Modelling and Optimization of Crude Oil Hydrotreating Process in Trickle Bed Reactor: Energy Consumption and Recovery Issues
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Aysar T. Jarullah
Energy consumption is a very important consideration for reducing environmental impact and maximizing the profitability of operations. Since high temperatures are employed in hydrotreating (HDT) processes, hot effluents can be used to heat other cold process streams. The aim of the present paper is to describe and analyze the heat integration (during hydrotreating of crude oil in trickle bed reactor) of a hydrotreating plant’s process based upon experimental work.In this work, crude oil is hydrotreated upon a commercial cobalt-molybdenum on alumina catalyst presulfided at specified conditions. Detailed pilot plant experiments are conducted in a continuous flow isothermal trickle bed reactor (TBR) in which the main hydrotreating reactions, are hydrodesulfurization (HDS), hydrodenitrogenation (HDN), hydrodeasphaltenization (HDAs) and hydrodemetallization (HDM). The latter includes hydrodevanadization (HDV) and hydrodenickelation (HDNi). The reaction temperature, the hydrogen pressure, and the liquid hourly space velocity (LHSV) are varied within certain ranges, with constant hydrogen to oil ratio (H2/Oil).Experimental information obtained from a pilot plant, together with kinetics and reactor modeling tools,and a commercial process data are employed for heat integration process model. The optimization problem to minimize the overall annual cost is formulated as a Non-Linear Programming (NLP) problem, which is solved using Successive Quadratic Programming (SQP) within gPROMS.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- Editorial: Special Issue of CAPE FORUM 2011
- Optimisation of Emulsion Copolymerization of Styrene and MMA in Batch and Semi-batch Reactors
- Modelling and Optimization of Crude Oil Hydrotreating Process in Trickle Bed Reactor: Energy Consumption and Recovery Issues
- An Optimization-Based Framework for Process Planning under Uncertainty with Risk Management
- Modelling and Control of Reactive Polymer Composite Moulding Using Bootstrap Aggregated Neural Network Models
- Numerical Simulation of Fluid Flow and Heat Transfer in a Counter-Current Reactor System for Nanomaterial Production
- Knowledge Based System Implementation for Lean Process in Low Volume Automotive Manufacturing (LVAM) with Reference to Process Manufacturing
- Novel Heuristic for Low-Batch Manufacturing Process Scheduling Optimisation with Reference to Process Engineering
- CFD Modelling of Reverse Osmosis Channels with Potential Applications to the Desalination Industry
