•A novel modified Allam cycle for co-generating power and water is proposed.•Seawater evaporation as a means to generate a cold source and produce freshwater.•The efficiency is 49.11%, marking a ...6.18% improvement over the original system.•The proposed system is particularly well-suited for hot and water-stressed regions.
The efficient performance of the Allam cycle deteriorate in hot regions, where water scarcity is a common issue. To overcome these challenges, a novel modification of the Allam cycle is proposed to simultaneously generate power and water. In this study, the seawater evaporation is utilized to provide cold sink for CO2 liquefaction, and the heat from compressing the vapor separated during seawater evaporation is utilized in a multi-effect evaporation desalination subsystem to produce freshwater. Detailed techno-economic analyses are conducted to assess the feasibility of the proposed system. The calculations indicate that the net efficiency of the new system is 49.11 %, representing a 6.18 % increase compared to the original system. Additionally, the system has the capacity to produce 292.64 kg/s of freshwater. For every 1 °C rise in ambient temperature, freshwater production increases by 0.6 %, and efficiency decreases by 0.06 %. Although the proposed system incurs an investment cost increment of approximately 5.89 %, it is projected to recoup this cost within less than 2.1 years. Moreover, the parametric analyses indicate that the proposed co-generation system is highly competitive in regions with higher electricity prices and water prices. These findings contribute to the application and promotion of Allam cycle technology in hot and water-stressed regions.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, SAZU, SBCE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
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•The efficiency of Allam cycle deteriorates when ambient temperature exceeds 30℃.•A modified Allam cycle with a multi-stage pump/compressor is proposed.•The efficiency of the modified ...cycle is higher than that of Allam cycle by 6.71%.•The proposed cycle is economical and efficient, making it suitable for hot region.
The Allam cycle is renowned for its zero-carbon power generation and high efficiency. However, it faces challenges in hot regions where there is no available cold source for carbon dioxide liquefaction, leading to deterioration in its performance. In this study, a multi-stage pump/compressor is introduced, aiming to enhance the net electric efficiency of the conventional Allam cycle. Various potential enhancement methods are explored and analyzed through comprehensive thermodynamic and economic analyses. Among the configurations under consideration, the Allam cycle combined with two-stage pump/compressor and bypass compressor exhibits the best performance, achieving a 6.71 % increase in the efficiency of the conventional cycle. For the conventional Allam cycle, the efficiency decreases by 0.42 % for every 1 ℃ increase in ambient temperature, however, it is 0.17 % for the Allam-MPC cycle, which indicates it is less responsive to changes in ambient temperature. Moreover, the economic performance of the proposed cycle is better than that of the conventional cycle, which has higher revenue and lower levelized cost of electricity. The capital costs of the modified equipment represent around 1.43 % of the total capital costs of the conventional Allam cycle, and the investment-increment payback period is less than 0.5 years when the ambient temperature exceeds 30 ℃. Sensitivity analyses suggest that the proposed cycle will be more economically viable in hot regions with lower natural gas prices and higher electricity prices. Overall, this study provides a promising approach to improving the performance of Allam cycle in hot regions and offering valuable references for its practical implementation.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, SAZU, SBCE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
Fiber Bragg gratings (FBGs) have become a promising sensor element for measuring temperature, strain and other parameters. However, there exists a cross-sensitivity between temperature and strain. An ...all-fiber sensing system composed of gold-plated FBGs and acrylic FBGs has been proposed and fabricated, which can achieve simultaneous measurement of axial strain and temperature. Using a spectrometer to monitor the shift in the transmission spectrum in real time, the strain and ambient temperature applied to the sensor can be measured conveniently. The experimental results show that the temperature sensitivity of a gold-plated FBG is 3.2 times of that of an acrylate-plated FBG, which can distinguish the temperature changes efficiently. In other words, its sensitivity is increased 2.2 times compared with ordinary fiber grating. The strain sensitivity of gold-plated FBG and acrylate-plated FBG is the same, which is 1.19 pm/µ . The sensor has the advantages of having a simple structure and high sensitivity and can be used to monitor the running state of rail traffic.
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•The formation of embossments on the film is realized by rGO accumulation.•The ultrahigh sensitivity (133 kPa−1) of pressure sensor is achieved.•The ultrawide detection range ...(0–300 kPa) of pressure sensor is achieved.•Our sensor has application universality.•A novel and simple strategy to achieve the superior sensor performance.
