scholarly journals ENVIRONMENTAL POLLUTION BY WASTEWATER FROM BROWN COAL PROCESSING ¬ A REMEDIATION CASE STUDY IN GERMANY

2014 ◽  
Vol 22 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Arndt Wiessner ◽  
Jochen A. Müller ◽  
Peter Kuschk ◽  
Uwe Kappelmeyer ◽  
Matthias Kästner ◽  
...  
Keyword(s):  
Environmental Pollution ◽  
Large Scale ◽  
Pilot Scale ◽  
Wastewater Disposal ◽  
Interdisciplinary Approaches ◽  
Phenolic Wastewater ◽  
Term Monitoring

The large scale of the contamination by the former carbo-chemical industry in Germany requires new and often interdisciplinary approaches for performing an economically sustainable remediation. For example, a highly toxic and dark-colored phenolic wastewater from a lignite pyrolysis factory was filled into a former open-cast pit, forming a large wastewater disposal pond. This caused an extensive environmental pollution, calling for an ecologically and economically acceptable strategy for remediation. Laboratory-scale investigations and pilot-scale tests were carried out. The result was the development of a strategy for an implementation of full-scale enhanced in situ natural attenuation on the basis of separate habitats in a meromictic pond. Long-term monitoring of the chemical and biological dynamics of the pond demonstrates the metamorphosis of a former highly polluted industrial waste deposition into a nature-integrated ecosystem with reduced danger for the environment, and confirmed the strategy for the chosen remediation management.

Full-scale bioremediation and long-term monitoring of a phenolic wastewater disposal lake

2010 ◽  
Vol 82 (1) ◽  
pp. 161-173 ◽  
Author(s):  
Ulrich Stottmeister ◽  
Peter Kuschk ◽  
Arndt Wiessner
Keyword(s):  
Disposal Site ◽  
Surface Zone ◽  
Wastewater Disposal ◽  
Color Transparency ◽  
Final Volume ◽  
Phenolic Wastewater ◽  
One Year ◽  
Term Monitoring ◽  
Open Cast

A former open-cast pit was used as the disposal site for wastewater from lignite low-temperature coking processes. The formed lake with a strong stratification had a final volume of 2 × 106 m3. Its water was highly toxic and of dark brownish color (transparency about 3 cm). Traditional remediation methods were too expensive, and it was therefore necessary to develop a new, ecologically and economically acceptable remediation strategy. This strategy is based on a first precipitation step of the dark-brown-colored macromolecules as iron-humate flocs at pH 4.0. These macromolecules are similar to fulvic/humic acid and are formed abiotically by autoxidation of polyphenolic compounds. Thus, the dissolved organic matter (DOM, about 280 mg/l) was decreased by about 50 % and transparency improved. After a neutralization step with 2200 m3 of 20 % calcium carbonate suspension and a subsequent "fertilization" with 0.75 % phosphoric acid (3 × 0.8 m3) "enhanced natural attenuation" was initiated. Due to higher transparency, intense growth of algae ("blooming") and bacteria started in the upper zone of the lake. In winter, algae sedimented and one year after the precipitation step, the water of the surface zone was almost clear (transparency 1 m, after 10 years over 3 m) and odorless.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1261
Author(s):  
Christopher Gradwohl ◽  
Vesna Dimitrievska ◽  
Federico Pittino ◽  
Wolfgang Muehleisen ◽  
András Montvay ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Failure Detection ◽  
Energy Yield ◽  
Combined Approach ◽  
Levelized Cost Of Electricity ◽  
Term Operation ◽  
Model Based

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

In Situ Tests on Creep Behavior of Rock Mass with Joint or Shearing Zone in Foundation of Large-Scale Hydroelectric Projects

2004 ◽  
Vol 261-263 ◽  
pp. 1097-1102 ◽  
Author(s):  
Jian Liu ◽  
Xia Ting Feng ◽  
Xiu Li Ding ◽  
Huo Ming Zhou
Keyword(s):  
Rock Mass ◽  
Creep Behavior ◽  
Large Scale ◽  
Time Dependent ◽  
Three Gorges Project ◽  
Three Gorges ◽  
The Three Gorges ◽  
The Three Gorges Project

The time-dependent behavior of rock mass, which is generally governed by joints and shearing zones, is of great significance for engineering design and prediction of long-term deformation and stability. In situ creep test is a more effective method than laboratory test in characterizing the creep behavior of rock mass with joint or shearing zone due to the complexity of field conditions. A series of in situ creep tests on granite with joint at the shiplock area of the Three-Gorges Project and basalt with shearing zone at the right abutment of the Xiluodu Project were performed in this study. Based on the test results, the stress-displacement-time responses of the joints and basalt are analyzed, and their time-dependent constitutive model and model coefficients are given, which is crucial for the design to prevent the creep deformations of rock masses from causing the failure of the operation of the shiplock gate at the Three-Gorges Project and long-term stability of the Xiluodu arc dam.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

Long-Term Monitoring and Research in the Clarion-Clipperton Zone

2021 ◽  
Vol 55 (6) ◽  
pp. 16-21
Author(s):  
Laurence P. Madin
Keyword(s):  
Large Scale ◽  
Renewable Energy Sources ◽  
Academic Research ◽  
Climate Science ◽  
Polymetallic Nodules ◽  
Continuous Presence ◽  
Offshore Oil Industry ◽  
Offshore Oil ◽  
Term Monitoring

Abstract The widely recognized need for large-scale transition from fossil to renewable energy sources has led to renewed effort to obtain metals needed for battery-based electric transportation and other functions. A potential source of some of these metals is the deposits of polymetallic nodules on the deep seafloor. If mining of these deposits proceeds in the coming decade, the enterprise creates an opportunity for extensive, long-term oceanographic research in the mining locations. The need to monitor environmental effects of mining activity can best be met with a near-continuous presence of a research platform in the vicinity. The platform could be a ship or potentially a semi-submersible platform like those used in the offshore oil industry. Such a facility might be supported by a consortium of mining companies and also provide opportunities for academic research in ocean and climate science.

Sign in / Sign up

Export Citation Format

Share Document