The seven stages of chemical engineering technology from concept to industrialization (Issue 19/100) -- Introduction to technology research and development

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This post was last edited by xiouxingzhe on 2026-6-23 15:26. Seven stages of chemical technology from concept to industrialization (Issue 19/100). Technology development: from “chemistry” to “chemical engineering”. Dear friends: Hello everyone! The second phase, “Innovation Incubation,” has come to an end. Starting from this issue, we officially enter the third phase — technology research and development. If the first two stages were just theoretical discussions, the third stage is when actual action is taken. You need to go into the laboratory and turn the hypotheses on paper into tangible data. This is the most fascinating part of the seven-stage model—this is the first time you truly see whether this idea will work or not. I. For the same chemical reaction equation, the behavior is completely different when it takes place in a glass bottle versus in a reactor. In this episode, I’d like to discuss a conceptual hurdle first. Only by overcoming this hurdle can subsequent technology research and development proceed in the right direction. In the glass flask in the laboratory, you achieved a nice conversion rate of 98%, with a selectivity of 95%. The data looks great, and everyone is excited. But you need to know: under what conditions were these data obtained? It features constant-temperature magnetic stirring with a temperature control accuracy of ±0.5 degrees; the addition of materials is done slowly via a peristaltic pump, eliminating the limitations associated with industrial-level mass and heat transfer. None of these conditions are met in industrial installations. In industrial reactors, the stirring effect far fails to achieve the uniformity of magnetic stirring. The temperature field is uneven – the temperature near the wall surface and in the central area can differ by several degrees or even a dozen degrees. Mass transfer is limited—the added raw materials do not mix evenly instantly. This is why many “perfect” reactions in the laboratory fail when moved to pilot-scale production. In terms of physical principles, the difference between laboratory scale tests and industrial plants stems from the three aspects of \"mass transfer, heat transfer, and momentum transfer\" within the concept of \"three transfers and one reaction\". The rates of these three transfer processes depend on the size and geometry of the device, rather than the rate of the chemical reaction. The same reaction equation undergoes completely different physical fates at two different scales. Realizing this, you know that good pilot plant data does not necessarily mean it will work on an industrial scale. The pilot test only provides an \"upper limit that can be achieved under ideal conditions,\" while engineering technology aims to determine \"how to get as close as possible to this upper limit under non-ideal conditions.\" The gap between the two is the chasm that must be bridged during the technical amplification process. This is the most important cognitive foundation of the third stage. II. Data-driven belief: Every decision must be backed by data. In the stage of technology development, the biggest obstacle is not technical complexity, but the notion of \"I think it will work.\" “I think increasing the reaction temperature should improve the yield” — let’s get the data to prove it. “I think this catalyst should be able to last a thousand hours” — let’s get the data to prove it. “I think adding both ingredients at once should work pretty well” — let’s get the data to back that up. Data faith is the most fundamental discipline at this stage. All process adjustments and all decision-making must be supported by data. This is not just a matter of scientific rigor – in the chemical industry, making decisions based on guesswork can have serious consequences; in mild cases, the facilities fail to meet operational standards, while in severe cases, safety accidents occur. I have learned a habit through practice: when determining any process parameter, one must answer three questions—where does this value come from? Who tested it? Is there any independent review? Only if all three questions can be answered can this data qualify for the subsequent amplification stage. III. Key decision points in the third phase: Technical research and development is not a straight path to the end. It has several key decision points. First node: After the concept verification is completed. Can the core reaction occur? Yes—keep going. No – stop losses immediately and do not invest any more. This approach seems simple, but it’s not easy to implement, as time and effort have been invested, and people are reluctant to accept negative outcomes. But stopping losses at this point only took a few weeks ； Going further, the investment will be several months or even longer. Second node: After the pilot run is completed. Is continuity possible? Feasible — proceed to the amplification stage. Unfeasible — return to the intermittent route for reevaluation, or admit that continuity does not work in this system. Third node: After the amplification verification calibration is completed. It should be specifically noted here—that when I mentioned \"pilot scale testing\" earlier, I was using a broad meaning for it. The third phase actually encompasses the entire process from laboratory scale tests to pilot-scale trials, and then to industrial-scale testing (in some cases). Not all projects require industrial-scale testing. For some processes that are already well-developed and have controllable risks, once solid data is obtained from the pilot testing, it is possible to proceed directly to the fourth stage of developing the process package. However, in some cases—such as when the process is entirely new, the scale of amplification is very large, or there are regulatory requirements related to hazardous chemical processes—it is necessary to conduct industrial-scale tests after pilot testing, in order to further verify the scaling laws and the reliability of long-term operation. This industrial-scale test is larger in scale than pilot tests and more similar to actual industrial plants, but it still falls within the scope of the technology development phase; its purpose is to provide more comprehensive data support for the preparation of the process package. Whether to conduct industrial-scale trials depends on the maturity of the process, the risks associated with scaling up, and the requirements of relevant regulations; this needs to be determined in advance during the risk assessment phase of innovation development in the second stage. Regardless of the number of amplification and verification steps involved, the end of the third phase is marked by the fact that the quality of the data packets meets the required standards, allowing them to proceed to the fourth phase of technical finalization. The data packet does not meet the requirements and cannot proceed further. IV. What abilities are needed for the third stage? The third stage is different from the previous two stages. The first two stages rely mainly on the mind and pen and paper, while the third stage begins to depend heavily on experimental skills. Solid analytical and testing skills are required. Conversion rate, selectivity, and impurity profile – each of these must be measured accurately. A certain sense of engineering amplification is required. When doing experiments, one should not focus only on chemistry but also on physics. How is the mixing effect? Is the temperature distribution even? Is the sampling representative? For these issues, one should consciously observe and record them during pilot tests. Strong data management skills are required. The third phase will generate a large amount of experimental data, which needs to be organized clearly, labeled accurately, and stored completely. If data management is not done properly, it will lead to difficulties when aggregating data packets later on. V. Content structure of the third phase: Starting from the next issue, the third phase will be developed step by step in the following order. Let’s start with the proof-of-concept experiment—using the simplest experiment to answer whether a reaction can occur. Then we discuss catalyst screening and reaction condition optimization—how to use a systematic approach to find the optimal operating window. Let’s discuss the exploration of separation solutions again—separation often plays a more decisive role in determining economic efficiency than reaction, but it is easily overlooked. Next is the setup of a pilot-scale continuous system and its long-term operation – transitioning from batch to continuous mode in order to expose issues that do not arise in short-term experiments. Then comes the scale-up verification phase—which includes pilot-scale scale-up design, pilot-scale construction and operation, as well as scale-up sensitivity experiments. If industrial testing is involved, it will also be discussed. Finally, there are the formation and quality standards for data packets—the quality of these data packets determines the efficiency and reliability of the subsequent process package development. Each issue will include real-world examples and specific methods for implementation. Preview for the next issue: Issue 20 – Proof of concept experiment: First answer “is it possible?” Before investing a lot of time and resources in optimization, use the simplest experiments to answer a fundamental question: Can this reaction take place in the laboratory? Next time, we’ll discuss how to design and conduct proof-of-concept experiments, and how to determine whether to continue or stop. Table of Contents link: Seven-Stage Model of Chemical Technology from Idea to Industrialization (Issue 100) – Table of Contents (updated in real time)