CDU optimization

CDU optimization

Modcon Systems Ltd. supports cdu optimization applications with process analyzers, analyzer systems and optimization technologies for industrial operators.

Crude distillation, the cornerstone of refining processes, involves heating crude oil to separate it into various components based on boiling points. Optimizing this process is critical for enhancing yield, reducing energy consumption and ensuring high-quality outputs. Traditional methods often struggle to adapt to the variable nature of crude inputs, particularly with the rising use of unconventional crudes.

Efficient crude distillation unit (CDU) optimization includes a complex of different components. A major strategy in achieving the goal to run CDU at highest economic performances is to involve different technologies of hardware and software (HW/SW) solutions. Deep reinforcement learning (DRL) is a powerful machine learning technique, which can be effectively used to optimize CDU processes for different strategic goals, allowing to shift focus intelligently and confidently. Reinforcement learning makes use of algorithms that do not rely only on historical data sets, to learn to make a prediction or perform a task. Just like we humans learn using trial and error, these algorithms also do the same. A reinforcement learning agent is given a set of actions that it can apply to its environment to obtain rewards or reach a certain goal. These actions create changes to the state of the agent and the environment. The DRL agent receives rewards based on how its actions bring it closer to its goal.

DRL agents can start by knowing nothing about their environment and selecting random actions. Because reinforcement learning systems figure things out through trial and error, it works best in situations where an action or sequence of events is rapidly established, and feedback is obtained quickly to determine the next course of action — there is no need for reams of historical data for reinforcement learning to crunch through. That makes DRL perfectly suitable for hydrocarbons processing optimization tasks using established metrics in the form of inputs, actions, and rewards.

Being a powerful tool, which requires no historical data for reinforcement learning, DRL has one significant weakness, which makes it more difficult to implement for hydrocarbons processing with wide ranges of operation. DRL is likely to improve performance only where the pre-trained parameters are already close to yielding the correct process steams quality. The observed gains may be due to effects unrelated to the training signal, but rather from changes in the shape of the distribution curve. Therefore, there is a need in real-time monitoring of crude oil quality parameters, rather than only their prediction using the pre-trained models. This can be reached using the on-line analyzers installed in the process to determine the chemical composition or physical properties of substances involved in hydrocarbons processing.

DRL’s effectiveness in hydrocarbons processing is enhanced by real-time monitoring, a function supported by the online MOD-4100 analyzer. This monitoring is crucial for validating the predictive models’ outputs, ensuring that adjustments in process streams are based on accurate and current data.

MODCON.AI CDU Optimization Suite is a state-of-the-art system that enhances crude oil distillation processes by leveraging AI models to replicate complex relationships between technological parameters. The suite optimizes the distillation process, identifying the most appropriate setpoints to ensure efficient operation and minimize costs. Its strength lies in adapting to crude oil’s fluctuating properties, which significantly impact the distillation process.

Crude oil’s composition varies, and these variations affect the distillation dynamics, such as boiling points and component separability. Efficient process control requires real-time adjustments based on these fluctuations. The challenge becomes more pronounced during “crude switch” periods, when the feedstock composition changes, leading to variations in oil properties.

The MODCON 4100 Crude Oil NIR Analyzer is critical in addressing these fluctuations. It provides real-time data on crude oil’s composition, viscosity, and other vital properties, feeding this information directly into the AI-driven control model of the MODCON AI CDU Optimization Suite. This allows the suite to continuously adjust the distillation process to align with the crude oil’s current properties, even during transitions between different feedstocks.

Combining the AI suite and the MODCON 4100 Crude Oil NIR Analyzer ensures the distillation process remains optimized during feedstock changes. The AI models adapt to fluctuations by predicting and adjusting for changes in crude oil composition, reducing the risk of inefficiencies. This results in improved consistency of refined products, such as gasoline and diesel, while minimizing energy use and operational costs.

By integrating AI-driven control with real-time analysis of crude oil properties, the MODCON.AI CDU Optimization Suite provides a comprehensive solution that enhances distillation process efficiency and sustainability in the oil and gas industry.

In addition to optimizing the distillation process, the MODCON.AI CDU Optimization Suite offers significant benefits in terms of operational flexibility and scalability. The system’s ability to adjust to varying crude oil compositions in real time ensures that refineries can handle a wide range of feedstocks without compromising performance. This adaptability is particularly valuable in environments where crude oil sources are diverse, and the demand for refined products fluctuates. The suite’s integration with existing refinery infrastructure further enhances its value, providing a seamless connection between real-time crude analysis, process control, and operational decision-making. By continuously learning from the data supplied by the MODCON 4100 Crude Oil NIR Analyzer, the AI model becomes increasingly effective at predicting the optimal process parameters, leading to long-term improvements in the quality of refined products and overall plant efficiency. Ultimately, this system reduces operational costs and supports sustainability goals by optimizing energy consumption and reducing waste.