Integrated Design of a Conventional Crude Oil Distillation Tower Using Pinch Analysis

Liebmann, K.; Dhole, V.R.; Jobson, Megan

doi:10.1205/026387698524767

Cited by 51 publications

(31 citation statements)

References 4 publications

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“…Liebmann et al put forward design procedures of a heat-integrated conventional crude oil distillation tower using pinch analysis [12]. Suphanit adjusted reflux, flow rate of steam stripping and other degrees of freedom to reduce operating cost considering the interaction of distillation column and heat exchange network [13].…”

Section: Intrductionmentioning

confidence: 99%

Simultaneous optimization of heat-integrated crude oil distillation systems

Luo

Wang

et al. 2015

Chinese Journal of Chemical Engineering

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Section: Intrductionmentioning

confidence: 99%

Simultaneous optimization of heat-integrated crude oil distillation systems

Luo

Wang

et al. 2015

Chinese Journal of Chemical Engineering

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“…Controlled variables are those variables that must be maintained at a precise value to satisfy column objectives [24]. The stages involved in transforming crude oil into finished products using crude oil distillation column in a petroleum refinery had been discussed ( [19] [25] [28] [29]). Figure 1 shows a typical configuration of a crude oil distillation system.…”

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

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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

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“…Rigorous, simplified and statistical/empirical models have been employed to simulate the complex distillation column. Liebmann et al (1998) and Bagajewicz and Ji (2001) presented step-by-step procedures to design heatintegrated crude distillation units using rigorous models. Rigorous models have been used to optimize the crude oil distillation column (Basak et al, 2002;Inamdar et al, 2004;Uppaluri et al, 2010).…”

Section: Introduction and Previous Workmentioning

confidence: 99%