Curriculum

The curriculum for the B.Tech Process Instrumentation program at School of Instrumentation, Devi Ahilya Vishwavidyalaya is meticulously designed to provide students with a comprehensive understanding of both theoretical principles and practical applications in the field of process instrumentation. The program spans eight semesters over four academic years, ensuring a balanced progression from foundational sciences to specialized engineering concepts.

Semester-wise Course Structure

Semester	Course Code	Course Title	Credit (L-T-P-C)	Pre-requisites
1st Semester	PH101	Physics for Engineers	3-1-0-4	-
1st Semester	CH101	Chemistry for Engineers	3-1-0-4	-
1st Semester	MA101	Mathematics I	4-0-0-4	-
1st Semester	EE101	Basic Electrical Engineering	3-1-0-4	-
1st Semester	ME101	Engineering Graphics and Workshop	2-2-0-3	-
1st Semester	CS101	Introduction to Computer Programming	2-0-2-3	-
1st Semester	HS101	English Communication Skills	2-0-0-2	-
1st Semester	GE101	General Education	2-0-0-2	-
2nd Semester	PH102	Physics II	3-1-0-4	PH101
2nd Semester	CH102	Chemistry II	3-1-0-4	CH101
2nd Semester	MA102	Mathematics II	4-0-0-4	MA101
2nd Semester	EE102	Electronics Circuits and Devices	3-1-0-4	EE101
2nd Semester	ME102	Engineering Mechanics	3-1-0-4	-
2nd Semester	CS102	Data Structures and Algorithms	3-0-2-5	CS101
2nd Semester	HS102	Communication Skills II	2-0-0-2	HS101
3rd Semester	PH201	Physics III	3-1-0-4	PH102
3rd Semester	CH201	Chemistry III	3-1-0-4	CH102
3rd Semester	MA201	Mathematics III	4-0-0-4	MA102
3rd Semester	EE201	Digital Electronics and Logic Design	3-1-0-4	EE102
3rd Semester	ME201	Mechanics of Materials	3-1-0-4	ME102
3rd Semester	CS201	Object-Oriented Programming in C++	2-0-2-4	CS102
3rd Semester	IN101	Introduction to Instrumentation	3-1-0-4	-
4th Semester	PH202	Physics IV	3-1-0-4	PH201
4th Semester	CH202	Chemistry IV	3-1-0-4	CH201
4th Semester	MA202	Mathematics IV	4-0-0-4	MA201
4th Semester	EE202	Signals and Systems	3-1-0-4	EE201
4th Semester	ME202	Thermodynamics and Heat Transfer	3-1-0-4	ME201
4th Semester	CS202	Database Management Systems	3-0-2-5	CS201
4th Semester	IN201	Process Dynamics and Control	3-1-0-4	IN101
5th Semester	PH301	Physics V	3-1-0-4	PH202
5th Semester	CH301	Chemistry V	3-1-0-4	CH202
5th Semester	MA301	Mathematics V	4-0-0-4	MA202
5th Semester	EE301	Electrical Machines and Drives	3-1-0-4	EE202
5th Semester	ME301	Fluid Mechanics and Hydraulic Machines	3-1-0-4	ME202
5th Semester	CS301	Computer Networks	3-0-2-5	CS202
5th Semester	IN301	Instrumentation Electronics	3-1-0-4	IN201
5th Semester	IN302	Process Control Systems	3-1-0-4	IN201
6th Semester	PH302	Physics VI	3-1-0-4	PH301
6th Semester	CH302	Chemistry VI	3-1-0-4	CH301
6th Semester	MA302	Mathematics VI	4-0-0-4	MA301
6th Semester	EE302	Power Electronics and Drives	3-1-0-4	EE301
6th Semester	ME302	Mechanical Vibrations and Acoustics	3-1-0-4	ME301
6th Semester	CS302	Software Engineering and Project Management	3-0-2-5	CS301
6th Semester	IN401	Industrial Automation and PLC Programming	3-1-0-4	IN302
6th Semester	IN402	Data Acquisition and Signal Processing	3-1-0-4	IN301
7th Semester	PH401	Physics VII	3-1-0-4	PH302
7th Semester	CH401	Chemistry VII	3-1-0-4	CH302
7th Semester	MA401	Mathematics VII	4-0-0-4	MA302
7th Semester	EE401	Control Systems Design	3-1-0-4	EE302
7th Semester	ME401	Manufacturing Technology	3-1-0-4	ME302
7th Semester	CS401	Artificial Intelligence and Machine Learning	3-0-2-5	CS302
7th Semester	IN501	Advanced Process Modeling and Simulation	3-1-0-4	IN401
7th Semester	IN502	Cybersecurity for Industrial Systems	3-1-0-4	IN402
8th Semester	PH402	Physics VIII	3-1-0-4	PH401
8th Semester	CH402	Chemistry VIII	3-1-0-4	CH401
8th Semester	MA402	Mathematics VIII	4-0-0-4	MA401
8th Semester	EE402	Modern Control Theory and Optimization Techniques	3-1-0-4	EE401
8th Semester	ME402	Energy Systems and Renewable Technologies	3-1-0-4	ME401
8th Semester	CS402	Internet of Things (IoT) and Cloud Computing	3-0-2-5	CS401
8th Semester	IN601	Capstone Project - Final Year Thesis	3-0-0-9	IN502

