Environmental Engineering
M.J. Zoqi
Abstract
BACKGROUND AND OBJECTIVES: Leachate recirculation has become a global practice for anaerobic digestion of municipal solid waste. Implementation of artificial neural networks for modeling and prediction of this process still remains challenging. Additionally, there has been a lack of research regarding ...
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BACKGROUND AND OBJECTIVES: Leachate recirculation has become a global practice for anaerobic digestion of municipal solid waste. Implementation of artificial neural networks for modeling and prediction of this process still remains challenging. Additionally, there has been a lack of research regarding the generalization capacity of neural networks using the data from other studies. This study aimed to enhance methane production rates and decrease biostabilization time in municipal solid waste treatment. It addressed the research gap in applying and generalizing neural networks to predict biogas production based on laboratory-measured parameters.
METHODS: Two distinct systems were utilized for leachate treatment. System 1 involved collecting the leachate delivered by a new municipal solid waste reactor and transferring it to a recirculation tank. System 2 consisted of passing the fresh municipal solid waste leachate through a degraded municipal solid waste and then returning the obtained liquid back to the waste reactor. The experimental data were employed to develop an artificial neural network to predict methane content and cumulative biogas production. The model was trained and optimized using the experimental data. The effectiveness and generalizability of the optimal neural network were evaluated by using it for the unseen data from other studies, ensuring its ability to make accurate predictions beyond the training dataset.
FINDINGS: The results demonstrated that in System 1, ammonium and chemical oxygen demand concentrations in the leachate progressively increased to high levels. In System 2, the average removal efficiencies for chemical oxygen demand and ammonium were found to be 85 percent and 34 percent respectively. The methane yield in biogas reached 59 liters per kilogram of dry weight, with a corresponding methane fraction of 63 percent. The neural network model showed an excellent performance, with validation performances of 0.716 and 0.634. The overall performance of the dataset resulted in correlation coefficients of 0.9991 and 0.9975. Finally, high correlation coefficients of 0.88 and 0.82 were achieved by incorporating the test data from other studies.
CONCLUSION: Leachate recirculation enhanced the reduction of chemical oxygen demand and the production of methane in bioreactors. Ammonium concentrations initially increased and later decreased due to waste adsorption and bacterial assimilation. The artificial neural network applied for predicting the cumulative methane production from municipal solid waste displayed a robust generalizability when tested on the data from other studies. The neural network was not significantly affected by changes in waste chemical properties, laboratory conditions, and recirculation rate. However, it showed a significant sensitivity to variation of waste mechanical properties.
Environmental Engineering
Z. Farajzadeh; M.A. Nematollahi
Abstract
BACKGROUND AND OBJECTIVES: The rank of Iran in terms of pollutant emissions, which mainly originate from the consumption of energy products, is much higher than the rank of gross domestic product, placing Iran the fourth in the production and consumption of gas and oil, among the cases with the highest ...
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BACKGROUND AND OBJECTIVES: The rank of Iran in terms of pollutant emissions, which mainly originate from the consumption of energy products, is much higher than the rank of gross domestic product, placing Iran the fourth in the production and consumption of gas and oil, among the cases with the highest emission intensity in the world. Different driving forces account for the high emission intensity. This study decomposes the changes in the aggregate emission intensity of the selected pollutants into a broader scope of driving forces including energy, urbanization, output, labor, and trade-related variables. The examined pollutants were far beyond carbon dioxide, including nitrogen oxides, sulphur dioxide, and carbon monoxide, emitted from energy product consumption. The aim of this study was to investigate the emission intensity of the selected pollutants and their components. METHODS: Decomposition analysis was done to decompose the emission intensity into a broader scope of the driving forces far beyond what examined in the literature. For this purpose, two well-known artificial neural networks, multilayer perceptron, and wavelet-based neural network were applied to forecast the emission intensity of the selected pollutants and their components.FINDINGS: The emission intensity of nitrogen oxides and sulphur dioxide illustrated a decreasing trend. In contrast, a general increasing trend with significant fluctuation was observed for carbon monoxide and carbon dioxide emission intensity. Among the components, energy structure, population-labor ratio, and trade openness showed an intensity decreasing effect, while urban per capita output, urbanization, energy intensity, and industrial output-trade ratio contributed to higher emission intensity of the pollutants. Moreover, the multilayer perceptron and wavelet-based neural networks were recommended to examine the predictability of the emission intensity and its components.CONCLUSION: It was found that intensive and extensive growth and energy structure were the most significant driving forces of the emission intensity. The forecast results indicated that the emission intensity of nitrogen oxides, sulphur dioxide, and carbon monoxide might be predicted by the applied networks with a prediction error of less than 0.2 percent. However, the prediction error for carbon dioxide emission intensity was much higher.
Environmental Management
U. Muksin; E. Riana; A. Rudiyanto; K. Bauer; A.V.H. Simanjuntak; M. Weber
Abstract
BACKGROUND AND OBJECTIVES: Soil or rock types in a region are often interpreted qualitatively by visually comparing various geophysical properties such as seismic wave velocity and vulnerability, as well as gravity data. Better insight and less human-dependent interpretation of soil types can be obtained ...
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BACKGROUND AND OBJECTIVES: Soil or rock types in a region are often interpreted qualitatively by visually comparing various geophysical properties such as seismic wave velocity and vulnerability, as well as gravity data. Better insight and less human-dependent interpretation of soil types can be obtained from a joint analysis of separated and independent geophysical parameters. This paper discusses the application of a neural network approach to derive rock properties and seismic vulnerability from horizontal-to-vertical seismic ratio and seismic wave velocity data recorded in Majalengka-West Java, Indonesia.METHODS: Seismic microtremors were recorded at 54 locations and additionally multichannel analyses of surface wave experiments were performed at 18 locations because the multichannel analyses of surface wave experiment needs more effort and space. From the two methods, the values of the average shear wave velocity for the upper 30 meters, peak amplitudes and the dominant frequency between the measurement points were obtained from the interpolation of those geophysical data. Neural network was then applied to adaptively cluster and map the geophysical parameters. Four learning model clusters were developed from the three input seismic parameters: shear wave velocity, peak amplitude, and dominant frequency.FINDINGS: Generally, the values of the horizontal to vertical spectral ratios in the west of the study area were low (less than 5) compared with those in the southeastern part. The dominant frequency values in the west were mostly low at around 0.1–3 Hertz, associated with thick sedimentary layer. The pattern of the shear wave velocity map correlates with that of the horizontal to vertical spectral ratio map as the amplification is related to the soil or rock rigidity represented by the shear wave velocity. The combination of the geophysical data showed new features which is not found on the geological map such as in the eastern part of the study area.CONCLUSION: The application of the neural network based clustering analysis to the geophysical data revealed four rock types which are difficult to observe visually. The four clusters classified based on the variation of the geophysical parameters show a good correlation to rock types obtained from previous geological surveys. The clustering classified safe and vulnerable regions although detailed investigation is still required for confirmation before further development. This study demonstrates that low-cost geophysical experiments combined with neural network-based clustering can provide additional information which is important for seismic hazard mitigation in densely populated areas.
