Process system integration
- May 1, 2023
- Posted by: rsispostadmin
- Categories: Engineering, IJRIAS
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VIII, Issue IV, April 2023|ISSN 2454-6194
Jelenka Savkovic Stevanovic
Faculty of Technology and Metallurgy Belgrade University, Karnegijeva 4, 11000 Belgrade, Serbia
DOI: https://doi.org/10.51584/IJRIAS.2023.8409
Received: 01 March 2023; Accepted: 18 March 2023; Published: 1 May 2023
Abstract. Process engineering today is concerned with the understanding and development of systematic procedures for design and optimal operation and control of the chemical process systems. This work motivated by the need to provide a more flexible than existing approaches framework for changes in chemical process engineering in particular, and for predicting the behavior of process systems in general. Integrated method of the process systems development make possible seeking out the most adequate model for simulation of real process design and optimization, that leads to improving current and development a new processes. The study state plant simulation model and dynamic simulation model make significant tools for observation behavior of the process. Comparative study of different processes can established more concurrent process alternatives.
Keywords: Process system, models, design, operation, skills, teaching.
I. Introduction
This paper proposes an approach to learn chemical process systems. Knowledge representation has always been central topic research identify there presentation scheme of logic, procedural representation, semantic networks, production systems, direct analogical representations, semantic primitives, and frames and scripts [1]. Logic is too powerful because the need to acquire knowledge automatically from teacher or environment and integrate it with what is already knows. A representation of facts or rules only becomes knowledge when used by a program to behave in a knowledgeable way.
Current process acquisition systems perform routine housekeeping, permit rote learning of explicitly presented facts, and are able to elicit from experts simple rules based on the attributes. Methods of concept learning may be able to overcome these imitations, although the present state of the art is primitive and suggests ideas rather than well developed algorithms for the knowledge acquisition tool box.
