In the present work, an experimental study was conducted to investigate the sulfur distribution in the products during thermal degradation of vacuum residues in the different operating conditions. In this regard, three vacuum residues with different sulfur contents were selected. The effects of main operating conditions such as pressure and temperature on products yields and properties were studied. The results show that for sulfur distribution, which indicates as the ratio of the amount of sulfur in each product to the initial mass of sulfur in the feed, increasing temperature will be followed by increased sulfur distribution of liquid fuel and reduced sulfur distribution of petroleum coke. Furthermore, effects of reaction temperature on yields of light coker gasoil (150-350 °C) as well as heavy coker gasoil (350-500 °C), as the dominant fractions of thermal cracking products, were evaluated using simulated distillation method.
The growing demand towards life cycle sustainability has created a tremendous interest in non-destructive evaluation (NDE) to minimize manufacturing defects and waste, and to improve maintenance and extend service life. Applications of Magnetic Sensors (MSs) in NDE of civil engineering structures have become of great interest in recent years due to their non-contact data collection, and their high sensitivity under the influence of external stimuli such as strain, temperature, and humidity, to detect damage and deficiencies. There have been several advancements in MSs over the years for strain evaluation, corrosion monitoring, etc. based on the magnetic property changes. However, these MSs are at their nascent stages of development, and thus, there are several challenges that exist. This paper summarizes the recent advancements in MSs and their applications in civil engineering. Principle functions of different MSs are discussed, and their comparative characteristics are presented. The research challenges are highlighted and the roadmap towards high technology readiness level is discussed.
The present paper addresses the thermodynamic modeling and multi-objective optimization of a solar-based multi-generation system producing hot water, heating, cooling, hydrogen, and freshwater using a humidification and dehumidification (HDH) unit. Usually, in areas with high radiation intensity, the shortage of drinking water is severe; therefore, using multi-generation systems with solar energy as the prime mover can be a promising option in these areas. The main goals of the current work are multi-aspect assessment and optimization of a solar system to generate potable water and other valuable products. The proposed system is examined using thermodynamic modeling and environmental simulation from different aspects in the present study. The exergy destruction evaluation rate showed that the heliostat had the highest exergy destruction rate, gauged at 1867 kW. Also, in terms of exergy efficiency, the pump and heliostat units had the lowest exergy efficiency, with values of 52.09 % and 65.39 %. Parametric analysis was implemented to find the effect of changing different parameters on the yield of produced fresh water, exergy efficiency, exergy destruction rate, coefficient of performance (COP), sustainability index (SI), and produced hydrogen. Results showed that increasing the compressor pressure ratio from 2 to 6 elicits a reduction in freshwater flow rate and COP. Similarly, increasing the outlet pressure from 70 to 80 bar reduced exergy efficiency and freshwater production. Furthermore, owing to the different effects of the parameters on the studied system, multi-objective optimization was performed using the evolutionary genetic algorithm.
Energy and mass storage in various single-phase fluid flows is of particular interest, as the world currently faces energy challenges. Double-diffusive natural convection in an n-shaped storage tank is numerically studied which can be a general guideline to maintain a storage tank with higher exergy. Lattice-Boltzmann's approach in an in-house computational code is used to simulate the problem. To display the results, it is considered that the Rayleigh number lies between 10³ and 10⁵, and the Lewis number in the range of 0.1 and 10. The average Nusselt and Sherwood number, as well as entropy generation, showing the energy loss, are illustrated. It is observed that the average Nusselt and Sherwood number rises with increasing Rayleigh number and buoyancy ratio. Further, the average Sherwood number boosts by increasing the Lewis number. The most promising parameter in increasing the heat and mass transfer are found to be Rayleigh and Lewis number, respectively, with a maximum 300 percent improvement. The flow friction can be regarded as the main source of entropy generation, with a share of 90 percent. The Rayleigh number increment from 10³ to 10⁵ leads to the rise in the total entropy generation by approximately fivefold.
