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A self-optimising microreactor system runs a reaction, measures the result, and uses that measurement to choose conditions for the next run. This closed feedback loop can search for useful settings—such as temperature, flow rate, or reactant concentration—more systematically than manual trial and error. It does not decide what chemistry to pursue or guarantee a universal optimum: researchers define the objective and the system works within the experiments and constraints they set.
How does a self-optimising microreactor system work?
The system links a flow reactor to an analytical instrument and a computer-controlled optimisation method. In each cycle, pumps deliver reactants, the reactor carries out the reaction, an instrument measures an outcome, and software uses that result to select conditions for another cycle.
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- Set conditions: Pumps deliver reactants at specified flow rates and concentrations; the system also sets controllable variables such as temperature.
- Run the reaction: The reactants pass through a mixer and a small reactor while the selected conditions are maintained.
- Measure the outcome: An analytical instrument estimates a response, such as product yield or concentration.
- Choose the next experiment: An optimisation procedure considers the measurement and earlier runs, then selects a new set of conditions.
- Repeat against the objective: The cycle continues until a stopping rule is met or the chosen objective is sufficiently satisfied.
The objective matters. A system asked to maximise yield may choose different conditions from one asked to increase production, reduce cost, or balance several goals. “Optimal” therefore means best according to the objective and constraints supplied for that campaign, not best in every possible sense.
What equipment does the system need?
The physical platform must deliver and mix reactants, carry out the reaction, measure a relevant response, and communicate that result to the control software. The exact equipment depends on the chemistry and on what the experiment is meant to learn.
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The 2010 demonstration
A 2010 Chemistry World report on an MIT research team’s system describes three syringe pumps feeding reaction components into a mixer and a 140 μl microreactor. High-performance liquid chromatography (HPLC) measured product yield; a computer then adjusted settings including flow rate, temperature, and reactant concentration across cycles. The report says that this particular reaction reached an 83% yield after two days and multiple cycles. That figure describes the reported reaction and setup, not a general performance benchmark for microreactors. Read the Chemistry World report.
Inline measurement in a later platform
Fath, Kockmann, Otto, and Röder’s 2020 study paired an automated flow microreactor with inline FT-IR spectroscopy. Because the measurement was sent directly to the optimisation procedure, the platform could use the response during the campaign. The authors report that the studied optimisation problems were solved within one working day; this is a result for their investigated scenarios, not a runtime guarantee for other reactions. Read the 2020 study.
The 140 μl reactor and three-pump arrangement are historical details of the 2010 demonstration, not specifications every modern system must meet. HPLC and inline FT-IR are examples of measurement choices, not interchangeable requirements: the appropriate method depends on the reaction and the response the system needs to track.
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Different algorithms choose experiments for different purposes. A search for useful operating conditions is not the same task as identifying a precise kinetic model. The cited studies illustrate those distinctions rather than establish a universally best method.
| Approach | Purpose and measurement in cited work | Reported result or capability |
|---|---|---|
| Modified simplex | Compared by Fath et al. (2020) for reaction optimisation using inline FT-IR. | The study reports successful resolution of its investigated optimisation problems within one working day. The paper does not establish a universal runtime. |
| Model-free design of experiments (DoE) | Also compared by Fath et al. (2020) for reaction optimisation using inline FT-IR. | Considered alongside modified simplex; the authors frame method choice as dependent on the scenario. |
| Model-based DoE | Used by Waldron et al. (2019) to select experiments for kinetic-model identification, with HPLC outlet-concentration measurements. | In their studied case, a transient-experiment campaign took two hours versus eight hours for the steady-state campaign, but produced less precise parameter estimates. |
The two-hour and eight-hour comparison concerns kinetic-model identification in Waldron et al.’s reported case. It should not be read as a general speed comparison for finding reaction-yield conditions. The campaign’s goal and acceptable precision affect which experimental strategy is useful. Read Waldron et al.’s 2019 study.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can the system optimise—and what can it not decide?
A self-optimising setup automates experimental iteration; researchers still define the chemistry, response, variables, and limits. If multiple goals conflict—for example, improving product output while controlling cost—the procedure must work with an objective that expresses how those goals should be balanced. The 2020 authors describe their platform as enabling multivariate and multi-objective optimisation in real time, and as modular and flexible. That is the authors’ conclusion about their platform, not an independent assessment of every system.
Automation also does not remove the need for meaningful measurements. A feedback procedure can only make decisions using the response it receives. The measurement method and the optimisation objective therefore need to fit the question being asked; a system configured to optimise one measured outcome does not automatically identify every useful property of a reaction.
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- 83% yield: reported for one MIT-team reaction in the 2010 Chemistry World account, after two days and multiple cycles.
- One working day: the 2020 Fath et al. paper’s result for its investigated optimisation problems.
- Two hours versus eight: Waldron et al.’s 2019 comparison of transient and steady-state campaigns for kinetic-model identification; the shorter campaign had less precise parameter estimates in their case.
These values describe different studies, reactions, objectives, and experimental setups. They are not directly comparable measures of general reactor performance.
What equipment should a laboratory evaluate?
A laboratory investigating flow chemistry can use syringe pumps as a starting equipment category because the 2010 system used three to feed components. That report does not identify a current product or specify the flow-rate range, pressure rating, wetted materials, or connections needed for a new application. Those requirements must be checked against the planned chemistry and reactor. The cited studies likewise do not establish a particular commercially available microreactor, analytical instrument, or automation package.
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