Deoki N. Saraf

Hydrocracking: a review

Abstract In this article the technology, kinetics, chemistry and reactor modelling of hydrocracking have been reviewed. While it is intended to provide a general overview of the recent advances in this process, greater emphasis has been given to technology and reactor modelling because of their industrial importance. Over ninety references have been cited.

Multi-objective Optimization of an Industrial Crude Distillation Unit Using the Elitist Non-Dominated Sorting Genetic Algorithm

This study provides insights into the optimal operation of one of the most important refinery units, namely, the crude distillation unit (CDU). A steady state model based on (C+3) iteration variables is used to simulate an industrial column. The model is first tuned using some industrial data. The elitist non-dominated sorting genetic algorithm (NSGA-II) is used to solve a few meaningful multi-objective optimization problems. It is observed that current plant operation is sub-optimal and more profit can be realized for the same amount of energy cost using the optimal operating conditions found. Also, it was found that profit can be increased keeping the product properties within acceptable limits. The procedure used is quite general and can be applied to other CDUs.

Modeling of a hydrocracking reactor

Abstract A three parameter model has been developed for a two-stage vacuum gas oil hydrocracker unit. The feed and the products were lumped into 23 pseudocomponents, each characterized by its boiling range and specific gravity. The model assumes that each pseudocomponent can only form lighter products by a pseudohomogeneous first order reaction. The model parameters were determined using information from literature and plant data. Given the feed characterization and the inlet conditions, the concentration and temperature profiles throughout the reactors, the amount of recycle and hydrogen consumption can be calculated. The model was validated against plant data and the agreement was generally good. The effect of variation in the model parameters and operating conditions has also been discussed.

Use of genetic algorithms in the optimization of free radical polymerizations exhibiting the trommsdorff effect

The genetic algorithm (GA) is adapted and used to obtain optimal temperature histories for methyl methacrylate polymerizations. The reaction time is minimized, while simultaneously requiring the attainment of design values of the final monomer conversion and number average chain length. The technique is robust, and gives near-global optimal solutions. As such, it can easily be used for on-line optimizing control of free radical polymerization reactors in which the reaction is associated with the Trommsdorff effect. The results obtained from GA can be improved further if these are provided as initial guesses to a computer code using the Pontryagin minimum principle with the first order control vector iteration method.

Design of neural networks using genetic algorithm for on-line property estimation of crude fractionator products

The products from the crude distillation unit (CDU) of a petroleum refinery need to conform to certain standards and hence it is important that the properties, which characterize these products, be measured on-line so that necessary control action can be taken through feedback mechanism. In view of nonavailability of on-line hardware sensors, software-based sensors have been developed for prediction of product properties on-line. Artificial neural network (ANN) based models have been used for this purpose. To overcome the problem of designing the networks by a cumbersome trial and error procedure, a methodology based on genetic algorithm (GA) has been developed. This involves optimally designing the network architecture including number of hidden layers, number of neurons in each layer, connectivity and activation functions. For on-line prediction of any property, the network design can be completed in a matter of a few hours. Additionally GA was also used for the selection of most relevant set of input features to the neural network. The above methodology was used for designing networks to predict properties of various side-draw products from a crude fractionator. The properties include ASTM temperatures, specific gravities and Flash Points of various products. All the predicted results were generally in good agreement with lab measurements with average deviation ranging from 0.5 to 3% for the properties investigated. The methodology is quite general and can be used to design similar other nets.