Random field Monte Carlo (MC) reliability analysis is a robust stochastic method to determine the probability of failure. This method, however, requires a large number of numerical simulations demanding high computational costs. This paper explores the efficiency of machine learning (ML) models and Artificial Neural Networks used as surrogate models trained on a limited number of random field slope stability simulations in predicting the results of large datasets. The paper explores the efficiency of the predictions on the probability of failure using databases with and without factor of safety (FOS) computations. An extensive range of soil heterogeneity and anisotropy is examined on unstratified and layered slopes. On datasets requiring only the examination of failure or non-failure class of slopes (without FOSs), the performance of ML classification of the random field slope stability results generally reduces with higher anisotropy and heterogeneity of the soil. However, using the probability summation method proposed here, ML prediction of the probability of failure is shown to be highly accurate for the whole range of soil heterogeneity and anisotropy. The errors in the predicted probability of failure using 5% of MC data is only 0.46% in average for the prediction of the remaining unseen 95% of data. Offering such accuracies, the approach accelerates the computations for about 100 folds. The models also proved similarly efficient in predicting FOSs for stratified random field anisotropic heterogenous slopes.
The main objective of this study is to compare and optimize two power-to-gas energy storage systems from a thermo-economic perspective. The first system is based on a solid oxide electrolyzer cell (SOEC) combined with a methanation reactor, and the second is based on a polymer electrolyte membrane electrolyzer cell (PEMEC) integrated into a Sabatier reactor. The first system relies on the co-electrolysis of steam and carbon dioxide followed by methanation, whereas the basis of the second system is hydrogen production and conversion into methane via a Sabatier reaction. The systems are also analyzed for being applied in different countries and being fed by different renewable and non- renewable power sources. Simulation results of both systems were compared with similar studies from the literature; the errors were negligible, acknowledging the reliability and accuracy of the simulations. The results reveal that for the same carbon dioxide availability (i.e., flow rate), the SOEC-based system has higher exergy and power-to-gas efficiencies, and lower electricity consumption compared to the PEMEC-based system. However, the PEMEC-based system produces 1.2 % more methane, also with a lower heating value (LHV) of the generated gas mixture that is 7.6 % higher than that of the SOEC-based system. Additionally, the levelized cost of energy (based on the LHV) of the SOEC-based system is found to be 11 % lower. A lifecycle analysis indicates that the lowest lifecycle cost is attained when solar PV systems are employed as the electricity supply option. Eventually, the SOEC-based system is found to be more attractive for power-to-gas purposes from a thermo-economic standpoint.
Among different carbon capture and sequestration (CCS) systems, ones that are based on the electrochemical processes are at the center of attention. Developing a CCS system with a high energy efficiency can facilitate their applications. In this study, two electrochemical CO2 capture systems are optimized and compared from energy consumption perspective. The considered systems are electrochemically mediated amine regeneration (EMAR) and electrochemically proton concentration modulation (EPCM). Both systems are modeled using reactions equilibrium constants and species activity methods and then, based on the presented chemical model, their heat and electricity loads are calculated. The chemical and electrochemical models are validated by comparing the species concentrations and electrochemical cell’s potential with those of previous works. Also, a comprehensive parametric study is presented based on the design parameters, where the performances of the two systems are also compared. Additionally, the energy performances of the systems are modeled using artificial neural network (ANN) to the optimization intention. The obtained ANNs, as a multi-objective optimization function considering heat and electricity loads as objectives, are introduced to the genetic algorithm. Applying the genetic algorithm, both objectives are minimized simultaneously and illustrated in a Pareto front diagram. The results show that the EMAR system at different optimum states always exhibits lower heat and electricity loads than the EPCM system. In trade-off mode, consumption of electricity and heat load for the EMAR system are 38.8 kJ/mol CO2 and 79.2 kJ/mol CO2, respectively, while those of the EPCM system are respectively 45.58 kJ/mol CO2 and 200.3 kJ/mol CO2.
This paper proposes a self-reset pulse frequency modulation (PFM) digital pixel sensor (DPS) with in-pixel variable reference voltage for optical brain imaging systems. The sensor demonstrates a wide dynamic range and very low power consumption that can detect small signals of brain activity in brain. The high dynamic range, high SNR (signal-to-noise ratio), high speed and low power consumption image sensor are suitable for optical brain imaging systems. Since the comparator part consumes high power inside pixel, sub-threshold, self-biased and bulk-driven techniques are used to achieve both ultra-low-voltage and low power in the PFM DPS. Moreover, High speed (high frame rate) is achieved by image capturing in-parallel for all pixels. The proposed image sensor is post layout simulated in 0.18 µm Complementary Metal Oxide Semiconductor (CMOS) technology with 0.6 V supply voltage, resulting in the dynamic range of 152 dB and the power consumption of 11.25 nW and the fill factor of the proposed sensor is 11%. Hence, this device has significant potential to be used for brain signal detection in pre-clinical and clinical studies, cognitive process, diagnose diseases in exploring brain structure and function.
