Introduction

Hello everyone, I'm SK. I'm a chemical engineer who completed my master's from IIT Guwahati, one of India's leading institutes, and I'm currently pursuing my PhD in chemical engineering. My PhD topic focuses on controllers based on artificial intelligence (AI). I'm well-versed in subjects like mass transfer, heat transfer, numerical methods, process control, and mathematical modeling. I'm a core chemical engineer, and today I want to guide you on these topics and share my experiences.

Understanding Chemical Engineering

Chemical engineering basically deals with the modeling part of processes. We design chemical reactors, distillation columns, and all the chemical setups you see in the industry. Students have to have knowledge of what are the basic things happening there, what are the primary things happening at the molecular level, and then they have to scale that up. Understanding what's happening at the molecular level is crucial because it forms the foundation for designing and optimizing large-scale processes.

Fundamental Principles

In chemical engineering, we focus on fundamental principles like mass balance and energy balance. These principles are essential for designing chemical equipment. To grasp these concepts, students need to learn subjects like:

• Mass Transfer: This subject teaches about separation processes like distillation and gas-liquid operations. It involves understanding how molecules diffuse when you apply heat, how equilibrium is established when two phases are in contact, and what calculations are needed to achieve your desired composition.

• Heat Transfer: Heat transfer is about how to supply or remove heat from reactors or materials. It teaches how to maintain reactors at a constant temperature, which is critical in many chemical processes.

• Numerical Methods: For the calculation part, students need a good understanding of numerical methods. Numerical methods involve techniques for differentiation and integration, especially when we don't get analytical solutions for differential equations. These methods are essential for solving the complex equations that model chemical processes.

• Mathematical Modeling: We often deal with differential equations to model systems. Students should be proficient in solving simultaneous differential equations, as they are the backbone of modeling chemical reactors and processes.

Process Control

Process control is my backbone; I've been working almost four years in this field—two years during my master's and two years in my PhD. Process control deals with automation, which is the backbone of the industry. In the current industry scenario, everybody is going for automation, and in the future, AI is expected to take over many aspects.

I chose process control because I want to be a pioneering engineer in AI-based controllers. Automation is very interesting, and there are very few people doing process control at the PhD level. It is a bit of a tricky subject, but if you understand it and really work in it, you can enjoy it and have various opportunities. Students who are pursuing chemical engineering definitely should have a good understanding of process control.

My PhD Project: AI-Based Controllers

In my PhD project, I'm working on AI-based controllers. Basically, it's about system identification. Every system has an input and an output. I designed an artificial intelligence model, which trains based on that input and output of the model, and it mimics that model, whether it is a real-life model or a mathematical model. This AI model helps us to gain insights about the real-life model.

Importance of Data Analysis and Python

For students, I strongly recommend learning Python, especially for data analysis. Nowadays, data analysis is entering every field—biotechnology, mechanical engineering, civil engineering, and of course, chemical engineering. Even in research, if you go into any area like molecular dynamics or any other part, people will ask you to analyze the results and the data. If you learn data analysis methods, it will be really helpful in your life and career.

At the core level, data analysis and AI are full of mathematics. The mathematics you should deal with includes linear regression, extrapolation, interpolation—these kinds of things. This all comes into the machine learning part. If you go deeper, the next level is deep learning, where you encounter artificial neural networks. These are advanced concepts, but students should at least have an understanding of basic concepts like linear regression, interpolation, and extrapolation, which we use in every field of engineering.

Mathematical Models and Numerical Methods

In process control, the mathematical models we deal with are actually differential equations. We deal with simultaneous differential equations. For example, if you go to the industry and look at a satellite, it has many parameters—position variables, thrust variables. All these variables are written in the form of differential equations.

You will be working on solving those differential equations. How can you solve them? You have a desired path or desired output. Let's say in a chemical reactor, you're trying to produce a new substance or cook some mixture or catalyst. Your desired value or composition requires you to control all the process variables. How these process variables depend on the output is written in terms of differential equations.

Students need to have a good understanding of solving differential equations. In the industry, if you have a good understanding of mathematics and know the basic principles like mass balance and energy balance, you will be invaluable. Wherever you go, you'll keep coming back to these basic principles.

For example, whether you see a chilling tower in a chemical industry, a distillation column, a heat exchanger, a valve, or any instrument, the supervisor or manager might come and ask you what are the basic principles this instrument is running on. If you have a basic understanding of mathematics and fundamental principles, you can understand and build your career, and you can understand any equipment in the industry.

Mass Transfer and Heat Transfer

Mass Transfer

Mass transfer is all about separation processes. If you have a mixture and you want to separate some component out of it, you have to perform a mass transfer operation. For instance, crude oil is a mixture of many things—petrol, kerosene, diesel, and more. We perform distillation, heating the crude oil based on the differences in the boiling points of the materials contained in the solution. At different stages, we get different components.

In mass transfer, students learn how molecules are diffusing when you apply heat, how equilibrium is achieved when two phases are in contact, and what calculations are necessary to get your desired composition. Understanding the molecular-level interactions and scaling that up is crucial.

