ANALYTICAL CHEMISTRY OF INDUSTRIAL PROCESSES

Second cycle degree in INDUSTRIAL CHEMISTRY

Campus: PADOVA

Language: English

Teaching period: Second Semester

Lecturer: MARCO FRASCONI

Number of ECTS credits allocated: 6


Syllabus
Prerequisites: Analytical Chemistry I and II, in particular knowledges of instrumental analysis.
Examination methods: The exam consists of a written assay, on a focused topic on process analytical control, and an oral exam, on the core topics of the course.
Course unit contents: 1) Introduction to Process Analytical Chemistry.
2) Sampling for analytical purposes. Sampling systems.
3) Data domains and signal elaboration. Sources of noise in instrumental analysis and signal-to-noise optimization strategies.
4) On-line chromatographic techniques. Process gas-chromatography (GC) and liquid-chromatography. Applications of GC and GC-MS in the petrochemical industry.
5) Optical spectroscopy for process analyses. UV-Vis, infrared and Raman spectroscopy: instrumentation design and sampling interface. Analytical applications of IR and Raman spectroscopies in the pharmaceutical industry.
6) Analytical electrochemistry. Potentiometry and ion-selective electrodes. Ion-sensitive field-effect transistors (ISFET). High-temperature potentiometric oxygen sensor in combustion process monitoring. Amperometric methods for on-line analysis.
7) Principles of chemical sensors. Origins of sensor selectivity: thermodynamic and kinetic aspects. Types, preparation and properties of sensors. Immunosensors and DNA-based sensors. Enzyme biosensors. Applications of biosensors in bioprocess monitoring and control.
8) Optical sensors. Integrated sensor arrays for high throughput analysis.
9) Automated methods of analysis. Flow injection analysis and applications in industrial biotechnology.
10) Microanalytical systems. Overview of miniaturization of analytical instruments utilizing microfabrication technology. Application of lab-on-chip detection techniques in bioanalytical studies.
11) Chemometrics methods for process control and monitoring. Feedback optimization algorithms.