Foresight has recently emerged as one of the most attractive and practical fields of study, while being used to draw up a preferable future and formulate appropriate strategies for achieving predetermined goals. The present research aimed at providing a framework for foresight with a primary focus on the role of a cognitive approach and its combination with the concept of fuzzy cognitive map in the environments of uncertainty and ambiguity. The proposed framework consisted of the 3 phases: pre-foresight, foresight, and post-foresight. The main stage (foresight) focused on the role of imagination and intuition in drawing the future in the experts’ minds and depicting their perceptions above perceptions in the form of a fuzzy cognitive map influenced by variables related to the subject under study in order to determine a preferable future. The use of a Z-number concept and integrating it with fuzzy cognitive maps in the foresight-oriented decision-making space, which was mainly saturated with uncertainty and ambiguity, was one of the main strengths of the proposed framework in the current investigation. The present paper focused primarily on the evolution of expert’s knowledge with regard to the topic of foresight. The role of Z-number in various processes, from data collection to illustration, analysis, and aggregation of cognitive maps, was considered for gaining knowledge and understanding into the nature of future. Moreover, an ultimate objective was realized through identifying, aggregating, and selecting the variables from each expert’s perspective and then the relationship between each variable was determined in the main stage of foresight. Finally, the proposed framework was presented and explicated in the form of a case study, which revealed satisfactory results.
Worldwide, most treatment systems are retuning sidestreams to the wastewater treatment plant head without treatment. This study established an innovative process to separately treat all sidestream lines (supernatant gravity thickener, underflow mechanical thickener, and centrate) away from plant mainstream and return treated sidestream effluents to plant wastewater outfall instead of wastewater head. It aims to start up and operate a novel EN-MBBR to eliminate side-streams impacts on a full-scale A 2 /O sewage treatment plant. The effects of DO, RAS, and media portion on the reactor were modeled using GPS-X. The system successfully started and reached a steady-state in 28 days. The pilot system processed 30 m 3 /d of the sidestreams, and the average of 8 months effluent concentrations for COD, BOD, TSS, PO 4 , NH 4 , NO 2 , NO 3 , H 2 S were 55, 4, 11, 0.16, 0.2, 0.17, 100, and 0.11 mg/L, respectively. Adding 3 kg/day of calcium hydroxide contributed to improving the nitrification process and reducing phosphates from 40 mg/L to 0.16 mg/L, but it caused an initial shock to the system that lasted more than a week, and then it was stabilized. Modeling results showed that DO concentrations affected the nitrification process but stabilized at a concentration of 3 mg/L. When operating the EN-MBBR as an EN-IFAS system, the RAS has positively contributed to reducing sludge in this system, whereas the sludge proportion is reduced by 60%. The media portion had a significant effect on the removal of nutrients, as it gave the best results when the rate of reactor filling with carriers was between 40 and 50%. Ó 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Modeling and control of hybrid-electric propulsion systems (HEPSs) is a challenging area, in particular for regional aircraft. In this research, modeling and rule-based control design for a hybrid-electrified aircraft are presented. For this purpose, real data from the ATR 42-600 as well as the modified CT7-9 turboprop engine, commercial electric machines, and battery experimental characteristics have been used to size a new version of hybrid-electric regional aircraft. Based on the dynamic modeling of HEPS, practical controllers are then designed for the turboprop engine and electric machines. It should be noted that one of the critical tasks of the control strategy for hybrid-electric propulsion is to ensure the aircraft’s safe landing. In this study, a rule-based tunable regulator is proposed to modify the conventional optimal control strategy to achieve the aircraft’s required performance over a real flight mission, in particular, to fulfill the safe landing of aircraft using the remaining SOC in the event of all-turbine failure. Finally, the results demonstrate that the proposed approach is effective at minimizing fuel consumption when HEPS requirements, such as battery SOC and turbine over-temperature, are fulfilled at different phases of the flight mission.
Metaplates, i.e. 2D extruded structures made by a periodic repetition of unit cells that exhibit a complete and wide 3D bandgap, are attracting increasing interest thanks to the variety of emerging applications. In this work, an innovative planar unit cell suitable for metaplates is proposed and optimized through a genetic algorithm, to achieve wide and low frequency bandgaps. Four objective functions are employed to show the potentiality of the proposed topology with respect to the state of the art. Metaplates obtained through the periodic repetition of the optimized unit cells are then simulated and experimentally tested. High performance metaplates, endowed e.g. with a nondimensional opening frequency of 0.0029 and bandgap width equal to 95%, are here obtained, thus opening the path to challenging applications also in the world of Micro-Electro-Mechanical Systems (MEMS).
