AI for Chemical Engineering: The Next Frontier

AI in Chemical Process Design

Learning Outcome: Understand how AI improves chemical process design and safety.

• AI-powered process simulation and optimization
• Machine learning for material and reaction selection
• AI-assisted safety and hazard analysis

• AI-driven refinery process optimization
• AI-based HAZOP and safety risk prediction

• Design and simulate a chemical process using AI techniques

Optimizing Chemical Production with AI

Learning Outcome: Improve production efficiency and reliability using AI.

• Predictive maintenance techniques
• Reaction yield and throughput optimization
• Waste and loss reduction

• Predictive maintenance of pumps and compressors
• Reaction yield optimization in petrochemical units

• Build an AI-powered production optimization model

AI in Sustainable Chemical Engineering

Learning Outcome: Apply AI to achieve sustainable and energy-efficient operations.

• Energy optimization using AI
• Environmental impact reduction
• Resource and waste management

• Energy efficiency optimization in refining units
• Emissions prediction and compliance support

• Develop an AI solution for a sustainable chemical process

AI for Chemical Process Control

Learning Outcome: Use AI for real-time monitoring and stable process control.

• Real-time process monitoring
• Anomaly detection
• Predictive analytics for control systems

• Process anomaly detection and early warning
• Pipeline monitoring and leak detection

• Build an AI model for process monitoring and control

Advanced & Quality Applications

Learning Outcome: Apply AI to quality control and advanced chemical engineering problems.

• Quality prediction models
• Process optimization for specialty chemicals
• AI-driven inspection systems

• Product quality prediction in downstream units
• Off-spec reduction and compliance monitoring

• Design an AI-powered quality optimization model

Learning Outcome: Solve a real Oil & Gas operational problem using AI.

• Process optimization
• Predictive maintenance
• Energy efficiency
• Safety and risk reduction
• Sustainability improvement

• AI-driven operational decision support

• Final project presentation and expert evaluation

Purpose: Make the capstone concrete and credible for leadership.

Participants will work in small teams on realistic operational problems such as:

• Reducing unplanned downtime of compressors and pumps using AI-based failure prediction
• Improving energy efficiency of distillation and separation units through predictive optimization
• Early detection of abnormal operating conditions to prevent safety incidents
• Minimizing off-spec production using AI-driven quality prediction
• Predicting fouling, corrosion, or degradation trends in critical assets

• Operational problem definition and context
• AI-driven solution approach (decision focused, minimal coding)
• Engineering insights and recommended actions
• Final presentation with impact assessment

• Identify where AI can replace manual analysis or reactive decision-making
• Interpret AI outputs to support process, maintenance, and safety decisions
• Translate operational challenges into AI-ready problem statements
• Collaborate effectively with data science and digital transformation teams
• Apply AI thinking in day-to-day plant and production workflows

• Faster and more confident engineering decisions
• Reduced dependence on external analytics teams
• Better alignment between operations, maintenance, and digital initiatives

• Refinery process optimization
• Reaction pathway and yield optimization
• AI-assisted HAZOP and safety prediction

• Predictive maintenance of rotating equipment
• Failure prediction using sensor data
• Throughput and bottleneck optimization

• Energy consumption optimization
• Emissions forecasting and compliance
• Waste and loss reduction

• Product quality prediction
• Process anomaly detection
• Pipeline monitoring and leak detection