Experimental and numerical study on tube-side flow and heat transfer characteristics of superheated vapor flow in catalyst-filled LH2 spiral wound heat exchanger

High energy consumption is considered to be one of the most persistent problems in liquid hydrogen (LH₂) plants. The combination of heat exchanger and ortho-para (O–P) hydrogen conversion has attracted considerable attention as a cutting-edge technology to reduce energy consumption. The flow and heat transfer characteristics of O–P hydrogen conversion catalyst-filled spiral wound heat exchanger (SWHE) were investigated in this study in two steps. In the first step, pressure-drop experiments were performed in a tube filled with porous media. The results indicated that the pressure drop was overestimated when using Ergun's equation. Therefore, a new empirical pressure-drop correlation for a channel filled with O–P catalyst was formulated. Subsequently, a novel heat transfer model was established based on this correlation for further numerical simulations. The distributions of the temperature, pressure, and para hydrogen content in a catalyst-filled tube were determined. In addition, the influence of the flow rate on the heat exchange coefficient and outlet para hydrogen was clarified; it was found that, with an increase in the flow rate, the heat exchange coefficient increased, whereas the outlet para hydrogen content decreased. At a flow rate of 0.5 m³/h, the para hydrogen content increased by 44% after hydrogen flowed through the channel filled with the O–P catalyst. Furthermore, a prediction model for the para hydrogen content with a flow rate range of 0–1.5 m³/h was derived. This study provides promising theoretical evidence for the engineering application of SWHEs filled with O–P catalysts in large-scale hydrogen liquefaction units.     

切顶卸压技术在双鸭山矿区沿空留巷中的应用

为了使双鸭山矿区煤炭资源最大程度地被采掘,减少资源浪费,提高矿井效益,延长矿井服务年限及促进矿井安全生产,以双鸭山矿区3个缓倾斜中厚煤层综采工作面为工程背景,对切顶卸压无煤柱开采技术进行深入探索,经过实际的检验证明,在双鸭山矿区缓倾斜中厚煤层中,采用切顶卸压技术进行沿空留巷,技术可行,经济合理,工艺简单,成巷率高。     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Experimental evaluation of the luminescence performance of fired clay brick coated with SrAl2O4:Eu/Dy phosphor

This study evaluates the luminescence performance of fired clay bricks coated with SrAl₂O₄:Eu/Dy phosphor. To do so, SrAl₂O₄:Eu/Dy phosphor was first produced using the traditional solid-state reaction synthesis technique. The prepared phosphor was then used for coating fired clay bricks to analyze the luminescence performance via spectral analysis, decay characteristics, and microstructure of the bricks. The results reveal that excitation and emission spectra of the phosphor coated bricks range from 200 to 480 nm and 455 to 650 nm, respectively, suggesting that the phosphor coated bricks have the capacity of absorbing light with a wide range of wavelengths. The peak wavelength projected at 511 nm in the emission spectrum is achieved, which indicates 4f⁶5 d¹-4f⁷ transition of Europium (Eu²⁺). The repeated excitation and deexcitation of Eu²⁺ by using hole traps and trap levels offered by Dysprosium (Dy³⁺), exist between the ground and the excited state of Eu²⁺ leads to luminescent phenomenon. Moreover, the decay characteristics has revealed that phosphor coated bricks can emit light for a considerable amount of time (>8.5 min) upon the removal of the excitation source. The results reveal that phosphor coated bricks has the potential of increasing energy efficiency of residential and commercial buildings.     

