탄소 포집 최적화 퀴즈의 AI

퀴즈를 풀면 정답과 설명이 나타납니다.

1. AI가 탄소 포집에서 해결하려고 하는 주요 병목 현상은 무엇입니까?
   • CO2 포집 및 방출에 드는 높은 비용과 에너지 사용
   • 장비의 색상
   • 대기 중 CO2 부족
   • 전 세계적으로 발전소가 너무 적다
2. What solvent family did the cited machine-guided discovery study investigate?
   • Lithium salts for battery storage
   • Aqueous amines for acid-gas scrubbing carbon capture
   • Solid sorbents for water purification only
   • Hydrogen catalysts for fuel cells
3. 탄소 포집에서 '용매 재생'이란 무엇을 의미하나요?
   • 용제 탱크 페인팅
   • 사무실 청소
   • 재사용이 가능하도록 용매에서 포집된 CO2를 방출하는데 많은 에너지가 필요합니다.
   • 연소가스 추가
4. What is the role of a surrogate model in capture-process optimization?
   • Certify that geological storage is secure
   • Replace all process measurements
   • Measure carbon emissions with satellite imagery
   • Approximate an expensive process simulation so candidate settings can be explored more quickly
5. What objectives did the cited 2026 amine-capture simulation study optimize together?
   • CO₂ recovery, product purity, and specific energy consumption
   • Worker safety, building daylight, and HVAC comfort
   • Only solvent color and viscosity
   • Reservoir pressure and earthquake risk
6. How did the cited solvent-discovery study verify promising model-selected candidates?
   • It launched an autonomous robotic lab for every candidate
   • It used a rapid laboratory assay for CO₂ absorption and then experimental checks
   • It used only a language-model rating
   • It inferred capture performance from satellite images
7. Which two solvent properties did the 2023 study use for molecular-fingerprint prediction?
   • Worker injury risk and building energy use
   • Reservoir pressure and seismic amplitude
   • Absorption capacity and observed initial absorption rate
   • CO₂ purity and stack temperature only
8. What did the 2023 solvent-discovery paper report on its external test set?
   • 100% accuracy on every outcome
   • 17% for all operating settings
   • No external test set
   • 60% accuracy for both material parameters and 80% for a single parameter