Ultrahigh sensitivity and wide detection range are critical for flexible pressure sensors in the further application of electronic skin and wearable electronics. Here, a flexible pressure sensor with rGO coated on a micro cone array was fabricated. By using the molecular dynamics simulation to investigating the formation mechanism of rGO, its optimized micro-morphology can be obtained and understood for improving the performances of sensors. Both an ultrahigh sensitivity (133.003 kPa−1, < 40 kPa) and a wide detection range (0–300 kPa, >10 kPa−1) were remarkably achieved due to the multiple embossments within interfaces of rGO. Furthermore, the designed sensor using rGO with embossments enables several practical applications, showing a fast response time (27 ms) in child door lock monitoring and a distinct step-shape response in robotic arm load monitoring, respectively. And no frequency dependence under loading is also observed obviously. In addition, micro-pressure monitoring, including heart beat (excellent robustness over 10,000 cycles) and vocal cord vibration (sound track), can be monitored clearly owing to embossments morphology of rGO as well. The simulation model of sub-microstructures on rGO shows that the embossments can build the multiple contacts within the interfaces for higher signals output. The designed sub-microstructures of rGO can be an effective strategy to afford superior performances of pressure sensors for universal applications in physiological signal monitoring and physical motion analysis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
To solve the problem of near-field measurement of aeroengines a novel large-range high-precision Fabry–Perot interferometer (FPI) is developed, which is verified by a high-temperature ...experiment. Based on the principle of FPI wavelength drift and frequency spectrum drift, a double-beam-interference FPI is designed. Through an analysis of the optical path difference between the two beams, the conclusion that the spectrum drifts to the long-wave direction with the increase of temperature is obtained. Moreover, through frequency spectrum analysis, the measurement error caused by the distortion of the spectrum is avoided, and it is found that the increase in temperature will cause the change in frequency spectrum. The temperature sensitivity of the glass-type FPI is only 0.0011 nm °C
−1
. A ceramic material with a higher thermal expansion coefficient is selected as the collimating tube to make the sensitivity of the temperature sensor as high as 0.691 nm °C
−1
from normal temperature to 100 °C. To meet the needs of a wide range of measurements from room temperature to 1000 °C the frequency drift method is utilized. A field experiment is carried out on the ceramic FPI at the tail spray of the aeroengine simulation platform. The temperature response test from normal temperature to 1000 °C is completed, and the accuracy of the sensor reached 0.043%. In this study, the principle, design, production, and testing of optical fiber sensors are carried out. The developed optical fiber sensor has significance for high temperature monitoring.
Flexible and compact sensors for collecting essential information from the environment are showing growing importance in robotic perception. In particular, flexible, multimodal, and low-form-factor ...sensors are among the major needs. In this article, a new sensor based on flexible printed circuits and flexible pressure-sensitive material was fabricated and characterized. To minimize fabrication complexity and improve reliability, the presented sensor builds on an established technology and a simple fabrication process. A layered device that can measure temperature, pressure, and surface material relative permittivity was designed, modeled, and tested. With a response time of 0.3 s, the sensor has high linearity in temperature measurement in the range of −30 °C to 120 °C. The sensor maintained its structural integrity and functional performance after undergoing a cumulative 10 h of exposure at 120 °C, demonstrating its resilience to harsh environments. In pressure measurement, the sensor monitors pressure from 0 to 65 kPa with a response time of 0.01 s, even after being overloaded approximately 50 times above the measuring range. In addition to tactile sensing, the sensor is integrated with the material recognition function based on relative permittivity measurement. This integration allows robots to recognize materials with relative permittivity between 1 and 9.3. Such functionality not only improves the adaptability of robots in various environments but also significantly augments their operational intelligence by providing crucial information about object materials, which is essential for complex task executions. Finally, the sensor was installed on a robotic gripper to simultaneously measure temperature, pressure, and material relative permittivity of surfaces. The flexible and lightweight sensor with its easy integration into robotic manipulators is promising for applications in intelligent sorting, smart factories, and intelligent prosthetics.
As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business ...owners and customers. Most of the existing methods for relationship inference are not targeted at POI, thus failing to capture unique spatial characteristics that have huge effects on POI relationships. In this work we propose PRIM to tackle POI relationship inference for multiple relation types. PRIM features four novel components, including a weighted relational graph neural network, category taxonomy integration, a self-attentive spatial context extractor, and a distance-specific scoring function. Extensive experiments on two real-world datasets show that PRIM achieves the best results compared to state-of-the-art baselines and it is robust against data sparsity and is applicable to unseen cases in practice.
RippleNet Wang, Hongwei; Zhang, Fuzheng; Wang, Jialin ...
Proceedings of the 27th ACM International Conference on Information and Knowledge Management,
10/2018
Conference Proceeding
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation ...performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.
xDeepFM Lian, Jianxun; Zhou, Xiaohuan; Zhang, Fuzheng ...
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,
07/2018
Conference Proceeding
Combinatorial features are essential for the success of many commercial models. Manually crafting these features usually comes with high cost due to the variety, volume and velocity of raw data in ...web-scale systems. Factorization based models, which measure interactions in terms of vector product, can learn patterns of combinatorial features automatically and generalize to unseen features as well. With the great success of deep neural networks (DNNs) in various fields, recently researchers have proposed several DNN-based factorization model to learn both low- and high-order feature interactions. Despite the powerful ability of learning an arbitrary function from data, plain DNNs generate feature interactions implicitly and at the bit-wise level. In this paper, we propose a novel Compressed Interaction Network (CIN), which aims to generate feature interactions in an explicit fashion and at the vector-wise level. We show that the CIN share some functionalities with convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We further combine a CIN and a classical DNN into one unified model, and named this new model eXtreme Deep Factorization Machine (xDeepFM). On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly. We conduct comprehensive experiments on three real-world datasets. Our results demonstrate that xDeepFM outperforms state-of-the-art models. We have released the source code of xDeepFM at https://github.com/Leavingseason/xDeepFM.
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