Chemical Engineering Thermodynamics 2

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Chemical Engineering Thermodynamics 2

Coursera · Beginner ·🏭 MLOps & LLMOps ·3mo ago

Key Takeaways

Covers Chemical Engineering Thermodynamics principles and applications

Original Description

An appreciation of thermodynamics is required to become a chemical and biomolecular engineer. Thermodynamics can assess the viability of a process and is one of the curriculum's most essential topics. The principles are utilized in following engineering courses (kinetics, mass transfer, design, materials) and are applicable to numerous engineering disciplines. The increased emphasis on energy usage and transformation as a result of rising demand, diminishing supply, and global warming necessitates that the engineers who will tackle these issues have a firm grasp of thermodynamics. The first and second laws will be studied in this course. Non-ideal features of single-component and multicomponent systems will be emphasized. A substantial portion of the course is devoted to solution thermodynamics, which is crucial for separations (e.g., distillation, extraction, membranes), and chemical equilibrium, which is crucial for reaction engineering. "A theory is more striking when its premises are simpler, when it relates more diverse types of things, and when its scope of applicability is broader. Consequently, the profound impact that classical thermodynamics had on me. It is the only physical theory with universal content that I am confident, within the range of its applicability, will never be overthrown." — Albert Einstein
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
Building a self-healing MLOps pipeline on AWS: from raw data to a model that fixes itself
Learn to build a self-healing MLOps pipeline on AWS that automates model fixing, increasing model reliability and reducing downtime
Medium · Machine Learning
📰
Building a self-healing MLOps pipeline on AWS: from raw data to a model that fixes itself
Learn to build a self-healing MLOps pipeline on AWS that automates model fixes, increasing model reliability and reducing downtime
Medium · DevOps
📰
qModel Open-Source Platform v1.2.0 Released: Streamlined Python Model Integration & Execution Pipeline
Learn how to streamline Python model integration and execution with qModel Open-Source Platform v1.2.0, a tool for MLOps and AI development
Dev.to AI
📰
Inference Infrastructure Best Practices for High-Traffic AI Applications
Learn best practices for building scalable inference infrastructure for high-traffic AI applications to ensure reliable and efficient deployment
Dev.to AI
Up next
Pole Pruner How A Rope Lever Shears High Branches
Innoforge Studio
Watch →