Identification of Bioethanol Destilator of Sugarcane Drops Kerosene Substitute Using Artificial Neural Network

Tuti Anggraini; Maualana Naseem  Hamed; Roza Susanti; Anton; Farhan  Ahmad

doi:10.59890/ijatss.v2i12.83

Authors

Tuti Anggraini Padang State Polytechnic
Maualana Naseem Hamed Andalas University
Roza Susanti Padang State Polytechnic
Anton Padang State Polytechnic
Farhan Ahmad Padang State Polytechnic

DOI:

https://doi.org/10.59890/ijatss.v2i12.83

Keywords:

Bioethanol, Kerosene, MQ-3 Sensor, MQ-135 Artificial Neural Network

Abstract

The decreasing availability of crude oil as an energy source has driven efforts to develop alternative technologies, such as bioethanol distillators, to replace kerosene. This research focuses on creating an automatic bioethanol distillator equipped with artificial neural network (ANN) technology to assess the quality of bioethanol derived from sugarcane molasses. The system incorporates an electronic nose (e-nose) sensor and a temperature sensor to detect ethanol levels during the distillation process, operating at a maximum temperature of 80°C. The collected data on bioethanol quality is processed using an artificial neural network with a backpropagation algorithm, designed with two input variables, six neurons in the hidden layer, and one output variable. Experimental results reveal that the tool achieves a 79.21% success rate in identifying bioethanol quality through MATLAB simulations. This innovation demonstrates the potential to enhance bioethanol production by ensuring quality control and promoting energy independence. The developed system is intended to serve as an efficient and practical engineering solution that benefits both societal and industrial applications, contributing to a sustainable energy future

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