Application of Machine Learning for Predictive Maintenance Cooling System in Nam Ngum-1 Hydropower Plant

Xayyasith, Sisavath; Promwungkwa, Anucha; Ngamsanroaj, K.

doi:10.1109/ictke.2018.8612435

Cited by 13 publications

(11 citation statements)

References 3 publications

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“…The study revealed that areas with increasing HP development are prone to geological and hydro-climatic hazards which portends multiple environmental and technological risks to lives. This submission is in agreement with previous studies such as Wei et al, 6 Ibrahim and Waziri, 8 Bordin et al, 10 and Xayyasith et al 11 HP remains a renewable and eco-friendly source of energy. It is responsible for 16% of the world's generated electricity and 78% of renewable electricity generation.…”

Section: Literature Reviewsupporting

confidence: 93%

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The effects of Trichoderma atroviride pretreatment on the biogas production from anaerobic digestion of water hyacinth

Ilo

Nkomo

Mkhize

et al. 2022

Energy & Environment

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Water hyacinth is an invasive alien plant with several impacts on the environment, economy and society. The plant’s high degree of proliferation makes its mitigation difficult and sometimes complex. However, existing evidence suggests that water hyacinth is a sustainable substrate for biogas production. Using the pretreatment processes for the optimisation conditions for biogas production from water hyacinth, this study analysed the effects of moisture content (60%, 70% and 75%) on Trichoderma atroviride pretreatment of water hyacinth and the impact of the pretreatment on biogas production. Anaerobic digestion of the water hyacinth process was performed at 35°C for 35 days. The modified Gompertz model was used to analyse and predict the appropriate kinetic variables of the digestion process. Biogas yields from untreated, pretreated-60%, pretreated-70% and pretreated-75% were optimal at 135, 210, 217 and 223.4 mL/g of volatile solids (VS). These results suggest the pretreatment of water hyacinth enhanced the degradability of water hyacinth by breaking down the cell wall structure and facilitating its use by microorganisms. Furthermore, the results also confirmed that the higher the moisture content, the easier the biodegradation rate and, consequently, the higher the biogas yield. The model predicted maximum methane production potential ranging from 91.84 to 201 mL/g VS, and the maximum methane yield rate was within 10.12–15.12 mL/day. The lag phase varied between 2.46 and 6.94 days. The percentage error between experimental and model outcomes for untreated, pretreated-60%, pretreated-70% and pretreated-75% are 17.96%, 16.67%, 14.20% and 4.68%, respectively, while the coefficients of determination of the model varied between 0.905 and 0.975, demonstrating significant reliability on attained factors.

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Section: Literature Reviewsupporting

confidence: 93%

“…This has made it difficult to have a mitigating approach with a wide level of acceptance among stakeholders, especially in a multi-stressed environment where solutions could still not be ascertained. 11 The main research questions raised for this study are:…”

Section: Introductionmentioning

confidence: 99%

The effects of Trichoderma atroviride pretreatment on the biogas production from anaerobic digestion of water hyacinth

Ilo

Nkomo

Mkhize

et al. 2022

Energy & Environment

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“…For the mean squared error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), correlation coefficient (R), and coefficient of determination (R2) were calculated the error in regression type. And the confusion matrix has calculated the error in classification type [25]. The measurement indicates the accuracy of the model for training when compared to the predictive result of the data testing.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Optimal Operation of Geothermal Power Plant by Artificial Neural Network

2023

IJML

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“…The prediction result shows acceptance criteria. This model is more convenient for the operator; they can further visualize and monitor this system in the hydropower plant (Xayyasith et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning

Azyus

2022

JCSTS

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Predictive maintenance (PdM) is indicated state of the machine to perform a schedule of maintenance based on historical data, integrity factors, statistical inference methods, and engineering approaches that are currently often applied to aircraft maintenance. The Predictive maintenance on aircraft to avoid the worse event (failure) and get information about the status of aircraft machines by applied on Machine Learning (ML) to get high accuracy and precision. The research aims to look for the method and technique of ML, which is the best applied on PdM for aircraft in accuracy indicators. The techniques of ML have been divided by classification and regression, which are compared on three ML methods: Random Forest (RF), Support Vector Machine (SVM), and simple LSTM. The result of the study for classification technique are LSTM 98,7%, SVM 95,6%, and RF 900,3%. On other hand, Regression technique for ML result on MAE and RMSE are LSTM 13,55 and 22,13, SVM 15,77 and 20,51, RF 15,06 and 19,98. Classify technique is better and faster than regression when calculating the PdM on an aircraft engine. The LSTM method of ML is the best applied to it because of the accuracy higher and time process faster than other methods in this study. Finally, the LSTM method is highly recommended while using with classify technique on ML to determine the PdM on an aircraft engine.

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Application of Machine Learning for Predictive Maintenance Cooling System in Nam Ngum-1 Hydropower Plant

Cited by 13 publications

References 3 publications

The effects of Trichoderma atroviride pretreatment on the biogas production from anaerobic digestion of water hyacinth

The effects of Trichoderma atroviride pretreatment on the biogas production from anaerobic digestion of water hyacinth

Optimal Operation of Geothermal Power Plant by Artificial Neural Network

Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning

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