A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

Popoola, Lekan Taofeek; Gutti, Babagana; Susu, Alfred A.

doi:10.4236/aces.2013.32020

Cited by 11 publications

(6 citation statements)

References 11 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…work is intended to generate a prognosis P n for the subsequent process value ( ) n z t with a minimum prediction error given as [2]:…”

Section: Artificial Neural Network Back-propagation Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

Popoola¹,

Susu²

2014

ACES

View full text Add to dashboard Cite

This research work investigated comparative studies of expert system design and control of crude oil distillation column (CODC) using artificial neural networks based Monte Carlo (ANNBMC) simulation of random processes and artificial neural networks (ANN) model which were validated using experimental data obtained from functioning crude oil distillation column of Port-Harcourt Refinery, Nigeria by MATLAB computer program. Ninety percent (90%) of the experimental data sets were used for training while ten percent (10%) were used for testing the networks. The maximum relative errors between the experimental and calculated data obtained from the output variables of the neural network for CODC design were 1.98 error % and 0.57 error % when ANN only and ANNBMC were used respectively while their respective values for the maximum relative error were 0.346 error % and 0.124 error % when they were used for the controller prediction. Larger number of iteration steps of below 2500 and 5000 were required to achieve convergence of less than 10 −7 for the training error using ANNBMC for both the design of the CODC and controller respectively while less than 400 and 700 iteration steps were needed to achieve convergence of 10 −4 using ANN only. The linear regression analysis performed revealed the minimum and maximum prediction accuracies to be 80.65% and 98.79%; and 98.38% and 99.98% when ANN and ANNBMC were used for the CODC design respectively. Also, the minimum and maximum prediction accuracies were 92.83% and 99.34%; and 98.89% and 99.71% when ANN and ANNBMC were used for the CODC controller respectively as both methodologies have excellent predictions. Hence, artificial neural networks based Monte Carlo simulation is an effective and better tool for the design and control of crude oil distillation column. L. T. Popoola, A. A. Susu 267

show abstract

“…work is intended to generate a prognosis P n for the subsequent process value ( ) n z t with a minimum prediction error given as [2]:…”

Section: Artificial Neural Network Back-propagation Algorithmmentioning

confidence: 99%

“…An ANN has the ability to learn relationships between given sets of input and output data by changing the weights. This process is called training the ANN [2]. The most well known training algorithm is the Back Propagation (BP) algorithm ( [3] [4]).…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

Popoola¹,

Susu²

2014

ACES

View full text Add to dashboard Cite

show abstract

“…In PSA, air will pass through a bed of zeolite in which nitrogen will be adsorbed while oxygen will pass due to the adsorption and molecular sieving properties of zeolite [15]. In membranes, the separation mechanism is based on the solution-diffusion model in which oxygen dissolves and diffuses through the polymer [11]. The polymer contains voids that act similar to physical pores but they differ as their location and size can change depending on the operating conditions [16].…”

Section: Introductionmentioning

confidence: 99%

Enriched Oxygen for Crude Oil Preheating in Petroleum Refining

Alqaheem

Alomair

2021

International Journal of Thermodynamics

View full text Add to dashboard Cite

The crude distillation unit is one of the energy-intensive processes in the refinery. This is because of the crude preheater that suffers from excessive energy loss due to the use of air in the combustion furnace. Alternatively, fuel combustion by enriched oxygen can improve heat efficiency, minimize fuel consumption and reduce emissions. In this paper, enriched oxygen has been simulated by UniSim for preheating Kuwaiti crude in one of the distillation columns in the Clean Fuels Project. Results show that the use of 30 mol% of concentrated oxygen reduced fuel consumption by 5%. Carbon dioxide emissions were also minimized by 22,240 tons per year. A membrane system made from perfluoropolymer was simulated for the production of 5,298 tons of enriched oxygen (per day) and it required an area of 39,000 m 2 with a capital investment of 6.9 million $.

show abstract

“…The crude oil distillation unit (CDU) fractionation column in the petroleum refinery industry separates the feed which is the crude oil into different products that is suitable for the different refinery processing units [2]. Currently, in other to improve fuel properties, quality of products and maximize the yield of the products, many CDU operates with different feed as compared to their original feed conditions in other to fulfil global market demand and at the same time fulfilling environmental laws [2,3]. The atmospheric distillation unit (ADU) of the CDU, have different physical properties depending on the characteristics of the crude oil [4].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have used ANN in the design of CDU [3,10,11], however, ANN use has not been very popular in the optimization of operating variables of CDU in the existing refineries. It is this void in the literature that the present study hopes to fill by studying the use of ANN in the optimization of CDU of an existing refinery.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network (ANN) in the Optimization of Crude Oil Refinery Process: New Port-Harcourt Refinery

Braimah

2020

JENRR

View full text Add to dashboard Cite

Background: Optimizing the process conditions of the crude distillation unit is a main challenge for each refinery. Optimization increases profit by producing the required range of distillates at maximum yield and at minimum cost. To achieve an acceptable control of product quality an artificial neural network (ANN) can be used. ANNs are used for engineering purposes, such as pattern recognition, forecasting, and data compression. In the petroleum refinery industry, ANN has been used as controller in for the crude distillation unit. The aim of the current study was to use ANN to optimize and achieve control of product quality of crude distillation unit of an oil refinery. Materials: The research was carried out using the following materials; The design flowchart and the operating data of the crude distillation unit of the New Port Harcourt refinery, Simulation software (HYSYS 2006.5) and Matlab for the ANN. Results: The ANN predicted the optimum operating conditions at which the atmospheric distillation unit (ADU) can operate with the least irreversibility and without changing the design and compromising the products quality. The corresponding exergy efficiency after optimization with ANN for the input variable combinations was 70.6% which was a great improvement because the exergy efficiency increased as compared to the base case of 51.9%. Conclusion: Optimization using ANN, improved the efficiency of the ADU with the least irreversibility and without changing the design and compromising the products quality.

show abstract

A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

Cited by 11 publications

References 11 publications

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

Enriched Oxygen for Crude Oil Preheating in Petroleum Refining

Application of Artificial Neural Network (ANN) in the Optimization of Crude Oil Refinery Process: New Port-Harcourt Refinery

Contact Info

Product

Resources

About