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
• big data (velocity, variety and volume of 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 trouble-shoot sensor/actuator systems
• to develop, test and tune soft sensors
• to report findings/conclusions of his work in writing using standard terminology and diagrams
• to tackle key issues involving hardware/software integration involving sensors/actuators

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/SciLAB, 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
• on-line resources (webinars, videos etc.)
• student presentations
• mandatory intermediate assignments with elements of PBL (Problem Based Learning) using group case-projects with oral presentations and/or mid-term tests

The present course will also be available online from the fall semester of 2016, in addition to the traditional campus-based course. The course contents and learning material used in the course will be the same in both programmes, except that in the online programme, the lectures will be partly in the form of offline video-based lectures in addition to relevant e-Resources. The mandatory laboratory assignments will be organized at a gathering on the campus at the end of the semester.

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: 6 assignments (two of which are group works) and case-projects (group work), distributed over two semesters” with a grade pass or not passed.

We reserve the rights for making any changes to the contents and plans.

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