Dependability Evaluation Based on System Monitoring

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Boosting resonant switched-capacitor voltage tripler

This elaboration presents the concept of a unidirectional DC–DC switchedcapacitor converter operating as a voltage tripler. The system consists of two resonant cells with switched capacitors and chokes. This proposed converter topology achieves low voltages on semiconductor switches (diodes and transistors) compared to the classic SC series-parallel converter or the boost topology. The output voltage on the capacitors is reduced in the proposed converter because it is divided into two series-connected capacitors with asymmetric distribution. The presented results describe the analytical description of the system operation and the analytical equation for semiconductor currents. A simulation and experimental results have been performed. The system efficiency and three voltage gain values were measured in the experimental setup. The efficiency measured was also compared with the analytical determination curve for loss analysis and further converter optimization

AutoML-based predictive framework for predictive analysis in adsorption cooling and desalination systems

Adsorption cooling and desalination systems have a distinct advantage over other systems that use low-grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real-life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI-based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three-bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three-bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods

CO₂ Capture by Virgin Ivy Plants Growing Up on the External Covers of Houses as a Rapid Complementary Route to Achieve Global GHG Reduction Targets

Global CO2 concentration level in the air is unprecedently high and should be rapidly and significantly reduced to avoid a global climate catastrophe. The work indicates the possibility of quickly lowering the impact of changes that have already happened and those we know will happen, especially in terms of the CO2 emitted and stored in the atmosphere, by implanting a virgin ivy plant on the available area of walls and roofs of the houses. The proposed concept of reducing CO2 from the atmosphere is one of the technologies with significant potential for implementation entirely and successfully. For the first time, we showed that the proposed concept allows over 3.5 billion tons of CO2 to be captured annually directly from the atmosphere, which makes even up 6.9% of global greenhouse gas emissions. The value constitutes enough high CO2 reduction to consider the concept as one of the applicable technologies allowing to decelerate global warming. Additional advantages of the presented concept are its global nature, it allows for the reduction of CO2 from all emission sources, regardless of its type and location on earth, and the fact that it will simultaneously lower the air temperature, contribute to oxygen production, and reduce dust in the environment

Modeling of Thermal Cycle CI Engine with Multi-Stage Fuel Injection

This work presents a complete thermal cycle modeling of a four-stroke diesel engine with a three-dimensional simulation program CFD - AVL Fire. The object of the simulation was the S320 Andoria engine. The purpose of the study was to determine the effect of fuel dose distribution on selected parameters of the combustion process. As a result of the modeling, time spatial pressure distributions, rate of pressure increase, heat release rate and NO and soot emission were obtained for 3 injection strategies: no division, one pilot dose and one main dose and two pilot doses and one main dose. It has been found that the use of pilot doses on the one hand reduces engine hardness and lowers NO emissions and on the other hand, increases soot emissions

Modelling of SO2 and NOx Emissions from Coal and Biomass Combustion in Air-Firing, Oxyfuel, iG-CLC, and CLOU Conditions by Fuzzy Logic Approach

Chemical looping combustion (CLC) is one of the most advanced technologies allowing for the reduction in CO2 emissions during the combustion of solid fuels. The modified method combines chemical looping with oxygen uncoupling (CLOU) and in situ gasification chemical looping combustion (iG-CLC). As a result, an innovative hybrid chemical looping combustion came into existence, making the above two technologies complementary. Since the complexity of the CLC is still not sufficiently recognized, the study of this process is of a practical significance. The paper describes the experiences in the modelling of complex geometry CLC equipment. The experimental facility consists of two reactors: an air reactor and a fuel reactor. The paper introduces the fuzzy logic (FL) method as an artificial intelligence (AI) approach for the prediction of SO2 and NOx (i.e., NO + NO2) emissions from coal and biomass combustion carried out in air-firing; oxyfuel; iG-CLC; and CLOU conditions. The developed model has been successfully validated on a 5 kWth research unit called the dual fluidized bed chemical looping combustion of solid fuels (DFB-CLC-SF)

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector

Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO2 measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO2, CH4, N2O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector

Evaluation of carbetocin (Pabal) efficacy in the prevention of the postpartum hemorrhage in women after cesarean section – preliminary report

Abstract Objectives: The aim of this study was to evaluate the efficacy of carbetocin in prevention of PPH in women after cesarean section. Material and Methods: We enrolled 60 patients who had undergone cesarean section in tertiary referential center, Department of Perinatology, Medical University of Lodz, Poland, between January and June 2008. Each patient obtained a single 100μg dose of carbetocin intravenously during cesarean section, immediately after the delivery of the baby and prior to the delivery of the placenta . We evaluated postoperative blood parameters in 2 and 12 hours after the operation, the proportion of patients requiring additional uterotonic agents and adverse events in the whole population and in the group of women with high risk of PPH. Results: 58.1% of patients underwent emergency and 41.3% elective cesarean section delivery. The risk factor of PPH was identified in 38 women (63.3%). The results of this study indicate that carbetocin produces rapid and longlasting uterine tone. A small drop in mean hemoglobin and hematocrit levels 2 and 12 hours after the operation was observed. 15% of patients required the use of additional uterotonic agents. In the group of women with high risk of PPH, carbetocin appeared to be effective in 79% of the patients. Only 11.4% of patients had minor adverse events. Conclusions: Carbetocin appears to be an effective new drug in the prevention of postpartum hemorrhage, not only among women undergoing cesarean section but also in the group of women with PPH risk factors

A fuzzy logic approach for the reduction of mesh-induced error in CFD analysis: A case study of an impinging jet

A crucial step in any computational fluid dynamics (CFD) analysis is the discretization of the domain because it influences truncation errors, numerical stability, and the convergence of the model. Therefore, the appropriate selection of numerical mesh parameters crucially contributes to the reliability of the obtained results. Therefore, an innovative approach to reducing the mesh-induced error in CFD analysis of an impinging jet using fuzzy logic is proposed within the paper. The flow parameters were obtained using the Reynolds-averaged Navier–Stokes calculations, based on the mesh parameters obtained using the grid convergence index and fuzzy logic, were compared to each other and to experimental research results. The fuzzy logic approach to define mesh parameters turned out to be a very promising method as it allowed us to obtain results that are qualitatively and quantitatively comparable to commonly used but far more time-consuming methods.Web of Science2111art. no. 104

The influence of computational domain discretization on CFD results concerning aerodynamics of a vehicle

The paper presents research concerning the influence of computational domain discretization on the results of CFD analysis. Tetrahedral and polyhedral numerical mesh types are analyzed and the mesh convergence index is calculated. The obtained results are compared to the experimental measurements concerning the estimation of drag coefficient of the vehicle model. The research carried out indicates the great influence of pre-processing on the reliability of the obtained results. Moreover, the advantages of polyhedral mesh over tetrahedral mesh are pointed out in the paper