Introducing Learn Design and Simulation of chemical plants and equipment from Industry Expert.

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.

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, 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.

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.