AI for Sustainable Chemical Process Design

Module: Sustainable Tech | Difficulty: Premium

Atom Economy

E-Factor

Process Mass Intensity

Comparison

| Metric | Traditional | Green | Best Practice | |--------|------------|-------|---------------| | Atom economy | 30-60% | 70-95% | > 90% | | E-factor | 5-50 | 1-5 | < 1 | | PMI | 10-100 | 2-10 | < 5 | | Solvent recovery | 50-70% | 90-99% | > 95% |

Python Implementation

import numpy as np

class GreenChemistryOptimizer:
    def atom_economy(self, product_mw, reactant_mws):
        return product_mw / sum(reactant_mws) * 100

    def e_factor(self, waste_mass, product_mass):
        return waste_mass / product_mass

    def process_mass_intensity(self, inputs, product_mass):
        return sum(inputs) / product_mass

    def solvent_selection(self, solvents, criteria):
        scores = {}
        for name, props in solvents.items():
            score = 0
            for criterion, weight in criteria.items():
                if criterion in props:
                    score += weight * props[criterion]
            scores[name] = score
        return sorted(scores.items(), key=lambda x: x[1], reverse=True)

    def reaction_optimization(self, conditions, yield_data):
        from sklearn.ensemble import GradientBoostingRegressor
        model = GradientBoostingRegressor(n_estimators=100)
        X = np.array([[c['temp'], c['pressure'], c['catalyst'], c['time']] for c in conditions])
        model.fit(X, yield_data)
        from scipy.optimize import differential_evolution
        result = differential_evolution(lambda x: -model.predict([x])[0],
                                       [(50, 200), (1, 50), (0.01, 5), (0.5, 24)])
        return result.x, -result.fun

Research Insight: AI-guided reaction optimization can improve atom economy by 20-40% while reducing waste by 50-70%.