Decomposition Of Components Research Articles

Decomposition of individual components of bio-organic waste: volatile organic compounds and the impact on health and psycho-emotional state

The study considers the impact on the person’s health and psycho-emotional state of unpleasant odors arising from the storage and accumulation of bioorganic waste, including food waste (meat, poultry, fruits and vegetables) and bird droppings. The survey data of poultry and livestock farms’ employees in Serbia are given. The impact of unpleasant odors on the Waste sorting complex (Moscow region) employees’ vital signs is assessed. The impact of high-intensity odor groups for individual components of food waste (pork meat, poultry meat, vegetables and fruits) and bird droppings was studied by interviewing volunteers and measuring their vital signs.

Fast permeability estimation using NMR well logging data log-normal decomposition

Nuclear magnetic resonance (NMR) well logging and NMR core analysis provide a non-invasive approach to a fast reservoir rock pore structures evaluation. The NMR data is presented as a time distribution called transverse relaxation time spectrum (T2 spectrum) and, with proper adjustment, the area under the T2 distribution curve is directly related to the porosity of the reservoir rock. Based on the assumption that immovable and free fluid are present in pores of different sizes, NMR well logging and core analysis use these differences to characterize the fluids found inside the rock pores. In this way, a fixed T2 value can be selected as a cutoff. The amplitudes of the T2 spectrum that have their T2 coordinates below this fixed value are related to the fluids that are in the small pores (immovable fluids), and those that have their T2 coordinates above this value are related to the large pores (free or movable fluids). This T2 value is called T2cutoff and can be estimated in a laboratory with NMR data acquisition on water-saturated core samples. This is done by comparing the NMR acquisition data from a plug fully saturated with water with the NMR acquisition data taken from the same partially saturated plug. In this work, a new method to estimate the pore size distribution, a variable T2cutoff curve, and the Timur–Coates permeability curve of the formation along the reservoir rock is presented. The method is based on the decomposition in log-normal components of the T2 spectra of NMR well logging and on the permeability and porosity of plugs, measured in a laboratory.

Detection and time–frequency analysis of multiple plant-wide oscillations using adaptive multivariate intrinsic chirp component decomposition

Analyzing plant-wide oscillations is a challenging task owing to the presence of noise, nonstationarity, and multiple modes in a process control system. Multivariate intrinsic chirp component decomposition (MICCD) is a novel powerful tool for multivariate signal processing. Nevertheless, MICCD requires users to provide component number in advance, which restricts its adaptability. This study proposes an adaptive MICCD (AMICCD) that can adaptively determine the component number by utilizing the permutation entropy of instantaneous frequency. An AMICCD-based time–frequency analysis framework is presented to detect and characterize the multiple plant-wide oscillations. Compared to the latest methods, such as multivariate empirical mode decomposition and multivariate intrinsic time-scale decomposition, the proposed method can process not only single/multiple plant-wide oscillations, but also time-invariant/time-varying plant-wide oscillations. In particular, the proposed method can directly provide the time–frequency curves of multiple plant-wide oscillations, which have not been achieved by the state-of-the-art techniques. Finally, the effectiveness and advantages of the proposed approach are demonstrated on a wide variety of simulations and industrial cases.

MSGCN-ISTL: A multi-scaled self-attention-enhanced graph convolutional network with improved STL decomposition for probabilistic load forecasting

Probabilistic load forecasting has gained widespread attention for its effectiveness in providing scientific power dispatch plans in recent years. Most existing works adopted decomposition strategies to handle the temporal features of load data, but ignored the fact that decomposition components have different levels of importance, resulting in the incomplete extraction of temporal correlation information. Moreover, previous works have primarily concentrated on the geographical correlation among neighboring load zones and overlooked the semantic correlation among load zones, resulting in insufficient extraction of spatial correlation information. In this study, a novel multi-scaled self-attention-enhanced graph convolutional network with improved Seasonal-Trend decomposition based on Loess (STL) decomposition (MSGCN-ISTL) is proposed for probabilistic load forecasting. First, an improved STL decomposition is proposed, which adopts a convolutional neural network with dual convolutional channels to separately handle the different decomposition components, thus improving the extraction of temporal correlation information. Second, a new multi-scaled self-attention-enhanced graph convolutional network is proposed to extract the spatial correlation information from geographical and semantic aspects simultaneously, which enriches the receptive fields and extracts the multi-scaled enhanced spatial features. Experimental results conducted under various conditions demonstrate the superiority and robustness of the proposed model.

