Courses Industrial Analysis


Tuition periods (4 weeks) and project period (4 weeks) will alternate throughout the first semester. Then the training takes place from March to September.


The main target of the master program "Industrial analysis" is to answer the question : How to perform measurement in an industrial plant ? 

It is paramount to monitor and optimise industrial processes in the chemical, petro-chemical, pharmaceutical, biotechnological, nuclear, or food industries with integrated high performance analytical methods directly on the production line. The analytical methods employed must not only support the control and optimisation of processes, they also have to be fast, and easy to implement.

The implementation of analytical methods in an industrial context requires a variety of skills which we focus on:

  • General and industrial analytical chemistry
  • Process engineering
  • Advance quality control
  • Information technology applied to manufacturing
  • Risk management
  • Health and Safety


You will find below an overview of the subjects covered during the time at University and some course content outline

Sampling on an industrial plant

Basics for reading a process diagram: backpressure regulator, valve.
Comparison Off-line / At-line / On-line / In-line
Requirements for the collection and transport: location, representativeness, physical or chemical changes, means of measuring and alarm control sample transport time response filters, return of the sample.
Sampling devices for pre-treatments: Filtration, Precipitation, Degassing, Homogenising, Flow metering, Phase separation, Cooling, Pressure reduction, Heat tracing, Digestion with acid, heat and/or UV, Dilution
Components and material for sampling, up to
Applied statistics for sampling 

Case study approach
The sampling loop seen as a mini-process (thermodynamics of phase equilibrium, dew point, calculation of pressure drop, thermal calculations, response time)

Exponential dilution
Case study without trivial solution, a gas solubility problem
Sampling gases in working place

Visit of an industrial plant (IFPEN)

  Instrumentation for industrial analysis

Critical review of analytical techniques in relation to industrial analysis : Vibrational spectroscopy (UV, IR, PIR, Raman), NMR, elemental analysis, x-ray fluorescence, LIBS, chromatography, electrochemical sensors, physical sensors, biological sensors.
Non-destructive measurements: ultrasonic sensors, radiography, acoustic emission, eddy current, magnetic particle inspection, liquid penetrant inspection, infrared thermography
Analysis in nuclear environment

Participation to professionnal event :
Industrial Analysis Exhibition in Paris

Visit of an industrial plant (Solvay)


Industrial IT and Automation

Introduction to systems theory: signals, input, output, dynamic, control, measurement, disturbance introduction to various issues related to automatic
Modelling: how to get a model, nonlinear system, linearization, linear system using the Laplace transform, transfer function, interconnections of linear systems
Parametric identification: principle, method appearance, appearance software
Command endpoint performance, open loop, closed loop, all or nothing, PID, PID control methods
CM bus and fieldbus
Implementation of PLC and human machine interface
Software suite


Industrial measurement strategy

General Introduction
Mastering the principles of functional analysis
Learning to do a needs analysis
Learn to describe the requirements, constraints
Learn to express functions
Practice writing a functional specification
Learn to develop solutions
Learning to select solutions

Case study approach

Master the general design principles of an analytical system in industry
Develop a functional analysis
Needs and requirements of an analytical system
Understand the principles of project management (WBS - GANTT - PERT)
Mastering safety and security
Understand the roles and missions in project management
Innovation in industrial measurement
Overview of major trends in analytical systems
Understand the main approaches to sustainable development, life cycle, value analysis
Project Manager


Data Analysis

Representations, analysis and modelling of multiple data
How to represent and interpret information gathered from an array of data to make comparisons, analyse behaviour, make classifications, establish relationships between groups, provide graphical representations (or factorial axes) which will need to assess the overall quality and the local quality
How to develop techniques for signal processing and / or experimental data collected in the laboratory.
How to build relationships structure-activity relationship (QSAR) properties or structures
Treatment of qualitative multidimensional data (Principal Component Analysis, Window analysis, hierarchical classification)
Treatment of multidimensional data PLS methods, Neural Networks, genetic Algorithm.


Experimental design

Relevant experiments for the choice of parameters of a study,
Influence of factors, development of a method, optimization:Screening of experimental factors, Hadamard matrices, Measure of effects and interactions between factors, full and fractional factorial matrices.
Response surface methods (RSM) composite matrices, Doehlert matrices

Case study approach

Organize a series of test
Exploit the results of an experimental study
Experimental optimization of a method, a process


Communication and Management

Marketing your job
Knowledge of the Industrial World
Management of industrial innovation
Communication in SMEs / SMIs / Groups
Resource management
Key management methods
Management of financial resources
Human Resource Management
Management of material resources
Resource management for quality
Resource management by security
Resource management project
Resource management for client / business
Management of external resources
Resource management in continuous improvement