As the world looks to reduce its carbon footprint, renewable fuels is poised to experience substantial growth in the coming decades.

At first glance, the transition to renewable fuels may seem like a straightforward progression. However, the reality is far more complex. Those who have embarked on the renewables journey can attest that executing this strategy is anything but simple. Numerous variables come into play, including feed composition, ambient conditions, and catalyst activity. As these variables change, the optimal operating conditions of the process unit must adapt accordingly to maximize its value. Whether renewable feeds are co-processed with traditional hydrocarbon feeds or processed independently, the challenges remain the same. Achieving the desired outcome necessitates dynamic optimization of process parameters. 

To tackle these challenges effectively, advanced hardware and software solutions that leverage closed-loop optimization diagrams have emerged. The application of the integrated system of on-line process analyzers and optimization software provides advanced tool to economize blending operations based on real time analytical data, combined with economic parameters. This is achieved by incorporation of on-line analyzers such as Beacon-3000, MOD–4100 and Modcon.AI optimization software. 

Modcon.AI offers a comprehensive optimization toolkit designed to empower refineries with modern tools for connectivity, validation, and prediction of main KPIs. The implemented artificial neural network (NN) dynamic models enable accurate calculation and prediction of physical properties and chemical compositions for different process streams. These predictions, along with the suggested set points, facilitate effective decision-making and process optimization. 

DRL is a powerful machine learning technique that can effectively optimize industrial processes to achieve various strategic goals. Unlike traditional machine learning algorithms, DRL agents learn through trial and error, receiving rewards based on their actions’ impact on reaching a predefined goal. This makes DRL particularly suitable for hydrocarbon processing optimization, where rapid feedback is crucial, and historical data is not essential. 

To ensure the success of DRL implementation in hydrocarbon processing optimization, real-time monitoring of process yields becomes crucial. On-line analyzers installed in the process play a vital role in determining the chemical composition and physical properties of substances involved in hydrocarbon processing. By integrating these analyzers’ data, refineries can validate predictions and make accurate adjustments to improve process efficiency. 

The selection of KPIs is critical for downstream industry optimization. KPIs directly related to process stream quality, such as physical properties and chemical compositions, form the foundation of a simplified digital twin. Additional KPIs, including safety, security, and environmental requirements, are considered as constraints. By integrating network input and target KPIs, linear programming techniques can be applied to maximize overall profit. 

By embracing dynamic process optimization and leveraging cutting-edge process analyzer technologies, refineries can navigate the complexities of renewable fuels production while maximizing their economic output and contributing to a greener future.