Total Site Hydrogen Integration with fresh hydrogen of multiple quality and waste hydrogen recovery in refineries

This paper proposes a novel method combining Pinch Methodology and waste hydrogen recovery, aiming to minimise fresh hydrogen consumption and waste hydrogen discharge. The method of multiple-level resource Pinch Analysis is extended to the level of Total Site Hydrogen Integration by considering fresh hydrogen sources with various quality. Waste hydrogen after Total Site Integration is further regenerated. The technical feasibility and economy of the various purification approaches are considered, demonstrated with a case study of a refinery hydrogen network in a petrochemical industrial park. The results showed that fresh hydrogen usage and waste hydrogen discharge could be reduced by 21.3% and 67.6%. The hydrogen recovery ratio is 95.2%. It has significant economic benefits and a short payback period for Total Site Hydrogen Integration with waste hydrogen purification. The proposed method facilitates the reuse of waste hydrogen before the purification process that incurs an additional environmental footprint. In line with the Circular Economy principles, hydrogen resource is retained in the system as long as possible before discharge.     

Furan fatty acid concentrations in tea infusions prepared from green,black, and herbal teas

Furan fatty acids (FuFAs) are valuable antioxidants with highly effective radical scavenging properties which are widely distributed at low levels in food. Previous research indicated that tea is a valuable source of FuFAs. However, tea is only consumed in form of infusions. To fill this gap, we prepared infusions from different herbal, green, and black teas. Initial measurements with GC-MSMS of tea verified previous findings that 11-(3,4-dimethyl-5-penylfuran-2-yl)-undecanoic acid (11D5) was the prevalent FuFA in tea matrix. Therefore, 11D5 was quantified in tea infusions by means of UHPLC-MSMS equipment after mild alkaline hydrolysis. While herbal tea infusions were low or free of FuFAs, 11D5 was detectable in all samples of green and black tea infusions. Amounts of 11D5 were higher in green tea than in black tea. Moreover, Darjeeling tea infusions were by ~30% richer in 11D5 than black and green teas from other regions. Each cup of green and black tea infusion may provide 20–60 μg 11D5, which is about 5% of the amounts found in tea samples. Spread over the day, regular tea consumption may contribute to the intake of valuable FuFAs.     

Pattern design and optimization of yarn-dyed plaid fabric using isolation niche genetic algorithm and rough set theory

Under the circumstance of perceptual consumption, it is still challenging to grasp consumer's emotions and demands due to the large search space, diversified preferences, and easy fatigue of consumers. To reduce user fatigue and enlarge search space, a novel method was presented to design and optimize the pattern of yarn-dyed plaid fabric using the isolation niche genetic algorithm and rough set theory. Each pattern was encoded as a chromosome based on the real number code. The population was initialized and evolved using INGA to maintain the diversity. The rough set theory was adopted as the fitness function of isolation niche genetic algorithm to extract the consumer's demands. After multiple evolutions, a large set of practical patterns of the yarn-dyed plaid fabric are obtained. Experiments were carried out by 24 testers of different ages and genders. The results prove that the proposed method based on the isolation niche genetic algorithm and rough set theory is feasible and effective, supplying references to the designer.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Data requirements for improving the Quantitative Risk Assessment of liquid hydrogen storage systems

Quantitative Risk Assessment (QRA) supports the development of risk-informed safety codes and standards which are employed to enable the safe deployment of hydrogen technologies essential to decarbonize the transportation sector. System reliability data is a necessary input for rigorous QRA. The lack of reliability data for bulk liquid hydrogen (LH₂) storage systems located on site at fueling stations limits the use of QRAs. In turn, this hinders the ability to develop the necessary safety codes and standards that enable worldwide deployment of these stations. Through a QRA-based analysis of a LH₂ storage system, this work focuses on identifying relevant scenario and probability data currently available and ascertaining future data collection requirements regarding risks specific to liquid hydrogen releases. The work developed consists of the analysis of a general bulk LH₂ storage system design located at a hydrogen fueling station. Failure Mode and Effect Analysis (FMEA) and traditional QRA modeling tools such as Event Sequence Diagrams (ESD) and Fault Tree Analysis (FTA) are employed to identify, rank, and model risk scenarios related to the release of LH₂. Based on this analysis, scenario and reliability data needs to add LH₂-related components to QRA are identified with the purpose of improving the future safety and risk assessment of these systems.