Download Learn Design and Simulation of Chemical Plants and Equipment
Learn Design and Simulation of Chemical Plants and Equipment video course download, Are you ready to take your skills in chemical engineering to the next level? Look no further! We are thrilled to present our comprehensive online course on Design and Simulation of chemical plants and equipment. Whether you’re a seasoned professional or a student aspiring to enter the industry, this course is designed to equip you with the knowledge and skills needed to excel in the field.
In this course, we delve into the key concepts and methodologies of design and simulation. We start by exploring the fundamentals, differentiating between design and simulation and discussing their respective roles in chemical engineering. We then explore two fundamental approaches: phenomenological model-based simulation and data-driven machine learning-based simulation. By understanding the advantages and disadvantages of these approaches, you will be able to choose the most suitable method for different scenarios.
We take a hands-on approach, utilizing Aspen, a leading simulation software widely used in the industry. You will be guided through various simulations, starting with property analysis, flash drum simulation, pump and pipeline simulation, heat exchanger simulation, and reactor simulation. We cover both basic and advanced simulations, including plug flow reactors, CSR, batch reactors, industrial ethylene glycol and ethyl acetate reactors, and more.
A significant portion of the course is dedicated to distillation column simulation. You will gain an overview of different distillation column models available in Aspen, including DSTWU and Radfrac rigorous distillation simulations. We also provide a comprehensive case study involving six distillation column simulations, including an industrial benzene column simulation. Additionally, we explore how to identify the optimum feed tray location, and we introduce the exciting topic of simulation by machine learning, discussing its relevance and the steps involved.
We guide you through the steps of developing a data-driven regression model, including model selection and performance evaluation. Through engaging case studies, such as bioreactors and soft sensors in distillation columns, you will witness the practical application of machine learning in simulation.
What you’ll learn
- Whether you’re a seasoned professional or a student aspiring to enter the industry, this course is designed to equip you with the knowledge and skills.
- In this course, we delve into the key concepts and methodologies of design and simulation.
- We start by exploring the fundamentals, differentiating between design and simulation and discussing their respective roles in chemical engineering.
- We then explore two fundamental approaches: phenomenological model-based simulation and data-driven machine learning-based simulation.
- We take a hands-on approach, utilizing Aspen, a leading simulation software widely used in the industry.
- Speaking of machine learning, we delve into its foundations, understanding the key terms and concepts.
Course content
- Section 1: Introduction and course overview
- Lecture 1 Introduction
- Lecture 2 Why to learn design and simulation in the chemical industry?
- Section 2: Understanding the concept of design and simulation
- Lecture 3 Understanding the concept of simulation
- Lecture 4 Differentiating between design and simulation
- Section 3: Two Fundamental Approaches of Modeling and Simulation
- Lecture 5 Two Fundamental Approaches of Modeling and Simulation
- Lecture 6 Comparitive Advantages and Disadvantages
- Section 4: Aspen based simulation
- Lecture 7 Overview of flowsheet simulation
- Lecture 8 Run the first Aspen simulation
- Lecture 9 Introduction part2
- Lecture 10 Assessing the variable in Aspen
- Section 5: Chemical Property Analysis in Aspen for accurate simulation results
- Lecture 11 Propert Analysis: What is it?
- Lecture 12 Guideline for selecting appropriate proety method
- Lecture 13 How to calculate pure component property of any compund?
- Lecture 14 How to calculate properties of any binary mixtures?
- Lecture 15 How to calculate properties of any ternary mixtures?
- Section 6: Simlation of Flash drum and separators
- Lecture 16 SImulation of mixer and splitter
- Lecture 17 Flash drum simulation
- Lecture 18 Simualtion of separator
- Section 7: Design of pump, compressor and pipeline
- Lecture 19 Pump
- Lecture 20 Pressure changer overview
- Lecture 21 Design of pipeline
- Section 8: Design and simulation of heat exchanger
- Lecture 22 Overview
- Lecture 23 Heat exchanger simulation
- Lecture 24 heat excahnger simulation2
- Lecture 25 Rigorous heatechanger design and simulation
- Section 9: Design and simualtion of Reactor
- Lecture 26 Reactor overview
- Lecture 27 Batch reactor simulation 1
- Lecture 28 Batch Reactor simlation 2
- Lecture 29 Plug flow reactor
- Lecture 30 CSTR
- Lecture 31 Gibbs Reactor
- Lecture 32 Case study : Ethylene glycol reactor
- Lecture 33 Case study : Ethyl acetate reactor
- Section 10: Design and SImulation of distillation column
- Lecture 34 Overview of distillation column simulation
- Lecture 35 Firststep of distillation column design
- Lecture 36 Distillation column simulation : casrstudy1
- Lecture 37 Distillation column simulation : casrstudy2
- Lecture 38 Distillation column simulation : casrstudy3
- Lecture 39 Distillation column simulation : casrstudy4
- Lecture 40 Distillation column simulation : casrstudy5
- Lecture 41 Distillation column simulation : casrstudy6
- Lecture 42 Distillation column simulation : Benene column
- Lecture 43 Overview of rigoruos distillation simulation
- Lecture 44 Radfrac
- Lecture 45 Optimum feed tray location
- Section 11: Design spec and sensitivity analysis Features in Aspen
- Lecture 46 Design spec
- Lecture 47 Sensitivity
- Lecture 48 Sensitivity 2
- Lecture 49 Sensitivity analysis case study
- Section 12: Overview of machine learning in design and simulation
- Lecture 50 Machine learning : what is it ?
- Lecture 51 Regression analysis
- Lecture 52 Overfitting and undetfitting
- Lecture 53 Definitions and components of machine learning
- Section 13: Steps to build model by machine learning
- Lecture 54 Steps
- Section 14: Data cleaning and data preprocessing
- Lecture 55 Data Preprocessing
- Lecture 56 Outlier
- Lecture 57 Outliers detection
- Section 15: How to build ML models?
- Lecture 58 Regression model
- Lecture 59 Performance of regression
- Section 16: How to build machine learning based simulations
- Lecture 60 Matlab regression learner app
- Lecture 61 Steps
- Lecture 62 Import data
- Lecture 63 Identify model type
- Lecture 64 Rn and evalate performnace
- Lecture 65 Result interpretation
- Section 17: Casestudy : BioReactor simulation
- Lecture 66 Bio reactor simulation part1
- Lecture 67 Bio reactor simulation part2
- Lecture 68 Bio reactor simulation part3
- Lecture 69 Bio reactor simulation part4
- Section 18: Data driven catalytic reactor simulation
- Lecture 70 catalytic reactor simulation part1
- Lecture 71 catalytic reactor simulation part2
- Lecture 72 catalytic reactor simulation part3
- Section 19: Real life case study to build softsensor for distillation column
- Lecture 73 Real life case study to build softsensor for distillation column part1
- Lecture 74 Real life case study to build softsensor for distillation column part2
- Lecture 75 Real life case study to build softsensor for distillation column part3
Who this course is for
- Whether you’re a seasoned professional or a student aspiring to enter the industry, this course is designed to equip you with the knowledge and skills needed to excel in the field.
- Chemical negineering students (under gradate or postgraduate)
- Process engineers working in chemical industry
- Chemical engineers working in chemical industry
- Supervisors, technicians, operators working in chemical industry
- Simulation and design engineers
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Last updated 11/2023
- Video duration: 14h 44m
- Number of lessons: 19 sections, 74 lectures
- Language: Language: English
- Compressed file size: 8.64 GB
