Methods For Analysis Of Products Research Articles

Iron Ore Tailing Composition Estimation Using Fused Visible–Near Infrared and Thermal Infrared Spectra by Outer Product Analysis

Iron ore tailings are mainly composed of SiO2 and iron, whose content determines the potential reuse strategy of the tailings. Compared with the traditional wet chemistry approach, spectroscopy has proven its superior effectiveness in characterizing and predicting minerals, such as iron oxides, clay, and SiO2. This study aims to estimate the content of SiO2 and TFe in iron ore tailings based on visible–near infrared (VIS–NIR, 350–2500 nm) and thermal infrared (TIR, 8–14 μm) spectroscopy. The outer product analysis (OPA) method is used to combine VIS–NIR and TIR spectral domains, from which an outer product matrix of fusion data can be generated. The study area is the iron ore tailing dam from Waitoushan, which is one of the super-large iron deposits in the Anshan–Benxi iron cluster of northeastern China. The spectral analysis results demonstrated the following: (1) The reflectance feature at 1163–2499 nm in the VIS–NIR range correlates with TFe and the emissivity feature at 8–9.4 and 10.7–12 μm in the TIR range correlates with SiO2. (2) Compared with the original absorbance spectra, the correlation coefficients of fusion spectra improve from 0.66 to 0.87 for TFe and from 0.64 to 0.84 for SiO2. (3) The partial least squares regression, random forest (RF), and extreme learning machine exploiting particle swarm optimization modeling methods are established for SiO2 and TFe estimation. The prediction accuracy results indicate that the prediction model with OPA-fused spectra performs significantly better than with individual VIS–NIR and TIR spectra. The RF model with input-fused spectra provides the highest accuracy with the coefficients of determination of 0.95 and 0.91, root mean square errors of 0.97% and 0.96%, and ratios of performance to interquartile distance of 6.49 and 2.31 for SiO2 and TFe content estimation, respectively. These outcomes provide a theoretical basis and technical support for tailing composition estimation using spectroscopy.

Energy loss of radial inflow turbine for organic Rankine cycle using mixture based on entropy production method

The radial inflow turbine is the core component of the organic Rankine cycle(ORC) system. Based on the internal flow field of the turbine analyzed by computational fluid dynamics (CFD), the energy dissipation of a radial inflow turbine with variable guide vane is determined by the entropy production analysis method. In the presented work, the mixture of R245fa/R134a is selected as the working fluid, and the effects of tip clearance and mixing ratio on the flow loss characteristics are investigated. The results show that the high entropy production region in the stator is caused by leakage vortex and shock phenomenon, while the passage vortex and leakage vortex are the reasons for the high loss area in the rotor. The total volume entropy production of rotor is mainly composed of the passage zone, which is significantly higher than that of the stator. As the tip clearance increases from 3% to 10%, the total entropy production of stator and rotor increases by 54.5 and 161.7% respectively, and the output power and isentropic efficiency of the turbine decrease by 14.7% and 14.6% respectively. With the increase of R245fa mass fraction, the total volume entropy production of the stator and rotor decreases linearly. Compared with R134a, the turbine isentropic efficiency of R245fa increases by 3.1%.

Vulnerability of 14 elasmobranchs to various fisheries in the southern Gulf of Mexico

Context Shark and ray fisheries in the southern Gulf of Mexico (GOM) are multispecific and use diverse fishing gear in multiple coastal areas. Aim Because no catch and fishing effort records by species exist, the aim of this study was to estimate the species’ vulnerability to fisheries through limited-data assessment methods. Methods The vulnerability to 14 fisheries through the productivity and susceptibility analysis (PSA) and rebound-potential method were estimated for 14 elasmobranchs. Key results Carcharhinus falciformis, Carcharhinus plumbeus and Sphyrna lewini among sharks, besides Hypanus americanus and Aetobatus narinari among rays, had the highest vulnerability and the lowest rebound potential. The sensitivity tests showed that the most significant differences in cumulative vulnerability (Cv) occur between two alternative widely applied PSA approaches. Cv determined from scores averaged over a range of susceptibility attributes tends to be higher than the product of the scores of four standard susceptibility attributes. Conclusions Significant correlations between Cv of Approach 1 and rebound-potential scenarios were estimated, indicating a general pattern in which the species’ rebound-potential increases while the vulnerability decreases. Implications The correlation between Cv (Approach 1) and rebound-potential highlights the suitability of PSA when enough information is available on the interactions of fisheries and populations.

