Kinetic Modelling Simulation and Optimal Operation of Trickle Bed Reactor for Hydrotreating of Crude Oil. Kinetic Parameters Estimation of Hydrotreating Reactions in Trickle Bed Reactor (TBR) via Pilot Plant Experiments; Optimal Design and Operation of an Industrial TBR with Heat Integration and Economic Evaluation.

Catalytic hydrotreating (HDT) is a mature process technology practiced in the petroleum refining industries to treat oil fractions for the removal of impurities (such as sulfur, nitrogen, metals, asphaltene). Hydrotreating of whole crude oil is a new technology and is regarded as one of the more difficult tasks that have not been reported widely in the literature. In order to obtain useful models for the HDT process that can be confidently applied to reactor design, operation and control, the accurate estimation of kinetic parameters of the relevant reaction scheme are required. This thesis aims to develop a crude oil hydrotreating process (based on hydrotreating of whole crude oil followed by distillation) with high efficiency, selectivity and minimum energy consumption via pilot plant experiments, mathematical modelling and optimization. To estimate the kinetic parameters and to validate the kinetic models under different operating conditions, a set of experiments were carried out in a continuous flow isothermal trickle bed reactor using crude oil as a feedstock and commercial cobaltmolybdenum on alumina (Co-Mo/¿-Al2O3) as a catalyst. The reactor temperature was varied from 335°C to 400°C, the hydrogen pressure from 4 to10 MPa and the liquid hourly space velocity (LHSV) from 0.5 to 1.5 hr-1, keeping constant hydrogen to oil ratio (H2/Oil) at 250 L/L. The main hydrotreating reactions were hydrodesulfurization (HDS), hydrodenitrogenation (HDN), hydrodeasphaltenization (HDAs) and hydrodemetallization (HDM) that includes hydrodevanadization (HDV) and hydrodenickelation (HDNi). An optimization technique is used to evaluate the best kinetic models of a trickle-bed reactor (TBR) process utilized for HDS, HDAs, HDN, HDV and HDNi of crude oil based on pilot plant experiments. The minimization of the sum of the squared errors (SSE) between the experimental and estimated concentrations of sulfur (S), nitrogen (N), asphaltene (Asph), vanadium (V) and nickel (Ni) compounds in the products, is used as an objective function in the optimization problem using two approaches (linear (LN) and non-linear (NLN) regression). The growing demand for high-quality middle distillates is increasing worldwide whereas the demand for low-value oil products, such as heavy oils and residues, is decreasing. Thus, maximizing the production of more liquid distillates of very high quality is of immediate interest to refiners. At the same time, environmental legislation has led to more strict specifications of petroleum derivatives. Crude oil hydrotreatment enhances the productivity of distillate fractions due to chemical reactions. The hydrotreated crude oil was distilled into the following fractions (using distillation pilot plant unit): light naphtha (L.N), heavy naphtha (H.N), heavy kerosene (H.K), light gas oil (L.G.O) and reduced crude residue (R.C.R) in order to compare the yield of these fractions produced by distillation after the HDT process with those produced by conventional methods (i.e. HDT of each fraction separately after the distillation). The yield of middle distillate showed greater yield compared to the middle distillate produced by conventional methods in addition to improve the properties of R.C.R. Kinetic models that enhance oil distillates productivity are also proposed based on the experimental data obtained in a pilot plant at different operation conditions using the discrete kinetic lumping approach. The kinetic models of crude oil hydrotreating are assumed to include five lumps: gases (G), naphtha (N), heavy kerosene (H.K), light gas oil (L.G.O) and reduced crude residue (R.C.R). For all experiments, the sum of the squared errors (SSE) between the experimental product compositions and predicted values of compositions is minimized using optimization technique. The kinetic models developed are then used to describe and analyse the behaviour of an industrial trickle bed reactor (TBR) used for crude oil hydrotreating with the optimal quench system based on experiments in order to evaluate the viability of large-scale processing of crude oil hydrotreating. The optimal distribution of the catalyst bed (in terms of optimal reactor length to diameter) with the best quench position and quench rate are investigated, based upon the total annual cost. The energy consumption is very important for reducing environmental impact and maximizing the profitability of operation. Since high temperatures are employed in hydrotreating (HDT) processes, hot effluents can be used to heat other cold process streams. It is noticed that the energy consumption and recovery issues may be ignored for pilot plant experiments while these energies could not be ignored for large scale operations. Here, the heat integration of the HDT process during hydrotreating of crude oil in trickle bed reactor is addressed in order to recover most of the external energy. Experimental information obtained from a pilot scale, kinetics and reactor modelling tools, and commercial process data, are employed for the heat integration process model. The optimization problem is formulated to optimize some of the design and operating parameters of integrated process, and minimizing the overall annual cost is used as an objective function. The economic analysis of the continuous whole industrial refining process that involves the developed hydrotreating (integrated hydrotreating process) unit with the other complementary units (until the units that used to produce middle distillate fractions) is also presented. In all cases considered in this study, the gPROMS (general PROcess Modelling System) package has been used for modelling, simulation and parameter estimation via optimization process.Tikrit University, Ira

