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Fees
₹6,00,000
Placement
92.0%
Avg Package
₹5,00,000
Highest Package
₹9,00,000
Fees
₹6,00,000
Placement
92.0%
Avg Package
₹5,00,000
Highest Package
₹9,00,000
Seats
150
Students
250
Seats
150
Students
250
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisite |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHE101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | MAT101 | Mathematics for Environmental Engineering | 3-1-0-4 | - |
| 1 | ENG102 | Introduction to Environmental Engineering | 2-0-0-2 | - |
| 1 | ENV101 | Environmental Science Fundamentals | 3-1-0-4 | - |
| 1 | ESC101 | Engineering Drawing & Graphics | 2-0-0-2 | - |
| 2 | ENG103 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | PHY102 | Thermodynamics and Heat Transfer | 3-1-0-4 | PHY101 |
| 2 | CHE102 | Physical Chemistry | 3-1-0-4 | CHE101 |
| 2 | MAT102 | Statistics and Probability | 3-1-0-4 | MAT101 |
| 2 | ENG104 | Fluid Mechanics | 3-1-0-4 | - |
| 2 | ENV102 | Environmental Biology and Ecology | 3-1-0-4 | ENV101 |
| 2 | ESC102 | Computer Programming for Engineers | 3-0-0-3 | - |
| 3 | ENG201 | Advanced Mathematics | 3-1-0-4 | ENG103 |
| 3 | CHE201 | Environmental Chemistry | 3-1-0-4 | CHE102 |
| 3 | ENV201 | Water Resources Engineering | 3-1-0-4 | ENG104 |
| 3 | ENV202 | Environmental Impact Assessment | 3-1-0-4 | ENV102 |
| 3 | ENG202 | Heat and Mass Transfer | 3-1-0-4 | PHY102 |
| 3 | ENV203 | Atmospheric Pollution Control | 3-1-0-4 | - |
| 3 | ESC201 | Data Structures and Algorithms | 3-0-0-3 | ESC102 |
| 4 | ENG203 | Operations Research | 3-1-0-4 | ENG201 |
| 4 | CHE202 | Biochemistry and Microbiology | 3-1-0-4 | CHE201 |
| 4 | ENV204 | Solid Waste Management | 3-1-0-4 | ENV202 |
| 4 | ENV205 | Environmental Monitoring and Modeling | 3-1-0-4 | - |
| 4 | ENG204 | Systems Analysis and Design | 3-1-0-4 | ESC201 |
| 4 | ENV206 | Renewable Energy Technologies | 3-1-0-4 | - |
| 5 | ENV301 | Water Treatment Engineering | 3-1-0-4 | ENV201 |
| 5 | ENV302 | Industrial Pollution Control | 3-1-0-4 | ENV203 |
| 5 | ENV303 | Environmental Management Systems | 3-1-0-4 | - |
| 5 | ENV304 | Sustainable Urban Planning | 3-1-0-4 | - |
| 5 | ESC301 | Database Management Systems | 3-0-0-3 | ESC201 |
| 5 | ENG301 | Project Management | 2-0-0-2 | - |
| 6 | ENV401 | Advanced Environmental Analysis Techniques | 3-1-0-4 | ENV301 |
| 6 | ENV402 | Climate Change and Adaptation Strategies | 3-1-0-4 | - |
| 6 | ENV403 | Biodiversity Conservation and Restoration | 3-1-0-4 | ENV202 |
| 6 | ENV404 | Green Building Technologies | 3-1-0-4 | - |
| 6 | ESC401 | Artificial Intelligence for Environmental Applications | 3-0-0-3 | ESC301 |
| 6 | ENG401 | Research Methodology | 2-0-0-2 | - |
| 7 | ENV501 | Specialized Topics in Environmental Engineering | 3-1-0-4 | - |
| 7 | ENV502 | Environmental Policy and Governance | 3-1-0-4 | - |
| 7 | ENV503 | Circular Economy Principles | 3-1-0-4 | - |
| 7 | ENV504 | Field Research Project | 3-0-0-3 | - |
| 7 | ESC501 | Big Data Analytics for Environmental Science | 3-0-0-3 | ESC401 |
| 8 | ENV601 | Final Year Thesis/Capstone Project | 6-0-0-6 | - |
Water Treatment Engineering (ENV301): This course provides an in-depth understanding of the principles and practices involved in water treatment processes. Students learn about physical, chemical, and biological methods used to remove contaminants from water sources. The curriculum includes design considerations for filtration systems, chlorination, ozonation, reverse osmosis, and membrane technologies.
