Pengaruh Nitrat terhadap Biokorosi Logam oleh Konsorsium Bakteri Pereduksi Sulfat dari PLTA Saguling

<div><strong>The Influence of Nitrate in Metal Biocorrosion caused by Sulfate Reducing Bacteria from Saguling Hydropower</strong>. The corrosion facilitated and accelerated by the activities of microorganism is called biocorrosion. Sulfate reducing bacteria (SRB) is known as the bacteria that cause biocorrosion in anaerobic condition by using sulfate as the final electron acceptor. Biocorrosion reduces equipment lifetime and increases maintenance cost in industry. In the cooling system in Saguling hydropower, corrosion was commonly caused by utilization of contaminated water due to anorganic and organic waste, especially sulfate. In this research, sulfate reducing bacteria was isolated from biofilms in the cooling system of Saguling Hydropower. Molecular analysis using PCR-DGGE method with dsrB gene (350 bp) as molecular markers showed that SRB consortium contained 12 bands and assumed as different species of SRB. SRB consortium was tested to determine its biocorrosion activity over metal material of ST37 (carbon steel) and SUS304 (stainless steel). The consortium then treated with 7 different nitrate concentrations to determine its effect against the sulfate reducing bacteria activity. SRB consortium caused higher corrosion to ST37 than SUS304L, with the corrosion rate of 0.07660 mm/year and 0.00265 mm/year, respectively. Concentration of 10 mM nitrate effectively inhibited corrosion rate on ST37 and caused the changes in sulfate reducing bacteria communities, indicated by the disappearance of 6 bands in DGGE profile</div>

PREDICTIVE MAINTENANCE USING LONG SHORT-TERM MEMORY ALGORITHM ON SENSOR DATA

Think of a complex system with very expensive parts. We can't risk running into failure as it will be extremely costly to repair highly damaged parts. But more importantly, it's a safety issue. This is why numerous organizations attempt to avoid failure beforehand by performing regular inspections on their equipment. One big challenge is to determine when to do maintenance. Since we don't know when failure will occur, we have to be conservative in our planning. LTSM can be used to predict the remaining useful life. But if we schedule maintenance very early, we will end up wasting machine life that is still usable, and this will add up to our costs. However, if we can predict when machine failure will occur, we can schedule maintenance right before it. Recurrent Neural Networks can predict when this machine failure is bound to happen. Predictive maintenance lets us estimate time to failure. It also pinpoints problems in complex machinery and helps us identify what parts need to be fixed. This way, we can minimize downtime and maximize equipment lifetime.

DETECTION OF POTENTIALLY UNRELIABLE VLSI BASED ON THE ANALYSIS OF TEMPERATURE-SENSITIVE PARAMETERS

The paper discusses methods for non-destructive diagnostic testing of very large scale integration circuits (VLSI) based on the “junction-case” thermal resistance parameter. This parameter is important because VLSI’s failure rate depends on junction temperature, which in turn depends on thermal resistance “junction-case”. There are three known methods for detecting potentially unreliable VLSIs with increased thermal resistance value: 1) non-destructive measurement of thermal resistance; 2) scanning acoustic microscopy; 3) an approach based on the statistical analysis of temperature-sensitive electric parameters. The paper presents advantages and disadvantages of each method. Special attention is paid to statistical analysis of temperature-sensitive electric parameters because this method allows detecting of potentially unreliable VLSIs without using expensive equipment. This method does not require changes in existing measurement programs. Electric parameters, which depend on temperature, are temperature-sensitive parameters. These parameters are useful for detecting VLSIs with deviations from the main batch. This allows decreasing of risk of potentially unreliable VLSIs application in high reliable equipment. With the proposed approach the high reliable equipment lifetime can be increased.

Assessing the Status of Quality Control Parameters of Four Computed Tomography Scanners in Ibadan

Background: Quality control (QC) of computed tomography (CT) scanners is important to evaluate succinctly quality image and radiation dose obtainable in a clinical environment. The aim of this study was to evaluate the quality of images generated by CT scanners used at some diagnostic facilities in Ibadan, Nigeria. Materials and Methods: A cross sectional design was employed in this study, four centers were studied, one government hospital and three private hospitals. The head CT phantom was used to verify the accomplishment of the CT scanners performance to the international quality requirements. Regions of interest were selected at the center of the image and at the periphery to obtain results for the CT number for water test, uniformity test, noise, and artifact test. Results: The mean CT number for water across the centers ranged from –0.12 HU to –2.2 HU which were within ±3 HU recommended by the equipment manufacturer. Values of standard deviation of the mean CT number ranged from 2.41 to 5.77 HU which to a little extent exceeded the set ±5 HU tolerance range. Similarly, the presence of streak artifact was observed in the images obtained at one center. Conclusion: Two out of the four computed tomography scanners assessed passed the four tests performed. Noise and artifact were the problem observed at centers B and C respectively. There was however no likelihood of periodic performance of these basic quality control tests at two of the centers in this study. Adequate records of quality control data should be kept regularly to allow in-depth analysis of failure rates of different tests, changes occurring during equipment lifetime and comparisons among CT scanners.

