4.6 Article

A Feedforward Model Predictive Controller for Optimal Hydrocracker Operation

Related references

Note: Only part of the references are listed.
Article Computer Science, Interdisciplinary Applications

Optimizing profit and reliability using a bi-objective mathematical model for oil and gas supply chain under disruption risks

Seyed Babak Ebrahimi et al.

Summary: This study designs a multi-echelon network for oil and gas supply chain and formulates a biobjective mathematical model to optimize profit and reliability. The proposed model is validated through a real-world case study and sensitivity analysis, demonstrating its effectiveness and feasibility.

COMPUTERS & INDUSTRIAL ENGINEERING (2022)

Article Energy & Fuels

Effects of Chemical Compositions and Cetane Number of Fischer-Tropsch Fuels on Diesel Engine Performance

Haoyu Yuan et al.

Summary: In this study, the effects of chemical compositions and cetane number of FT fuels on diesel engine performance were investigated. It was found that FT fuels with higher cetane numbers exhibited shorter combustion time, while FT fuels with lower cetane numbers showed better premixing and lower smoke emissions at low intake oxygen concentration conditions.

ENERGIES (2022)

Article Chemistry, Multidisciplinary

Numerical Investigations on the Molecular Reaction Model for Thermal Cracking of n-Decane at Supercritical Pressures

Limei Zhang et al.

Summary: This study establishes a high-accuracy cracking reaction model of n-decane and demonstrates its accuracy in predicting species distribution through experimental verification. In the computational fluid dynamics (CFD) simulation, the high-accuracy model shows better accuracy in terms of fuel conversion, temperature, and product distribution compared to a one-step global model.

ACS OMEGA (2022)

Article Chemistry, Analytical

Characterization of crude oils with a portable NIR spectrometer

Francine D. Santos et al.

Summary: This study evaluates the performance of a portable NIR spectrometer and compares it with a benchtop NIR instrument for analyzing crude oil properties. The results show that the portable instrument performs well in estimating certain properties, but the benchtop instrument is superior in estimating others. The accuracy of the portable instrument can be improved with the application of the SVR algorithm.

MICROCHEMICAL JOURNAL (2022)

Article Engineering, Chemical

Product tri-section based crude distillation unit model for refinery production planning and refinery optimization

Fupei Li et al.

Summary: A mixed-integer linear model for CDU is proposed in this work, with accuracy comparable to rigorous CDU models, relying on the observation that a line through the middle of the product true boiling point curve depends on crude feed properties and adjacent product yields. The novelty of the product tri-section CDU model lies in its simplicity by not requiring individual distillation tower models within the CDU, leading to significant reduction in computational effort for nonlinear refinery model optimization.

AICHE JOURNAL (2021)

Article Computer Science, Interdisciplinary Applications

An online operating performance evaluation approach using probabilistic fuzzy theory for chemical processes with uncertainties

Yalin Wang et al.

Summary: The study proposed an online OPE scheme based on probabilistic fuzzy theory to consider uncertainties in chemical processes, including methods for establishing prediction models, improving prediction accuracy of evaluation indicators, and a probabilistic fuzzy inference method to enhance evaluation results by considering uncertainty of evaluation indicators.

COMPUTERS & CHEMICAL ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

Real-time refinery optimization with re duce d-order fluidize d catalytic cracker model and surrogate-based trust region filter method

Xinhe Chen et al.

Summary: Real-Time Optimization (RTO) is widely appreciated for optimizing process decision variables in refineries. With increasing demand for improved optimization strategies, surrogate models and the trust region filter (TRF) optimization strategy have been embedded within the RTO framework. This approach has been shown to improve efficiency and reduce computational burden in real-world refinery applications.

COMPUTERS & CHEMICAL ENGINEERING (2021)

Article Engineering, Chemical

Online Adaptive Modeling Framework for Deep Belief Network-Based Quality Prediction in Industrial Processes

Xiaofeng Yuan et al.