FBG strain monitoring data denoising in wind turbine blades based on parameter-optimized variational mode decomposition method

Herein, we aimed to solve the problem of difficulty in filtering the noise components in the monitoring of strain on wind turbine blades using fiber bragg grating, a denoising method based on parameter-optimized variational mode decomposition (VMD) is proposed. This method uses the minimum envelope entropy as the fitness function and the slime mould algorithm for self-adaptive optimization to find the optimal combination of modal decomposition components K and the quadratic penalty factor α of VMD. The optimized VMD was used to decompose the strain data of wind turbine blades over time into K intrinsic mode components, and the noise mode was removed using the sample entropy to obtain the effective signal. The proposed method was compared to ensemble empirical mode decomposition and complementary ensemble empirical mode decomposition using the simulated signals and engineering data. The experimental results show that the proposed method can effectively remove noise from the strain data of wind turbine blades and has better denoising performance than the other two methods, which provides a reliable basis for analyzing the strain data of wind turbine blades.

Effect of Environmental Factors on the Decomposition of organic Components of Archaeologicals sites in Najaf Governorate (Al Farathy sites case study)

Environmental factors play an important role in the process of decomposition of the components of archaeological sites, especially the organic components. The environmental elements are in a group of chemical, physical or biological influences. These environmental factors combine with each other in influencing the components of the archeological sites by dismantling the basic properties of the materials used in the installation of the buildings of the archaeological cities In addition to the environmental factors, the factors of time and space have a multiplier effect on the degree of influence of environmental factors on archaeological sites, whether visible or not

Effect of organic carbon structures on the degradation of nonylphenol by hydrogen peroxide in sediment-water system.

A laboratory experiment is conducted to investigate the effects of organic carbon (OC) from riverine and marine sediments on the degradation of ring-14 C-labeled nonylphenol (14 C-NP) by hydrogen peroxide (H2 O2 ). Researchers have isolated demineralized OC (DM) before and after oxidation, namely, DM and resistant OC (ROC) fractions, respectively. The structures of DM and ROC are characterized using solid-state 13 C nuclear magnetic resonance. Unstable structures (O-alkyl, OCH3 /NCH, and COO/NC=O) show a significant and positive correlation with the degradation of 14 C-NP (R2 >0.73, p<0.05), thus suggesting that the NP absorbed in the unstable structures is easily degraded because of the decomposition of unstable components. The stable structures (alkyl C and non-protonated aromatic C [Arom C─C]) exhibit a significant and negative correlation with the degradation of 14 C-NP (R2 >0.69, p<0.05), thus suggesting that the NP absorbed and protected in these resistant structures is minimally degraded. The significant correlations among the degradation kinetic parameters (Frap and Fslow ), OC structures (Falip and Farom ), and microporosity further illustrate the important protective roles of OC structures and micropores in the degradation of 14 C-NP by H2 O2 (R2 >0.69, p<0.05). The parent NP fraction that desorbed into the aqueous solution or extracted is completely degraded, indicating preferential degradation of the easily desorbed NP. This study provides important insights into the NP degradation mechanism in sediment-water systems, particularly regarding sediment OC structures and microporosity.

DESTformer: A Transformer Based on Explicit Seasonal–Trend Decomposition for Long-Term Series Forecasting

