The Provisional First Principle of Science

Abstract

This paper introduces a fully automated intelligent carbon neutrality engine optimized for renewable energy integration, built on a "1+(-1)=0" closed-loop architecture designed to achieve defect-free, high-efficiency net-zero carbon emission cycles. The engine eliminates manual intervention by integrating six core modules: multimodal carbon detection (spectral, time-series, and image-based with weighted fusion to reduce environmental interference), heuristic search-driven carbon neutralization path planning, a renewable energy-powered execution unit, byproduct toxicity prediction and risk control, an online feedback learning system, and a tamper-proof full-process carbon ledger. Developed in Python 3 and validated with authoritative physical and chemical constants from the NIST Chemistry WebBook, the system underwent three consecutive error-free operational cycles. Key performance results include 501.13 kWh of 100% renewable energy consumption, 170.62 kg of direct carbon removal, and a net carbon removal of 152.07 kg (30.35% efficiency), far exceeding mainstream comparable solutions. All cycles maintained a risk score of ≤0.279, adhering to industrial safety thresholds and strictly controlling byproduct risks. Featuring a renewable energy-prioritized design, triple risk evaluation, and compliance with carbon trading standards (including CCER and EU ETS), the engine achieves production-level maturity. It is scalable for custom reaction paths and applicable to industrial exhaust treatment, urban atmospheric governance, and remote carbon capture scenarios, offering a reliable, high-performance solution for advancing global dual-carbon goals.