Summary: Soft sensors play important roles in industries for monitoring and optimization, with deep learning showing great potential for complex modeling. A new adaptive updating strategy, OAFDBN, is proposed to address performance degradation in deep networks due to time-varying processes. OAFDBN first trains an initial DBN model through offline pre-training and fine-tuning with labeled data, then adaptively fine-tunes the model for each online query sample.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2021)

Article Computer Science, Interdisciplinary Applications

Actor-critic reinforcement learning to estimate the optimal operating conditions of the hydrocracking process

Dong-Hoon Oh et al.

Summary: This study proposes an actor-critic reinforcement learning optimization strategy using a DNN surrogate model, which achieved high accuracy in determining the optimal operating conditions for hydrocracking units. Case studies demonstrated the reliability and consistency of the strategy, with an average efficiency of 98%.

COMPUTERS & CHEMICAL ENGINEERING (2021)

Article Chemistry, Physical

Catalytic Hydrocracking of Fresh and Waste Frying Oil over Ni- and Mo-Based Catalysts Supported on Sulfated Silica for Biogasoline Production

Karna Wijaya et al.

Summary: The study successfully prepared a sulfated silica catalyst through sulfation and metal promotion modification, which exhibited high activity and selectivity in the hydrocracking of waste frying oil and fresh frying oil. Among the investigated catalysts, Ni-SS2 showed the best performance for waste frying oil hydrocracking, while NiMo-SS3 exhibited the highest activity and selectivity for fresh frying oil hydrocracking.

CATALYSTS (2021)

Article Computer Science, Interdisciplinary Applications

Multi-period optimal schedule of a multi-product pipeline: A case study in Algeria

Wassila Abdellaoui et al.

Summary: This article proposes an operational schedule for a multi-product pipeline system in an Algerian oil company to meet the demands of different distribution centers while respecting quality and cost requirements. The model utilizes a mixed integer linear programming (MILP) approach and is tested on a real case to show that it satisfies variable demand while meeting quality and cost constraints.

COMPUTERS & INDUSTRIAL ENGINEERING (2021)

Article Thermodynamics

Debottlenecking of existing hydrocracking unit by improved heat recovery for energy and carbon dioxide savings

Stanislav Boldyryev et al.

Summary: The proposed method aims to reduce energy consumption and emissions at oil refineries by conducting energy audits, analyzing process flows, improving energy recovery, and identifying reliable and economically viable process changes. A case study at a hydrocracking unit demonstrated significant potential for energy efficiency improvement, resulting in a 54% reduction in energy consumption. Environmental benefits included an annual saving of 18,915 tons of carbon dioxide.

ENERGY CONVERSION AND MANAGEMENT (2021)

Article Chemistry, Physical

Comparison between Artificial Neural Network and Rigorous Mathematical Model in Simulation of Industrial Heavy Naphtha Reforming Process

Ali Al-Shathr et al.

Summary: In this study, an artificial neural network (ANN) model was developed and compared with a rigorous mathematical model (RMM) to estimate the performance of an industrial heavy naphtha reforming process. The results showed that the ANN slightly outperformed the RMM in simulating the process, and the computational time was significantly reduced. However, a disadvantage of the ANN model is its inability to predict the performance at internal points of reactors.

CATALYSTS (2021)

Article Engineering, Chemical

Modeling the Hydrocracking Process with Deep Neural Networks

Wenjiang Song et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2020)

Review Green & Sustainable Science & Technology

Hydrocracking: A Perspective towards Digitalization

Esin Iplik et al.

SUSTAINABILITY (2020)

Article Materials Science, Multidisciplinary

Using deep neural network with small dataset to predict material defects

Shuo Feng et al.

MATERIALS & DESIGN (2019)

Proceedings Paper Computer Science, Artificial Intelligence

DNN-based Speech Synthesis for Small Data Sets Considering Bidirectional Speech-Text Conversion

Kentaro Sone et al.

19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES (2018)

Article Computer Science, Interdisciplinary Applications

A single events microkinetic model for hydrocracking of vacuum gas oil

P. J. Becker et al.

COMPUTERS & CHEMICAL ENGINEERING (2017)

Article Engineering, Chemical

On the construction of a continuous concentration-reactivity function for the continuum lumping approach

Jagannathan Govindhakannan et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2007)

Article Engineering, Chemical

Constrained model predictive control for a pilot hydrotreating plant

HMS Lababidi et al.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2004)