DLPC Technology Plant Process Models

NEURAL PREDICTION MODELS

Nonlinear dynamic models

Only deep learning models can represent and ultimately predict, control and optimize the most complex processes at your plant

Dynamic plant process models and visualizations

We use our proprietary process modeling platform to build, verify and continuously improve your deep learning process models that run closed-loop in production. Working alongside your process engineers we identify the hidden governing dynamics between variables and refine, validate and build operator confidence in the models.

Process modeling platform

Our modeling platform lets you integrate chemical engineering decisions, historical and continuous process data analysis to train your deep learning process control prediction models.

Deep learning process models

High-dimensional deep learning neural networks 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

Monte Carlo simulations on the trained models generate visualizations of 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.

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Imubit has completely transformed what I thought was possible in process control and optimization. Deep learning process control can truly understand and optimize the non-linear, highly dynamic relationships found in complex processes. Being able to predict and control these behaviors is going to forever change the process industry.”

– Process control team lead

An interconnecting neural network aligns all teams and stakeholders

lmubit is built for hydrocarbon processors who are looking for new ways to unlock margin from their assets. Unlike AI solutions that are bolted onto your existing methodologies, procedures and technologies, Imubit’s Closed Loop Neural Network™ crosses process unit and plant area boundaries. It flows through the traditional layers and interconnects all of the various teams. This allows everyone to speak the same language and drive towards a common economic goal. It manipulates carefully selected key variables by interconnecting real-time product prices with process constraints and economically critical properties. Instead of each team managing its own model in a discrete stack layer, all of the various disciplines interface with the Closed Loop Neural Network™.

Planning and economics feed direct real-time product and feedstock prices directly into the neural network. Process engineers input true unit operational constraints along with known process relationships. Process control engineers manage the interaction between the neural network and the existing advanced process control (APC) and distributed control system (DCS). Operators interact with the neural network on a 24/7 basis, constraining it to their desired bounds and limits in real time to allow for a safe and compliant closed-loop operation.

Hydrocarbon processing expertise

Our solution is built on over 220 years planning and economics, process engineering, process control engineering and operations at refining and chemical plants.

Closed-loop optimization

We go beyond open-loop data-driven insights, predictions, and recommendations. Our solution integrates to your plant and optimizes your process in closed-loop.

Deep reinforcement learning

Our interconnected AI solution is built on a deep reinforced learning architecture that uses your minute-by-minute data for real-time process optimization.

Ready to learn more about the Imubit Closed Loop Neural Network™?

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