On-line estimation of product properties for crude distillation units

Abstract The stringent quality requirement of petroleum products in a highly competitive market makes on-line monitoring and control of product properties essential. But unfortunately few on-line hardware sensors are available and these are also difficult to maintain. It is, therefore, necessary to develop ‘software sensors’ to predict the quality using other easily measurable secondary variables. This study presents an algorithm that uses the crude true boiling point (TBP) curve and other routinely measured flow rates, temperatures and pressures in the crude distillation unit (CDU) to predict the product properties. The measured top plate, side-stripper draw plates and flash zone temperatures are corrected for hydrocarbon partial pressures to obtain equilibrium flash vaporization (EFV) temperatures. These product EFVs are converted to product TBPs and are superimposed on the crude TBP curve. An assumption, that the initial boiling point (IBP) of the next heavier product lies vertically below the final boiling point (FBP) of the product under consideration and the two points are equidistant from the crude TBP curve, allows estimation of the IBP and FBP temperatures of all the distillate products. A straight line approximation of the product TBP curve is used to obtain intermediate temperatures. These TBP temperatures are converted to product ASTM (American Society for Testing Materials) temperatures which are correlated with the desired product properties. Several properties have been predicted using the above procedure. These include densities of all the CDU products, Flash Points for all the side-stream products, Reid Vapor Pressure (RVP) for the distillate, Freeze Point for kerosene, Pour Point and the recovery for the gas oils etc. It is possible to predict these properties repeatedly every minute as long as steady state conditions prevail in the CDU. The algorithm has been applied off-line with the available on-line data from two different operating refineries. A satisfactory match between the predicted and the measured properties validated the developed soft sensors. However, extensive testing is recommended before the implementation of these soft sensors on the actual process.

A crude distillation unit model suitable for online applications

Abstract A steady state, multicomponent distillation model particularly suited for fractionation of crude oil has been developed based on equilibrium stage relations. For a mixture of C components, the present formulation uses C +3 iteration variables namely the mole fractions of the components, temperature, total liquid and total vapor flow rates on each stage. This choice of variables makes the present model numerically stable and robust rendering a separate initial guess computation unnecessary. An improved scheme of numbering the equilibrium stages when side strippers are present, was found to be advantageous with respect to computation time. Selected example problems have been included from literature as well as industry to demonstrate the efficacy and usefulness of the method. The accuracy of predictions and speed of solution of the model equations are particularly suited for online applications such as online optimization.

Online tuning of a steady state crude distillation unit model for real time applications

Abstract The steady state simulators, used for on-line performance prediction and for on-line optimization in crude distillation units are often sensitive to small variations in the feed composition, which is specified in terms of a True Boiling Point (TBP) vs volume percent distilled curve. The exact feed TBP is often not available during the plant operation. Also stratification of raw crude oil into layers in the large tank farm sections cause severe operating problems in terms of the stability of the column. If feed TBP can be predicted online, necessary feedforward action can considerably reduce the operating problems. A model has been developed for backcalculation of feed TBP using measured plant parameters. A heat balance is performed around an envelope encompassing the rectifying section of the fractionator and is followed by the calculation of Equilibrium Flash Vaporization (EFV) temperatures at six different locations of the column which are correlated with corresponding feed TBP temperatures. The second part of model tuning consists of calculating model parameters in the form of point efficiencies so as to minimize the discrepancy between the simulator predicted and measured column parameters which arises out of modelling approximations such as assumption of phase equilibria at each stage and use of imperfect thermodynamics correlations. The simulator results, after tuning, were found to match the plant measurements within two percent in all the cases investigated. The simulator output was used to predict various product properties using a Property Prediction package and these were also found to match well with those of laboratory measurements. Both the backcalculation of feed TBP and the efficiency tuning need to be implemented on-line for inferential control and supervisory optimization.

Adaptive dynamic matrix control of a distillation column with closed-loop online identification

Abstract This paper presents an online identification technique where a process is identified in terms of pseudo impulse response coefficients and subsequently used to update convolution type models to accommodate process-model mismatch. As an example, dynamic matrix control has been applied adaptively to control the top product composition of a distillation column for both servo and regulatory problems. The algorithm automatically detects a large step-like disturbance requiring fresh identification of the process and subsequently adapts the controller to the new model. Simulation studies using an analytical dynamic full order model of a distillation column demonstrated the usefulness of the adaptation scheme. Experimentation on a pilot scale distillation unit vindicated the simulation results.

Predict heat of vaporization of crudes and pure components Revised II

Abstract Modeling of non-isothermal vapor–liquid equilibrium processes requires the availability of heat of vaporization data. An equation for calculation of heat of vaporization at normal boiling point has been developed which is particularly suited for pure hydrocarbons and petroleum fractions. The equation is simple to use and provides an improvement over the existing equations for pure hydrocarbons in wide boiling range. The equation is equally valid for crude oils, thus enabling the estimation of heats of vaporization needed for modeling of distillation units in oil refineries.