Vision-Based Productivity Analysis of Cable Crane Transportation Using Augmented Reality–Based Synthetic Image

The productivity analysis of cable crane transportation in the construction field is of great significance to improve crane equipment management and reduce operation costs. However, the traditional manual recording method of analyzing cable crane productivity is time-consuming and tedious. The existing vision-based method requires significant amounts of time to collect extensive images at construction sites and does not achieve high-precision detection in complex scenes. Thus, an automated vision-based method for productivity analysis of cable crane transportation is proposed using a new synthetic image approach based on an augmented reality (AR) technique. The unmanned aerial vehicle-based three-dimensional (3D) reconstruction of a crane bucket model is superimposed on a realistic scene using AR to synthesize the images for vision-based model training without manual image acquisition at a construction site. The feature pyramid network and attention module are integrated into Faster region-based convolutional neural network (Faster R-CNN) to enhance the capability of feature extraction for the high-precision detection of a crane bucket and its ID number, which provides the logical basis for calculating productivity. The proposed vision-based productivity analysis method is evaluated on large-scale hydraulic engineering. The results demonstrate that the mean average precision (mAP) of detection performance is 98.01% using the model trained by AR-based synthetic images, which confirms the proposed AR-based synthetic image method could provide a new image generation mode for the construction industry. Additionally, the bias of productivity between the proposed method and ground truth is 0.03%, which confirms the effectiveness and accuracy of the proposed method.

Laplacian Echo-State Networks for production analysis and forecasting in unconventional reservoirs

Production data analysis for low permeability shale reservoirs is crucial in characterizing flow regimes and reservoir properties, and the forecasting of production is essential for portfolio and reservoir management. However, traditional methods have failed due to incorrect physics or complicated convolution from the well control history. In this research, we provide a physics-assisted analytics workflow using Laplacian Eigenmaps Coupled Echo-State Network (LEESN) to facilitate and accelerate the analysis of noisy historical production data. Pressure-rate deconvolution is an ill-posed, complex time-series problem. When using the traditional Echo-State Network (ESN), the number of training sets is less than the number of neurons. To solve this problem, we apply LEESN to first deconvolve noisy variable-pressure variable-rate histories into smooth constant-pressure rate responses. The physics-based training features and training algorithm provide additional benefits in addition to the analytic approach by honoring transient flow physics. After training, the constant-pressure rate response can be predicted and used for reservoir characterization, and the trained model could be further used for production and EUR forecasting through long-term rate predictions to the economic limit. The proposed workflow was first validated by synthetic cases where the production data were obtained through simulation. The short-term flow rate history was obtained by specifying highly variable controlling pressures. We also added artificial white Gaussian noise to mimic measured signals collected in the field and input this information into LEESN for deconvolution. The constant-pressure rate response was generated after training to determine flow regimes and properties such as permeability using a traditional transient testing specialized plot. All outcomes from the analytics approach were validated by comparison against the input data of the synthetic simulation model. The advantages of the analytics approach were maintained with a moderate variation of noisy pressure-rate signals. For production forecasting, both the trained analytics model and simulator were used to predict for an extended period, and the results indicated good agreement between the response predictions. We performed a further sensitivity analysis on important parameters such as the training scale as well as the capability of Laplacian Eigenmaps handling moderate noise in training data. The comparison between the model predictions and simulation data showed significantly increased accuracy in production estimates. The efficacy was further demonstrated from additional single-phase and multiphase synthetic and field cases. This study shows that the LEESN approach is a powerful alternative to interpret pressure-rate-time information from production data. The discussion and comparison of LEESN with other traditional production analysis and forecast methods are included as well. Deconvolved pressure-rate data greatly enhances traditional rate-transient analysis (RTA) used to characterize reservoir parameters, and the trained model enables engineers to predict future production even with noisy, highly-variable production histories. The robustness of the proposed analytics methodology is strengthened by coupling the training features with transient flow physics and provides a unique approach for production analysis and forecasting for unconventional reservoirs.