Seasonal–trend-decomposed transformer has empowered long-term time series forecasting via capturing global temporal dependencies (e.g., period-based dependencies) in disentangled temporal patterns. However, existing methods design various auto-correlation or attention mechanisms in the seasonal view while ignoring the fine-grained temporal patterns in the trend view in the series decomposition component, which causes an information utilization bottleneck. To this end, a Transformer-based seasonal–trend decomposition methodology with a multi-scale attention mechanism in the trend view and a multi-view attention mechanism in the seasonal view is proposed, called DESTformer. Specifically, rather than utilizing the moving average operation in obtaining trend data, a frequency domain transform is first applied to extract seasonal (high-frequency) and trend (low-frequency) components, explicitly capturing different temporal patterns in both seasonal and trend views. For the trend component, a multi-scale attention mechanism is designed to capture fine-grained sub-trends under different receptive fields. For the seasonal component, instead of the frequency-only attention mechanism, a multi-view frequency domain (i.e., frequency, amplitude, and phase) attention mechanism is designed to enhance the ability to capture the complex periodic changes. Extensive experiments are conducted on six benchmark datasets covering five practical applications: energy, transportation, economics, weather, and disease. Compared to the state-of-the-art FEDformer, our model shows reduced MSE and MAE by averages of 6.5% and 3.7%, respectively. Such experimental results verify the effectiveness of our method and point out a new way towards handling trends and seasonal patterns in long-term time series forecasting tasks.

Adsorption and sensing performances of air decomposition components (CO, NOx) on Cr modified graphene surface

The adsorption and sensing abilities of air decomposition components such as CO, NO, and NO2 on a Cr-graphene monolayer were investigated using the first principles of density functional theory. Firstly, three possible doping points of Cr atoms on the surface of graphene were studied, and the most stable Cr-graphene monolayer was obtained through modeling and computational analysis. Then, various adsorption structures were constructed and optimized based on the model. The most stable adsorption structure of Cr-graphene monolayer for the three characteristic gases was determined by comparing their adsorption energy and other relevant parameters.Furthermore, state density, differential charge, and molecular orbital theory analyze the gas sensing mechanism and desorption time. The results show that the surface of the pristine graphene has a weak adsorption effect on these gases, and the Cr-graphene monolayer has good adsorption performance for the three gases. Although the calculation results show that Cr-graphene monolayer has strong adsorption and gas sensitivity performance for the three components, the recovery performance of the Cr-graphene monolayer needs to be improved because of the immense adsorption energy of Cr-graphene monolayer for the three components. The calculation can provide a theoretical guidance for gas sensors based on graphene and its modified materials.

Bidirectional Catalysis Disintegration and Mineral Polymerization via Endogenous Iron(III) from Iron-Rich Sludge in Synergy with Waste Incineration Fly Ash.

To enhance the utilization of solid waste in cement kiln co-processing, this study analyzed the multifaceted synergy of pyrolysis and mineralization processes of iron-rich sludge (SS) and waste incineration fly ash (FA) at optimal blending ratios. Based on the physicochemical properties of SS and co-pyrolysis experiments, it was found that Fe acted as a positive catalyst in pyrolysis between 700 and 1000 °C, while the endogenous polymerization effect of Fe(III) mineral groups dominated above 800 °C. Additionally, the study investigated the solidification and migration of heavy metals and the transformation of harmful elements (S, Cl, and P). Results indicated that the best mixture ratios for SS and FA were 6:4 and 9:1, respectively, and synergistic pyrolysis and mineral co-curing effects were observed in the pyrolysis temperature range of 50-1000 °C. The synergy between SS and FA allowed for the decomposition and solidification of harmful organic components and heavy metals, reducing environmental risks. Furthermore, in actual production, by mixing 100 tons of SS and FA with Portland cement with a daily output of 2500 tons, the compressive strength during early hydration stages can reach 34.52 MPa on the third day, exceeding the highest performance of Portland cement (62.5R) strength index specified in the standard.

Multicomponent Cholesky Decomposition: Application to Nuclear-Electronic Orbital Theory.

The Cholesky decomposition technique is commonly used to reduce the memory requirement for storing two-particle repulsion integrals in quantum chemistry calculations that use atomic orbital bases. However, when quantum methods use multicomponent bases, such as nuclear-electronic orbitals, additional challenges are introduced due to asymmetric two-particle integrals. This work proposes several multicomponent Cholesky decomposition methods for calculations using nuclear-electronic orbital density functional theory. To analyze the errors in different Cholesky decomposition components, benchmark calculations using water clusters are carried out. The largest benchmark calculation is a water cluster (H2O)27 where all 54 protons are treated quantum mechanically. This study provides energetic and complexity analyses to demonstrate the accuracy and performance of the proposed multicomponent Cholesky decomposition method.

