Design And Simulation Of Chemical Plants And Equipment

Learn Aspen Simulation and Machine Learning Techniques to Simulate and Optimize Chemical Processes from industry expert.

What you'll learn

Module 1 provides an overview of the course, highlighting the importance of design and simulation in the chemical industry.

Module 2 introduces the two fundamental approaches to simulation in chemical engineering, allowing learners to understand the strengths and weaknesse

Modulen3 familiarizes learners with the popular Aspen simulation software, providing hands-on experience in navigating the Aspen simulator interface

Module 4 guides learners in analyzing properties efficiently, ensuring accurate simulation results. It covers both single-component and binary mixture analyses.

Module 5 covers the simulation of flash drums and explores separator simulation techniques, providing learners with practical knowledge in simulation.

Module 6 teaches learners how to simulate and analyze these systems, considering factors such as pressure drop, flow rates, and efficiency.

Module 7 covers both simplified and rigorous heat exchanger simulations, enabling learners to optimize heat transfer and improve process efficiency.

Module 8 covers various reactor types and their simulations, allowing learners to analyze reaction kinetics, conversion rates, select the appropriate reactor

Module 9 explores different distillation column models and simulations, including rigorous distillation simulations. You gain insights into optimizing column

Module 10 explores the application of machine learning in simulation. You will understand when and how to utilize machine learning techniques for simulation

Module 11 discusses real-life Case Study to Develop a Soft Sensor in Distillation Column

Module 12. discusses real-life Case Study of Industrial Catalytic Reactor Simulation

Requirements

Chemical engineering knowledge is desirable but not essential

Basic Aspen plus simulation knowledge is desirable but not essential

Description

Learn Design and Simulation of chemical plants and equipment from Industry ExpertAre 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.Why learn design and simulation in the chemical industry? The answer is simple: it is the backbone of modern chemical engineering. Simulation allows engineers to predict and optimize process behavior, minimize risks, and improve efficiency. By understanding the principles and techniques of design and simulation, you gain a competitive edge and open up a world of possibilities in your career.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, CSTR, 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.Speaking of machine learning, we delve into its foundations, understanding the key terms and concepts. You will learn how to clean data, detect outliers, handle missing values, and encode data for machine learning purposes. 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.–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––-Course Content: Design and Simulation of chemical plants and equipmentSection 1: IntroductionWhy learn design and simulation in the chemical industry?Understanding the concept of simulationDifferentiating between design and simulation in chemical engineeringSignificance: This section provides an overview of the course, highlighting the importance of design and simulation in the chemical industry. It sets the foundation for understanding the subsequent topics and their practical applications.Section 2: Two Fundamental Approaches of Modeling and SimulationPhenomenological model-based simulation and its advantages and disadvantagesData-driven machine learning-based simulation and its advantages and disadvantagesSignificance: This section introduces the two fundamental approaches to modeling and simulation in chemical engineering, allowing learners to understand the strengths and weaknesses of each method. It helps learners make informed decisions about choosing the appropriate approach for specific scenarios.Section 3: Aspen-Based SimulationWalkthrough of the Aspen software interfaceFirst Aspen-based simulationSignificance: This section familiarizes learners with the popular Aspen simulation software, providing hands-on experience in navigating the interface and conducting their first simulation. It builds confidence and prepares learners for more advanced simulations throughout the course.Section 4: Property AnalysisUnderstanding the property analysis quick method assistantPure component property analysisProperty analysis of binary mixtureSignificance: Property analysis is a crucial step in chemical engineering design and simulation. This section guides learners in analyzing properties efficiently, ensuring accurate simulation results. It covers both single-component and binary mixture analyses.Section 5: Flash Drum SimulationSeparated simulationSignificance: Flash drum simulation is commonly used in chemical processes. This section covers the simulation of flash drums and explores separated simulation techniques, providing learners with practical knowledge in simulating and optimizing these operations.Section 6: Pump and Pipeline SimulationSimulating pump and pipeline systemsSignificance: Pump and pipeline systems are integral components of chemical processes. This section teaches learners how to simulate and analyze these systems, considering factors such as pressure drop, flow rates, and efficiency.Section 7: Heat Exchanger SimulationSimple heat exchanger simulationRigorous heat exchanger simulationSignificance: Heat exchangers play a vital role in energy transfer within chemical processes. This section covers both simplified and rigorous heat exchanger simulations, enabling learners to optimize heat transfer and improve process efficiency.Section 8: Reactor SimulationPlug flow reactor simulationCSR simulationBatch reactor simulationIndustrial ethylene glycol reactor simulationGibbs reactor simulationIndustrial ethyl acetate reactor simulationSignificance: Reactor simulation is crucial for understanding chemical reactions and optimizing reactor designs. This section covers various reactor types and their simulations, allowing learners to analyze reaction kinetics, conversion rates, and select the appropriate reactor configuration for specific applications.Section 9: Distillation Column SimulationOverview of different models of distillation columns available in AspenDSTWU moduleRadfrac rigorous distillation simulationCase study of 6 distillation column simulations, including an industrial benzene column simulationIdentifying optimum feed tray locationSignificance: Distillation is a widely used separation technique in the chemical industry. This section explores different distillation column models and simulations, including rigorous distillation simulations. Learners gain insights into optimizing column design, tray location, and achieving desired separation efficiency.Section 10 : Simulation by Machine LearningCase study of bioreactors simulation by machine learningSteps to develop a machine learning modelRegression learner toolsData preparation for machine learningModel selection for machine learning simulationsPerformance evaluation of the developed modelVisualizing simulation resultsSignificance: This section explores the application of machine learning in simulation. Learners will understand when and how to utilize machine learning techniques for simulation purposes. The case study on bioreactors demonstrates the practical implementation of machine learning models in chemical engineering processes. Learners will gain insights into data preparation, model selection, performance evaluation, and result visualization, enabling them to apply machine learning effectively in their own simulations.Section 11: Case Study to Develop a Soft Sensor in Distillation ColumnDeveloping a soft sensor using simulation dataUnderstanding the role of soft sensors in distillation columnsPractical implementation of a soft sensor for real-time monitoring and controlSignificance: Soft sensors play a crucial role in distillation column operations, providing real-time monitoring and control. In this case study, learners will learn how to develop a soft sensor using simulation data and understand its significance in optimizing distillation processes.Section 12: Case Study: Industrial Catalytic Reactor SimulationSimulation of an industrial catalytic reactorAnalyzing reaction kinetics and reactor performanceOptimization of operating conditions for improved productivitySignificance: This case study focuses on simulating an industrial catalytic reactor, allowing learners to explore reaction kinetics, reactor performance, and the optimization of operating conditions. Learners will gain practical knowledge in simulating and improving the efficiency of catalytic reactors.Takeaways from this course:This comprehensive online course on Design and Simulation in Chemical Engineering covers essential topics, techniques, and case studies to equip learners with the knowledge and skills needed to excel in the field. By understanding the principles of design and simulation, as well as gaining hands-on experience using industry-standard software and machine learning techniques, learners will be well-prepared to tackle real-world challenges in chemical engineering. Enroll in this course today and embark on a journey of professional growth and mastery in design and simulation.Join us on this exciting journey as we equip you with the skills to design and simulate chemical engineering processes effectively. Our expert instructors bring years of industry experience and a passion for teaching, ensuring that you receive top-quality instruction and guidance throughout the course. Take the first step towards mastering design and simulation in chemical engineering and enroll in our course today!

