The seven stages of chemical engineering technology from concept to industrialization (Issue 5/100) – Insights from theoretical reasoning

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This post was last edited by xiouxingzhe on 2026-6-23 at 15:21. Seven stages of chemical technology from idea to industrialization (Issue 5/100). Another source of inspiration: insights from theoretical reasoning. Dear friends: Hello everyone! In the last issue, we discussed the first source of inspiration: accidental observations from experiments. Today, let’s discuss the second approach: inspiration from theoretical reasoning. Some might ask: Is it possible to find innovative directions just by thinking, without conducting experiments? My experience is: it’s possible. And sometimes, using a pen and a few sheets of paper to work through things is more efficient than going straight to the laboratory. I. What is theoretical reasoning? The term theoretical reasoning may sound a bit abstract, but in essence it boils down to one thing: based on the scientific principles you already know, systematically asking yourself “If… then…”. If I increase the reaction temperature from 200 degrees to 220 degrees, how will the equilibrium conversion rate change? If I change the feed ratio from 1:1 to 1:1.2, will the side reactions be suppressed? If I evaporate the product while reacting, can the equilibrium be disrupted? For these problems, it is not necessary to go to a laboratory; using the basic principles of chemical engineering thermodynamics and reaction engineering that you have already learned, it is possible to make preliminary judgments on paper. But to carry out this reasoning, I think two basic skills are needed. First, there must be a sufficient theoretical foundation. It’s not about memorizing physical chemistry textbooks, but you do need to know the principles that form the basis for these calculations: the first and second laws of thermodynamics, Le Chatelier’s principle, the basic framework of reaction kinetics, and the fundamental principles of mass and heat transfer. These things are learned in university, but to truly turn them into mental tools that can be used at any time, it is necessary to practice consciously in one’s work. Second, there must be a sense of the “quantity” of data. The calculations do not require data accurate to two decimal places, but they need to be reliable in terms of order of magnitude. Is the order of magnitude of the reaction heat in the tens or hundreds of kilojoules per mole? The temperature dependence of the equilibrium constant: by how much does the K value change when the temperature varies by ten degrees? You need to have a general idea in mind of these order-of-magnitude figures. When I was young, I learned a habit from an experienced mentor: when encountering any set of numbers, instead of rushing to record them, I first ask myself: Is this order of magnitude correct? If it’s correct, why is it correct? If it’s not correct, what’s the problem? This habit was very helpful for subsequent theoretical derivations. II. Ask yourself three questions when making predictions. When I make predictions, I repeatedly ask myself three questions. What is the basis for this judgment? What principle did you use to arrive at this conclusion? Is it thermodynamics? Is it dynamics? Is it still an empirical rule? Where does this data come from? Where did you get the order-of-magnitude data you’re using? Which database was it searched in again? Is it still estimated based on experience? If this assumption is not true, how will the derived results change? If one of your underlying assumptions is wrong, will your final conclusion be completely reversed, or will it just shift slightly? By asking these three questions, the more vulnerable links in the derivation are naturally exposed. You’ll know where data needs to be supplemented and where design verification experiments are required. Some people might think this kind of reasoning is too slow. But my experience is that spending half a day working out the logic on paper is much more cost-effective than spending half a year taking detours in the laboratory. I’ve learned from my own experiences: when working on projects in the early stages, I would rush to the laboratory without thoroughly considering certain ideas on paper, and it was only after months of experimentation that I realized there was something wrong with the approach. I learned my lesson later on: whenever I had a new idea, I would first work through it on paper. III. An advanced approach: Finding the coupling between two principles. Another use of theoretical reasoning is to combine two seemingly unrelated principles and see whether they can work together. For example. In chemical engineering, chemical reactions and separation have traditionally been separated: reactions take place in reactors, while separation is carried out in columns. But you just need to ask, “Is it possible to do both at the same time on one device?” ”That’s how reactive distillation was born. The key to this approach is that you need to be aware of the limitations of each principle when used alone. The equilibrium limit of reversible reactions – when only the reaction takes place, the conversion rate cannot increase. Separation itself requires energy – performing separation alone represents a pure cost. When these two are combined, the heat generated by the reaction helps with the separation, and the removal of the separated components in turn drives the reaction forward – two elements that each had their own limitations, but when coupled together they complement each other. How can one practice this type of thinking? My own approach is: whenever I come across a new technical principle, instead of just remembering what it is, I ask myself – what is the core problem that this principle addresses? Are there any limitations to it? Are there any other principles that address problems in the opposite direction? What would happen if we put them together? It’s not always possible to come up with some groundbreaking idea every time. But over time, this way of thinking will gradually change the way you view things. IV. Rapid elimination using the first laws of thermodynamics. At the end of this reasoning process, I believe there is one fundamental principle that cannot be ignored: the first laws of thermodynamics. No matter how elegant the logic may be, if ΔG is positive, this reaction is thermodynamically unfeasible. No matter how many clever coupling schemes you propose, no matter how exciting they are, calculate ΔG first. Many ideas that seem beautiful crumble instantly in the face of thermodynamics. The value of this rapid elimination lies in the fact that a few hours of computation can save months of futile laboratory work. I have a habit in practice: for any new idea, I first calculate ΔG using paper and pen. If it doesn’t make sense, then I won’t go any further. Moreover, it is not only necessary to calculate ΔG under standard conditions, but also to estimate the change in Gibbs free energy under the actual operating temperature and pressure conditions. Sometimes, for a given reaction, ΔG is negative at room temperature, but becomes positive at higher temperatures – because the effect of the entropy change is amplified at high temperatures. If this change is not taken into account in advance, wrong judgments may be made. V. Theoretical reasoning is not omnipotent. After discussing so many advantages of theoretical reasoning, it is also necessary to talk about its limitations. Theoretical derivation cannot replace experiments. It can only rule out those directions that are clearly invalid, or help narrow down the possible range. Whether it will work in the end still needs to be tested in the laboratory. But it at least gives you a rough idea before entering the laboratory, so you know which directions are worth spending time on and which ones might be a waste of effort. My own experience is that theoretical derivation and experimental verification are not in a relationship of one replacing the other; rather, they complement each other. Inference guides the direction of experiments, while experiments provide feedback and corrections for inference. Only through repeated iteration of the two can cognition rise in a spiral manner. Preview for the next issue: Issue 6 – The Third Gateway to Inspiration: Associating While Reading Literature. Reading literature is not just about searching for data. How can one find inspiration to solve one’s own problems from a paper in a field that is not related? Next time, we’ll talk about how I read literature and the little trick of “functional searching”. Table of Contents link: Seven-Stage Model of Chemical Technology from Idea to Industrialization (Issue 100) – Table of Contents (updated in real time)