An accelerated primal‐dual method for semi‐definite programming relaxation of optimal power flow

AbstractThe application of a semi‐definite programming (SDP) approach to the Alternating Current Optimal Power Flow problem has attracted significant attention in recent years. However, the SDP relaxation of optimal power flow (OPF) can be computationally intensive and lead to memory issues when dealing with large‐scale power systems. To overcome these challenges, we have developed APD–SDP, an optimisation solver based on a first‐order primal–dual algorithm. This framework incorporates various acceleration techniques, such as rescaling, step size decay and reset, adaptive line search, and restart, to improve efficiency. To further speed up computations, we have developed a customised eigenvalue decomposition component by exploiting the 3 × 3 block structure in the dual SDP formulation. Experimental results demonstrate that APD–SDP outperforms other commercial and open‐source SDP solvers on large‐scale and high‐dimensional PGLib‐OPF datasets.

A novel adaptive multi-scale Rényi transfer entropy based on kernel density estimation

The utilization of Rényi Transfer Entropy (RTE) as a powerful model for analyzing causal relationships among variables has become pervasive in the field of complex systems time series causality detection. However, there are two major challenges in using RTE: the inherent multilevel structure of causal associations in the systems, and the precision in RTE estimation. To address these challenges, this paper proposes an adaptive multi-scale Rényi transfer entropy based on kernel density estimation. The framework of causal detection based on the novel RTE consists of two parts: adaptive discrete wavelet transform (ADWT)-based time series decomposition and multivariate kernel density estimation (MKDE)-based causality network generation. In the ADWT-based time series decomposition, the original series are decomposed into different frequency bands by optimal wavelet coefficients, which is generated adaptively by an auto-encoder. In the MKDE-based causality network generation, the causal network between the variables is represented by an adjacency matrix composed of their decomposition components in each layer, and the values of the matrix are the RTE values between the variables. In order to accurate estimation of RTE values an evaluation criterion for KDE under a uniform measure in both univariate and multivariate cases and the optimal bandwidth selection is provided in this part. To validate the effectiveness of the novel causal measure in this paper, the proposed method is tested on the synthetic and real data, and the results show that it can effectively detect causal relationships among variables at different levels in non-stationary time series of both bidirectional and undirectional complex systems. Compared to the other RTE estimators, the proposed method can detect the causality accurately and avoid the spurious causality.

Experimental evaluation of damping in beams using the acceleration generalized coordinates: A comparison of the FDD and PCA methods

The correct evaluation of damping from experimental measurements is important for subsequent analyses, be it in the field of structural dynamics, earthquake engineering, vibration control, structural health monitoring, etc. In this work, we demonstrate how an accurate evaluation of modal damping is possible by using a minimal amount of acceleration sensors and exciting the structure using a simple hammer. However, the transient signals thus recovered must be carefully processed so as to extract as much accuracy from these measurements as possible. The particular experiment used here involved a metallic bridge span, which was simply supported and used for testing moving loads. Both Frequency Domain Decomposition and Principal Component Analysis were employed and subsequently compared, with the latter method showing a clear separation of modes. Thus, an accurate estimation of modal damping values from the free vibration regime is possible using the logarithmic decrement method.

Background initialization in video data using singular value decomposition and robust principal component analysis

Background initialization is used in video processing applications to extract a scene without the foreground scene. In recent times, the issue of background initialization has gained researchers’ attention in different fields such as video surveillance, video segmentation, computational photography, and so on. The initialization of the background is affected due to different complex dissimilarities such as shadow, intermittent movement, illumination, camera jitter, and clutter. To overcome the aforementioned issues, this paper proposes a decomposition using the combination of the Singular Value Decomposition (SVD) and Robust Principal Component Analysis (RPCA) for Singular Spectrum Analysis (SSA) to perform an effective background initialization. The incorporation of RPCA in SVD is used to overcome the issues related to non-Gaussian noise and it also uses an effective structural knowledge of the video input i.e. sparse and low rank matrix which improves the Peak-Signal-to-Noise-Ratio (PSNR) of the background image. The SBI dataset was used to analyze the performances of SSA-SVDRPCA. The SSA-SVDRPCA is analyzed using MultiScale Structural Similarity Index (MSSSIM), Average gray-level error (AGE), Percentage of clustered error pixels (pCEPS), Percentage of error pixels (pEPs), and PSNR. The existing approaches such as Background Initialization Singular Spectrum Analysis (BISSA) and Quaternion-based Dynamic Mode Decomposition (Q-DMD) are used to compare with the SSA-SVDRPCA method. The PSNR of the SSA-SVDRPCA for Board class is 30.39 dB which is higher than the BISSA and Q-DMD.

