Open AccessArtificial Neural Network (ANN) Analysis of Co-pyrolysis of Waste Coconut Husk and Laminated Plastic Packaging
Author(s) -
Joselito Abierta Olalo
Publication year - 2021
Publication title -
asean journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.153
H-Index - 5
eISSN - 2655-5409
pISSN - 1655-4418
DOI - 10.22146/ajche.69521
Subject(s) - husk , pyrolysis , biomass (ecology) , yield (engineering) , artificial neural network , pulp and paper industry , waste management , materials science , environmental science , process engineering , composite material , engineering , agronomy , computer science , botany , artificial intelligence , biology
Co-pyrolysis of plastic with biomass was used in the possible mitigation of environmental health problems associated with plastic waste. The pyrolysis method possessed the highest solution in the reduction of waste problems. Fuel oil can be produced through the pyrolysis of plastic and biomass waste. Many researchers used pyrolysis technology to produce a suitable amount of pyrolytic oil through different optimization techniques. This study will predict the percentage mass oil yield using an artificial neural network. It uses an input layer, hidden layer and an output layer. Three input factors for the input layer were (i) temperature, (ii) particle size, and (iii) percentage coconut husk. The structure has one hidden layer with two neurons. The artificial neural network was designed to predict the percentage oil yield after 15 pyrolysis runs set by the Box-Behnken design of the experiment. Percentage oil yields after pyrolysis were calculated. Results showed that temperature and percentage of coconut husk significantly influenced the percentage oil yield. Predicted values from simulation in the artificial neural network showed a good agreement through a correlation coefficient of 99.5%. The actual percentage oil yield overlaps the predicted values, which ANN demonstrates as a viable solution.