A ONE DIMENSIONAL MODEL FOR TRICKLE BED HYDRODESULFURIZATION

The need for processing of heavy sour crudes is increasing as good quality crude oil reserves are depleting fast. Presence of sulfur in the crude oil can corrode the process equipment, poison the catalysts and can lead to environmental pollution. Trickle bed reactors are widely used for hydrodesulfurization by reacting sulfur in the crude with hydrogen. Optimal design of these units is possible through development of easy to use performance models for trickle bed reactors recognizing the multiphase nature of the reactor and nonlinearities in the parameters. Liquid holdup in trickle bed reactors is an important hydrodynamic parameter which controls the liquid residence time in the bed and hence the degree of sulfur conversion. A new model to estimate liquid holdup in trickle beds is developed considering gas to flow around particles enveloped by trickling liquid. Ergun’s equation for gas phase pressure drop is modified incorporating the effect of presence of liquid phase on gas phase voidage and tortuosity for gas flow. The model is compared with large experimental database available in the literature to evaluate the effect of parameters such as gas and liquid velocities, liquid properties, particle shape and size, operating temperature and pressure. The model equations compare reasonably well with the experimental observations. A one-dimensional multiphase cells-in-series model is developed to predict the steady state behavior of trickle bed reactor applied to the hydrodesulfurization of vacuum gas oil (VGO). The reactor model is established through mass and enthalpy balances with reaction using carefully selected correlations and hydrodesulfurization reaction kinetics based on Langmuir-Hinshelwood mechanism from the literature. The model is validated with experimental data on hydrodesulfurization of VGO reported in the literature. The model is simulated to investigate the effect of various parameters to analyze ways and means to improve the sulfur reduction

Modelling and simulation of diesel catalytic dewaxing reactors

Designing catalytic dewaxing reactors is a major challenge in petroleum refineries due to the lack of kinetic studies related to this operation. Also, the measurements of the cloud point of diesel fuels produced from those units are still being carried out using inaccurate visual procedures, which bring difficulties to the process control design. In this thesis, a single event kinetic model is developed for catalytic dewaxing on Pt/ZSM-5, which application has not been explored in the scientific literature. A total of 14 kinetic parameters have been estimated from experimental data, which are independent on the feedstock type. Then, the obtained parameters were used to propose a soft-sensor consisting of three different modules that handles the feedstock distillation data and integrates a mechanistic reactor model to a solid-liquid flash algorithm to predict the cloud point of the diesel product. Finally, a surrogate model is developed using a sequential design of experiments to simplify the sensor framework and reduce the computing time so that it can be industrially implemented to perform on-line cloud point estimations. The results have showed that the proposed kinetic model was in agreement with observed data and suitable to simulate the industrial operation. Pressure, temperature, and liquid hourly space velocity (LHSV) were found to be the main process variables controlling the conversion in the hydrodewaxing mechanism. The proposed sensor showed to be suitable to study the reactor performance for different set of operating conditions. Also, the surrogate model drastically reduced the computing time to obtain the cloud point estimations and showed to be suitable for on-line prediction purposes. Finally, the employed sequential design strategy revealed that nonlinearities can strongly affect the sensor accuracy if not properly handled

