & Communication Systems. Lecture Notes in Electrical Engineering, vol 403. Springer, Singapore. https://doi.org/10.1007/978-981-10-2999-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-10-2999-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2998-1
Online ISBN: 978-981-10-2999-8
eBook Packages: EngineeringEngineering (R0)