Circular Economy Strategies for Equipment Lifetime Extension: A Systematic Review

Even if the economy nowadays is still locked into a linear model of production, tighter environmental standards, resource scarcity and changing consumer expectations are forcing organizations to find alternatives to lighten their impacts. The concept of Circular Economy (CE) is to an increasing extent treated as a solution to this series of challenges. That said, the multitude of approaches and definitions around CE and Life Cycle Extension Strategies (LCES) makes it difficult to provide (Small and Medium Enterprise) SMEs with a consistent understanding of the topic. This paper aims at bridging this gap by providing a systematic literature review of the most prominent papers related to the CE and lifetime extension, with a particular focus on the equipment and machinery sector. A taxonomy was used to define and cluster a subset of selected papers to build a homogeneous approach for understanding the multiple strategies used in the industry, and the standards in maintenance and remanufacturing strategies. As a final research step, we also propose a Strategy Characterization Framework (SCF) to build the ground for the selection of the best strategy to be applied for production equipment life cycle extension on several industrial use cases.

Improving the Ambient Temperature Control Performance in Smart Homes and Buildings

Currently, it is becoming increasingly common to find numerous electronic devices installed in office and residential spaces as part of building automation solutions. These devices provide a rich set of data related to the inside and outside environment, such as indoor and outdoor temperature, humidity, and solar radiation. However, commercial of-the-shelf climatic control systems continue to rely on simple controllers like proportional-integral-derivative or even on-off, which do not take into account such variables. This work evaluates the potential performance gains of adopting more advanced controllers, in this case based on pole-placement, enhanced with additional variables, namely solar radiation and external temperature, obtained with dedicated low-cost sensors. This approach is evaluated both in simulated and real-world environments. The obtained results show that pole-placement controllers clearly outperform on-off controllers and that the use of the additional variables in pole-placement controllers allows relevant performance gains in key parameters such as error signal MSE (17%) and control signal variance (40%), when compared with simple PP controllers. The observed energy consumption savings obtained by using the additional variables are marginal (≈1%, but the reduction of the error signal MSE and control signal variance have a significant impact on energy consumption peaks and on equipment lifetime, thus largely compensating the increase in the system complexity.

Total cost of ownership as a base of Transneft fleet operation

Total cost of ownership (TCO) accounting is a brand new optimization process aimed at gaining overall investment and maintenance benefit which is being first applied in Transneft to vehicles and heavy equipment. Commonly fleet renewal and model choice decisions are built on outdated technical standards, personal experience or declared properties, however such way cannot provide an objective result in a modern condition of engineering. IT development in vehicles and heavy equipment monitoring allows accumulating primary data on the operation of each technical unit. Transneft has worked out the methodology of AV annual TCO calculating and analysis to describe the procedure and methods of applying the primary data to AV annual TCO calculating, optimal equipment lifetime determination and models comparison. The authors of this paper have developed the methodology based on the identification of primary data correspondence to different intervals of the same model lifetime using a Big data logic and further operating cash flow long-term forecasting for each model. Forecasting process is selected between averaging, linear extrapolation and probabilistic model based on reliability theory methods. AV annual TCO is calculated by applying equivalent annual annuity to the overall lifetime cash flow. Optimal equipment lifetime is calculated in correspondence to the minimum AV annual TCO. Models are compared by the AV annual TCO corresponded to optimal equipment lifetime. AV annual TCO calculating and analysis provides a whole new level of optimal equipment lifetime determination based on overall investment and maintenance benefit and models overall lifetime costs comparison. Thus we have a mathematical founded answer to the questions «How often to renew the fleet?» and «Which model to choose?».

Stationary gas turbines: an exergetic approach to part load operation

As renewables are progressively displacing thermal plants in the power generation scene worldwide, the vocation of stationary Gas Turbines (GT) is deeply evolving. In this irreversible move GT plants are called upon to become cycling units with increasingly variable load profiles. This is dictated by the need to compensate for the fluctuations of renewable energy sources and secure the spinning reserve that is indispensable for the stability of the grids. This new scenario creates a serious challenge for gas turbine designers and operators in terms of investment policy, plant management and equipment lifetime. Indeed, operating a gas turbine at part, variable load requires reducing its firing temperature and possibly its air flow. While part load operation entails efficiency losses with respect to the full load mode, load variations cause maintenance penalties due the premature component ageing tied namely with thermal and low cycle fatigue effects on machine materials. As far as efficiency is concerned, an exergy analysis of a contemporary, air-based Brayton cycle is useful for quantifying and comparing the losses incurred by the various engine components. Such study reveals the high sensitivity of compressor efficiency to load decreases. Among possible counter-measures, heating the air at the compressor intake represents a simple mitigation measure, as it enables reducing the air flow rate while preserving to some extent the efficiency of the compressor and consequently GT efficiency.

Monitoring Fugitive Methane Gas Emission From Natural Gas Pads

Identifying fugitive methane leaks can improve predictive maintenance of the extraction process, can extend gas extraction equipment lifetime, and eliminate hazardous work conditions. We demonstrate a wireless sensor network based on cost effective and robust chemi-resistive methane sensors combined with real time analytics to identify leaks from 2 scfh to 1000 scfh. The chemi-resistive sensors were validated to have a sensitivity better than 1 ppm in methane plume detection. The real time chemical sensor and wind data is integrated into an inversion models to identify the location and the magnitude of the methane leak. This integrated sensing and analytics solution can be deployed in outdoor environment for long term monitoring of accidental methane plume emissions, generate recommendations about fixing them, and ensure compliance with local government regulations.