Industrial Pollution Control (ENV302): This elective focuses on identifying and controlling pollutants generated by industrial processes. Topics covered include emission control systems, waste minimization strategies, regulatory compliance frameworks, and hazardous material handling procedures. Practical components involve site visits to industrial facilities and simulation exercises using environmental modeling software.
Environmental Management Systems (ENV303): Designed for students interested in corporate sustainability, this course introduces ISO 14001 standards and their implementation within organizations. It covers life cycle assessment, environmental auditing, risk management, and continuous improvement strategies to achieve sustainable operations.
Sustainable Urban Planning (ENV304): This course explores how cities can be planned to minimize environmental impact while enhancing livability. Students examine green infrastructure, smart growth policies, urban heat islands, transportation planning, and resource efficiency in metropolitan areas.
Advanced Environmental Analysis Techniques (ENV401): This course delves into modern analytical tools used in environmental research. It includes advanced spectroscopy, chromatography, mass spectrometry, and remote sensing technologies for environmental monitoring. Students gain hands-on experience in interpreting complex data sets from real-world environmental samples.
Climate Change and Adaptation Strategies (ENV402): This elective examines the science behind climate change and its implications for ecosystems and human societies. Students learn about mitigation strategies, adaptation planning, resilience building, and policy frameworks designed to reduce vulnerability to climate impacts.
Biodiversity Conservation and Restoration (ENV403): Focusing on ecosystem integrity, this course teaches methods for assessing biodiversity, restoring degraded landscapes, and implementing conservation programs. It covers habitat restoration techniques, species reintroduction strategies, and the role of protected areas in maintaining ecological balance.
Green Building Technologies (ENV404): This course introduces sustainable construction practices that minimize environmental impact. Students explore energy-efficient designs, green materials, water conservation systems, indoor air quality management, and building performance evaluation using tools like LEED certification criteria.
Artificial Intelligence for Environmental Applications (ESC401): This interdisciplinary elective combines AI concepts with environmental science. It covers machine learning algorithms applied to pollution prediction, climate modeling, biodiversity monitoring, and resource optimization. Practical assignments include developing predictive models using Python libraries like TensorFlow and Scikit-learn.
Big Data Analytics for Environmental Science (ESC501): This course equips students with skills in processing large datasets from environmental sensors, satellite imagery, and weather stations. It introduces statistical modeling, data visualization tools, cloud computing platforms, and advanced analytics techniques to extract meaningful insights from environmental big data.
The department's approach to project-based learning emphasizes real-world problem-solving through structured mini-projects and capstone research initiatives. Students are encouraged to apply theoretical knowledge in practical settings, collaborate with peers, and engage with industry partners.
Mini Projects (Semesters 3-6): These projects last for one semester and typically involve working on a specific environmental challenge. For example, students might design a wastewater treatment system for a local community or develop an air quality monitoring network using IoT devices. Each project is supervised by faculty members who provide guidance, feedback, and mentorship throughout the process.
Final Year Thesis/Capstone Project (Semester 8): The capstone project represents the culmination of a student's academic journey. It involves conducting independent research under the supervision of a faculty advisor, culminating in a thesis report and oral presentation. Projects often address pressing environmental issues such as plastic pollution, carbon capture technologies, or renewable energy integration.
Students select their projects based on personal interests, available resources, and faculty expertise. The department maintains a database of ongoing research initiatives and encourages students to align their interests with current research themes. Faculty mentors are chosen based on their experience in relevant fields and their availability for guidance.
