Prediction of Compaction Characteristics of Coal Bottom Ash

Compaction is the process of artificially improving the mechanical properties of soil. However, determination of compaction characteristics in laboratory using Proctor compaction test is time consuming and expensive. Hence, there is a need of correlating compaction characteristics with other physical properties of bottom ash which can be obtained easily. This paper describes an innovative solution to predict the compaction properties of coal bottom ash for the preliminary assessment prior to geotechnical engineering related field applications. The data for required parameters of bottom ash for the model development were collected through a literature survey representing different parts of the world. After stepwise regression analysis, specific gravity and uniformity coefficient were found to be the most significant input parameters to predict the compaction characteristics of bottom ash. These parameters were then used to develop the models to predict maximum dry density and optimum moisture content of bottom ash using multiple regression analysis. The developed models were accurate with a prediction accuracy less than ±3% for both maximum dry density and optimum moisture content models. These empirical models were also presented graphically. According to those predictive curves, maximum dry density increases with increasing uniformity coefficient and specific gravity while optimum moisture content reduced

Prevalence of positive depression screen among post miscarriage women- A cross sectional study

Background: Miscarriages are a common pregnancy complication affecting about 10–15% of pregnancies. Miscarriages may be associated with a myriad of psychiatric morbidity at various timelines after the event. Depression has been shown to affect about 10–20% of all women following a miscarriage. However, no data exists in the local setting informing on the prevalence of post-miscarriage depression. We set out to determine the prevalence of positive depression screen among women who have experienced a miscarriage at the Aga Khan University hospital, Nairobi. Methods: The study was cross-sectional in design. Patients who had a miscarriage were recruited at the post-miscarriage clinic review at the gynecology clinics at Aga Khan University Hospital, Nairobi. The Edinburgh postpartum depression scale was used to screen for depression in the patients. Prevalence was calculated from the percentage of patients achieving the cut –off score of 13 over the total number of patients. Results: A total of 182 patients were recruited for the study. The prevalence of positive depression screen was 34.1% since 62 of the 182 patients had a positive depression screen. Moreover, of the patients who had a positive depression screen, 21(33.1%) had thoughts of self-harm. Conclusion: A positive depression screen is present in 34.1% of women in our population two weeks after a miscarriage. Thoughts of self-harm are present in about a third of these women (33.1%) hence pointing out the importance of screening these women using the EPDS after a miscarriage

Coal Bottom Ash as an Anthropogenic Soil to Prevent Soil Erosion during Post Mine Rehabilitation in Sri Lanka

The clay mines are abandoned due to the higher cost and non-availability of suitable fill material creating severe environmental issues. Coal bottom ash (CBA) is an industrial waste by-product generated in coal power plants and open dumped which causes soil and water pollution. Hence, CBA is a potential anthropogenic soil for post mine rehabilitation. Soil erosion is one of the major environmental problems in post mine land with the change in soil conditions, mainly in the tropical countries like Sri Lanka. Therefore, main objective of this study is to evaluate the potential of CBA to prevent soil erosion in the post mine land. The engineering properties of the samples were evaluated, and annual soil loss due to rainfall was measured by artificial rainfall test for six different CBA and soil mixtures. CBA exhibits high potential to prevent soil erosion with higher permeability and water holding capacity values. The annual soil loss is very low when the fine fraction of the CBA-soil mixture is lesser than 20%. Further, soil erosion can be significantly reduced when CBA fraction of the CBA-soil mixture is greater than 75%. Interestingly, the micropore structure of CBA is significantly influential on the soil erodibility

Utilization of Bottom Ash for Clay Mine Rehabilitation

At the end of mining activities, clay mines were abandoned due to the cost and non-availability of filling materials. These abandoned clay mines cause adverse environmental and social impacts. In addition, large quantities of bottom ash (BA) are generated as a by-product of coal combustion process. This BA is disposed by open dumping in the lands, which creates severe environmental pollution. Therefore, conducted research on utilization of BA for mine rehabilitation is beneficial. The main focus of this research is applicability of BA generated from Lakvijaya power plant, Sri Lanka as a potential backfill material and a soil amendment during the clay mine rehabilitation. Initially tests were conducted to investigate the basic properties of BA. Next, chemical composition of BA was analysed to select the suitable crops for vegetation. Further pH, electrical conductivity and water holding capacity were checked and micro structural morphology of BA was determined through Scanning Electron Microscope. The results showed that BA has good engineering properties and the potential to improve agronomic characteristics of soil. It has better water holding capacity and permeability. BA can adjust soil pH to a desirable plant growth range. As BA has a very porous structure, the root system can easily develop and helps to uptake nutrients by the plant. However, a considerable percentage of trace metals is accumulated in BA which will increase the bioavailability of some trace metals to levels that poses risk to human. Thus, investigations were carried out to identify the heavy metal concentration in leachate of BA using column leaching test. Results showed that leachability potential of trace metals in BA does not exceed the allowable limits

Maximizing Efficiency in Commercial Power Systems with an Optimized Load Classification and Identification Method Using Deep Learning and Ensemble Techniques

Due to the continuous rise of energy demand and electricity costs, the need for a detailed metering option has become crucial. Non-intrusive load monitoring is such an approach that requires less hardware compared to the other load monitoring options, significantly improving consumer comfort. Due to this reason, researchers are encouraged to implement more advanced machine learning techniques capable of accurate load classification and identification; among them, most focus on residential applications due to fewer complications. However, commercial power systems present considerable challenges compared to residential power systems due to the greater diversity of loads and significant imbalances. In order to overcome these challenges, we introduce a novel neural network design that incorporates sequence-to-sequence, WaveNet, and Ensembling techniques to identify and classify single-phase and three-phase loads in commercial power systems. We tested our approach by identifying and classifying nine appliances - five single-phase and four three-phase - for three months, revealing a significant improvement in accuracy