In a world of fast technological advancements, it is increasingly important to see how hydrocracking applications can benefit from and adapt to digitalization. A review of hydrocracking processes from the perspective of modeling and characterization methods is presented next to an investigation on digitalization trends. Both physics-based and data-based models are discussed according to their scope of use, needs, and capabilities based on open literature. Discrete and continuous lumping, structure-oriented lumping, and single event micro-kinetic models are reported as well as artificial neural networks, convolutional neural networks, and surrogate models. Infrared, near-infrared, ultra-violet and Raman spectroscopic methods are given with their examples for the characterization of feed or product streams of hydrocracking processes regarding boiling point curve, API, SARA, sulfur, nitrogen and metal content. The critical points to consider while modeling the system and the soft sensor are reported as well as the problems to be addressed. Optimization, control, and diagnostics applications are presented together with suggested future directions of interdisciplinary studies. The links required between the models, soft sensors, optimization, control, and diagnostics are suggested to achieve the automation goals and, therefore, a sustainable operation.

Hydrocracking is an energy-intensive process, and its control system aims at stable product specifications. When the main product is diesel, the quality measure is usually 95% of the true boiling point. Constant diesel quality is hard to achieve when the feed characteristics vary and feedback control has a long response time. This work suggests a feedforward model predictive control structure for an industrial hydrocracker. A state-space model, an autoregressive exogenous model, a support vector machine regression model, and a deep neural network model are tested in this structure. The resulting reactor temperature decisions and final diesel product quality values are compared against each other and against the actual measurements. The results show the importance of the feed character measurements. Significant improvements are shown in terms of product quality as well as energy savings through decreasing the heat duty of the preheating furnace.

Hydrogen has been seen as a decarbonization enabler for the last few decades, and in the last couple of years, there have been many investments in its production through renewables and use in different industrial applications. It is often researched for energy storage, and combustion is an excellent alternative to recover the energy stored in hydrogen. It might be the most viable alternative, especially when it comes to energy-intensive metal and glass production processes. The utilization of hydrogen as a fuel in these processes would reduce greenhouse gas emissions significantly, considering their share in total emissions. Since these industries already benefit from oxyfuel combustion with traditional fuels for fuel savings, part of the infrastructure already exists for hydrogen oxyfuel combustion. Fuel change is expected to require some minor adjustments other than simply changing the oxidizer; however, each industry has specific points to consider. This chapter investigates metal and glass production processes based on their needs and challenges in using hydrogen oxyfuel combustion for heating. Additionally, possible exhaust gas stream improvements are suggested to recover energy and reduce emissions. Finally, safety aspects of hydrogen and oxygen use are discussed together with the community acceptance of hydrogen use.

This work presents an oxidation model that integrates high-temperature steel oxidation kinetics with CFD simulations to predict oxide scale formation during steel reheating under varying combustion atmospheres in the temperature range of 800–1200 °C, over residence times in the rage of 60–160 min. The model accounts for the water vapor content in the furnace atmosphere and evaluates scale thickness under both natural gas and hydrogen combustion, using air or oxygen as oxidizing agents. Oxide scale growth is described using a combined linear–parabolic approach to capture mixed growth mechanisms. Simulation results were validated against experimental measurements of scale thickness obtained for two low-carbon steel grades. The model predictions show good agreement with experimental measurements, with average deviations of approximately 10%, while maximum deviations of up to approximately 17% are observed for specific cases and operating conditions. The model captures scale growth trends under non-isothermal conditions and highlights the impact of water vapor and combustion atmosphere on oxidation behavior.

Oxyfuel combustion complements decarbonization efforts by reducing the energy needs in high-temperature industries. Steel reheating furnaces are good candidates for full oxyfuel operation since this can lead to up to 30% energy savings. Linde uses an in-house tool to simulate reheating furnaces for airfuel to oxyfuel conversion. This paper follows a real customer case, starting with an airfuel simulation setup used to analyze the furnace, followed by oxyfuel simulations for burner design and energy savings estimations. These simulations lead to a successful installation of oxyfuel burners for the reheating furnace located at Ovako Imatra site. After the commissioning is completed, performance evaluation is done by comparing a reference airfuel operation period with an oxyfuel combustion period. Full oxyfuel conversion results in 27% energy savings for hot charge and high production rate periods thanks to significantly lower flue gas losses. Removing nitrogen from the oxidizer decreases the flue gas volume, reducing the total heat capacity of the off-gas stream. The savings are around 30% for cold charge and average production rate periods.