An innovative liquid cooling system that contains stair and wavy channels by alumina nanofluid with copper sheath is numerically analyzed to improve the battery thermal management system's temperature distribution and cooling capacity during discharge/charge processes. The effects of the charge/discharge current rate, alumina nanofluid coolant, inflow velocity, ambient/fluid inflow temperature, stair channel, interfacing regions between adjacent LIBs, and the contact regions between LIBs and wavy channel shell on the thermal efficiency of the battery module was investigated. Based on the outcomes, it is concluded that the addition of coolant alumina nanofluid with a 2% volume fraction notably reduces the highest temperature and temperature non-uniformity across the battery module. Simulation results illustrated that by using a 2% volume fraction of Alumina nanofluid, the peak temperature and temperature difference for the discharge process decreased 1.2 °C and 0.4 °C, compared to DI water, respectively. In addition, increasing the coolant inlet velocity led to a decrease in maximum temperature and temperature difference. Different ambient/fluid inflow temperatures of the battery module are evaluated to simulate different weather climates. An innovative design using the stair channel cooling is finally developed for the LIB thermal management system. Comparing the two stair and straight channels, it is accomplished that the temperature non-uniformity for the stair-type channel is almost reduced by 0.19 °C and 0.22 °C for the discharge and charge processes, respectively.
Observations of past earthquakes as well as numerical and experimental studies have confirmed the acceptable performance of Steel Plate Shear Walls (SPSW). Although the SPSW has a number of advantages, it has two major flaws: low elastic-buckling capacity of the infill plate and significant stresses generated by the infill plate on the boundary columns. There are some techniques to overcome these shortcomings. Among them, using the semi-supported SPSW is the most effective one. However, the weak point of this system is the reduction of stiffness and strength of the system in comparison with the conventional SPSW. To resolve this issue, an innovative four-layer semi-supported SPSW has been introduced recently. The system is composed up of the main frame, secondary columns, two corrugated infill plates, and two flat infill plates. Since there is no connection between the infill plates and the mainframe, there is no stress transmission from plates to columns. This fact, in turn, reduces the ductility demand. As a result of the combination of the corrugated and flat plates, the buckling capacity of the wall increases nearly up to the yielding point. This results in a more cost-effective system. The current study presents the results of a comprehensive numerical study to investigate the effect of plate thickness, wall length, and aspect ratio on the behavior of this system under the monotonic lateral load. To obtain the pushover curves, the finite element (FE) software package ABAQUS was utilized. The findings showed that, as the aspect ratio of the wall increases, the wall capacity increases and exceeds the capacity of the frame. Furthermore, the relevant equations for achieving the pushover curve were proposed without the need for FE simulation. Finally, the results showed a good match between the FE results and the intended relations.
The smart railway stations (SRSs), as prosumer microgrids, are considered active users in smart grids. By utilizing regenerative braking energy (RBE) and renewable energy resources (RERs) along with energy storage systems (ESSs), these SRSs can participate in the prosumer market. The uncertainties of RERs in SRSs due to meteorological factors have been studied in the literature. However, there is a research gap in developing a stochastic method for optimized operating of SRSs considering the RBE uncertainties besides the RER, load, and number of passengers’ uncertainties. In this paper, a new probabilistic clustering-based framework for the optimal operation of SRSs is presented. By applying Monte Carlo Simulations (MCS), several scenarios are generated and then clustered by the k-means algorithm. The introduced method is applied to an actual SRS in Tehran Urban and Suburban Railway Operation Company. The test results of the MCS, deterministic, and proposed scenario-based approaches are compared to illustrate the proposed method. Test results imply that the related error of the scenario-based method under the real-time pricing can be less than 4.4%, while the computation time significantly decreases. Furthermore, sensitivity analysis is done to determine how the exchanging power constraints and ESS capacity might influence the SRS operation.
