Machine Learning Applications in Process Simulation

The chemical industry is increasingly looking towards artificial intelligence to optimize operations. Traditionally, process simulation relies on rigorous first-principles models—mass and energy balances supplemented by thermodynamic equations of state.

While accurate, these models can be computationally expensive, particularly when embedded in real-time optimization loops.

Hybrid Modeling Approach

One of the most promising avenues is hybrid modeling. In this approach, we do not throw away first-principles. Instead, we use machine learning (often neural networks) to represent only the most complex, poorly understood phenomena—such as complex reaction kinetics or highly non-ideal thermodynamic behavior.

"The future of chemical engineering lies at the intersection of deep domain knowledge and data-driven learning algorithms."

Code Example: Basic Neural Net

Here is a simplified example of how one might define a neural network architecture in Python using PyTorch to predict phase equilibrium properties:

import torch
import torch.nn as nn

class ThermoPredictor(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(ThermoPredictor, self).__init__()
        self.network = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, output_size)
        )
        
    def forward(self, x):
        # x contains T, P, and compositions
        return self.network(x)

# Initialize the model
model = ThermoPredictor(input_size=5, hidden_size=64, output_size=2)
print(model)

Conclusion

By blending these data-driven models with classic conservation laws, engineers can build simulations that are both highly accurate and orders of magnitude faster. I'll be sharing more concrete examples of integrating these predictors into flowsheet simulators in my upcoming articles.

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