Generative Ai For Chemical Engineers

1000+ GenAI prompts for Chemical Engineers- R&D, Plant, and Lab Excellence

What you'll learn

Learn the fundamentals of Generative AI and its application to chemical engineering.

Explore 1000+ domain-specific prompts tailored to core chemical engineering operations.

Understand the differences between classical and generative approaches for engineering tasks.

Master zero-shot, one-shot, and few-shot prompting strategies for technical workflows.

Apply instructional and analytical prompting to solve design, simulation, and lab problems.

Generate novel molecules, catalysts, and reaction pathways using prompt-based tools.

Create Process Flow Diagrams (PFDs) and P&IDs from structured and unstructured data.

Simulate and optimize separation, crystallization, and polymerization processes with AI support.

Design control logic narratives and safety SOPs using AI-generated language.

Prompt for hazard identification, emergency scenarios, and safety margin calculations.

Generate technical documentation, regulatory compliance reports, and patent drafts using AI.

Translate lab notes and experimental data into summarized logs and research artifacts.

Configure AI-driven digital twins, simulations, and predictive maintenance protocols.

Enhance productivity across design, safety, sustainability, and regulatory domains.

Build prompt fluency and iterative chaining techniques for advanced workflows.

Requirements

Basic understanding of chemical engineering concepts

Description

This course, Generative AI for Chemical Engineers, introduces participants to the transformative role of AI in modern chemical engineering workflows—from molecular design to digital twins. Beginning with a clear explanation of what Generative AI is and how it differs from classical AI systems, learners gain foundational insight into how AI models can not only predict outcomes but also create new molecules, processes, and documentation. Through an engineering-centric lens, students will explore zero-shot, one-shot, and few-shot prompting methods tailored for real-world applications in labs and process industries. The course then guides learners through instructional versus analytical prompting styles—empowering them to tailor AI behavior for specific use cases.In practical modules, learners will generate novel molecules using generative models, simulate reaction pathways, and optimize catalysts and reaction conditions. With a focus on downstream design, they’ll use prompts to create process flow diagrams (PFDs), piping and instrumentation diagrams (P&IDs), and control logic narratives. The course further enables learners to apply AI for unit operations like distillation, crystallization, and separation, as well as in polymer design, structure–property exploration, and lab batch report generation. Safety and regulatory compliance are integrated into the curriculum with lessons on HAZOP prompts, LOPA diagram generation, emergency SOPs, and AI-assisted simulation of runaway reactions and overpressure scenarios.In addition, learners will develop skills to configure digital twins, convert SOPs into simulation-ready logic, and tune control system behaviors using AI. Document automation is emphasized in modules covering patent drafting, technical report summarization, and sustainability analysis—including lifecycle and emission-related documentation. The capstone component offers over 1000 domain-specific prompts, giving engineers a powerful toolkit for daily work in research, process optimization, plant safety, and regulatory reporting. This course blends creativity and control—redefining how chemical engineers interact with AI in complex, safety-critical environments.

Overview

Section 1: Generative AI in Chemical Engineering

Lecture 1 Introduction- What is Generative AI?

Lecture 2 Differences Between Classical and Generative AI Approaches

Lecture 3 Course Setup- Downloadable File

Lecture 4 Zero-shot, One-shot, and Few-shot Prompting for Chemical Engineers

Lecture 5 Instructional vs Analytical Prompts for Chemical Engineers

Section 2: AI-Enhanced Molecular and Reaction Design

Lecture 6 Generating Novel Molecules Using Generative Models

Lecture 7 AI-Powered Reaction Pathway Predictions

Lecture 8 Optimizing Catalysts and Reaction Conditions Using AI

Section 3: Generative AI for Process Simulation and Optimization

Lecture 9 Generating Process Flow Diagrams (PFDs) and P&IDs

Lecture 10 Prompting AI for Distillation, Crystallization, and Separation Processes

Section 4: Material and Polymer Innovation with Generative AI

Lecture 11 AI in Polymer Chain Design and Cross-Linking Predictions

Lecture 12 Using AI to Explore Structure–Property Relationships

Section 5: Process Control, Quality, and Sustainability

Lecture 13 Generating Control Logic Narratives and SOPs with AI

Lecture 14 Prompting AI for Quality Control Analysis and Batch Reports

Lecture 15 Sustainability and Lifecycle Analysis Prompts

Section 6: AI-Driven Documentation, Reporting, and Compliance

Lecture 16 Prompting for Regulatory Document Generation

Lecture 17 Technical Paper and Patent Drafting with Generative AI

Lecture 18 AI for Lab Report Summarization and Experimental Logs

Section 7: AI for Process Safety Intelligence and Hazard Prediction

Lecture 19 Prompting AI for HAZOP Scenario Generation

Lecture 20 Generating Bowtie Diagrams and Layer of Protection Analysis (LOPA) Narratives

Lecture 21 Simulating Runaway Reactions, Overpressure, and Fire/Explosion Hazards