Kinetic of Oil Sludge Thermolysis Process in Presence of Nickel, Cobalt and Iron-Supported Microsilicate

Abstract The article is devoted to the experimental determination of thermokinetic parameters of oil sludge thermal degradation using the model-free Ozawa-Flynn-Wall method in the presence of a nanocatalyst (nickel, cobalt and iron-supported microsilicate) by calculating Arrhenius kinetic parameters (activation energy and pre-exponential factor). The phase composition of the reflex microsilicate was established – 4.12; 2.51 Å – SiO2, nickel-supported microsilicate reflexes: 2.09; 1.48 Å – NiO, reflexes: 4.25 Å – SiO2 and acid numbers of microsilicate – 64 μmol/g of prepared nanocatalysts. Using the method of Brunauer, Emmett and Teller, the specific surface area of the microsilicate was established – 18.3 ± 0.3 m2/g, the microsilicate with nickel applied – 20.9 ± 0.2 m2/g and the adsorption isotherm of the prepared nanocatalysts (microsilicate with nickel, cobalt and iron). Thermokinetic parameters of thermal decomposition of oil sludge without a catalyst and with a catalyst at an increment of 0.9 are 99.0 and 93.3 kJ/mol nickel-supported microsilicate, 51.9 kJ/mol cobalt-supported microsilicate, 111.3 kJ/mol iron-supported microsilicate and non-metal-supported microsilicate 173.7 kJ/mol, respectively. The study of the kinetic parameters of pyrolysis of oil sludge using various catalysts makes it possible to assess their influence on the process of decomposition of organic components. The results of the experiments showed that the use of catalysts significantly affects the destruction of oil sludge. Dynamic thermal analysis at different heating rates studied the dynamics of oil sludge decomposition. The study of the effect of catalysts on the kinetic parameters of oil sludge pyrolysis is an important step in the development of new methods for the disposal of petroleum products and the reduction of their negative impact on the environment. The obtained experimental data on thermal degradation kinetics of oil sludge will find application in designing a reactor for the process of destructive hydrogenation of heavy hydrocarbon raw materials.

An Improved Intrusion Detection System Using Machine Learning with Singular Value Decomposition and Principal Component Analysis

An Improved Intrusion Detection System Using Machine Learning with Singular Value Decomposition and Principal Component Analysis

Sensing properties of SF6 decomposition gas sensor based on Rh doped α-AsP monolayer

The detection of decomposition components of SF6 insulating gas in gas-insulated switchgear (GIS) is of great significance to evaluating GIS's operating state and ensuring the equipment's safety. In order to detect and diagnose the main decomposition gases SO2, SOF2, and SO2F2 of SF6, this work systematically analyses the intrinsic α-AsP monolayer and two Rh-modified α-AsP monolayers. Specifically, the adsorption structure, energy, charge transfer and deformed charge density, the total density of states, the partial density of states, and recovery time were discussed and calculated in detail, and the internal mechanism of the entire adsorption process was clarified. Meanwhile, the results show that Rh adsorbed on α-AsP monolayer (Rh-AsP-O) has a good adsorption effect on SO2, SOF2, and SO2F2 gases. From the sensor's perspective, the Rh-AsP-O monolayer has potential applications in the gas-sensing field of SO2, SOF2, and SO2F2.

