Optimize your Ammonia plant
Optimize your Ammonia plant

Advance the operation of your reformers

Steam reforming or steam methane reforming (SMR) is the predominant technology for the generation of synthesis gas (syngas) via the catalytic conversion of natural gas and/or higher hydrocarbons with steam to produce hydrogen, ammonia and methanol. The performance of the SMR reactor is an important factor in determining the overall efficiency of the process.

Economic impact of steam methane reforming

While in hydrogen production plants steam reforming is responsible for nearly the entire energy consumption, in ammonia production the SMR typically consumes around 50-70% of the total energy, depending on plant design and other factors.

Optimizing the  hydrogen production efficiency together with the feed gas and fuel gas consumption is therefore an essential task for the economic operation and  for achieving the lowest possible CO₂ footprint.

AI-powered models tailored to your plant

The Interactive Reformer Optimization tool is a hybrid process model for primary- and secondary reformers. A pre-configured hybrid neural network for the SMR section is tailored to each plant and trained on historic data. 

Plant specific, machine-learning based data analytics are accessible to operators and engineers in an easy-to-use tool on the dashboard. It allows them to predict the impact of changes to process variables such as the plant load, primary reformer outlet temperature, steam to carbon ratio and steam flow to secondary reformer on selected KPIs of the reformers:

• Methane slip at the primary and secondary reformer outlet
• Specific fuel consumption
• Specific feed consumption
  

Operators and engineers can optimize the operating conditions in the SMR to: 

• Minimize fuel and feed consumption
• Predict the performance gain in terms ammonia make and energy savings

Advance the operation of your shift reactors

High temperature shift (HTS) and low temperature shift (LTS) converters play a key role in the economic operation of ammonia and hydrogen plants. Hybrid process models combined with machine learning offer a powerful tool for interactive optimization of these units.

Economic impact of shift reactors

AI-powered models tailored to your plant

The Interactive Shift Optimization tool is a hybrid process model for the HTS and LTS reactors. A preconfigured hybrid neural network for the shift unit is tailored to each plant and trained on historic data.

Optimize your hydrogen make by minimizing CO slip

Operators and engineers can simulate and optimize the operating conditions in the shift reactors based on a plant-specific hybrid process model. An interactive tool enables them to:

• Minimize HTS/LTS reactor CO slip to increase hydrogen gain
• Gain insights into operating conditions relative thermodynamic equilibrium
• Ensure sufficient distance to the dewpoint to protect the LTS catalyst from condensation
• Predict the performance gain in terms of H₂ or NH₃ make and energy savings

Advance the operation of your CO₂ absorber

Paired with the methanation process, the CO₂ absorber plays a crucial role in removing CO₂ from the process gas before it enters the ammonia synthesis loop. This step is vital for ammonia production, as any residual CO₂ in the process gas could compromise the efficiency and longevity of the ammonia synthesis catalyst.

Economic impact of CO₂ absorption

CO₂ removal from the process gas is an energy-intensive operation that utilizes energy from steam. Achieving lower CO₂ slip requires greater energy input. Any residual CO₂ in the process gas is converted quantitively in the methanator, consuming hydrogen and thereby reducing the overall process efficiency.

Balancing CO₂ removal efficiency with hydrogen consumption in the methanator is critical for both economic operation and minimizing the CO₂ footprint of the ammonia plant.

AI-powered models tailored to your plant

The Interactive CO₂ Absorber Optimization tool is a hybrid process model for CO₂ removal section of your ammonia plant. A pre-configured hybrid neural network for the CO₂ absorber and the steam network is tailored to each plant and trained on historic data. Plant specific, machine-learning based data analytics then allow a user to predict the impact of changes to defined process variables on selected KPIs of the CO₂ absorber.

Optimize your energy efficiency by optimizing the CO₂ slip

Operators  and engineers can simulate and optimize the operating conditions in the CO₂ absorber section based on a plant-specific hybrid process model. An interactive tool enables them to:

• Optimize the CO₂ slip after the absorber to gain hydrogen production efficiency
• Realize energy savings through improved operation

Advance the operation of your ammonia synthesis loop

The ammonia synthesis loop reactor is at the heart of efficient ammonia production, driving high conversion rates of hydrogen and nitrogen to ammonia. Advanced hybrid process models, augmented by machine learning, provide an innovative solution for interactive optimization of this critical unit.

Economic impact of the ammonia synloop

The ammonia synthesis loop is a critical driver of efficiency and profitability in ammonia production, as it determines the final conversion of hydrogen and nitrogen into ammonia. While it consumes less energy compared to upstream units like steam methane reforming, the loop's optimization significantly influences the plant's overall energy efficiency, product yield, and operating costs.

Maximizing ammonia yield and minimizing unconverted gas recycling reduce both energy consumption and operational wear on compressors and turbines. This not only lowers production costs but also improves the plant's CO₂ footprint by reducing the energy required per ton of ammonia produced.

Optimizing the ammonia synthesis loop is thus an essential task for enhancing profitability, minimizing environmental impact, and ensuring the sustainable operation of ammonia plants

AI-powered models tailored to your plant

The Interactive Synloop Optimization tool is a hybrid process model specifically designed for the ammonia synthesis loop reactor. A preconfigured hybrid neural network is customized for each plant and trained on its historical performance data.

Optimize ammonia yield and energy efficiency

Operators and engineers can simulate and optimize the operating conditions in the ammonia synloop using a plant-specific hybrid process model. This interactive tool enables them to:

• Maximize ammonia yield by optimizing reactor conditions
• Improve energy efficiency through fine-tuning of operating parameters