An optimisation approach for increasing the profit of a commercial VGO hydrocracking process

Sadighi, Sepehr; Ahmad, Arshad

doi:10.1002/cjce.21720

Cited by 16 publications

(13 citation statements)

References 23 publications

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“…In the proposed hybrid model, the following exponential law is applied for estimating the activity of the catalyst [32]: (5) (6) where ac is the activity of the catalyst; α is the decay constant which is dependent to the type of the catalyst; life is the days on stream (DOS) for the current test run; ts is the time interval between the current test run and the previous one; mf is the mass flow rate of feed; Wcat is the total weight of reforming catalysts, and tc is a dimensionless term, called accumulated feed, representing the total amount of feed passed through the total catalyst weight at a specific life time.…”

Section: Hybrid-mlp (Hmlp) Neural Network Applied To Heavy Naphtha Camentioning

confidence: 99%

Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique

Sadighi¹,

Mohaddecy²,

Norouzian

2015

Bull. Chem. React. Eng. Catal.

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In this paper, a hybrid model for estimating the activity of a commercial Pt-Re/Al2O3 catalyst in an industrial scale heavy naphtha catalytic-reforming unit (CRU) is presented. This model is also capable of predicting research octane number (RON) and yield of gasoline. In the proposed model, called DANN, the decay function of heterogeneous catalysts is combined with a recurrent-layer artificial neural network. During a life cycle (919 days), fifty-eight points are selected for building and training the DANN (60%), nineteen data points for testing (20%), and the remained ones for validating steps. Results show that DANN can acceptably estimate the activity of catalyst during its life in consideration of all process variables. Moreover, it is confirmed that the proposed model is capable of predicting RON and yield of gasoline for unseen (validating) data with AAD% (average absolute deviation) of 0.272% and 0.755%, respectively. After validating the model, the octane barrel level (OCB) of the plant is maximized by manipulating the inlet temperature of reactors, and hydrogen to hydrocarbon molar ratio whilst all process limitations are taken into account. During a complete life cycle results show that the decision variables, generated by the optimization program, can increase the RON, process yield and OCB of CRU to about 1.15%, 3.21%, and 4.56%, respectively.

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Section: Hybrid-mlp (Hmlp) Neural Network Applied To Heavy Naphtha Camentioning

confidence: 99%

Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique

Sadighi¹,

Mohaddecy²,

Norouzian

2015

Bull. Chem. React. Eng. Catal.

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“…According to the results reported by Sadighi and Arshad (2013), Expen  is about 6.5% of the total increase in the gross profit, and therefore it is ignored to mitigate the complexity of the model. Additionally, by considering some process limitations (Equation 4), the above objective function was optimized by using the Fmincon syntax that was available in the MATLAB.…”

Section: Optimization Methodsmentioning

confidence: 99%

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network

Sadighi¹,

Mohaddecy²,

Abbasi³

2018

IJTech

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In this research, based on actual data gathered from an industrial scale vacuum gas oil (VGO) hydrocracker and artificial neural network (ANN) method, a model is proposed to simulate yields of products including light gases, liquefied petroleum gas (LPG), light naphtha, heavy naphtha, kerosene, diesel and unconverted oil (off-test). The input layer of the ANN model consists of the catalyst, feed and recycle flow rates, and bed temperatures, while the output neurons are yields of those products. The results showed that the AAD% (average absolute deviation) of the developed ANN model for training, testing, and validating data are 0.445%, 1.131% and 0.755%, respectively. Then, by considering all operational constraints, the results confirmed that the decision variables (i.e., recycle rate and bed temperatures) generated by the optimization approach can enhance the gross profit of the hydrocracking process to more than $0.81 million annually, which is significant for the economy of the target refinery.

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“…According to the discrete lumping approach, there are many researches in which the hydrocracking process is modeled with threelump [11,12], four-lump [13,14], five-lump [15], six-lump [16] and eight-lump [17] partitions. In all these studies, the pilot or industrial scale reactor was modeled using a one-dimensional (1D) space in which hydrocracking and/or hydrotreating reactions were assumed to be carried out in the axial direction.…”

Section: Introductionmentioning

confidence: 99%

A two-dimensional discrete lumped model for a trickle-bed vacuum gas oil hydrocracking reactor

Sadighi¹

2016

Korean J. Chem. Eng.

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A two-dimensional (2D) computational fluid dynamics model based on discrete lumping approach was used to predict the product yields of a pilot scale vacuum gas oil (VGO) hydrocracking reactor. This model was developed by solving mass conservation equations in conjunction with the continuity and momentum balances in the z-r cylindrical plane. The kinetic parameters of the model were estimated from the experimental data, and validated by using actual values. Results show that the proposed model can appreciably improve the accuracy of the yield prediction in comparison to the predicted value using the 1D model. Moreover, it is confirmed that the order of magnitude of the radial liquid velocity against the axial one is considerably low, and there is no significant pressure drop along the r-direction. Additionally, results show that two-dimensional model is a reliable tool for evaluating the catalyst performance and also for designing commercial reactors.

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An optimisation approach for increasing the profit of a commercial VGO hydrocracking process

Cited by 16 publications

References 23 publications

Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique

Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network

A two-dimensional discrete lumped model for a trickle-bed vacuum gas oil hydrocracking reactor

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