Prediction of Volatile Organic Compounds (VOCs) From Decomposition of Local Household Food Waste Using the Artificial Neural Network
Siti Rohana Mohd Yatim1, Ku Halim Ku Hamid2, Kamariah Noor Ismail3, Zulkifli Abdul Rashid4, Nur Ain Mohd Zainuddin5
1Siti Rohana Mohd Yatim, Centre of Environmental Health and Safety, Faculty of Health Sciences, Universiti Teknologi MARA, Kampus Puncak Alam, Puncak Alam, Selangor, Malaysia.
2Ku Halim Ku Hamid, Faculty of Chemical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
3Kamariah Noor Ismail, Faculty of Chemical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
4Zulkifli Abdul Rashid, Faculty of Chemical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
5Nur Ain Mohd Zainuddin, Faculty of Chemical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 5773-5779 | Volume-9 Issue-1, October 2019 | Retrieval Number: A3061109119/2019©BEIESP | DOI: 10.35940/ijeat.A3061.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study examines the potential of artificial neural network (ANN) to predict Total Volatile Organic Compounds (TVOCs) released via decomposition of local food wastes. To mimic the decomposition process, a bioreactor was designed to stimulate the food waste storage condition. The food waste was modeled based on the waste composition from a residential area. A feed forward multilayer back propagation (Levenberg – Marquardt training algorithm) was then developed to predict the TVOCs. The findings indicate that a two-layer artificial neuron network (ANN) with six input variables and these include (outside and inside temperature, pH, moisture content, oxygen level, relative humidity) with a total of eighty eight (88) data are used for the modeling purpose. The network with the highest regression coefficient (R) is 0.9967 and the lowest Mean Square Error (MSE) is 0.00012 (nearest to the value of zero) has been selected as the Optimum ANN model. The findings of this study suggest the most suitable ANN model that befits the research objective is ANN model with one (1) hidden layer with fifteen (15) hidden neurons. Additionally, it is critical to note that the results from the experiment and predicted model are in good agreement.
Keywords: Multilayer back propagation; waste storage, Volatile organic compounds (VOCs), local household food waste.