AI Platform for Ammonia Production Optimization

AMMONIA

AI tools for ammonia production optimization

Deep Learning Process Control® can make ammonia production more profitable

Ammonia is one of the largest-volume synthetic chemicals produced in the world. While ammonia process technology has improved drastically over the past 50 years, there are some processes that are just too dynamic and can’t be modeled or optimized with your current tools.

Your engineering and operations teams have likely picked all the lower hanging fruit in terms of optimization and generally debottlenecked the plant. However, it’s mathematically impossible for ammonia producers to capture all the multivariate and dynamic relationships with linear models. That’s exactly why we built Imubit – the first AI tools built to optimize your ammonia production processes.

Our process optimization artificial intelligence (AI) tools for ammonia producers – help you maximize profitability on your ammonia production processes. It’s not just generic AI tools, but next-level artificial intelligence that understands the complexities and nuances of the ammonia production process.

Risk-free model

No obligation EVALUATION

If you qualify for our evaluation program, you get detailed scoping, modeling, closed-loop commissioning, and 3 months of continuous improvement before any payment decision

No upfront expenses

You pay a fixed annual subscription for an all-inclusive Deep Learning Process Control® solution and service for your application

Measure benefits in months

You can be up and running with your first Deep Learning Process Control® application running in closed loop in just 4 to 6 months

Learn about how Imubit’s AI tools helps ammonia production leaders discover, engineer and monetize new margin opportunities at your refineries:

Generalized first-principle economic models

for key chemical processes like blending, fractionation, conversion and reforming. The models consider unit constraints, operation modes, feeds, products, and fundamental behaviors of process units.

Steady-state baseline models

that estimate in real time the potential benefit from engaging closed-loop DLPC control. Once DLPC is commissioned in closed-loop, we use the baseline models to assess the value it created over time as well as the potential loss if it were disengaged.

Performance dashboards

let you track the unique KPIs for each AI application, perform economic debottlenecking and analyze constraints. Our dashboards help you devise strategies to adapt to feedstock, equipment and global or regional economic changes.

Process modeling platform

that leverages your process understanding, historical and ongoing data to analyze your process and regulatory control and train your deep learning prediction models.

Deep learning process models

capture the hidden governing dynamics between variables in all process states and model the relationships between feed properties, key process variables, operational constraints, and economic objectives.

Dynamic relationships visualization

of Monte Carlo simulations on the trained models show the learnt relationships between process model variables as well as model prediction errors, so your process engineers can see and understand how the model works.

Pre-optimized truly dynamic controller

is designed, trained and continuously improving based on your manipulated variables, constraints, objective function, and real-time stream pricing.

Open-loop simulations

let your team visualize the dynamic controller moves and predictions in every desired historical period, run what-if simulations for various operating scenarios, and fully validate the controller’s behavior before commissioning the controller in closed-loop.

Process control network software

is installed, configured and continually supported to run your controller inside your air-gapped secure environment. Our on-premise software is fully compliant to process control network cybersecurity and reliability requirements and supports all major DCS vendors.

Control room application

gives operators the ability to directly update controller constraints and move limits, while engineers can update constraint operator ranges and control priorities.

Customizable dashboards

provide chart and matrix views for operators to understand controller status, projected moves, and prediction trajectories. Multi-unit control room dashboards show data across all controllers, all units and every process in one place.

Remote monitoring

via our cyber-secure proprietary network protocols enables those not in the control room to run safe cloud-based simulations and analyses on your production data. No data is ever transmitted into your control network.

Imubit’s team understands what we’re trying to do from an operating and an economics perspective. Their Deep Learning Process Control® technology and their approach have aligned everyone from planning and operations around a shared goal of maximizing production profits.”

-Process control and optimization manager

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