ScalePilot

Process Equipment Selection Assistant for Early Process Scale-Up

A local-first engineering prototype that turns a lab-scale process brief into a preliminary equipment recommendation report. The demo case is the scale-up of a lab-scale hydrothermal synthesis of MnO₂ nanoparticle powder for aqueous zinc-ion battery cathodes.

It is an early-stage decision-support tool, not a certified process design tool. All equipment and supplier data is mock data for demonstration.

What it does

Given a structured process brief, it runs a simple, deterministic workflow:

data/sample_input.json
  -> process_basis        (product, scale, scale-up factor, missing data)
  -> unit_decomposer      (break process into unit operations)
  -> equipment_selector   (find candidate equipment from a mock database)
  -> engineering_reviewer (screen on temperature / pressure / volume, recommend)
  -> supplier_matcher     (match mock suppliers, rough CAPEX range)
  -> rfq_generator        (draft an RFQ-style supplier inquiry)
  -> outputs/sample_output.json   (structured result)
  -> outputs/sample_report.md     (human-readable report)

Every step is rule-based and transparent: the report shows why each equipment candidate was accepted or rejected.

Requirements

• Python 3.9+ (developed on 3.11).
• The local workflow (main.py / src/) uses the standard library only.
• The ASI:One agent (agent.py) additionally needs uagents (and openai for the optional LLM layer): pip install -r requirements.txt openai.

How to run

From the project root:

python main.py --input data/sample_input.json

On Windows, if python is not found (the Microsoft Store stub), use the Python launcher instead:

py main.py --input data/sample_input.json

This writes two files into outputs/:

• outputs/sample_output.json — full structured result
• outputs/sample_report.md — readable report for demo

ASI:One agent

The same workflow is wrapped as a conversational Fetch.ai uAgent (agent.py) using the chat protocol, so it can be reached from ASI:One. The deterministic workflow is the source of truth; an optional ASI:One LLM layer only makes the replies conversational and never invents equipment, prices, or suppliers.

# Standard-library workflow only needs Python; the agent needs uagents:
pip install -r requirements.txt

# Optional: enable the conversational LLM layer (else it uses rule-based replies)
#   PowerShell:  $env:ASI_ONE_API_KEY = "your-key"
#   bash:        export ASI_ONE_API_KEY=your-key

python agent.py     # on Windows: py agent.py

On startup the agent prints an Agent Inspector link; open it (while the agent is running) to connect an Agentverse mailbox, after which the agent is reachable from ASI:One. Sample prompts: analyze, rfq, or what equipment do you recommend for my MnO₂ process?. See AGENT_README.md for the Agentverse profile text.

Project structure

hackathon-project/
  main.py                     # workflow entry point / orchestration
  agent.py                    # ASI:One-compatible uAgent wrapping the workflow
  AGENT_README.md             # Agentverse profile text for the agent
  requirements.txt            # uagents (workflow itself is standard-library only)
  data/
    sample_input.json         # the demo process brief
    equipment_database.json   # mock equipment knowledge base
    supplier_database.json    # mock supplier + CAPEX data
  src/
    process_basis.py
    unit_decomposer.py
    equipment_selector.py
    engineering_reviewer.py
    supplier_matcher.py
    rfq_generator.py
  outputs/
    sample_output.json        # generated
    sample_report.md          # generated
  docs/
    project_guide.md
    architecture.md

Example result (MnO₂ demo)

For the 2 L bench-pilot batch, the workflow recommends, for example:

Unit operation	Recommended equipment
Hydrothermal batch reaction	Teflon-lined stainless steel batch autoclave
Solid-liquid separation	Lab or pilot vacuum filtration setup
Vacuum drying	Vacuum oven

• Rough total CAPEX (mock, deduplicated): $11,550 – $278,000 USD
• Equipment shared across steps (e.g. the reactor used for both reaction and cooling) is counted once in the CAPEX total.

Scope and limitations

• Preliminary and illustrative only — requires review by a qualified process engineer before any procurement.
• No process simulation, heat/mass balance, or pressure-vessel design.
• No real web search, supplier APIs, or ASPEN integration. The optional ASI:One LLM layer only phrases the deterministic results conversationally — it does not select equipment or generate data.
• Prices, lead times, and suppliers are mock placeholders.

Possible future work

• Split the single manager agent into specialist sub-agents (reactor, separation, drying, supplier matching) communicating via uAgents messages.
• Add human-in-the-loop "top-2" selection per unit operation.
• Replace the mock databases with real, curated equipment/supplier data.

See docs/architecture.md for the intended agent architecture.