The Economic Performance of Logistical Processes in Polish Production Companies

Purpose: The main aim of the article is to present the issue of the economic performance of logistical processes and the results of own research on their calculation in Polish manufacturing companies. Approach/Methodology/Design: The undertaken research issues and the set goal determined the choice of research methods, such as critical literature review, questionnaire research of Polish production companies, analysis and synthesis methods. Findings: A reliable system for measuring the economic performance of the company's logistical processes and the valuation of individual types of resources is undoubtedly a condition for the success of a manufacturing company's policy. Practical Implications: It was established that for each type of resource (logistics infrastructure, material and financial resources and human capital) there is a significant dependence of its valuation depending on the calculation of the economic performance of logistical processes.The results can be used to develop a long-term strategy on the development of Polish production companies. Originality/Value: The proposed solutions are to contribute to the development of Polish production companies.

Advanced applications of chemo-responsive dyes based odor imaging technology for fast sensing food quality and safety: A review.

Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemo-responsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application: The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.

Using Rate Transient Analysis and Bayesian Algorithms for Reservoir Characterization in Unconventional Gas Wells during Linear Flow

Summary The main objective of this work is to improve robust, repeatable interpretation of reservoir characteristics using rate transient analysis (RTA). This is to generate probabilistic credible intervals for key reservoir and completion variables. This resulting data-driven algorithm was applied to production data from both synthetic and actual case histories. Synthetic production data from a multistage, hydraulically fractured horizontal completion in a reservoir modeled after the Marcellus Shale reservoir were generated using a reservoir model. The synthetic production data were analyzed using a combination of RTA and Bayesian techniques. First, the traditional log-log plot was produced to identify the linear flow production regime. Using the linear flow production data and traditional RTA equations, Bayesian inversion was carried out using two distinct Bayesian methods. The “rjags” and “EasyABC” packages in the open-source statistical software R were used for the traditional and approximate inversion, respectively. Model priors were based on (1) information available about the Marcellus Shale from technical literature and (2) results from a hydraulic fracturing forward model. Posterior distributions and credible intervals were produced for the fracture length, matrix permeability, and skin factor. These credible intervals were then compared with true reservoir and hydraulic fracturing data. The methodology was also repeated for an actual case in the Barnett shale. The most substantial finding was that for nearly all the investigated cases—including complicated scenarios (such as including finite fracture conductivity, fracturing fluid flowback, and heterogeneity in fracture length in the reservoir/hydraulic fracturing forward model)—the combined RTA-Bayesian model provided a 95% credible interval that encompassed the true values of the reservoir/hydraulic fracture parameters. We also found that the choice of the prior distribution did not affect the posterior distribution/credible interval in a significant manner as long as it was moderately concentrated and consistent with engineering science. Also, a comparison of the approximate Bayesian computation (ABC) and the traditional Bayesian algorithms showed that the ABC algorithm reduced computational time by at least an order of magnitude with minimal loss in accuracy. In addition, the production history used, the number of iterations, and the tolerance of fitting in the ABC analysis had a minimal impact on the posterior distribution after an optimal point, which were determined to be at least 1-year production history, 10,000 iterations, and 0.001, respectively. In summary, the RTA-Bayesian production analysis method was implemented using relatively user-friendly computational platforms [R and Excel® (Microsoft Corporation, Redmond, Washington, USA)]. This methodology provided reasonable characterization of all key variables such as matrix permeability, fracture length, and skin when compared to results obtained from analytical methods. This probabilistic characterization has the potential to enable better understanding of well performance ranges expected from shale gas wells. The methodology described here can also be generalized to shale oil systems during linear flow.

Estimation of the reliability of determination of filtering parameters of productive formations using multi-dimensional regression analysis

Determination of reliable values of filtration parameters of productive strata is the most important task of monitoring the processes of developing reserves. One of the most effective methods for solving the problem is hydrodynamic testing of wells using the pressure recovery method, as well as modern methods - the pressure stabilization method and the method based on production analysis (Decline Analyze). This article is devoted to the assessment of the reliability of these three methods in determining the filtration parameters of terrigenous and carbonate productive deposits of oil fields in the Perm Krai. To solve the problem, multivariate regression analysis was used. A series of multidimensional mathematical models of well flow rates was built using filtration parameters determined for each of the methods. It is proposed to consider the filtration parameters included in the models with the maximum statistical estimates of performance as the most reliable. With regard to the fields under consideration, it was found that in terrigenous reservoirs, all three methods demonstrate stable results. In carbonate reservoirs, reliable values of filtration parameters are determined by processing pressure build-up curves. Pressure stabilization and production analysis methods show less robust results and require additional research in order to develop sound recommendations for their practical application. Keywords: permeability; skin factor; pressure stabilization curve; decline analyze; liquid flow rate; geological and technological parameters; oil deposit; carbonate deposits.