Heat Transfer

In heat transfer, you learn how to maintain reactors at a constant temperature. You have to supply heat or sometimes cool the system by taking away heat. Heat transfer teaches you the principles of how you can transfer energy between two components.

Interconnection of Subjects

All the mass transfer and heat transfer equations are in terms of differential equations. To solve them, you have to use numerical methods. These subjects are all interconnected. It's not like you can learn only one thing and forget the rest. One student has to learn all of them because one equipment will use all these principles.

Thesis Writing and Research Methods

Writing a Thesis

My thesis writing experience was really interesting. I know a lot of students use Google, Grammarly, and different online tools to produce content, but I strongly disagree with that approach. Students have to develop the skill to write and produce sentences on their own.

In writing a thesis, you typically have the following sections:

1. Introduction: Write whatever you feel about the subject. Describe what type of things got you interested in that subject, what evoked you to learn it, and discuss previous literature. This is a very standard process, and students should practice writing this on their own.

2. Methodology: Write the methodology in your own words. It's very easy, especially if you've conducted the experiments or simulations yourself. Just write the step-by-step procedures you followed.

3. Results and Discussion: There's a standard way to present results, often in terms of graphs showing correlations between variables. Analyze your results and compare them with previous studies.

4. Conclusion: Draw conclusions based on your results. Discuss the advantages and disadvantages of your experimental procedure or strategy. This requires a solid understanding of theoretical knowledge.

Simulation-Based vs. Experimental-Based Research

There are two types of researchers:

• Experimental-Based: You conduct experiments in lab-scale reactors, analyze the output using instruments like HPLC, and present your findings. Since you've done the experiments yourself, writing the methodology is straightforward.

• Simulation-Based: You simulate a real-life experimental setup on a computer. You're not conducting the experiment physically but simulating the environment using software. For example:

  • In process control, we use MATLAB.

  • In transport phenomena, people use HYSYS.

  • In the industry, people use Aspen.

  • In molecular dynamics, different specialized software is used.

You choose the appropriate software for your field, carry out calculations, and the software provides the results. In process control, sometimes we have to design a strategy to control. We test this strategy in MATLAB, write down how the strategy came to mind, how it was devised and formulated—all these formulations are written in the methodology. The results are produced from the strategies, and in the conclusion, you explain how the strategy improved the results and how it's better than previous strategies.

If a student wants to work in simulations, they have to deal with a great depth of mathematics because all the strategies are formulated in mathematical equations. That's why I always tell students to learn coding because coding is now entering all fields.

Motivation for Pursuing a PhD

Becoming a Pioneering Engineer

My motivation for doing a PhD is that I want to become a pioneering engineer in new technology. Technology is always evolving. If you go back 50 years, there was one technology; if you come to the early 2000s, we had keypad mobiles, and now we have smartphones. I want to be part of the evolving technology. I want to research the technology that's going to rule the world in the future.

I personally think the automation industry is going to rule the world in the future, and I have a huge interest in automation and process control. I encourage students to choose a field that has a strong future impact.

Identifying Future Trends

One way to know about future scope is to visit websites of different universities, especially foreign universities, and look at the postdoc positions. They have different objectives written on their websites, which are basically the future scope they've identified. They open positions for those particular areas.

A student can understand what types of scopes exist for different fields in engineering by seeing which positions are opening in research. Not only postdoc positions—there are different ways as well. You can track current publications or look at the current working topics of different professors in universities.

Industry Insights

If you want to work in the industry, talk to people who are in the industry. For example, I talked to a person working at Dow Chemicals in America. She told me they are currently working on hybrid models. Hybrid models mean there will be an analytical model, which is modeled in terms of differential equations, and then there is an AI model, which is a mathematical model or neural networks. They are working on combining these two models into a hybrid model.

Then I came to know that people are still working on these kinds of things. You have to talk to professionals in the industry to really get what's happening because it's difficult to get news about pilot-stage projects—they don't give these news outside.

For research, go through professors' profiles and postdoc positions. For industry insights, talk to professionals—you can connect with them on LinkedIn. Stay updated with current research in the industry and academia, and choose your future path accordingly.

Advice to Students

I want to tell everyone to be curious. Don't think about results in terms of money—like, how much will I get if I do this? That will ruin your brain. Just be curious and do whatever you are interested in. Do research, talk to people, and always be willing to learn something new.

In this era, every person should be learning continuously. Even if you are at the age of 40, you should still learn some new skill; otherwise, you won't survive in the industry. You should always be in the learning mode. I tell students to always be in the learning mode, be curious, and don't think about money and material things. Think in terms of knowledge, and just pursue whatever you want. Just do research and keep learning.

Conclusion

Chemical engineering is a fascinating field that combines fundamental principles with practical applications. By understanding what's happening at the molecular level and scaling that up, you can design and optimize complex processes. Subjects like mass transfer, heat transfer, numerical methods, process control, and mathematical modeling are all interconnected and essential.

Embrace coding and data analysis, as they are becoming integral parts of engineering. Be proactive in seeking knowledge, stay updated with industry trends, and connect with professionals. Most importantly, stay curious and keep learning.

Thank you for giving me the opportunity to share my experiences and insights. If you have any questions or need guidance, I'm always willing to help. Have a nice day!