TY - GEN
T1 - Predicting the effect of seasonal variation on the physical composition of municipal solid waste
T2 - 5th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2019
AU - Adeleke, Oluwatobi
AU - Akinlabi, Stephen A.
AU - Hassan, S.
AU - Jen, Tien Chien
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2020.
PY - 2020
Y1 - 2020
N2 - Several factors influence the physical, chemical, and thermal properties of waste at different sources. One of the major indexes to variation in the morphological composition of municipal solid waste is the season. A significant discrepancy in the composition of municipal solid waste at different seasons has been reported in the literature. However, this study explores the adaptive neuro-fuzzy inference system (ANFIS) with a fuzzy c-means (FCM) clustering technique to predict the physical content of waste in South Africa based on the varying weather parameters at different seasons. Four different models (I–IV) were developed to forecast the percentage fraction of organics, plastics, paper, and textile, respectively. The choice of these streams was because a closer look at the historical data reveals a significant variation in the percentage of these waste fractions at different seasons with little or no difference in other waste streams. The percentage composition of samples of waste collected and characterized at Marie Louise Landfill, Johannesburg, in summer 2015 and winter 2016 was used as the output variable. Weather parameters for the same period were extracted from South Africa Weather Service data and used as the input variables. M-file script was written and computed on a workstation with configurations of 64 bits, 4 GB ram Intel(R) core(TM) i3. The performance of the ANFIS models I–IV was evaluated using mean absolute deviation (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).
AB - Several factors influence the physical, chemical, and thermal properties of waste at different sources. One of the major indexes to variation in the morphological composition of municipal solid waste is the season. A significant discrepancy in the composition of municipal solid waste at different seasons has been reported in the literature. However, this study explores the adaptive neuro-fuzzy inference system (ANFIS) with a fuzzy c-means (FCM) clustering technique to predict the physical content of waste in South Africa based on the varying weather parameters at different seasons. Four different models (I–IV) were developed to forecast the percentage fraction of organics, plastics, paper, and textile, respectively. The choice of these streams was because a closer look at the historical data reveals a significant variation in the percentage of these waste fractions at different seasons with little or no difference in other waste streams. The percentage composition of samples of waste collected and characterized at Marie Louise Landfill, Johannesburg, in summer 2015 and winter 2016 was used as the output variable. Weather parameters for the same period were extracted from South Africa Weather Service data and used as the input variables. M-file script was written and computed on a workstation with configurations of 64 bits, 4 GB ram Intel(R) core(TM) i3. The performance of the ANFIS models I–IV was evaluated using mean absolute deviation (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).
KW - Adaptive neuro-fuzzy inference system
KW - Clustering technique
KW - Municipal solid waste
KW - Seasonal variation
UR - http://www.scopus.com/inward/record.url?scp=85091318347&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-5753-8_18
DO - 10.1007/978-981-15-5753-8_18
M3 - Conference contribution
AN - SCOPUS:85091318347
SN - 9789811557521
T3 - Lecture Notes in Mechanical Engineering
SP - 187
EP - 198
BT - Advances in Manufacturing Engineering - Selected Articles from ICMMPE 2019
A2 - Emamian, Seyed Sattar
A2 - Yusof, Farazila
A2 - Awang, Mokhtar
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 November 2019 through 21 November 2019
ER -