Advanced Departmental Elective Courses

The department offers a wide array of advanced departmental elective courses designed to deepen students' understanding and enhance their specialization skills:

1. Advanced Process Modeling and Simulation: This course explores mathematical modeling techniques, simulation software tools, and optimization methods for complex industrial processes. Students learn how to build dynamic models of real-world systems and validate them against experimental data.
2. Cybersecurity for Industrial Systems: Addressing the growing need for secure control systems, this course covers network security protocols, threat analysis, intrusion detection systems, and compliance with international standards such as NIST SP 800-82.
3. Data Analytics for Process Optimization: Focused on extracting insights from operational data, this course introduces students to statistical methods, machine learning algorithms, and visualization tools for improving process efficiency and reducing variability.
4. Renewable Energy Integration: This course examines the integration of renewable energy sources into existing industrial processes. Topics include solar thermal systems, wind power generation, energy storage solutions, and hybrid systems design.
5. Smart Sensors and IoT Integration: Students explore the development and deployment of wireless sensor networks, embedded systems, and Internet of Things (IoT) solutions in industrial environments. Practical sessions involve programming microcontrollers and designing sensor-based applications.
6. Process Safety and Risk Management: This course emphasizes the identification, assessment, and mitigation of hazards in industrial operations. Students learn risk analysis methodologies, safety management systems, and regulatory frameworks for ensuring compliance with international standards.
7. Energy Efficiency and Sustainability: Covering energy conservation techniques, environmental impact assessments, and sustainable practices in industrial processes, this course provides students with tools to optimize resource utilization while minimizing ecological footprint.
8. Industrial Automation and Control Systems: Designed to equip students with skills in designing and implementing automated control systems for manufacturing environments. Students gain hands-on experience with PLCs, SCADA systems, and HMI interfaces.
9. Artificial Intelligence in Process Control: This course explores the application of AI techniques such as neural networks, fuzzy logic, and genetic algorithms in process control and optimization. Practical projects include developing intelligent control systems for chemical plants and power generation units.
10. Advanced Signal Processing Techniques: Focuses on advanced signal processing methods including wavelet transforms, spectral estimation, and adaptive filtering. Applications in industrial monitoring, diagnostics, and data acquisition are emphasized.

Project-Based Learning Philosophy

The department places significant emphasis on project-based learning as a cornerstone of the educational experience. The philosophy behind this approach is rooted in fostering critical thinking, problem-solving abilities, and practical application skills essential for real-world engineering challenges.

Projects are structured across multiple levels:

• Mini Projects (First Year): These are introductory-level projects that allow students to apply fundamental concepts learned in class to solve simple problems. Mini-projects typically last 4-6 weeks and require a team of 3-5 students.
• Major Projects (Second Year): Students engage in more complex projects involving system design, implementation, and testing. These projects span 8-10 weeks and often involve collaboration with industry partners or faculty research initiatives.
• Final Year Thesis/Capstone Project: The capstone project represents the culmination of the student's academic journey. It involves extensive research, independent study, and innovation in a specialized area within process instrumentation. Students work closely with a faculty advisor and are expected to deliver a comprehensive report and presentation.

The evaluation criteria for these projects are rigorous and multi-faceted:

• Technical Proficiency: Assessment of the technical correctness, depth of understanding, and innovative approach applied to the project.
• Documentation Quality: Evaluation of written reports, diagrams, data analysis, and overall clarity of communication.
• Presentation Skills: Grading based on oral presentations, visual aids, and ability to articulate ideas effectively.
• Team Collaboration: Assessment of teamwork, contribution levels, and coordination among group members.
• Project Execution: Evaluation of project completion status, adherence to timelines, and resolution of unexpected challenges.

Students select projects based on their interests and career aspirations, guided by faculty advisors who provide mentorship throughout the process. The department encourages students to propose innovative ideas or work on real-world problems identified by industry partners or research laboratories.