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Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler

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Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler. / Shaha, Aditya Pankaj; Singamsetti, Mohan Sai; Tripathy, B. K. et al.
In: IEEE Access, Vol. 8, 9122496, 2020, p. 117467-117482.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Shaha, AP, Singamsetti, MS, Tripathy, BK, Srivastava, G, Bilal, M & Nkenyereye, L 2020, 'Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler', IEEE Access, vol. 8, 9122496, pp. 117467-117482. https://doi.org/10.1109/ACCESS.2020.3004156

APA

Shaha, A. P., Singamsetti, M. S., Tripathy, B. K., Srivastava, G., Bilal, M., & Nkenyereye, L. (2020). Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler. IEEE Access, 8, 117467-117482. Article 9122496. https://doi.org/10.1109/ACCESS.2020.3004156

Vancouver

Shaha AP, Singamsetti MS, Tripathy BK, Srivastava G, Bilal M, Nkenyereye L. Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler. IEEE Access. 2020;8:117467-117482. 9122496. doi: 10.1109/ACCESS.2020.3004156

Author

Shaha, Aditya Pankaj ; Singamsetti, Mohan Sai ; Tripathy, B. K. et al. / Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler. In: IEEE Access. 2020 ; Vol. 8. pp. 117467-117482.

Bibtex

@article{d70d5e56c2fd4453b3d1576f4234e248,
title = "Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler",
abstract = "In order to cater to the energy requirement in the form of steam at a reasonable cost, the process industries are relying on the waste incineration plants by engaging themselves through industry symbiosis. However, before the establishment of industrial symbiosis, it is very crucial to monitor and predict the operational performance of the boiler used in the waste incineration plants. The existing works focus on using Artificial Neural Networks (ANNs) for prediction of the performance of the boiler in terms of pressure, temperature, and mass flow rate of steam using the input parameters viz. feed water temperature, feed water pressure, incinerator exit temperature and conveyor speed. However, the problem with this approach is that shallow ANNs cannot model the complex mathematical non-linear relationships so precisely. In addition, ANNs are not interpretable which makes stakeholders apprehensive to use these networks in production. In this paper, we address these drawbacks of ANNs by modeling the complex relationship governing the boiler performance by using a set of machine learning and deep learning models. Also, the research paper introduces multiple techniques like feature importance, Partial Dependence Plots(PDP) plots etc. which interpret the reason behind the model's output to make it more reliable for the stakeholders. It has been empirically shown that the new Machine Learning and Deep Learning models performed better than the ANNs for predicting the boiler performance. The Random Forest model made a Mean Absolute Percentage Error (MAPE) of 1.12 and LSTMs had a MAPE of 1.14 in the prediction of steam temperature C^o which is a significant improvement in comparison to the original ANN model which had a MAPE of 6.93. In the case of the predictions for steam pressure kgf/cm2 the MAPE for the Random Forest model and LSTM was 5.54 and 4.21 respectively as compared to ANNs MAPE of 1.49. Similarly for steam mass flow rate(t/h), the MAPE was improved to 15.6 and 9.63 by Random Forest Model and LSTM respectively, which was originally 18.77 for ANN based model. These results clearly show that LSTM based models outperformed ANNs and Random Forests in terms of prediction accuracy.",
keywords = "boiler, green energy, LIME, Long short-term memory, neural networks, predictors, random forest, refuse plastic fuel",
author = "Shaha, {Aditya Pankaj} and Singamsetti, {Mohan Sai} and Tripathy, {B. K.} and Gautam Srivastava and Muhammad Bilal and Lewis Nkenyereye",
note = "Funding Information: This work was supported by the Sejong University New Faculty Program through the National Research Foundation of Korea NRF. Publisher Copyright: {\textcopyright} 2013 IEEE.",
year = "2020",
doi = "10.1109/ACCESS.2020.3004156",
language = "English",
volume = "8",
pages = "117467--117482",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler

AU - Shaha, Aditya Pankaj

AU - Singamsetti, Mohan Sai

AU - Tripathy, B. K.

AU - Srivastava, Gautam

AU - Bilal, Muhammad

AU - Nkenyereye, Lewis

N1 - Funding Information: This work was supported by the Sejong University New Faculty Program through the National Research Foundation of Korea NRF. Publisher Copyright: © 2013 IEEE.