Vulnerability analysis of pelagic and demersal fisheries in the Indian Ocean, Fisheries Management Area 572, Indonesia

Indonesian Fisheries Management Area (FMA) 572 situated in the Indian Ocean, includes the coastal waters of Bengkulu City, has great potential in terms of pelagic and demersal fisheries. However, the exploitation of these fisheries resources has been intensified due to the growing demand for human consumption. Unfortunately, information related to the susceptibility and productivity of fisheries resources in this area is still negligible. The purpose of this study was to determine the level of vulnerability to potential fishery impacts of several pelagic and demersal fisheries in FMA 572. The study was conducted in Bengkulu City from July to December 2019. Analysis of the productivity and susceptibility level of the fisheries used several productivity and susceptibility parameters through the Productivity and Susceptibility Analysis (PSA) method. The results of the study revealed that the vulnerability level of yellow stripe scad (Selaroides leptolepis) was higher than that of other target fishes. However, in general, the vulnerability of pelagic and demersal fisheries to potential overfishing in FMA 572 was still low.

A new production analysis method for shale gas well based on the evaluation of decline parameters in advance

Production forecasting for multi-fractured horizontal well (MFHW) in shale gas reservoir is of vital importance to the evaluation of development potential.In this paper, we develop a new method to predict production of the MFHW under constant bottom-hole Pressure (BHP) condition. Based on the analytical model, well's life cycle is divided into two stages, in which the decline models are established, respectively. According to these models, the decline exponent is a function of fluid properties, the pressure drawdown strategy and heterogeneous completion. It can be directly calculated without production data matching. Influence of desorption on decline exponent, calculation of “initial pressure” during boundary dominated flow (BDF) period, are discussed. A workflow is established to perform production forecasting for a MFHW with complex fracture configuration and constant BHP in shale reservoir. A preliminary scheme is also developed to extend the workflow to variable BHP condition. Results indicate that a special hyperbolic model in which decline exponent changes as pressure drops, can be employed to forecast production during BDF. A constant decline exponent may result in an underestimation/overestimation well production, especially, in the BDF period. This new method can be used to explore how each factor impacts well productivity rigorously. Compared with conventional methods, it is of higher accuracy when making production forecast, especially for wells with short history.

Waste cooking oil as starting resource to produce bio-polyol - analysis of transesteryfication process using gel permeation chromatography

This work presents a possibility of implementing Green Chemistry principles in the synthesis of bio-components. Five out of twelve principles have been highlighted taking into account the reagents used as well as the product analysis method and process monitoring. The influence of different contents of a catalyst (0.15−0.45 wt.%), transesterification agents (ethylene glycol, propylene glycol, diethylene glycol, glycerin and triethanolamine) as well as the reaction time and the properties of the bio-polyols on the transesterification process of used cooking oil (UCO) were analysed.In this work, gel permeation chromatography (GPC) was used for the first time to evaluate the yield of UCO transesterification into di- and monoglycerides as well as the reaction time. It was concluded that alcohols containing two primary hydroxyl groups were more reactive than alcohols containing three hydroxyl groups or two hydroxyl groups including one primary and one secondary group. The most reactive agent was triethanolamine and the transesterification reaction progress monitoring using gel permeation chromatography allowed us to complete the reaction in a time shorter by 75 %. In the case of glycerine, it was necessary to increase the temperature in order to increase the reaction yield. Selected physico-chemical properties: the density, viscosity, hydroxyl value, acid value, content of water as well as molecular weight and chemical structure of the bio-polyols based on UCO were studied too.

Free-drop simulation and analysis of Full-screen smartphone based on ABAQUS/CAE

Finite element analysis (FEA) provides numerical modeling and testing methods of stress analysis for products. The time of products development cycle can be shorten and the cost for products physical testing can be reduced with FEA. Drop test is one of the important tools uses for the impact behaviour study of cell phones, which identifies weak design points during the impact behaviour of cell phone. With the development of technology and growing of people’s demand, the screen of cell phone is getting bigger and bigger. Full-screen smartphones have become a trend. It is necessary that modeling and analyzing for full-screen smartphones are considered. This study conducts free-drop simulation and analysis on full-screen smartphones based on ABAQUS/CAE.

