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ÖgeData-driven modeling for the prediction of stack gas concentration in a coal-fired power plant in Türkiye(Springer, 2024)In this research, deep learning and machine learning methods were employed to forecast the levels of stack gas concentrations in a coal-fired power plant situated in Türkiye. Real-time data collected from continuous emission monitoring systems (CEMS) serves as the basis for the predictions. The dataset includes measurements of carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), oxygen (O2), and dust levels, along with temperatures recorded. For this analysis, deep learning methods such as multi-layer perceptron network (MLP) and long short-term memory (LSTM) models were used, while machine learning techniques included light gradient boosted machine (LightGBM) and stochastic gradient descent (SGD) models were applied. The accuracy of the models was determined by analysing their performance using mean absolute error (MAE), root means square error (RMSE), and R-squared values. Based on the results, LightGBM achieved the highest R-squared (0.85) for O2 predictions, highlighting its variance-capturing ability. LSTM excelled in NOx (R-squared 0.87) and SO2 (R-squared 0.85) prediction, while showing the top R-squared (0.67) for CO. Both LSTM and LGBM achieved R-squared values of 0.78 for dust levels, indicating strong variance explanation. Conclusively, our findings highlight LSTM as the most effective approach for stack gas concentration forecasting, closely followed by the good performance of LightGBM. The importance of these results lies in their potential to effectively manage emissions in coal-fired power plants, thereby improving both environmental and operational aspects.
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ÖgeDiscovering the perception differences of stakeholders on the sustainable and innovative stormwater management practices(Springer, 2024)The overarching aim of the present work is to explore the perception differences of stakeholders, i.e., municipalities (MN), water administrations (WS), non-governmental organizations (NGO), and universities (UN), playing vital roles in the decision mechanisms regarding one of the sustainable flood mitigation techniques, i.e., low impact development (LID) practices. As being rewarding alternative to conventional drainage techniques, four different LID strategies, i.e., green roof (GR), bioretention cells (BC), permeable pavement (PP), and infiltration trench (IT), and three of their combinations were adopted to the densely urbanized Ayamama River basin, Istanbul, Turkey. The performances of the LIDs were comprehensively evaluated based on three pillars of sustainability (i.e., social, economic, and environmental) using a hybrid multi-criteria decision-making (MCDM) framework containing the implementation of fuzzy analytical hierarchy process (fuzzy AHP) and the VIKOR (VIse KriterijumsaOptimiz acija I Kompromisno Resenje) for finding the weights of constraining criteria and prioritizing the LID scenarios, respectively. The major outcomes of this research showed that experts from MN, WS, and UN put forward the environmental dimension of sustainability, whereas respondents from NGO concentrated on the social aspect. Furthermore, MN and WS highlighted initial investment cost as the most determining criterion in optimal LID selection. On the other hand, criteria weights regarding the judgments of the experts attended from NGO revealed the significance of community resistance in specifying the optimal LID practices, while aesthetic appearance was the major concern of the academia. Hence, the present study, as an initial attempt, enabled critical standpoints for discovering perceptions of stakeholders.