Lecture 22 Prompting for Safety Margin Calculations and Emergency SOPs

Section 8: Generative AI in Digital Twin Creation and Process Simulation

Lecture 23 Prompt-Based Configuration of Digital Twins for Chemical Processes

Lecture 24 Using AI to Translate P&IDs and SOPs into Simulation Logic

Lecture 25 Simulating Control Behavior and Disturbance Response with AI

Section 9: 1000+ Prompts- Generative AI for Chemical Engineers

Lecture 26 Generating Novel Molecules and Reaction Pathways

Lecture 27 Retrosynthetic Analysis and AI-Based Reaction Planning

Lecture 28 Catalyst Design and Optimization Using AI

Lecture 29 Solvent Selection and Reaction Medium Optimization

Lecture 30 Kinetics and Rate Constant Estimation Prompts

Lecture 31 Thermodynamic Property Prediction (Cp, H, S, etc.)

Lecture 32 Estimating Activation Energy and Arrhenius Parameters

Lecture 33 Multi-Step Reaction Mechanism Prediction

Lecture 34 AI for Process Intensification and Alternative Reaction Routes

Lecture 35 Generating Green Chemistry Alternatives Using Prompts

Lecture 36 Process Flow Diagram (PFD) Generation via Prompting

Lecture 37 P&ID Generation with Control and Instrumentation Tags

Lecture 38 AI-Based Equipment Sizing and Specification Prompts

Lecture 39 Material and Energy Balance Prompts

Lecture 40 Distillation, Absorption, and Extraction Process Prompting

Lecture 41 Crystallization, Drying, and Filtration Prompt Scenarios

Lecture 42 Membrane Separation and Ion Exchange Prompting

Lecture 43 Chemical Process Optimization and Parameter Tuning

Lecture 44 Prompting for Process Simulation Configurations (Aspen/HYSYS-like logic)

Lecture 45 Utility System Design and Integration Using Prompts

Lecture 46 Equipment Specification Sheets Generation (Vessels, Reactors, etc.)

Lecture 47 Pump and Compressor Curve Estimation via AI

Lecture 48 Instrumentation Loop Diagrams and Tagging Prompts

Lecture 49 Control Narrative Generation for DCS and PLC Systems

Lecture 50 PID Controller Tuning Recommendations Using Prompts

Lecture 51 Alarm and Interlock Design Prompting

Lecture 52 Digital Twin Setup Prompts for Unit Operations

Lecture 53 Automation Logic Writing for Batch and Continuous Processes

Lecture 54 Control System Fault Diagnosis and Corrective Action Prompts

Lecture 55 Operator Training Simulations and Prompt-Driven Scenario Playbacks

Lecture 56 HAZOP Scenario Generation from PFD/P&ID Inputs

Lecture 57 Runaway Reaction and Overpressure Simulation Prompts

Lecture 58 Bowtie Diagrams and LOPA Prompt Chains

Lecture 59 Relief Valve Sizing and Safety Margin Prompts

Lecture 60 Prompting for Emergency Shutdown and Interlock Systems

Lecture 61 Evacuation Planning and Alarm Response Prompts

Lecture 62 Incident Root Cause Analysis Using Fault Logs

Lecture 63 Prompting for Process Safety Lifecycle Checklists

Lecture 64 Emergency Response SOP and Drill Simulation Prompts

Lecture 65 AI-Powered Lifecycle Analysis (LCA) Prompting

Lecture 66 Emission Estimation and Carbon Footprint Reporting Prompts

Lecture 67 Regulatory Compliance Prompting (REACH, EPA, ISO, etc.)

Lecture 68 SDS (Safety Data Sheet) Generation Using Prompts

Lecture 69 Process Waste Minimization and Recycle Loop Suggestions

Lecture 70 Greenhouse Gas Reduction Opportunities via AI Prompts

Lecture 71 Energy Optimization and Pinch Analysis Suggestions

Lecture 72 Technical Documentation and Audit-Ready Report Prompting

Lecture 73 Environmental Impact Report Summarization

Lecture 74 Patent Drafting, Innovation Logging, and Research Paper Prompts

Lecture 75 AI for Batch Record Review and Deviation Investigation

Chemical engineering students (undergraduate or postgraduate) looking to gain cutting-edge skills in AI-enhanced design, simulation, and process optimization.,Early-career chemical engineers aiming to integrate AI tools into workflows like PFD/P&ID generation, batch reporting, or safety analysis.,Process, production, and plant engineers interested in leveraging generative AI to automate technical documentation, SOP writing, and control logic.,R&D chemists and material scientists exploring molecule generation, polymer design, and structure–property relationships using generative models.,Academic researchers and PhD candidates focused on AI-assisted reaction pathway planning, retrosynthesis, and lab report summarization.,Industry professionals in quality, safety, or compliance roles, looking to auto-generate SDS documents, HAZOP scenarios, or LOPA diagrams.,Anyone curious about AI applications in chemical manufacturing, green chemistry, and digital twin simulation—no coding experience required.