A ONE DIMENSIONAL MODEL FOR TRICKLE BED HYDRODESULFURIZATION

The need for processing of heavy sour crudes is increasing as good quality crude oil reserves are depleting fast. Presence of sulfur in the crude oil can corrode the process equipment, poison the catalysts and can lead to environmental pollution. Trickle bed reactors are widely used for hydrodesulfurization by reacting sulfur in the crude with hydrogen. Optimal design of these units is possible through development of easy to use performance models for trickle bed reactors recognizing the multiphase nature of the reactor and nonlinearities in the parameters. Liquid holdup in trickle bed reactors is an important hydrodynamic parameter which controls the liquid residence time in the bed and hence the degree of sulfur conversion. A new model to estimate liquid holdup in trickle beds is developed considering gas to flow around particles enveloped by trickling liquid. Ergun’s equation for gas phase pressure drop is modified incorporating the effect of presence of liquid phase on gas phase voidage and tortuosity for gas flow. The model is compared with large experimental database available in the literature to evaluate the effect of parameters such as gas and liquid velocities, liquid properties, particle shape and size, operating temperature and pressure. The model equations compare reasonably well with the experimental observations. A one-dimensional multiphase cells-in-series model is developed to predict the steady state behavior of trickle bed reactor applied to the hydrodesulfurization of vacuum gas oil (VGO). The reactor model is established through mass and enthalpy balances with reaction using carefully selected correlations and hydrodesulfurization reaction kinetics based on Langmuir-Hinshelwood mechanism from the literature. The model is validated with experimental data on hydrodesulfurization of VGO reported in the literature. The model is simulated to investigate the effect of various parameters to analyze ways and means to improve the sulfur reduction

Soybean Oil Derivatives for Fuel and Chemical Feedstocks

Plant based sources of hydrocarbons are being considered as alternatives to petrochemicals because of the need to conserve petroleum resources for reasons of national security and climate change. Changes in fuel formulations to include ethanol from corn sugar and methyl esters from soybean oil are examples of this policy in the United States and elsewhere. Replacements for commodity chemicals are also being considered, as this value stream represents much of the profit for the oil industry and one that would be affected by shortages in oil or other fossil fuels. While the discovery of large amounts of natural gas associated with oil shale deposits has abated this concern, research into bio-based feedstock materials continues. In particular, this chapter reviews a literature on the conversion of bio-based extracts to hydrocarbons for fuels and for building block commodity chemicals, with a focus on soybean derived products. Conversion of methyl esters from soybean triglycerides for replacement of diesel fuel is an active area of research; however, the focus of this chapter will not reside with esterification or transesterification, except has a means to provide materials for the production of hydrocarbons for fuels or chemical feedstocks. Methyl ester content in vehicle fuel is limited by a number of factors, including the performance in cold weather, the effect of oxygen content on engine components particularly in the case of older engines, shelf-life, and higher NOx emissions from engines that are not tuned to handle the handle the enhanced pre-ignition conditions of methyl ester combustion [1]. These factors have led to interest in synthesizing a hydrocarbon fuel from methyl esters, one that will maintain the cetane number but will achieve better performance in an automobile: enhanced mixing, injection, and combustion, and reduce downstream issues such as emissions and upstream issues such as fuel preparation and transportation. Various catalytic pathways from oxygenated precursor to hydrocarbon will be considered in the review: pyrolysis [2], deoxygenation and hydrogenation [3, 4], and hydrotreatment [5]. The focus of many of these studies has been production of fuels that are miscible or fungible with petroleum products, e.g., the work published by the group of Daniel Resasco at U. Oklahoma [6]. Much of the published literature focuses on simpler chemical representatives of the methyl esters form soybean oil; but these results are directly applicable to the production of chemical feedstocks, such as ethylbenzene that can be used for a variety of products: polymers, solvent, and reagent [3]. Although many chemical pathways have been demonstrated in the laboratory, the scale-up to handle quantities of bio-derived material presents a number of challenges in comparison with petroleum refining. These range from additional transportation costs because of distributed feedstock production to catalyst cost and regeneration. Other chapters in the book appear to address the cultivation and harvesting of soybeans and production of oil, so these areas will not be dealt with directly in this chapter except as they may relate to chemical changes in the feedstock material. However, the feasibility of the production of hydrocarbons from soybean triglycerides or methyl esters derived from these triglycerides will be considered, along with remaining technical hurdles before soybeans can make a significant contribution to the hydrocarbon economy

