Here you will find real-world uses of the ALAMO tool, including green chemistry, carbon capture and environmental science, process optimization, surrogate modeling, and advanced engineering design.
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Green solvent design
A team of bioenergy engineers and computational scientists used ALAMO to identify a 50 vol% methanol/water solvent system, optimizing lignin-solvent interactions with thermodynamic constraints. The approach reduced the bioproduct minimum selling price by 24% and carbon impact by 28% while operating at 48 bar.
David Goldman/AP
Carbon capture cost optimization
In a study from Imperial College London, researchers used ALAMO to convert thousands of carbon capture simulations into a reusable algebraic cost model, enabling accurate cost prediction across operating conditions. The model showed that amine-based post-combustion systems can cost-effectively achieve 99% carbon capture rates. Details of the ALAMO model development are provided in the Supporting Information accompanying the study.
Patrick T. Fallon/AFP
Real-time energy optimization
Researchers in Greece used ALAMO to develop nonlinear algebraic surrogate models for optimizing pressure/vacuum swing adsorption carbon capture systems. ALAMO generated models that matched detailed process simulations within 1% while reducing optimization time by 3–4 orders of magnitude, enabling faster process design and real-time optimization.
Manjusha Films/Air Products
Optimization of CO₂ networks
Researchers used ALAMO to develop surrogate models for CO₂ capture plant optimization, handling large-scale problems with approximately 45,000 variables and equations in an average of 176 seconds. The methodology ensured physical consistency and managed risk through iterative screening, balancing model accuracy with simplicity.
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Process flowsheet optimization
A collaboration with the U.S. Department of Energy utilized ALAMO to replace intensive reactor simulations with compact formulas, achieving a 100% success rate by solving 64 out of 64 optimization instances. This approach outperformed neural networks, which solved 48 cases, while delivering superior speed and efficiency.
Google DeepMind/Pexels
Benchmarking surrogate models
In a study published in Computers & Chemical Engineering, ALAMO outperformed traditional modeling approaches like Kriging and neural networks. Findings indicated that ALAMO offered superior computational efficiency, enhanced interpretability, and improved integration with process optimization frameworks.
Best Data-Driven Modeling Tool
ALAMO has become a leading modeling tool for data-driven optimization. Its capabilities enable users to build accurate models and accelerate development of optimization solutions with minimal effort.
Technical support
ALAMO support is handled by ALAMO's developers (PhD-level) who work on real-world models and enterprise deployments. They can advise on algorithmic or software features that can materially improve performance in your ALAMO runs.
Updates and new features
The development team works continuously to provide several ALAMO releases each year. Each release includes performance improvements and new capabilities driven by user feedback, so you can generate models faster.