Overview

Section 1: Introduction

Lecture 1 Introduction and overview of course content

Lecture 2 Why to learn Design and Simulation in Chemical Engineering?

Lecture 3 What is simulation ? Why we do simulation of chemical processes?

Lecture 4 What is the difference between Design and Simulation?

Section 2: Two Fundamental approach of modelling and simulation

Lecture 5 Two types of simulation

Lecture 6 Comparison of two types of simulations

Section 3: What is Simulation?

Lecture 7 what is simulation?

Lecture 8 Unlocking the first simulation

Lecture 9 Unlocking the first simulation part2

Lecture 10 Accessing variables

Section 4: Property Analysis

Lecture 11 Understand Property analysis

Lecture 12 Quick method assistance

Lecture 13 Pure component property analysis

Lecture 14 Property analysis of binary mixture

Section 5: Flash Drum simulation

Lecture 15 Flash drum simulation

Lecture 16 Separator Simulation

Section 6: Pump and Pipeline simulation

Lecture 17 Pressure Changer

Lecture 18 Pump simulation

Lecture 19 Pipeline Simulation

Section 7: Heat Exchanger simulation

Lecture 20 Heat Exchanger simulation overview

Lecture 21 Simple Heat Exchanger Simulation

Lecture 22 Heater Simulation

Lecture 23 Rigorous Heat exchanger simulation

Section 8: Reactor Simulation

Lecture 24 Understanding Reactor simulation

Lecture 25 Plug flow reactor simulation

Lecture 26 CSTR simulation

Lecture 27 Batch reactor simulation

Lecture 28 Batch reactor part 2

Lecture 29 Ethylene glycol reactor

Lecture 30 Gibbs Reactor

Lecture 31 Ethyl Acetate Reactor

Section 9: Distillation column simulation

Lecture 32 Overview of Distillation column

Lecture 33 DSTWU module

Lecture 34 Radfrac rigorous distillation

Lecture 35 Radfrac 2

Lecture 36 Distillation column simulation 1

Lecture 37 Distillation column simulation 2

Lecture 38 Distillation column simulation 3

Lecture 39 Distillation column simulation 4

Lecture 40 Distillation column simulation 5

Lecture 41 Distillation column simulation 6

Lecture 42 Benzene column simulation

Lecture 43 Optimum feed tray identification

Section 10: Simulation by Machine Learning

Lecture 44 What is machine learning?

Lecture 45 Understanding Machine learning terms

Section 11: Steps of simulation by machine learning

Lecture 46 Steps

Lecture 47 Data Cleaning

Lecture 48 Outlier

Lecture 49 Detecting outlier

Lecture 50 Handling missing value

Lecture 51 Data Encoding

Section 12: Data driven regression model development

Lecture 52 Regression analysis

Lecture 53 Performance of regression

Lecture 54 How to use performnace matrices

Lecture 55 Over fitting and under fitting

Section 13: Case study of Bio Reactor simulation

Lecture 56 Bio Reactor Simulation part1

Lecture 57 Bio Reactor Simulation part2

Lecture 58 Bio Reactor Simulation part3

Lecture 59 Bio Reactor Simulation part4

Section 14: Developing machine learning models

Lecture 60 Regression Learner tools

Lecture 61 Steps to develop ML models

Lecture 62 data Preparation

Lecture 63 Model selection

Lecture 64 Performance evaluation of developed model

Lecture 65 Visualize data

Section 15: Case study to develop soft sensor in a distillation column

Lecture 66 Soft sensor development part1

Lecture 67 Soft sensor development part2

Lecture 68 Soft sensor development part3

Section 16: Case study catalytic reactor simulation

Lecture 69 catalytic reactor simulation part1

Lecture 70 catalytic reactor simulation part2

Lecture 71 catalytic reactor simulation part3

Chemical Engineering students,Professional chemical engineers working in industry,Process engineer,Production engineer,Instrument engineer working in chemical industry,Operator and technicians working in chemical industry,Faculties in chemicals and allied engineering working in technical colleges