Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm

Wisnu Jatmiko; Dwi M.J. Purnomo; Machmud Alhamidi; Ari Wibisono; Hanif Wisesa; Petrus Mursanto; Anom Bowolaksono; Dian Hendrayanti; Fajar Addana

doi:10.1109/MHS.2015.7438293

Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm

Wisnu Jatmiko
, Dwi M.J. Purnomo
, Machmud Alhamidi
, Ari Wibisono
, Hanif Wisesa
, Petrus Mursanto
, Anom Bowolaksono
, Dian Hendrayanti
, Fajar Addana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

The cause of global warming is the existence of greenhouse gases that trap the emitted infrared wave and cause the increasing of the earth's temperature. One of the predominant greenhouse gases in the atmosphere is CO2. Biosequestration by utilizing micro algae is one of the promising method to reduce the concentration of CO2 in the atmosphere. This research focuses on the modeling of the algal growth which is one of the parameter that defines the amount of CO2 which can be fixated by algal. From the observation data, the growth behavior is modeled by regression method, Multilayer Perceptron (MLP) algorithm. To optimize the algorithm, MLP is also combined with The Canonical Particle Swarm Optimization (CPSO). The result shows that modeling using MLP-CPSO is more accurate than the original MLP and MLP-PSO respectively by 25% and 15% in RAE. MLP-CPSO also shows the best performance in RMSE with 0.091 and coefficient correlation (r) with 0.92.

Original language	English
Title of host publication	2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479966783
DOIs	https://doi.org/10.1109/MHS.2015.7438293
Publication status	Published - 21 Mar 2016
Event	International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015 - Nagoya, Japan Duration: 23 Nov 2015 → 25 Nov 2015

Publication series

Name	2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015

Conference

Conference	International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015
Country/Territory	Japan
City	Nagoya
Period	23/11/15 → 25/11/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

RAE
RMSE
algal growth
coefficient correlation
microalgae
multilayer perceptron
the canonical particle swarm optimization

Access to Document

10.1109/MHS.2015.7438293

Cite this

Jatmiko, W., Purnomo, D. M. J., Alhamidi, M., Wibisono, A., Wisesa, H., Mursanto, P., Bowolaksono, A., Hendrayanti, D., & Addana, F. (2016). Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm. In 2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015 Article 7438293 (2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MHS.2015.7438293

Jatmiko, Wisnu ; Purnomo, Dwi M.J. ; Alhamidi, Machmud et al. / Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm. 2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015. Institute of Electrical and Electronics Engineers Inc., 2016. (2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015).

@inproceedings{0bc8291f0219422c9283e31305b339d6,

title = "Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm",

abstract = "The cause of global warming is the existence of greenhouse gases that trap the emitted infrared wave and cause the increasing of the earth's temperature. One of the predominant greenhouse gases in the atmosphere is CO2. Biosequestration by utilizing micro algae is one of the promising method to reduce the concentration of CO2 in the atmosphere. This research focuses on the modeling of the algal growth which is one of the parameter that defines the amount of CO2 which can be fixated by algal. From the observation data, the growth behavior is modeled by regression method, Multilayer Perceptron (MLP) algorithm. To optimize the algorithm, MLP is also combined with The Canonical Particle Swarm Optimization (CPSO). The result shows that modeling using MLP-CPSO is more accurate than the original MLP and MLP-PSO respectively by 25\% and 15\% in RAE. MLP-CPSO also shows the best performance in RMSE with 0.091 and coefficient correlation (r) with 0.92.",

keywords = "RAE, RMSE, algal growth, coefficient correlation, microalgae, multilayer perceptron, the canonical particle swarm optimization",

author = "Wisnu Jatmiko and Purnomo, \{Dwi M.J.\} and Machmud Alhamidi and Ari Wibisono and Hanif Wisesa and Petrus Mursanto and Anom Bowolaksono and Dian Hendrayanti and Fajar Addana",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015 ; Conference date: 23-11-2015 Through 25-11-2015",

