Prediction of B20 Storage Tank Precipitate Removal Based on Biodiesel Monoglyceride Content

Precipitate in B20 fuel stored in storage tanks can accumulate at the bottom level of the tank and affect the fuel filter, clogging in the fuel distribution and engine system. This study examines the precipitate formation prediction in B20 fuel based on the monoglyceride content in biodiesel. This research used a modified CSFT method of ASTM D7501 for the precipitation test. Monopalmitin was added to biodiesel with a variation of monoglyceride content. Each biodiesel sample was then blended with petroleum diesel fuel to produce two groups of samples. Each sample was separately soaked in the cooling chamber at constant and room temperature for 21 days. The bottom layer of each B20 fuel sample stored in the measuring cylinder was then pipetted and filtered, washed with petro-ether, vacuum-dried, and weighed for a constant amount of precipitate retained on the filter. The simulation results show that the ratios between the amount of collected precipitate at the bottom layer of the 2-liter measuring cylinder and the total amount of collected precipitate for the 2-liter measuring cylinder increased with the monoglyceride content biodiesel. This ratio was used to predict the amount of accumulated sludge for a given volume of B20 fuel loaded into the storage tank. This study shows the effect of monoglyceride content on the precipitation behaviour in the storage tank concerning general tank storage dimension parameters and B20 loading frequency. This approach can be applied to estimate the sludge removal frequency for biodiesel storage.

A Cost-Aware DNN-Based FDI Technology for Solenoid Pumps

Fluid Pumps serve a critical function in hydraulic and thermodynamic systems, and this often exposes them to prolonged use, leading to fatigue, stress, contamination, filter clogging, etc. On one hand, vibration monitoring for hydraulic components has shown reliable efficiencies in fault detection and isolation (FDI) practices. On the other hand, signal processing techniques provide reliable FDI parameters for artificial intelligence (AI)-based data-driven diagnostics (and prognostics) and have recently attracted global interest across different disciplines and applications. Particularly for cost-aware systems, the choice of diagnostic parameters determines the reliability of an FDI/diagnostic model. By extracting (and selecting) discriminative spectral and transient features from solenoid pump vibration signals, accurate diagnostics across operating conditions can be achieved using AI-based FDI algorithms. This study employs a deep neural network (DNN) for fault diagnosis after a correlation-based selection of discriminative spectral and transient features. To solve the problem of hyperparameter selection for the proposed model, a grid search technique was employed for optimal search for parameters (number of layers, neurons, activation function, weight optimizer, etc.) on different network architectures.The results reveal the high accuracy of a three-layer DNN with ReLU activation function, with a test accuracy of 99.23% and a minimal false alarm rate on a case study.

Convolutional Neural Network Model for Estimating Clogging of Ash-Mixed Sand Filters

A deep learning model was developed to estimate the clogging of voids in bottom-ash-mixed sand filters. Filter clogging is characterized by a decrease in the infiltration ratio with an increase in the outflow. The estimation of clogging for time-series forecasting is a regression problem. Various algorithms for regression problems have been applied through machine learning and deep learning. Despite the various algorithm applications, an application for clogging appears to be emerging. Studies are required to develop dependable models for precisely predicting clogging. A convolutional neural network (CNN) clogging model was developed in this study to estimate the relationship between the infiltration ratio and outflow quantity using features defined by the mixing ratio between sand and ash. Significant clogging data required for the effective maintenance and stable operation of stormwater infiltration filters were obtained using the proposed model. The CNN clogging model is recommended as a supplement to conventional theoretical models and expensive experimental models.

Water pre-filtration methods to improve environmental DNA detection by real-time PCR and metabarcoding

Environmental DNA (eDNA) analysis is a novel approach for biomonitoring and has been mostly used in clear water. It is difficult to detect eDNA in turbid water as filter clogging occurs, and environmental samples contain various substances that inhibit the polymerase chain reaction (PCR) and affect the accuracy of eDNA analysis. Therefore, we applied a pre-filtration method to better detect the fish species (particularly pale chub, Opsariichthys platypus) present in a water body by measuring eDNA in environmental samples containing PCR inhibitors. Upon conducting 12S rRNA metabarcoding analysis (MiFish), we found that pre-filtration did not affect the number or identities of fish species detected in our samples, but pre-filtration through pore sizes resulted in significantly reduced variance among replicate samples. Additionally, PCR amplification was improved by the pre-filtration of environmental samples containing PCR inhibitors such as humic substances. Although this study may appear to be a conservative and ancillary experiment, pre-filtration is a simple technique that can not only improve the physical properties of water, such as turbidity, but also the quality of eDNA biomonitoring.

Groundwater resources of the Republic of Belarus: Problems and solutions

The quantity and quality of water resources determines sustainable development of any country, as well as the standard of living and the health of the population. According to the national water cadastre, in 2009, 2.35 million m3 of water per day was taken from 20 thousand production wells in Belarus. The Republic of Belarus has significant mineral water resources, and there are 224 wells with mineral water on its territory. There are 130 wells in operation, and 94 wells are reserve wells. Drinking water supply in the Republic of Belarus is mostly based on the use of underground sources, with the exception of the cities of Minsk and Gomel, which makes it possible to obtain better and cleaner water than water from surface sources. At the same time, a significant part of wells operate at lower rate due to the phenomena of mechanical, biological and chemical clogging. Chemical clogging is an unavoidable process. Even with high-quality drilling operations, during well operation, due to the appearance of hydrodynamic disturbances in the reservoir, the gas equilibrium in underground water is shifted, which results in precipitation of hard-to-dissolve iron, calcium, manganese, and silicon compounds on the filter and in the filter zone. The analysis of the service life of water intake wells has shown that the main causes of their failure are filter clogging and sanding. The service life of most wells, even if mudding sediments are removed by the known methods, is never longer than 16–20 years. Operation of inefficient wells, their subsequent abandonment and re-drilling require significant financial resources. Currently, outdated wells are being designed and constructed at water intakes in the Republic of Belarus. They are insufficiently maintainable and short-lived, have a significant cost and high operating costs for lifting water. One of the ways to solve the problem is to build wells with annular systems of circulating reagent regeneration and replaceable filters to provide a significant increase in resource, stability of flow rate and reduction in operating costs. This will allow us to modernize water intakes of underground water by switching to wells of new designs and to reach a better level in ground water production.

Ballast Water Treatment Performance Evaluation under Real Changing Conditions

We conducted a shipboard ballast water test using seawater of extreme turbidity collected from Shanghai Port (China) (>300 mg total suspended solids (TSS)/L), and normal seawater collected in other ports (<100 mg TSS/L). All three types of International Maritime Organization (IMO)-approved ballast water management system (BWMS) tested failed to properly operate because of filter clogging or insufficient generation of oxidants under near-fresh water conditions with extremely high concentration of suspended solid during ballasting. It was also found that the number of microorganisms increased with longer ballast water retention time, with higher numbers in the treated discharge water. The results suggest that when operating a BWMS involving a filter unit in areas with water having high concentrations of suspended solids, the filter unit should be used during ballast water discharge, rather than during ballasting. This method has the advantage of removing ≥50 µm organisms at discharge that could not be removed by a filter during ballasting. For ballast water retained for long storage times, the results suggest the use of BWMSs involving UV units or electrolysis during deballasting. In addition, BWMSs involving electrolysis units provide the opportunity to maintain residual total residual oxidant (TRO) levels, using a partial ballast tank. Although the BWMSs tested are a small subset of the large number of IMO-approved BWMSs, the results demonstrate that there is a significant gap between the technology currently available and capacity to meet IMO and US Coast Guard standards.