Developing an online predictor to predict product sulfur concentration for HDS unit

Hydrodesulfurization (HDS) is an important process in refining industries. Advanced control system (e.g. model predictive controller) requires on-line measurement of the product sulfur at the reactor outlet. However, most HDS processes do not have a sulfur analyzer at the reactor outlet. In order to predict product sulfur concentration usually a data based sulfur predictor is developed. Performance of data based predictor is usually poor since some of the input parameters (e.g. feed sulfur concentration) are unknown. The objective of this thesis is to overcome these limitations of data based predictors and develop an online product sulfur predictor for HDS unit. In this thesis, a hybrid model is proposed, developed and validated (using industrial data), which could predict product sulfur concentration for online HDS system. The proposed hybrid structure is a combination of a reaction kinetics based HDS reactor model and an empirical model based on support vector regression (SVR). The mechanistic model runs in off-line mode to estimate the feed sulfur concentration while the data based model uses the estimated feed sulfur concentration and other process variables to predict the product sulfur concentration. The predicted sulfur concentration can be compared with the lab measurements or sulfur analyzer located further downstream of the process at the tankage. In case there is a large discrepancy, the predictor goes to a calibration mode and uses the mechanistic model to re-estimate the feed sulfur concentration. The detailed logic for the online prediction is also developed. Finally a Matlab based Graphical User Interface (GUI) has been developed for the hybrid sulfur predictor for easy implementation to any HDS process

Developing an online predictor to predict product sulfur concentration for HDS unit

Hydrodesulfurization (HDS) is an important process in refining industries. Advanced control system (e.g. model predictive controller) requires on-line measurement of the product sulfur at the reactor outlet. However, most HDS processes do not have a sulfur analyzer at the reactor outlet. In order to predict product sulfur concentration usually a data based sulfur predictor is developed. Performance of data based predictor is usually poor since some of the input parameters (e.g. feed sulfur concentration) are unknown. The objective of this thesis is to overcome these limitations of data based predictors and develop an online product sulfur predictor for HDS unit. In this thesis, a hybrid model is proposed, developed and validated (using industrial data), which could predict product sulfur concentration for online HDS system. The proposed hybrid structure is a combination of a reaction kinetics based HDS reactor model and an empirical model based on support vector regression (SVR). The mechanistic model runs in off-line mode to estimate the feed sulfur concentration while the data based model uses the estimated feed sulfur concentration and other process variables to predict the product sulfur concentration. The predicted sulfur concentration can be compared with the lab measurements or sulfur analyzer located further downstream of the process at the tankage. In case there is a large discrepancy, the predictor goes to a calibration mode and uses the mechanistic model to re-estimate the feed sulfur concentration. The detailed logic for the online prediction is also developed. Finally a Matlab based Graphical User Interface (GUI) has been developed for the hybrid sulfur predictor for easy implementation to any HDS process