A novel multivariable hybrid model to improve short and long-term significant wave height prediction

Accurate significant wave height (Hs) prediction is crucial for marine renewable energy development. The hybrid models combining multi-resolution analysis techniques such as empirical mode decomposition and wavelet transform with intelligence algorithm have flourished in Hs forecasting. However, these hybrid models cannot fit multivariable input mode well. In this study, a novel multivariable hybrid model is proposed. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and recurrence quantification analysis (RQA) were integrated as the deterministic and stochastic components decomposition (DSD) method. Then three machine learning models was integrated with DSD method as hybrid models, respectively. For more sufficient forecasting information, wind speed (Ws), wind direction (Wd) and Hs were adopted as inputs to construct multivariable hybrid models. The forecasting experiment was benchmarked with those from univariate hybrid models, multivariable single models and univariate single models. Three buoy-measured datasets were utilized for validation. Results revealed the positive effect of wind data on long-term prediction and the improvement to prediction by the DSD method. Benefiting from the advantages of both, multivariable hybrid models outperformed other benchmark models. Among them, the multivariable hybrid model based on long short-term memory (LSTM) network, DSD-LSTM-m, achieved the best forecasting performance.

Analysis of the Decomposition of Sulfur-Based Electrolyte Additives in Spent LiNi0.6Co0.2Mn0.2O2||AG Cells

The development and optimization processes of the last decades in the context of lithium ion batteries (LIBs) have led to a variety of electrode chemistries, which enable application-specific targets such as high energy, high power, high safety or long lifetime. Most of these LIB systems rely on a liquid carbonate-based electrolyte, whose basic composition has changed scarcely during this period.1 In this respect, the usage of electrolyte additives is one of the most economical and effective ways to further improve the overall performance, while also impact the application-tailored characteristics of the battery cells at the elctrolyte level without changing the bulk properties.2 These improvements can range from overcharge protection to lowered flammability or interphase formation, among others. In particular, film-forming additives signifcantly influence the performance and life time of LIBs by preventing excessive decomposition of the electrolyte, which is in contact with the positive and negative electrodes, by the formation of a passivation layer. Investigating the electrochemical decomposition of the electrolyte components, especially electrolyte additives, is key to enable a better understanding of the composition from the electrode||electrolyte interface layer and the effects on LIB cell performance.3 Thus, aging mechanisms and parasitic reactions can be elucidated to improve and design new electrolyte additives.Herein, the irreversible decomposition of sulfur-containing electrolyte additives will be addressed. Sulfur-containing additives represent an attractive option for film-forming additives as these compounds endow lower energies in the lowest unoccupied molecular orbital (LUMO) compared to the organic carbonate analogs and therefore are more susceptible to electrochemical reduction.4 However, these class of electrolyte additives find only a limited use in state-of-the-art electrolyte mixtures due to the often reported or suspected carcinogenicity and toxicity towards the human body. Nevertheless, since sulfur-containing additives still play an important role in spent LIBs of recent decades, identification of these hazardous compounds and formed aging products, which should be also classified as potential dangers, is of great interest.1 This contribution focused on the identification of aging products emerging in electrolytes from LIB cells using sulfur-based electrolyte additives of different chemical classes such as sulfates, sulfites, sultones and sulfonates. Ion chromatography and gas chromatography hyphenated to high-resolution acurrate mass spectrometry (HRAM-MS) were used to obtain information on the formation of ionic and volatile compounds after electrochemical operation and to elucidate the corresponding structures. Thus, specific ionic and volatile aging marker molecules could be defined for target-analysis of the original electrolyte additive in LIB material, despite a potentially complete consumption during cycling. This allows improved reverse-engineering of LIB post-mortem analysis and risk assessment. Furthermore, mechanistic conclusions on the decomposition of the investigated electrolyte additives could be drawn, extending literature reports based on X-Ray methods with respect to species information and ultimately contribute to a better understanding of the interphase constitutions.[1] C. Peschel, S. van Wickeren, A. Bloch, C. Lechtenfeld, M. Winter, S. Nowak, Energy Technol. 2022, xxx.[2] S. S. Zhang, J. Power Sources 2006, 162, 1379–1394.[3] J. Henschel, J. M. Dressler, M. Winter, S. Nowak, Chem. Mater. 2019 31 (24), 9970-9976.[4] B. Tong, Z. Song, H. Wan, W. Feng, M. Armand, J. Liu, H. Zhang, Z. Zhou, InfoMat 2021, 3, 1364–1392.