Learning outcome

A candidate who has completed “SCE 1213 Hard/Soft Sensors in Process Measurements” will have the following learning outcomes in the form of acquired knowledge, skills, and general competence.

KNOWLEDGE

The candidate will understand

• the standard terminology of sensors (both hard and soft) , measurements and instrumentation with respect to a set of measurands
• the functional principles and ideas of most of the modern sensors and associated signal processing
• how the sensors are central in any system for its function and control and hence the need for a careful selection of sensor portfolio
• how to use sensors and actuators in a system and describe their layout using standard P&ID and flow diagrams
• multi sensor data fusion (Information fusion) and the convergence of computing, communication, measurements and big data and technology behind games
• basic concepts of wired/wireless sensor networks
• how to develop soft sensors (soft computing, inferential methods) using neural networks, fuzzy logic, support vector machines and relate these methodologies to other subjects in the SCE course
• how to implement these concepts in hardware and how to develop software to realize a working system

SKILLS

The candidate will be capable of

• selecting sensors, designing a measurement system with multimodal sensors and handling process measurands
• choosing, and implementing, the right approach to implement algorithms based on existing measurements for achieving soft sensors for the system
• developing dedicated programs to handle both hard/sensors in processes
• of finding, reading and understanding information from resources such as journals, books, internet, etc. relevant for the problems associated with sensors and actuators
• handling hands-on approach to tackle real system involving sensors/actuators and wired/wireless sensor networks

GENERAL COMPETENCE

The candidate will be able

• to communicate/discuss with peers problems related to hard/soft sensors in process measurements,
• to troubleshoot sensor/actuator systems
• to develop, test and tune soft sensors
• to report findings/conclusions of his work in writing using standard terminology and diagrams

Course Description

Measurands, conventional sensors including Pt-100, Pt-1000, Pt-25, thermocouples, strain gages, piezoelectric devices, flow meters etc., some currently evolving sensor technologies, P&ID, flow diagrams, importance of sensor data for control, multi sensor data fusion (information fusion),sensitivity, range, linearity, hysteresis, dead-band etc. of a measurement system, basic statistical aspects of measurement data and uncertainty calculations, soft sensors (soft computing, inferential methods), neural networks, fuzzy logic, neural-fuzzy methods, Support Vector Machines, some common actuators, interplay of sensors/actuators, dedicated programming using any one/ combination of these: NI LabVIEW, MATLAB, DELTA V

Teaching and Learning Methods

Learning/teaching will be based on

• lectures, exercises, and the use of relevant software.
• visit to relevant industries and guest lectures
• mandatory intermediate assignments with elements of PBL (Problem Based Learning) using group case-projects with oral presentations and/or mid-term tests

Assessment Methods

All mandatory exercises and assignments must be graded as “passed” in order to participate in the final exam.

Grades (A-F) with F-fail will be based on final written exam (60%), mandatory assignments (40%) and 4 mandatory assignments with a grade passe or not passed.

Minor adjustments may occur during the academic year, subject to the decision of the Dean