The Comparison of the Ability of the Neural Hammerstein-Wiener Model to Simulate the Remediation Process of Mining Acid Waste Water using Biochar-Cao Composite

Sudibyo Sudibyo; Gabriel Sianturi; Rifky Fauzi; Eristia Arfi; Aswan Anggun Pribadi; Fakhrony Shalahuddin Rohman; Asyraf Azmi

doi:10.2991/978-94-6463-589-8_20

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The Comparison of the Ability of the Neural Hammerstein-Wiener Model to Simulate the Remediation Process of Mining Acid Waste Water using Biochar-Cao Composite

Authors

Sudibyo Sudibyo¹^{, *}, Gabriel Sianturi², Rifky Fauzi², Eristia Arfi², Aswan Anggun Pribadi²^{, 3}, Fakhrony Shalahuddin Rohman³, Asyraf Azmi⁴

¹Research Center for Mining Technology, National Research and Innovation Agency (BRIN), 35352, Jakarta, South Lampung, Indonesia

²Department of Mathematics, Institute Technology of Sumatera, Bandar Lampung, Indonesia

³Process Systems Engineering Centre (PROSPECT), Research Institute for Sustainable, Faculty of Chemical and Energy Engineering Environment, Universiti Teknologi Malaysia UTM, 81310, Johor Bahru, Johor, Malaysia

⁴Faculty of Chemical Engineering, Universiti Teknologi MARA Shah Alam, 40450, Shah Alam, Selangor, Malaysia

^*Corresponding author. Email: sudibyo@brin.go.id

Corresponding Author

Sudibyo Sudibyo

Available Online 1 December 2024.

DOI: 10.2991/978-94-6463-589-8_20 How to use a DOI?
Keywords: Remediation; Absorption; Hammerstein-Wiener Neural
Abstract: Acid mine drainage waste is waste from which the impact is detri- mental to the environment and human health. To overcome the pollution of acid mine drainage waste, one of the studies is using biochar-CaO composites to reduce the level of metal content in the waste. Time constraints, expensive materials, and nonlinear data are the problems in this case. This Final Project research uses the Hammerstein-Wiener Neural model to predict the absorption of metal content in acid mine drainage waste. The model will be trained with various scenarios, namely variations in the distribution of training data, test data, validation data, and variations in the number of hidden nodes. The results showed that the Hammerstein-Wiener Neural model is the best model to predict the absorption of metal content in acid mine drainage using Biochar-CaO composite with MSE, MAE, and MAPE evaluation values of 0.7815, 0.0790, and 0.0130, respectively. These values are obtained from the data division of 35% training data, 30% validation data, and 35% testing data with 160 hidden nodes. The model outperforms other models to solve the problem.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Innovation & Entrepreneurship in Computing, Engineering & Science Education (InvENT 2024)
Series: Advances in Computer Science Research
Publication Date: 1 December 2024
ISBN: 978-94-6463-589-8
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-589-8_20 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Sudibyo Sudibyo
AU  - Gabriel Sianturi
AU  - Rifky Fauzi
AU  - Eristia Arfi
AU  - Aswan Anggun Pribadi
AU  - Fakhrony Shalahuddin Rohman
AU  - Asyraf Azmi
PY  - 2024
DA  - 2024/12/01
TI  - The Comparison of the Ability of the Neural Hammerstein-Wiener Model to Simulate the Remediation Process of Mining Acid Waste Water using Biochar-Cao Composite
BT  - Proceedings of the International Conference on Innovation & Entrepreneurship in Computing, Engineering & Science Education (InvENT 2024)
PB  - Atlantis Press
SP  - 198
EP  - 208
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-589-8_20
DO  - 10.2991/978-94-6463-589-8_20
ID  - Sudibyo2024
ER  -

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