Six organic acids in a process water of the paper industry
Applications | | MetrohmInstrumentation
The control of organic acid levels in paper industry process water is critical for maintaining equipment integrity, optimizing treatment steps and ensuring regulatory compliance. Accurate quantification of low molecular weight acids supports process optimization and environmental monitoring.
This application note describes a method for simultaneous determination of six key organic acids in paper mill process water. The targeted analytes are glycolic acid, formic acid, glutaric acid, acetic acid, propionic acid and butyric acid. The method employs ion exclusion chromatography with suppressed conductivity detection to achieve high sensitivity and selectivity.
The analytical workflow consists of minimal sample preparation and direct injection after cation removal.
The method delivered baseline separation of all six acids in less than 12 minutes. Quantified concentrations in the process water sample were:
The described ion exclusion chromatography method provides a robust, sensitive and efficient approach for quantifying key organic acids in paper industry process water. Its ease of use and broad applicability make it an excellent tool for routine quality control and environmental monitoring.
No literature references were provided in the original application note.
Ion chromatography
IndustriesEnergy & Chemicals
ManufacturerMetrohm
Summary
Importance of the topic
The control of organic acid levels in paper industry process water is critical for maintaining equipment integrity, optimizing treatment steps and ensuring regulatory compliance. Accurate quantification of low molecular weight acids supports process optimization and environmental monitoring.
Goals and overview of the application note
This application note describes a method for simultaneous determination of six key organic acids in paper mill process water. The targeted analytes are glycolic acid, formic acid, glutaric acid, acetic acid, propionic acid and butyric acid. The method employs ion exclusion chromatography with suppressed conductivity detection to achieve high sensitivity and selectivity.
Methodology and used instrumentation
The analytical workflow consists of minimal sample preparation and direct injection after cation removal.
- Sample preparation: Injection through H+ cartridge for matrix simplification
- Column: Metrosep Organic Acids 6.1005.200
- Eluent: 0.5 mmol/L perchloric acid
- Flow rate: 0.5 mL/min
- Suppressor: Membrane suppressor module using 10 mmol/L lithium chloride regenerant
- Injection volume: 20 µL
- Detection: Conductivity detection with suppressed background
Main results and discussion
The method delivered baseline separation of all six acids in less than 12 minutes. Quantified concentrations in the process water sample were:
- Glycolic acid 3.35 mg/L
- Formic acid 0.99 mg/L
- Glutaric acid 0.96 mg/L
- Acetic acid 723 mg/L
- Propionic acid 533 mg/L
- Butyric acid 24.5 mg/L
Benefits and practical application
- Rapid simultaneous analysis of multiple organic acids
- Minimal sample preparation reduces analysis time
- High sensitivity and low detection limits for trace components
- Automated suppression lowers chemical consumption and maintenance
- Applicability for on-site process control and environmental compliance monitoring
Future trends and opportunities
- Integration of ion chromatography with mass spectrometry for enhanced selectivity
- Development of greener eluents and suppressor regenerants
- Miniaturized systems for online and inline monitoring
- Advanced column materials for improved resolution of structurally similar acids
- Artificial intelligence-driven data analysis for real-time process feedback
Conclusion
The described ion exclusion chromatography method provides a robust, sensitive and efficient approach for quantifying key organic acids in paper industry process water. Its ease of use and broad applicability make it an excellent tool for routine quality control and environmental monitoring.
References
No literature references were provided in the original application note.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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