year = "2016",

month = mar,

day = "21",

doi = "10.1109/MHS.2015.7438293",

language = "English",

series = "2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015",

address = "United States",

}

Jatmiko, W, Purnomo, DMJ, Alhamidi, M, Wibisono, A, Wisesa, H, Mursanto, P , Bowolaksono, A , Hendrayanti, D & Addana, F 2016, Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm. in 2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015., 7438293, 2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015, Institute of Electrical and Electronics Engineers Inc., International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015, Nagoya, Japan, 23/11/15. https://doi.org/10.1109/MHS.2015.7438293

Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm. / Jatmiko, Wisnu; Purnomo, Dwi M.J.; Alhamidi, Machmud et al.
2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015. Institute of Electrical and Electronics Engineers Inc., 2016. 7438293 (2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm

AU - Jatmiko, Wisnu

AU - Purnomo, Dwi M.J.

AU - Alhamidi, Machmud

AU - Wibisono, Ari

AU - Wisesa, Hanif

AU - Mursanto, Petrus

AU - Bowolaksono, Anom

AU - Hendrayanti, Dian

AU - Addana, Fajar

PY - 2016/3/21

Y1 - 2016/3/21

N2 - The cause of global warming is the existence of greenhouse gases that trap the emitted infrared wave and cause the increasing of the earth's temperature. One of the predominant greenhouse gases in the atmosphere is CO2. Biosequestration by utilizing micro algae is one of the promising method to reduce the concentration of CO2 in the atmosphere. This research focuses on the modeling of the algal growth which is one of the parameter that defines the amount of CO2 which can be fixated by algal. From the observation data, the growth behavior is modeled by regression method, Multilayer Perceptron (MLP) algorithm. To optimize the algorithm, MLP is also combined with The Canonical Particle Swarm Optimization (CPSO). The result shows that modeling using MLP-CPSO is more accurate than the original MLP and MLP-PSO respectively by 25% and 15% in RAE. MLP-CPSO also shows the best performance in RMSE with 0.091 and coefficient correlation (r) with 0.92.

AB - The cause of global warming is the existence of greenhouse gases that trap the emitted infrared wave and cause the increasing of the earth's temperature. One of the predominant greenhouse gases in the atmosphere is CO2. Biosequestration by utilizing micro algae is one of the promising method to reduce the concentration of CO2 in the atmosphere. This research focuses on the modeling of the algal growth which is one of the parameter that defines the amount of CO2 which can be fixated by algal. From the observation data, the growth behavior is modeled by regression method, Multilayer Perceptron (MLP) algorithm. To optimize the algorithm, MLP is also combined with The Canonical Particle Swarm Optimization (CPSO). The result shows that modeling using MLP-CPSO is more accurate than the original MLP and MLP-PSO respectively by 25% and 15% in RAE. MLP-CPSO also shows the best performance in RMSE with 0.091 and coefficient correlation (r) with 0.92.

KW - RAE

KW - RMSE

KW - algal growth

KW - coefficient correlation

KW - microalgae

KW - multilayer perceptron

KW - the canonical particle swarm optimization

UR - https://www.scopus.com/pages/publications/84966461436

U2 - 10.1109/MHS.2015.7438293

DO - 10.1109/MHS.2015.7438293

M3 - Conference contribution

AN - SCOPUS:84966461436

T3 - 2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015

BT - 2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015

Y2 - 23 November 2015 through 25 November 2015

ER -

Jatmiko W, Purnomo DMJ, Alhamidi M, Wibisono A, Wisesa H, Mursanto P et al. Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm. In 2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015. Institute of Electrical and Electronics Engineers Inc. 2016. 7438293. (2015 International Symposium on Micro-NanoMechatronics and Human Science, MHS 2015). doi: 10.1109/MHS.2015.7438293

Algal growth rate modeling and prediction optimization using incorporation of MLP and CPSO algorithm

Abstract

Publication series

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UN SDGs

Keywords

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