# Atmospheric Distillation Design AI Agent Connect

> Atmospheric Distillation Design MCP provides the engineering math needed to build and optimize Atmospheric Distillation Units. You can use it to determine theoretical stages, predict product yields from specific crude assays, and calculate the energy required for furnaces and cooling systems. It bridges the gap between raw assay data and actionable column design.

## Overview
- **Category:** engineering
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_K9E4LENgFwyOr76lS5zu4u5vjecD02PnYvztI1ip/ai-agent-connect
- **Tags:** distillation, crude-oil, refinery, chemical-engineering, process-design

## Description

You can use this MCP to handle the heavy lifting of atmospheric distillation unit (ADU/CDU) design. Instead of manually running separate calculations for every design iteration, you can feed crude oil assay data directly to your AI client to get immediate results. It handles the core engineering math for column sizing and energy balancing. You can determine the necessary theoretical stages to hit your separation targets or predict exactly how much naphtha, diesel, and residue you'll get from a specific feedstock. It also manages the thermal side of the design, helping you find the best pumparound locations to save energy and calculating the total heat duty for your furnace and condensers. It's built to turn raw refinery data into a functional column blueprint.

## Tools

### calculate_column_stages
Use this to find the exact number of theoretical stages required to reach your separation goals.

### estimate_product_yields
This tool predicts the specific volume of each product fraction you'll get from a given crude oil.

### evaluate_energy_consumption
This calculates the total heat needed for the furnace along with the cooling requirements for products and pumparounds.

### optimize_pumparounds
This tool finds the best locations and flow rates for heat removal to maximize energy efficiency.

## Prompt Examples

**Prompt:** 
```
Calculate the number of stages needed for a throughput of 50000 kg/h with specific assay data.
```

**Response:** 
```
The required number of theoretical stages is 32, with a minimum of 24 stages.
```

**Prompt:** 
```
What are the expected yields for this crude oil?
```

**Response:** 
```
The predicted yields are: Naphtha 15%, Kerosene 12%, Diesel 25%, Gas Oil 30%, and Residue 18%.
```

**Prompt:** 
```
Calculate the energy consumption for the column design.
```

**Response:** 
```
The total energy requirement is 450 MW, consisting of 380 MW furnace duty and 70 MW condenser duty.
```

## Capabilities

### Column Sizing
Your agent calculates the theoretical stages needed for specific separation requirements.

### Yield Prediction
The AI estimates the volume of product fractions based on crude oil assays.

### Energy Optimization
Your agent identifies optimal pumparound locations to improve heat recovery.

### Thermal Duty Calculation
The tool computes furnace heat requirements and product cooling needs.

## Use Cases

### Initial Unit Design
Use the tool to set the foundation for a new atmospheric distillation unit based on feedstock properties.

### Feedstock Sensitivity Analysis
Predict how changing your crude oil source will impact your product yields.

### Energy Efficiency Audits
Run pumparound optimizations to find ways to reduce furnace duty.

### Capacity Planning
Calculate how throughput changes affect the required number of theoretical stages.

## Benefits

- Connects crude oil assay data directly to column configuration math.
- Reduces manual calculation time for theoretical stage determination.
- Provides rapid energy consumption estimates for furnace and condenser duty.
- Optimizes heat recovery by identifying efficient pumparound locations.

## How It Works

You connect the MCP to your AI client and start running engineering calculations immediately.

1. Connect your preferred client like Claude or Cursor to Vinkius.
2. Provide your crude oil assay data to your AI agent.
3. Ask the agent to run specific tools like stage calculations or yield estimates.
4. Review the calculated column configurations and energy requirements.

## Frequently Asked Questions

**What kind of data does this MCP need?**
It primarily uses crude oil assay data to perform distillation calculations.

**Can I use this with Claude or Cursor?**
Yes, you can use this MCP with any MCP-compatible client including Claude, Cursor, and Windsurf.

**Does this tool handle energy calculations?**
Yes, it can evaluate total energy consumption, including furnace duty and cooling requirements.

**How does it help with heat recovery?**
It uses the optimize_pumparounds tool to find the best locations and flows for heat removal.

**What is the output of the yield tool?**
The tool provides predicted volumes for product fractions like naphtha, kerosene, and diesel.

**What inputs are needed for column stage calculation?**
You need to provide the crude throughput, the assay data as a JSON string, and the target product specifications.

**Can I optimize energy efficiency?**
Yes, you can use `optimize_pumparounds` to find the best locations for heat removal to maximize energy recovery.

**How are product yields determined?**
Yields are calculated by integrating the boiling point distribution from your assay data over the specified cut points.