The seven stages of chemical engineering technology from concept to industrialization (Issue 20/100) -- Concept validation experiment

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This post was last edited by xiouxingzhe on 2026-6-23 at 15:26. The seven stages of chemical technology from concept to industrialization (Issue 20/100). Technology research and development: Concept validation experiments. Dear friends: Hello everyone! In the previous issue, we discussed the beginning of the third phase – the transition from “chemistry” to “chemical engineering”. Starting today, we will discuss the first stage of technology development: concept validation experiments. What needs to be done in this step can be summarized in one sentence: use the simplest experiments to answer a fundamental question—can this reaction take place in the laboratory? I. Purpose of the proof-of-concept experiment: To answer only whether it is “possible” or “not possible”. The proof-of-concept experiment is the first step in technology development. Its purpose is very simple: to verify whether the core reaction or separation process can be carried out under laboratory conditions and whether a product is produced. I’ve seen many engineers make a mistake at this stage: they try to optimize things right from the start. Before even conducting the proof of concept, they started to consider \"what temperature yields the highest yield\" and \"what ratio gives the best selectivity\". This is actually putting the order backwards. If the reaction itself cannot occur, what are you optimizing? If no product is formed at all, what’s the point of measuring the yield? Therefore, the design principle for the proof-of-concept experiment is one: simplicity. No complex experimental design is required, nor is there any need to consider optimization and scaling up. You only need a very simple experimental setup, under the most basic conditions, to verify whether the core reaction can occur. Take the development of the continuous flow process for diphenylamine as an example. Our first step at that time was not to go straight to microchannel reactors; instead, we first carried out the aniline diazotization reaction in the laboratory using the simplest continuous setup – to verify whether this approach of continuous processing could work. If this step fails, all the subsequent complex designs and efforts will be a waste. II. Experimental Design: Conditions should be simple, and records should be detailed. Although concept verification experiments are simple, there are still considerations regarding their design. How to choose the conditions? Usually, existing literature data or the results of theoretical derivations are referred to. Temperature, pressure, ratio, catalyst—choose the combination that is theoretically the most likely to work. There is no need to conduct orthogonal experiments or response surface optimization; try the option that seems most likely to work first. But one thing is particularly important, and it’s also something I’ve learned from my own mistakes: once the experiment starts, one cannot focus solely on the main product. Is the temperature stable? Is the color abnormal? Are there any bubbles? Has any precipitation occurred? Is there any significant exothermic or endothermic reaction during the process? This information is very valuable for subsequent judgments. There is another detail that is easily overlooked: recording the time series during the reaction process. When does the temperature start to rise, when does the color begin to change, and when does precipitation occur? The patterns in these time points often conceal important information on reaction kinetics. Recording should not rely on the brain. Write clearly in the experiment notebook: experiment number, date, operating conditions, amount of material used, procedure steps, observed phenomena, and test results. The handwriting does not need to be neat and beautiful, but the information must be complete. If you look back at it in a few months, you will be able to reconstruct the experimental scene from that day by recalling it in your mind. III. Repeat three times—to confirm it’s not a coincidence. This is a step that many beginners tend to overlook. The first experiment was successful; a product was produced, and the yield was quite good. At such a time, the natural reaction is excitement, and one wants to report to the supervisor right away and arrange for the next experiment promptly. But don’t rush. Do it two more times. With the same conditions and the same operations, will the results be identical? If there is a discrepancy, where is the fluctuation? Is it the yield that fluctuates, or is the selectivity changing, or are certain phenomena appearing intermittently? Three repetitions are a fundamental requirement for the reproducibility of science. It may seem like more time is required, but in fact it’s the most cost-effective approach. If one proceeds eagerly after just one attempt and later discovers that the result was merely a fluke – perhaps due to particularly good raw materials or because the laboratory temperature happened to be within the optimal range on that day – then all the work done in the following months will have to be redone. If it can be reproducibly achieved three times, then you can say with fairly high confidence that this reaction is feasible under laboratory conditions, and it’s time to move on to the next stage. IV. Judgment of failure: The courage to cut losses decisively. After completing the concept validation, it is possible to obtain an outcome that one does not want to see – no product is produced, or only a very small amount is produced, far below expectations; or the main product is indeed formed, but at the same time a large amount of by-products that cannot be separated are generated. At this point, what is most needed is to cut losses decisively. By stopping losses at this point, you only incur a few weeks of time, a few batches of reagents, and some costs for analysis and testing. Going further – catalyst screening, condition optimization, exploration of separation methods – the investment will increase several times over. But people have emotions. An idea that has been pondered for so long, with so many days and nights spent in the laboratory – it’s indeed difficult to simply give it up. I've been through similar situations myself. Every time this happens, I remind myself by asking three questions: “If this were the first time I saw these experiment results, with no prior investment, would I still go ahead?” ” “Is it possible to resolve this negative outcome by adjusting the conditions? If possible, what would be the approximate cycle time and cost for making such adjustments? ” “Should one continue with it to test a scientific question, or to justify the time and effort that have already been invested? ” The first two questions are technical judgments, while the third one is a psychological judgment. Technical judgments can be made together with the team, while psychological judgments have to be faced on one’s own. Many people consider \"setting a stop loss\" to be a sign of failure. I don’t think so. Stop-loss is about withdrawing limited resources from a path that isn’t working and investing them in a more promising direction. This is not a failure; it is an optimal allocation of resources. It takes weeks to realize it’s not feasible, while it takes a year to admit that it’s not feasible – the cost difference is not in the same order of magnitude at all. V. If the reaction is feasible, what should be paid attention to next? The concept validation was successful—the reaction can occur, the formation of the product has been confirmed, and the results are consistent across three repetitions. Congratulations, you chose the right direction. But before moving on to the phase of condition optimization and screening, there is one thing that should be done first: organize and archive all the experimental data from the proof-of-concept phase. Don’t think, “This is just a preliminary test; the data isn’t very useful.” In the data from the proof-of-concept phase, there are often many pieces of information that are overlooked later on. How does the temperature curve behave? How long is the induction period for reaction initiation? What will the spectrum of the product distribution be like? These early data can serve as a starting point for tracing back when problems arise later on. After the archiving is completed, the next steps are the screening of catalysts and materials, as well as the systematic optimization of reaction conditions. These topics will be explored in detail in subsequent issues. Preview for the next issue: Issue 21 – Catalyst and material selection: The trade-off among performance, lifespan, and cost. Once the concept validation is complete, the reaction can take place. But if the reaction involves a catalyst, screening must be carried out next. Screening is not just about activity – catalysts with high activity but low selectivity have no industrial value. How to balance performance, stability, and cost across these three dimensions? To be continued in the next issue. Table of Contents link: Seven-Stage Model of Chemical Technology from Idea to Industrialization (Issue 100) – Table of Contents (updated in real time)