The chlor-alkali process is a fundamental chemical process that produces chlorine, caustic soda and hydrogen through electrolysis of brine. Plants usually run continuously, are energy-intensive and, because they handle hazardous substances, depend heavily on process safety. These three characteristics also set the priorities for digital transformation.
Why is it a difficult field?
In the process industries, lack of data is rarely the problem. Control systems, lab instruments and meters generate data every second. The real problem is that this data is scattered across different systems, formats and teams.
- Process data sits in the control system; lab results in a separate spreadsheet.
- Maintenance records run independently of the production plan.
- Energy consumption is seen on the monthly bill, not linked to production in real time.
- Shift reports are prepared by hand and arrive late.
The result: everyone has data, but no one is looking at the same picture.
1. Build a single data backbone
The first step is to bring process, quality, maintenance and production data together in a common data layer. This does not mean replacing every system. Data is collected from existing control systems through standard protocols such as OPC UA or MQTT, and from business systems through integrations.
The data model is what matters here: you need to know which equipment, which batch and which shift a measurement belongs to. Data without context, however abundant, does not produce decisions.
2. Connect planning with the shop floor
Production planning in continuous processes differs from discrete manufacturing. Line rate, energy tariffs, storage capacity and the shipping schedule must be considered together. When the planning system sees actual production in real time, the plan does not drift away from reality.
3. Manage maintenance with data
Electrolysis cells, pumps, compressors and heat exchangers are the heart of the plant. An unplanned stoppage creates both production loss and a safety risk. Digitalising maintenance records makes it possible to analyse failure history together with process data. That is the prerequisite for moving from time-based to condition-based maintenance.
4. Link quality and traceability to process data
When product concentration, impurity values and lab results are linked to process data at batch level, finding the source of a deviation can take minutes instead of hours. When a customer asks, you can present the full history of a batch in a single report.
The measure of digital transformation is not the number of applications, but the data an operations manager brings to the morning meeting.
5. Prepare for digital twins and AI
Once clean, contextualised data starts to accumulate, more advanced steps become meaningful: testing process parameters on a digital twin, linking energy use to production, and detecting deviations in equipment behaviour early with AI. Attempted without a data backbone, these steps usually stall at the pilot stage.
Conclusion
Digital transformation in chlor-alkali plants starts not with a major platform change but with bringing data to the point of decision. ORELSIS has been developing process management and production systems in plants running the chlor-alkali process for years. That experience lets us reflect the specific needs of the process industries in our products.