PY - 2020

Y1 - 2020

N2 - In order to cater to the energy requirement in the form of steam at a reasonable cost, the process industries are relying on the waste incineration plants by engaging themselves through industry symbiosis. However, before the establishment of industrial symbiosis, it is very crucial to monitor and predict the operational performance of the boiler used in the waste incineration plants. The existing works focus on using Artificial Neural Networks (ANNs) for prediction of the performance of the boiler in terms of pressure, temperature, and mass flow rate of steam using the input parameters viz. feed water temperature, feed water pressure, incinerator exit temperature and conveyor speed. However, the problem with this approach is that shallow ANNs cannot model the complex mathematical non-linear relationships so precisely. In addition, ANNs are not interpretable which makes stakeholders apprehensive to use these networks in production. In this paper, we address these drawbacks of ANNs by modeling the complex relationship governing the boiler performance by using a set of machine learning and deep learning models. Also, the research paper introduces multiple techniques like feature importance, Partial Dependence Plots(PDP) plots etc. which interpret the reason behind the model's output to make it more reliable for the stakeholders. It has been empirically shown that the new Machine Learning and Deep Learning models performed better than the ANNs for predicting the boiler performance. The Random Forest model made a Mean Absolute Percentage Error (MAPE) of 1.12 and LSTMs had a MAPE of 1.14 in the prediction of steam temperature C^o which is a significant improvement in comparison to the original ANN model which had a MAPE of 6.93. In the case of the predictions for steam pressure kgf/cm2 the MAPE for the Random Forest model and LSTM was 5.54 and 4.21 respectively as compared to ANNs MAPE of 1.49. Similarly for steam mass flow rate(t/h), the MAPE was improved to 15.6 and 9.63 by Random Forest Model and LSTM respectively, which was originally 18.77 for ANN based model. These results clearly show that LSTM based models outperformed ANNs and Random Forests in terms of prediction accuracy.

AB - In order to cater to the energy requirement in the form of steam at a reasonable cost, the process industries are relying on the waste incineration plants by engaging themselves through industry symbiosis. However, before the establishment of industrial symbiosis, it is very crucial to monitor and predict the operational performance of the boiler used in the waste incineration plants. The existing works focus on using Artificial Neural Networks (ANNs) for prediction of the performance of the boiler in terms of pressure, temperature, and mass flow rate of steam using the input parameters viz. feed water temperature, feed water pressure, incinerator exit temperature and conveyor speed. However, the problem with this approach is that shallow ANNs cannot model the complex mathematical non-linear relationships so precisely. In addition, ANNs are not interpretable which makes stakeholders apprehensive to use these networks in production. In this paper, we address these drawbacks of ANNs by modeling the complex relationship governing the boiler performance by using a set of machine learning and deep learning models. Also, the research paper introduces multiple techniques like feature importance, Partial Dependence Plots(PDP) plots etc. which interpret the reason behind the model's output to make it more reliable for the stakeholders. It has been empirically shown that the new Machine Learning and Deep Learning models performed better than the ANNs for predicting the boiler performance. The Random Forest model made a Mean Absolute Percentage Error (MAPE) of 1.12 and LSTMs had a MAPE of 1.14 in the prediction of steam temperature C^o which is a significant improvement in comparison to the original ANN model which had a MAPE of 6.93. In the case of the predictions for steam pressure kgf/cm2 the MAPE for the Random Forest model and LSTM was 5.54 and 4.21 respectively as compared to ANNs MAPE of 1.49. Similarly for steam mass flow rate(t/h), the MAPE was improved to 15.6 and 9.63 by Random Forest Model and LSTM respectively, which was originally 18.77 for ANN based model. These results clearly show that LSTM based models outperformed ANNs and Random Forests in terms of prediction accuracy.

KW - boiler

KW - green energy

KW - LIME

KW - Long short-term memory

KW - neural networks

KW - predictors

KW - random forest

KW - refuse plastic fuel

UR - http://www.scopus.com/inward/record.url?scp=85087826287&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2020.3004156

DO - 10.1109/ACCESS.2020.3004156

M3 - Journal article

AN - SCOPUS:85087826287

VL - 8

SP - 117467

EP - 117482

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 9122496

ER -