AI for Carbon Capture, Utilization, and Storage (CCUS) Optimization

Module: Sustainable Tech | Difficulty: Premium

CO2 Absorption Kinetics

The rate of CO2 absorption in amine solutions follows:

where is the liquid-side mass transfer coefficient, is the interfacial area, is the equilibrium concentration, and is the bulk liquid concentration.

Storage Capacity Estimation

Comparison

Python Implementation

import numpy as np
from sklearn.ensemble import RandomForestRegressor

class CarbonCaptureOptimizer:
    def __init__(self):
        self.model = RandomForestRegressor(n_estimators=100)

    def predict_capture_rate(self, conditions):
        return self.model.predict(conditions)[0]

    def estimate_storage_capacity(self, porosity, density, volume, saturation):
        return porosity * density * volume * saturation

    def optimize_operating_conditions(self):
        from scipy.optimize import differential_evolution
        def objective(x):
            return -self.model.predict([x])[0]
        bounds = [(30, 60), (1, 10), (15, 40), (50, 200)]
        result = differential_evolution(objective, bounds, maxiter=100)
        return result.x, -result.fun

Research Insight: Graph neural networks for predicting CO2 storage site suitability have achieved 94% accuracy by modeling subsurface geological structures.