Recent advances in sample preparation technologies for analysis of harmful substances in aquatic products

水产品含有丰富的蛋白、维生素和多种微量元素,是人们摄取动物性蛋白质的重要来源之一,我国是世界上最大的水产品消费国,其质量安全问题一直备受关注。但水产样品基质复杂,有害物质的含量低,须对其进行分离富集后才能进行检测,传统的液-液萃取、固相萃取和快速固相分散萃取等样品前处理技术在水产品分析中得到广泛应用,同时针对一些挥发性和超痕量有害物质检测时,固相微萃取同样体现出巨大优势。这些样品前处理技术可以有效去除基体对分析对象的干扰,提高检测方法的灵敏度和准确度。根据目标分析物性质的不同,选择合适的样品前处理技术,是水产品中有害物质分析的关键步骤。该文以水产品中有害物的来源不同,将其分为3类:(1)水产品中环境污染物的分析;(2)养殖运输和加工过程中有害物的分析;(3)水产品中生物毒素的分析。以这3类有害物质的分析为主线,综述了近10年水产品中有害物质分析的样品前处理技术,包括液-液萃取、固相萃取、固相微萃取、快速固相分散萃取和磁性固相萃取等。此外,还对各种技术的优缺点进行了探讨,并对其未来发展方向进行了展望。

Mechanistic studies of NH3-assisted reduction of mononuclear Cu(ii) cation sites in Cu-CHA zeolites

Spectroscopic, titrimetric, and gas-phase product analysis methods reveal a six-electron process for NH3-assisted reduction of mononuclear Cu(ii) sites to Cu(i) in Cu-CHA zeolites of different Cu(ii) site speciation and density.

Quality Markers for Astragali Radix and Its Products Based on Process Analysis.

Due to the complex nature of traditional medicines, quality control methods need to cover two aspects: compliance of raw materials with quality standards and process control. Astragali radix (AR), the roots of Astragalus mongholicus Bunge, was selected in this study as an example of a widely used traditional medicine in various formulations. Astragaloside IV (AG IV) and calycosin 7-O-β-D-glucoside (CG) are used as the markers for the quality control of AR and its products in the Chinese Pharmacopoeia. However, in the raw materials, malic acid esters of the CG and acetate esters of the astragaloside are easily decomposed into CG and AG IV during storage and processing of AR to make extracts for various preparations. The thermal stability of the isoflavonoids and astragalosides in decoction was studied. The level of CG and astragalosides (AG I/AG II/AG IV) was strongly affected by prolonged heat during processing, while calycosin was stable in the conditions. Also the major astragalosides in AR could fully converted into AG IV which eventually reaches a stable level under certain conditions. With calycosin and AG IV as marker components, practical, reproducible, and precise methods were established and applied to the quality analysis of AR from its raw materials to its intermediates and products. This study demonstrates that a full chemical profiles analysis of the whole manufacturing process (from “raw materials—intermediates/extracts—final product”) is important to identify quality markers (Q-markers) and even to establish proper analysis methods for traditional Chinese medicine products.

Language and memory: an investigation of the relationship between autobiographical memory recall and narrative production of semantic and episodic information

ABSTRACT Background: The production of autobiographical narratives requires linguistic structures and the ability to access and generate both semantic information and episodic details of personal events. Aims: This study investigated autobiographical narratives produced by individuals with established semantic memory impairments (semantic variant primary progressive aphasia; svPPA) or episodic memory impairments (amnestic mild cognitive impairment; aMCI) in order to investigate whether different categories of memory impairment would manifest different linguistic deficits. Methods & Procedures: We used the Autobiographical Interview and Quantitative Production Analysis methods to investigate linguistic production during autobiographical recall. Additional investigations compared the production of present and past tense inflections in order to look for morpho-syntactic differences in the sets of episodic and semantic information. Outcomes and Results: The results showed that individuals with svPPA produced fewer well-formed sentences when producing episodic details and produced fewer past tense inflections when producing semantic details in comparison to healthy controls. The aMCI group produced fewer episodic utterances but produced a larger number of words in the set of semantic details, in comparison to healthy controls. Conclusions: It is possible that specific demands related to the type of message being conveyed, or high cognitive load during retrieval of episodic information may affect the narration process. Difficulty in the retrieval of episodic information is likely related to reduced production of episodic utterances in individuals with aMCI and may be related to deficits in linguistic production in svPPA. We propose that the results in the set of semantic details are connected to previous findings relating to semantic memory and deficits in discourse coherence in both groups.

