Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler

• Published in: IEEE access Vol. 8; pp. 117467 - 117482
• Main Authors: Shaha, Aditya Pankaj, Singamsetti, Mohan Sai, Tripathy, B. K., Srivastava, Gautam, Bilal, Muhammad, Nkenyereye, Lewis
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; boiler; Boilers; Combustion; Data models; Deep learning; Feeds; green energy; Incineration; Incinerators; Industrial plants; Learning theory; LIME; Long short-term memory; Machine learning; Mass flow rate; Neural networks; Performance prediction; Predictive models; predictors; random forest; Random forests; refuse plastic fuel; Scientific papers; Symbiosis; Waste disposal; Water pressure; Water temperature
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3004156

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 /cm 2 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.