There are a number of factors that affect the output of the Fischer Tropsch process, change in the process parameters like catalyst used, residence time, temperature, pressure, particle size, bed height, flowrate and even the type of reactor used. A small manipulation of the parameters is enough to change completely the conversion, selectivity; therefore, these parameters have to be monitored. This paper presents the effect of the operation parameters on the Fischer Tropsch synthesis. The method that was used for this observation was that all the adjustable process parameters were manipulated, one parameter was subjected to variation while others were kept constant so that its effect on the conversion and selectivity can be easily monitored. It is not always very easy to obtain results that favour all the aspects of measuring efficiency: conversion, yield and selectivity e.g. if the selectivity increases, conversion decreases and the yield also changes. It has been found that an increase in the residence time or lowering the concentration of the feed increases the conversion by up to 90% without a significant effect on the selectivity. Investigations of the operation of the fluidized bed reactor using mathematical models in polymerizing syngas was done, simulation results revealed that iron catalyst is best for the optimization of liquid hydrocarbon products. Operation at high reactor pressure has positive effect on the reaction i.e. there is an increase in overall CO conversion which, the chain length of the products. Coupling, high reactor pressure and the use of an iron catalyst leads to the production of heavy molecular weight hydrocarbons. At high pressure there is a reduction in the olefin/paraffin ratio. It can be concluded that iron catalyst, high feed rates and high pressure mostly favour the Fischer Tropsch reactions.
|Number of pages||7|
|Publication status||Published - 2019|
|Event||2nd International Conference on Sustainable Materials Processing and Manufacturing, SMPM 2019 - Sun City, South Africa|
Duration: Mar 8 2019 → Mar 10 2019
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
- Artificial Intelligence