Parameter Identification for Bernoulli Serial Production Line Model

Model-based analysis of production systems is one of the main areas in manufacturing research. The foundation of the successful application of these theoretical studies is the availability of valid and high-fidelity mathematical models that are capable of capturing the behavior of job flow in production systems. The modeling process of a production system, however, may require a significant amount of nonstandardized work that can only be done properly by someone with solid training in the area and extensive experience through real case studies. This poses a critical challenge in the effective implementation of these valuable theoretical results in the Industry 4.0 era. To overcome this, we propose a new production systems modeling paradigm inspired by system identification: calculate production system model parameters that best match the standard system performance metrics measured on the factory floor. Specifically, in this article, we consider production lines characterized by the Bernoulli serial line model and develop algorithms that identify model parameters to fit the system throughput and work-in-process. Analytical algorithms are derived to solve this problem in a two-machine line case and then extended to multi-machine lines. The accuracy and computational efficiency of the algorithms are demonstrated through extensive numerical experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A high-fidelity mathematical model is of critical importance to the implementation of any model-based production system analysis method. Currently, the construction of such models is carried out in an ad hoc manner. The quality of the resulting models may heavily depend on the training, experience, intuition, and personal preference of the modeler. The proposed model parameter identification method focuses on standard key performance indices commonly measured on the factory floor. The advantage is twofold. First, these standard performance metrics are consistently defined regardless of industry, thus avoiding any data-ambiguity issue that may occur when using complex machine/equipment status data. Second, measuring these performance metrics in real time is typically convenient and cost effective, even for manufacturing plants without high-end IT infrastructure, thus making the technology accessible to not only large but also small- and mid-sized manufacturers. Using the algorithms developed in this article, a practitioner can quickly construct a serial production line model and then utilize it to access the rich library of production analysis, design, and control methods available in the literature.

Production capacity identification and analysis using novel multivariate nonlinear regression: Application to resource optimization of industrial processes

Production capacity identification and analysis in industrial processes plays a more and more important role at home and abroad, which not only can improve the energy efficiency, but also reduce the carbon emission. However, the data of complex industrial processes exist multi-dimension and strong noise, which make traditional linear models difficult to identify and analyze the production capacity. Therefore, this paper proposes a novel production capacity identification and analysis method based on multivariate nonlinear regression (MNR) integrating the affinity propagation (AP) clustering algorithm (AP-MNR) for energy saving and resource optimization. The elements that mainly affect the production capacity are extracted by the AP algorithm. Then the extracted elements and the final yield are set as inputs and outputs to build the production capacity identification model by using multivariate nonlinear regression method. At last, the AP-MNR method has been applied for energy saving and resource optimization of actual ethylene and PTA industrial processes. The evaluation indexes with the goodness of fit in ethylene and PTA industrial processes are 0.984 and 0.993, which have proved the effectiveness of the proposed method. Furthermore, the reasonable resource allocation of complex industrial processes can be optimized to achieve energy saving and carbon dioxide emission reduction.

Visible Post-Data Analysis Protocol for Natural Mycotoxin Production.

Fungal natural products are routinely analyzed using target detection protocols by comparing to commercial standards. However, discovery of new products suffers from a lack of high-throughput analytical techniques. Post-data process techniques have become popular tools for natural product confirmations and mycotoxin family analysis. In this work, a visible post-data process procedure with MZmine, GNPS, and Xcalibur was used for efficient analysis of high-resolution mass spectrometry. Conjugated products were screened with an optimized diagnostic fragmentation filtering module in MZmine and further confirmed with Xcalibur by comparing to unconjugated commercial standards. MS/MS spectral data were processed and used to establish a feature based on a molecular networking map in GNPS (Global Natural Products Social Molecular Networking; https://gnps.ucsd.edu), for visualization of fungal natural product families. The results demonstrate the potential of combining MZmine-, GNPS-, and Xcalibur-based methods for visible analysis of fungal natural products.