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.
Several sensors are installed in the majority of chemical reactors and storage tanks to monitor temperature profiles for safety and decision-making processes such as heat demand or flow rate calculations. These sensors fail occasionally and generate erroneous measurement data that need to be detected and excluded from the calculations. However, due to the high number of process variables displayed in the chemical plants, this task is not trivial. In this work, a Bayesian network approach to detect faulty temperature sensors is proposed. By comparing the sensor measurements with each other, the faulty sensor is detected. A modular approach is preferred, and networks are created for 10 K temperature intervals to increase flexibility and sensitivity. Created networks can be adjusted for the operating temperature ranges; hence, they can be used for any catalyst and entire life cycle. The developed method is demonstrated on an industrial scale hydrocracker unit with 92 sensor couples installed in a series of reactors. From the investigated sensors, 16 of them showed a greater difference than the 2 K threshold chosen for the fault. In addition to that, 13 sensors showed an increasing temperature difference that may lead to a fault. Two scenarios were created to calculate the energy loss due to a faulty measurement, and a 5.5 K offset error was found to cause a 5.79 TJ energy loss every year for a small scale hydrocracker.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.