Metal-organic frameworks (MOFs), and layered double hydroxides (LDHs) as worthy hot-spot materials have gained substantial attention for electrochemical energy storage and conversion applications as a result of their structural diversities, excellent physical properties, and promising electrochemical performance. In this work, we synthesized composite materials comprised of ZIF-8 and NiCoAl-LDHs via a mechanochemical method and in-situ solid state ion exchange. The findings have shown that the synergetic effects between ZIF-8 and LDHs in composite materials and tuning of the percentages of Ni and Co in LDHs have a positive contribution to enhancing electrochemical performance in supercapacitors. In this regard, among pure LDHs and ZIF-8\NixCoyAl0.33 LDHs, ZIF-8\Ni(17%)Co(50%)Al(33%)-LDH indicated higher specific capacitance of 256 F g⁻¹ and higher rate of performance. In ZIF-8/Ni(17%)Co(50%)Al(33%)-LDH, LDH nanosheets’ structure improves the conductivity of ZIF-8, and optimized bimetal Ni-Co provides more active sites. In addition, the asymmetric supercapacitor (ASC) assembled by ZIF-8/Ni(17%)Co(50%)Al(33%)-LDH as the cathode and activated carbon (AC) as the anode achieved a good performance in terms of specific capacity, energy density, and cyclic stability. For instance, the ASC delivered a maximum energy density of 184 Wh kg⁻¹ at 1291 W kg⁻¹ and outstanding capacitance retention of 91% after 5000 cycles. This study demonstrates that the interaction between different element ratios in LDHs and the composition of LDHs with ZIFs (porous and redox-active MOFs) has significant effects on using ZIFs/LDHs electrode materials in advanced supercapacitor applications.
This paper proposes a cooperative game to schedule the day-ahead operation of multi-microgrid (MMG) systems. In the proposed model, microgrids are scheduled to achieve a global optimum for the cost of the multi-microgrid system. The minimum cost is achieved by transactions of microgrids with each other. Also, price-based demand response is implemented in the model to build a cost-reducing opportunity for consumers. Applying Shapley value, the optimum cost of the MMG system is fairly allocated between microgrids. To enhance the confidence level of results, data uncertainties are incorporated into the model. The uncertainties of renewable outputs, demand, and prices of trading with the main grid are applied into the model. The presented model is developed as a mixed-integer nonlinear programming problem, and its efficiency is evaluated on a standard test system containing three microgrids. The cost of the MMG system when microgrids form a cooperative game is compared with the isolated status that microgrids do not transact energy with each other. The results indicate that the cost of the MMG is declined using the proposed cooperative model in comparison with the isolated mode. Also, the cost of microgrid1, microgrid2, and microgrid3 are improved by 2.4, 2.7, and 11.8%, respectively.
Various derivatives of Hermia models (complete pore blocking, intermediate pore blocking, cake layer formation, and standard pore blocking) and different assessments of foulant characteristics have long been used to determine the membrane fouling mechanisms. Accordingly, this study aims to adapt Hermia models and their combination according to the operating conditions of an anoxic-aerobic sequencing batch membrane bioreactor (A/O-SBMBR). In addition, fouling mechanisms of the A/O-SBMBR were assessed using these models along with the main foulant characteristics. Models fitting with the transmembrane pressure (TMP) data indicated that the intermediate-standard model was accounting for the increased fouling during the whole regular operating period, with the residual sum of squares (RSS) of 58.3. A more detailed study on the distinct stages of TMP curve showed that the intermediate-standard model had the best fit in stages of 2 and 3, with the RSS equal to 2.6 and 2.8, respectively. Also, the complete-standard model provided the best description of the fouling mechanism in stage 4, with the RSS of 12.5. Different analyzes revealed how the main foulant characteristics affect the occurrence of intermediate, complete and standard fouling mechanisms in the A/O-SBMBR, which is consistent with the fitting results of the adapted Hermia models. The modeling and experimental methods used in the presented study provided a valuable basis to prevent and control membrane fouling in membrane bioreactors.
One of the main concerns in development of metros in historical cities is adverse effects of train-induced vibrations on Cultural and Historical Structures (CHS). In this regard, several approaches have been developed in the literature to predict the level of railway-induced vibration received by CHSs. One of the main limitations of the proposed prediction approaches is a lack of consideration of the effect of variation of water table level on the railway-induced vibrations. To fill this gap, a comprehensive field measurement was carried out in this research in the historical city of Isfahan. Based on the data obtained from the field measurement, significant effects of variation of water table level on the Peak Particle Velocity (PPV) and Soil Transfer Function (STF) were shown. Using the result obtained from the measurement, an adjustment factor was derived to consider the effect of variation of water table level in the conventional train-induced vibration prediction approach. The accuracy and validity of the water table level adjustment factor derived in this study were evaluated through an independent comprehensive field measurement performed in a different historical city.
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