How do I optimize water purification membranes with MatCraft?

The water membrane domain is one of MatCraft's most mature plugins. It supports optimization of polymeric membranes for reverse osmosis (RO), nanofiltration (NF), and ultrafiltration (UF) applications.

Quick Start

name: "ro-membrane-study"
domain: water_membrane

components:
  - name: polymer_concentration
    type: continuous
    bounds: [0.10, 0.35]
    unit: "wt%"
    description: "Polymer (PSf/PES/PVDF) concentration in casting solution"
  - name: additive_type
    type: categorical
    choices: ["PVP", "PEG", "LiCl", "ZnCl2", "none"]
  - name: additive_concentration
    type: continuous
    bounds: [0.0, 0.15]
    unit: "wt%"
  - name: coagulation_bath_temp
    type: continuous
    bounds: [15, 60]
    unit: "°C"
  - name: evaporation_time
    type: continuous
    bounds: [0, 120]
    unit: "seconds"

objectives:
  - name: water_flux
    direction: maximize
    unit: "L/m²/h"
  - name: salt_rejection
    direction: maximize
    unit: "%"
  - name: fouling_resistance
    direction: maximize
    unit: "FRR%"
    description: "Flux recovery ratio after BSA fouling"

constraints:
  - type: custom
    expression: "salt_rejection >= 90.0"
    description: "Minimum acceptable rejection for RO"

Built-In Physics Model

The water membrane plugin includes a simplified transport model based on the solution-diffusion framework:

• Water flux is estimated using the Hagen-Poiseuille equation with porosity and pore size derived from polymer concentration and additive effects.
• Salt rejection is modeled using the Kedem-Katchalsky equation, accounting for membrane thickness and charge effects.
• Fouling resistance uses empirical correlations based on surface hydrophilicity (influenced by additive type).

These physics models provide approximate evaluations for surrogate training when you do not have experimental data for every candidate. They are calibrated against published literature data for common PSf/PES systems.

Typical Results

In our benchmarks with PSf membranes, MatCraft found Pareto-optimal compositions that matched or exceeded manually optimized formulations from the literature within 12 iterations (60 total evaluations including 20 seed points). The key insight from the optimization was a non-obvious interaction between additive concentration and coagulation temperature that was not captured by one-variable-at-a-time experiments.

Experimental Workflow

For lab-based optimization, set the evaluator to manual mode. MatCraft will suggest 5 compositions per iteration, you fabricate and test them, enter the results, and the next iteration refines the search.