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Simulate wastewater treatment processes in natural language for AI-driven analysis and automation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "QSDsan Engine MCP" yet — see the docs or source repo.
Simulate a wastewater treatment process with these parameters: influent COD 450 mg/L, ammonia nitrogen 35 mg/L, daily flow 12,000 tons. Output the main treatment stages, key operating parameters, and expected effluent indicators.
A structured process description with treatment stages, key parameters, and expected effluent results.
Compare two wastewater treatment operating scenarios: Scenario A increases aeration intensity, and Scenario B extends sludge age. Explain their impact on COD removal, energy consumption, and effluent stability.
A comparative analysis of the two scenarios to help choose a more suitable operating strategy.
Explain how to call QSDsan Engine via MCP or CLI to run batch simulations on multiple wastewater treatment parameter sets and summarize the results in a table.
Integration-ready guidance with invocation steps and a summarized output format for automation workflows.
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Break down QA tasks into steps with confidence-scored tool recommendations.
Build, debug, and manage software tasks with natural language across LLMs.