- PROJECT TITLE: MODELLING, SIMULATION AND SENSITIVITY ANALYSIS OF A FATTY ACID METHYL ESTER (FAME) REACTIVE DISTILLATION (RD) PROCESS USING ASPEN PLUS DEPARTMENT: COMPUTER ENGINEERING
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Reactive distillation, being an intensified process of combining reaction and distillation in a single vessel, is an ongoing research. This work considered the use of this novel process to investigate the esterification of a fatty acid methyl ester, an alternative fuel, biodiesel, which is a potential economic bedrock via modelling, simulation and sensitivity analysis in Aspen Plus. The selection of FAME was conducted based on the source of the oil for quality biodiesel and on its compatibility with the software; these led to the selection of oleic acid as the fatty acid of the process. A reactive distillation process for a reaction between oleic acid and methanol was then set up in the Aspen environment and tested for convergence, after a successful simulation, two operating parameters (reflux ratio and reboiler duty) were varied from 2.0-5.5 and 1350-1800 W, respectively. Afterwards, graphical representations of composition profiles, temperature profiles and sensitivities of mole-fraction to reboiler duty at different reflux ratios were obtained. Results obtained showed that a reflux ratio of 2.0 was most compatible with a reboiler duty of 1800 W to produce a methyl oleate mole fraction of 0.7627 in the bottom product. Given the novelty of this process in comparison with the conventional independent reaction and separation, more experiments should be carried out to help show any discrepancy between reality and simulation world.
CHAPTER ONE 1.0 INTRODUCTION
Modelling and simulation may enhance the insight, clarify dependencies, predict behaviour, explore the system boundaries; however, they will not reveal knowledge that is unknown. A model is a reflection of the experiments that have been performed and a good trade-off between realism and simplicity (Diran, 1999)
Process engineering offers the knowledge about an application. Understanding a process is always the basis of modelling and control. A rigorous dynamic process model should be developed to increase the understanding about the operation fundamentals and to test the control hypothesis. Experimental model verification is essential to be aware of all uncertainties and peculiarities of the process (Luyben, 1996)
Generally, a model intended for a simulation study can be a type developed with the help of simulation software. Mathematical model classifications include deterministic (input and output variables are fixed values) or stochastic (at least one of the input or output variables is probabilistic), static (time is not taken into account) or dynamic (time-varying interactions among variables are taken into account). The solutions of modelling are often referred to as simulations, that is, they simulate or reproduce the behaviour of physical systems and processes. Typically, simulation models are stochastic and dynamic (Maria, 1997)
The art of foretelling and predicting the future with the use of computers has become increasingly popular, as the speed and memory of the machines have increased. In addition, the desire to understand what happens in systems in which measurements are impossible or impractical has brought about the development of many computational models. Regardless of the aims of these computer models, they all suffer the same drawback:
uncertainty (Ekberg, 1999)
To further increase the thoroughness of the investigation, a computer-simulated model is subjected to different conditions of process parameters. The response and reaction of the model to these parameters reveal parameters upon/to which the model is independent, unresponsive or insensitive, and those to which it is easily affected or reactive, that is, sensitivity analysis. Attunement of the computer model to these parameters in itself is an experiment, which helps to manifest the permissive of operating conditions applicable to the real life version of the model
The recent shortcomings of conventional petroleum have increased the research for alternative energy sources, which offer a lot of promise economically and otherwise. Biodiesel is a prominent subject in this area of research, hence the reason this project studies. Biodiesel is considered as a “direct-pour” alternative fuel to petroleum diesel, as it requires almost no modification to most modern diesel engines. It can be produced locally and, therefore, reduces foreign oil dependence. It has been reported that biodiesel combustion can result in less air pollutant emissions, such as carbon monoxide, sulphur di- oxide, particulate matter, hydrocarbons, but with slightly higher nitrogen oxides. Since the feedstock of biodiesel is mostly renewable, it significantly reduces carbon dioxide emission during its whole life cycle
Fatty acid methyl esters (FAME), valuable oleo-chemicals and main constituent of biodiesel, can be manufactured in a continuous process using reactive distillation. (Dimian, 2007)
Reactive distillation (RD) is the process in which chemical reaction and separation are carried out simultaneously within a fractional distillation apparatus. It may be advantageous for liquid-phase reaction systems when the reaction must be carried out with a large excess of one or more of the reactants, when a reaction can be driven to completion by removal of one or more of the products as they are formed, or when the product recovery or by-product recycle scheme is complicated or made infeasible by azeotrope formation (Perry et al 1997).
With regards to fatty acid ester production and purification, and more specifically to largescale production of biodiesel, it would appear that reactive distillation could provide an efficient and integrated approach to obtain the desired fatty acid esters. Biodiesel is a renewable, clean-burning diesel replacement that is reducing U.S. dependence on foreign petroleum, creating jobs and improving the environment. Technically, biodiesel is defined as a fuel comprised of mono-alkyl esters of long chain fatty acids derived from vegetable oils or animal fats, designated B100, and meeting the requirements of ASTM D 6751.
Computer simulations have become increasingly popular in many different areas over the years, owing mainly to more effective and cheaper machines. In many cases, the trend seems to be that computer simulations are replacing experiments, at least in areas in which experiments are very difficult, expensive or impossible. One such area is that of attempting to foresee what will happen in the future (Ekberg, 1999)
1.1 Problem Statement
Industrially, some operators do not operate at optimal process conditions because they are unaware of the dependency of the process outputs upon certain parameters. To gain insight into the favourable conditions and to make performance predictions of industrial processes of the subject matter to different operating conditions, the sensitivity of a simulated model process needs to be analysed.
The aim of this research is to ascertain the behaviour of an ASPEN PLUS simulated model of a fatty acid methyl ester reactive distillation process, when subjected to different operating parameter conditions.
1.3 Objectives Of Study
The objectives intended to be achieved in this work include:
1. Developing the model of the process in Aspen PLUS environment,
2. Subjecting the model to different operating conditions of deciding variables, and
3. Examining, discovering and interpreting the functional response of a reactive distillation process of a fatty acid methyl ester to these variables.
1.4 Significance of Study
The findings of this study will reveal the behaviour of the purity of a fatty acid methyl ester towards variations in some certain operating parameters involved in reactive distillation process. These findings will help in guiding the plant operators on how to choose the values of the operating parameters for efficient production.
1.6 Scope of Study
This work borders on the use of ASPEN PLUS to investigate the behaviour and functionality of a fatty acid methyl ester reactive distillation process when product mole
fraction is the selected output variable.