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catalyst-analyzer skill

/plugins/babysitter/skills/babysit/process/specializations/domains/science/chemical-engineering/skills/catalyst-analyzer

This skill analyzes catalyst performance, models deactivation, and optimizes regeneration, enabling data-driven selection and operational efficiency for

npx playbooks add skill a5c-ai/babysitter --skill catalyst-analyzer

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
name: catalyst-analyzer
description: Catalyst performance analysis skill for activity testing, deactivation modeling, and optimization
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Edit
  - Bash
metadata:
  specialization: chemical-engineering
  domain: science
  category: Reaction Engineering
  skill-id: CE-SK-008
---

# Catalyst Analyzer Skill

## Purpose

The Catalyst Analyzer Skill evaluates catalyst performance, models deactivation kinetics, and supports catalyst selection and optimization for chemical processes.

## Capabilities

- Catalyst activity measurement analysis
- Selectivity evaluation
- Deactivation kinetics modeling
- Regeneration cycle optimization
- Catalyst screening support
- Turnover frequency calculation
- Surface area and porosity analysis
- Poison identification

## Usage Guidelines

### When to Use
- Evaluating catalyst candidates
- Modeling catalyst deactivation
- Optimizing regeneration cycles
- Troubleshooting performance decline

### Prerequisites
- Experimental data available
- Reaction mechanism understood
- Operating conditions defined
- Reference catalyst identified

### Best Practices
- Use consistent testing protocols
- Account for mass transfer effects
- Consider long-term stability
- Validate deactivation models

## Process Integration

This skill integrates with:
- Catalyst Evaluation and Optimization
- Kinetic Model Development
- Reactor Design and Selection

## Configuration

```yaml
catalyst-analyzer:
  analysis-types:
    - activity
    - selectivity
    - deactivation
    - regeneration
  catalyst-types:
    - heterogeneous
    - homogeneous
    - biocatalyst
```

## Output Artifacts

- Activity profiles
- Deactivation models
- Regeneration protocols
- Screening comparisons
- Recommendation reports

Overview

This skill analyzes catalyst performance for activity testing, selectivity assessment, deactivation modeling, and regeneration optimization. It produces actionable artifacts—activity profiles, deactivation models, and screening comparisons—to support catalyst selection and process improvement. The focus is on practical, data-driven recommendations for lab and pilot-scale workflows.

How this skill works

The skill ingests experimental time-on-stream data, turnover frequency calculations, surface area and porosity metrics, and operating-condition metadata. It fits deactivation kinetics models, computes selectivity trends, flags potential poisoning signatures, and simulates regeneration cycle outcomes to recommend optimal intervals and conditions. Outputs include plots and parameterized models suitable for further integration in kinetic model development or reactor design.

When to use it

  • Evaluating and comparing catalyst candidates from screening campaigns
  • Modeling and predicting catalyst deactivation over operational time
  • Designing or optimizing regeneration frequency and conditions
  • Troubleshooting unexpected drops in activity or selectivity
  • Validating long-term stability and lifetime projections

Best practices

  • Provide consistent, time-resolved activity and composition data with clear operating-condition logs
  • Include reference catalyst runs and blank/reactor baseline measurements
  • Correct for mass and heat transfer artifacts before kinetic fitting
  • Use replicate experiments to quantify variability and confidence intervals
  • Validate model predictions against independent holdout datasets

Example use cases

  • Rank heterogeneous catalysts by activity and selectivity for a target reaction using normalized turnover frequency
  • Fit deactivation kinetics (e.g., first-order, power-law) and predict time-to-failure under projected conditions
  • Optimize regeneration scheduling by simulating activity recovery and cumulative productivity
  • Identify likely catalyst poisons by correlating performance drops with feed impurities and surface analyses
  • Generate screening comparison reports to support down-selection for pilot testing

FAQ

What data do I need to run robust deactivation modeling?

Time-resolved conversion and selectivity measurements, temperature/pressure logs, catalyst characterization (surface area, porosity), and known feed composition are required. Reference and replicate runs improve model reliability.

Can it suggest regeneration conditions or just scheduling?

It does both: the skill simulates regeneration cycles to estimate recovered activity and recommends intervals and basic conditions, but detailed regeneration protocols should be validated experimentally.

Does it handle different catalyst types?

Yes. The workflow supports heterogeneous, homogeneous, and biocatalyst data, but model choice and assumptions should be adapted to the catalyst class.