Controlling the Emission of Pollution from the Boiler of Palm Oil Mill by Using Neural Network and Genetic Algorithm
| dc.contributor.author | K.N.Seetharamu, Prof . | |
| dc.date.accessioned | 2017-06-02T01:44:38Z | |
| dc.date.available | 2017-06-02T01:44:38Z | |
| dc.date.issued | 2000 | |
| dc.description.abstract | Project Description The emission released from the palm oil mill in Malaysia has quite significant environmental impact. The use of biomass material from the palm oil byproduct, i.e. fiber and shell as fuel is identified as the main cause of the emission released. Economically, the usage of palm waste material as fuel is seen as productive as this material will not be wasted. However, the combustion process in the furnace of the boiler releases the emission such as particulate matters (PM), carbon Monoxide (CO), Nitrogen Oxide (NO.) and Sulphur Dioxide (Soz). The monitoring and control of these pollutants from the palm oil mill is a great concem to the community. It is well known that the emission released depends on many complex factors which are either directly or indirectly related to each other, and are influenced by many variables. These variables can appear from the combustion process, boiler process etc. These processes are related to each other and are highly non-linear. When a particular operation changes, the new optimum parameters can not be determined immediately. The idea is based on controlling the input parameters, such as fibre flow rate, etc, in order to control the output, i.e. the release of pollutants from the chimney. From the collected data from the palm oil mill, a simulation model has been constructed using tools of artificial intelligence to predict the pollution level of several constituents under any given operating conditions. Using this model, the control of the pollutants has been achieved by optimizing the operating conditions without sacrificing the maximum output of the mill. It has been established that several constituents of the pollutants can be brought down below the acceptable limits by the methodology developed using Artificial Neural Network and Genetic Algorithm. It is expected that several palm oil mill will optimize their operating conditions so as to have the emission of pollutants below the acceptable limit by implementing the methodology developed. This process prevents the deterioration of the environmental conditions | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/3988 | |
| dc.subject | Mechanical Engineering | en_US |
| dc.title | Controlling the Emission of Pollution from the Boiler of Palm Oil Mill by Using Neural Network and Genetic Algorithm | en_US |
| dc.type | Working Paper | en_US |
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