Cosmetics and Detergents
Real-time inline monitoring helps respond faster to adverse changes and fluctuations in the production process, reduce raw material losses, and maintain stable production quality.
In process industries, laboratory control alone often comes „after the fact” — because obtaining a result requires sampling, logistics, and analysis, while the process has already moved forward. That is why we implement process analytics, which provides continuous and immediate data under real production conditions (temperature, pressure, 24/7 operation).
Real-time inline monitoring helps respond faster to adverse changes and fluctuations in the production process, reduce raw material losses, and maintain stable production quality.
Petrochemical plants require continuous monitoring of process stream compositions to detect deviations faster and stabilize product quality. Spectrally supports inline monitoring, reduces raw material losses, and enables more efficient operational decisions.
Fertilizer production requires stable control of key component concentrations and rapid detection of process deviations. Spectrally supports inline monitoring, improves batch repeatability, and relieves the quality control laboratory.
Chemical and polymer production requires precise control of composition, reaction progress, and batch quality at every process stage. Spectrally supports inline monitoring, faster deviation detection, and stabilization of production parameters.
Environmental monitoring requires fast and reliable identification of contaminants in water, soil, and air to respond earlier to deviations and limit environmental risk. Spectrally supports inline and field measurements, helping shorten analysis time, increase inspection frequency, and streamline operational decisions.
Water and wastewater management requires continuous control of medium quality and rapid identification of composition changes to stabilize treatment and purification processes. Spectrally supports inline monitoring, reduces delays associated with laboratory analysis, and helps respond faster to process deviations.
In practice, plants gain:

Real-time quality control, directly in the process
Measurement without sampling and without downtime
Designed for 24/7 operation, even in demanding conditions
Integration of results with control systems / QA reports
The bridge between the laboratory and production
Rapid verification of batch composition without tedious sample preparation
Support for scaling formulations from R&D to production
Real-time data visualization and analysis
Mobile raw material identification and incoming quality control (IQC)
Fast PASS/FAIL, even without opening the packaging (for transparent packaging)
A tool for raw material control at warehouse intake and in the field
Non-destructive measurement with results in seconds
Dedicated software with proprietary analytical models
Analyte signal converted into a user-readable result
Rapid development and deployment of models in response to specific process issues
Real data for automating and optimizing the production process
Challenge.
Unstable production causing variable reagent concentrations in the final product. Additionally, long waiting times for laboratory results prevented tight control of free reagent concentrations.
Results
After implementation, it became possible to achieve: 24/7 detection. with detection information every. 10 minutes. and data collection at a level of. 150 points per day / 50 thousand per year., enabling incident analysis and problem source localization.
Challenge.
Unstable production and issues achieving final reagent concentrations, long laboratory response times, and lack of strict control over free reagent concentrations.
Results
After implementation, the measurement covered from. 750 points. (every. 30 seconds.) throughout the entire process, with relative errors against the reference method (HPLC) including. 0.02% for phenol. i 0.05% for formaldehyde.. Additionally, there was. batch time reduction (~6%). thanks to predicting the reaction endpoint and full quality documentation of batches.
The plant produces UAN 28, 30, or 32 to customer order on a single continuous mixing line. Hitting the specification window for the selected grade and reaching it quickly during grade changes determine the amount of transitional material and the nitrogen above declaration. We demonstrate how to control composition directly in the mixing line.
In acrylic dispersions and resins, the copolymer composition and its homogeneity determine the glass transition temperature (Tg) and coating properties, while residual monomer is subject to customer regulations. We demonstrate how to monitor monomer conversion and copolymer composition directly in the reactor.
The molar F/U ratio in UF resin is a fundamental parameter determining formaldehyde emissions from the finished board, and it directly results from the composition of the received UFC concentrate. We demonstrate how to verify the F/U ratio and formaldehyde content in UFC upon delivery – within 1–2 minutes and without reagents.
If you are considering inline analysis, the most important question is: will this method work under your conditions and deliver a result on which a decision can be based? Therefore, we start with a practical verification — using your samples and in the context of a specific goal (quality stabilization, batch shortening, contamination detection, loss reduction). In a short time, you get a clear answer:, whether measurement is possible, with what accuracy, where to install the measurement point. and how to translate data into real operational actions (trend, alarm, PASS/FAIL decision).
No. The list shows areas where we have the most implementation experience, but process analytics can also be adapted to other applications — the decision is made after a brief discussion and sample verification.
We start by identifying critical points (what and where to measure), and then conduct a short feasibility study using samples from your process. This way, you immediately know whether we can achieve the required accuracy and what the boundary conditions are.
The greatest impact is where laboratory results are „after the fact” and do not allow process control, and deviations generate raw material losses, complaints, or downtime risks. In such cases, frequent or continuous measurement enables faster response and quality stabilization.
Yes — process implementations are designed for continuous operation and real installation conditions (temperature, pressure, working environment). Configuration selection depends on the medium and measurement location.
Both scenarios are possible. Depending on the industry and process, we select the architecture: measurement. in-line. (in-process), at-line/lab. (in-line control during production) or portable (incoming quality control/IQC).
Yes. In such applications, response speed is crucial, which is why implementations are designed to detect events in real-time (with a short measurement interval), rather than only after laboratory results are available.
The most common limitations depend on the sample (e.g., fluorescence) and require proper selection of configuration and chemometrics. Therefore, we verify this in a feasibility study using your materials.
Yes — we often start with one critical point to confirm the business value and gather process data, then expand the system to additional stages or lines. This is a safe approach for implementation and cost control.
Yes. The result is presented in an operationally useful format (e.g., trend, alarm, PASS/FAIL, quality indicator), not as raw spectra. This allows personnel from technology, quality, and maintenance to all utilize the solution.
A brief process description (medium, measurement point, business objective) and information on which parameters you currently control and at what frequency is sufficient. If we proceed to testing, we will define the minimum sample set for a feasibility study.
Initial conclusions emerge after process analysis and preliminary sample testing — at that point, we can determine feasibility, expected accuracy, and the recommended implementation architecture.
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