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Chemical Process Simulation using DWSIM, SCILAB & ChemSep
Rating: 4.1 out of 5(31 ratings)
275 students
Created byAI Chemconnect
Last updated 9/2024
English

What you'll learn

  • Synthesis of Reaction Paths
  • Synthesis of Heat Exchanger Networks (HENs)
  • Synthesis of Separation Sequences
  • Synthesis of Energy Recovery Networks

Course content

20 sections20 lectures38h 22m total length
  • Engineering Fundamentals and Concepts2:03:41

Requirements

  • Basic Understanding of Chemical Engineering

Description


Each session shall be of two hours. 

Sessions 1 through 14 are on Advanced Process Simulations and

Sessions 15 through 21 are on Artificial Intelligence in Chemical Process Industry.


Sessions 1 through 7, 15 through 21 are contemporary industrial case study learning and sessions 8 through 14 are completely hands on exercises.

Special Feature: A complementary session on personal research work on India’s First BioCNG Plant and Green Hydrogen Production shall be delivered at the end.

Software to be used:

  • DWSIM

  • SCILAB

  • ChemSep

Skills those shall be earned/fostered after the internship:

  • Engineering Fundamentals and Concepts

  • Mathematical Modelling

  • Process Synthesis and Analysis

  • Process Design

  • Process Modelling

  • Process Simulation

  • Process Optimization

  • Process Retrofitting

  • Process Debottlenecking

  • Process Data Analytics

  • Data Modelling

  • Artificial Intelligence Tools

  • Deep Learning using Artificial Neural Networks

  • Industry 4.0 for Process Engineering

  • Contemporary Industrial case studies


At the end of this course, the participants shall be able to:

  • Review the developments of process design and simulation fostering economy


  • Show how advanced Process Simulators function


  • Share various contemporary industrial simulation case studies with facilitation of ICT enabled tools


  • Encourage use of process simulators for simulation of Chemical Manufacturing processes and plants Apply advanced Computational Intelligence as Process Data Analytics, Artificial Intelligence, Machine Learning with deep learning of Artificial Neural Networks

Who this course is for:

  • Chemical Engineering students