Total Projection To Latent Structures Research Articles

Edible insects as a novel source of lecithin: Extraction and lipid characterization of black soldier fly larvae and yellow mealworm

Edible insects with high fat and phosphorus content are a potential novel source of lecithin, however, studies on their minor lipids are limited. In this study, lecithin was extracted from black soldier fly larvae and yellow mealworm. Herein, the effects of lecithin extraction method, matrix and ultrasound pretreatment were explored based on the fatty acid composition and phospholipid profile with soy lecithin as a reference. The use of a wet matrix and ultrasound pretreatment increased the extraction efficiency of total PLs from both insects. Insect lecithin contained a considerable amount of sphingomyelin compared to soy lecithin. In insect lecithin, a total of 47 glycerophospholipid and sphingomyelin molecular species, as well as four molecular species of fatty acyl esters of hydroxy fatty acid, were detected. This study is the first comprehensive investigation of insects as a new source of lecithin with applications in food, cosmetics and in the pharmaceutical industry.

A decentralized operating performance assessment for geological drilling process via multi-block total projection to latent structures and Bayesian inference

Geological drilling process is an industrial process that contains a lot of variables, and the relationships and characters among them are also complex. Accordingly, implementing operating performance assessment by conventional methods has always been a problem that operators are dedicated to solving. In fact, geological drilling process is a complex industrial process involving multiple systems and stratigraphic uncertainties, which makes assessing its operating performance difficult. A decentralized operating performance assessment based on multi-block total projection to latent structures (T-PLS) and Bayesian inference is proposed for the geological drilling process. Utilizing the variational trends of the detection variables, the most related variables can be grouped in the same block, and the process capability index determines the operating performance grade. This is followed by a T-PLS algorithm-based operating performance assessment model and Bayesian inference. Each sub-block’s assessment results are integrated to achieve a comprehensive performance assessment. Last but not least, drill data show that the proposed method is effective and superior. The proposed method provides better accuracy and generalizability in assessing drilling performance. The novelty of the assessment scheme involves that a decentralized framework is proposed for operating performance assessment by identifying normal operating conditions first and then constructing the multi-block T-PLS-based monitoring model on the local sub-blocks.

Product design for batch processes through total projection to latent structures

Due to the short duration, batch processes are operated flexibly. With the same batch unit, a wide range of high value-added products can be produced to satisfy the demand of the rapidly varying market, within which the quick product design is of vital importance for the manufacturer. In the PLS-based product design method, a design space is available if the number of latent variables is larger than that of output variables. However, according to the development of PLS model, the latent variable may contain the output-irrelevant information, while through the model inversion, the derived latent variable only has the output-related information, so the existence condition of design space is largely relaxed and the dimension of design space may be reduced. This work introduces the total projection to latent structures (TPLS) model into the product design. The TPLS model decomposes the latent variable space into two subspaces: the output-related subspace and the output-orthogonal subspace. In the output-orthogonal subspace, the latent variable is irrelevant to the output, and it can be any value without any influence on the output, while in the output-related subspace, the latent variable is determined by the desired output, and it is derived by the model inversion. The input computed by any different output-irrelevant latent variable along with the output-related latent variable may result in the desired output, and the output-irrelevant latent variable spans the design space. Compared with the PLS-based method, the proposed TPLS-based method may derive the design space with higher dimension on less restrictive conditions. In addition, the design spaces by the two methods are compared. In the end, the validity of the method is confirmed in a numerical simulation and the penicillin fermentation simulation.

Hierarchical Multiblock T-PLS Based Operating Performance Assessment for Plant-Wide Processes

With the pursuit of profit, the operating performance assessment of industrial processes has gradually attracted attention and concentration in modern industrial production. However, owing to the complexity of plant-wide processes, many existing operating performance assessment techniques are lacking in interpretation of the assessment result, and the cause identification is also difficult for the nonoptimal operating performance. In this study, a new operating performance assessment method is proposed for plant-wide processes based on hierarchical multiblock total projection to latent structures (T-PLS). The proposed approach divides the process variables into several conceptually meaningful blocks and incorporates block information into high level for operating performance assessment of plant-wide processes. With the new method, the assessment task can be greatly reduced and the interpretability of the assessment models are greatly improved. Once the process operating performance is nonoptimal, the resp...

A Nonlinear Process Monitoring Approach With Locally Weighted Learning of Available Data

This paper proposes a data-driven approach for nonlinear process monitoring under the framework of locally weighted learning. Based on available process measurements, the locally weighted projection regression is used in the offline learning scheme to provide a series of locally weighted linear models, in which the algorithms of traditional projection to latent structures (PLS) and total PLS could be applied to establish improved test statistics suitable for complicated process monitoring. By using the weights of local models obtained from measurement learning, the developed test statistics are further online utilized to monitor potential abnormalities related or unrelated to process quality. The effectiveness of the proposed locally weighted total PLS monitoring approach is finally demonstrated by the comparisons with other relevant methods via simulations based on the wastewater treatment process benchmark under different abnormal conditions.

Enhanced quality-related fault detection approach based on OSC and M-PLS

Partial least squares (PLS) is an efficient tool widely used in multivariate statistical process monitoring. Since standard PLS performs oblique projection to input space X, it has limitations in distinguishing quality-related and quality-unrelated faults. Several postprocessing modifications of PLS, such as total projection to latent structures (T-PLS), have been proposed to solve this issue. Further studies have found that these modifications fail to reduce false alarm rates (FARs) of quality-unrelated faults when fault amplitude increases. To cope with this problem, this paper proposes an enhanced quality-related fault detection approach based on orthogonal signal correction (OSC) and modified-PLS (M-PLS). The proposed approach removes variation orthogonal to output space Y from input space X before PLS modeling, and further decomposes X into two orthogonal subspaces in which quality-related and quality-unrelated statistical indicators are designed separately. Compared with T-PLS, the proposed approach has a more robust performance and a lower computational load. Two case studies, including a numerical example and the Tennessee Eastman (TE) process, show the effeteness of the proposed approach.

Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process

Increasing global competition is setting even higher demands for the safety, quality and operating efficiency of industrial processes. The traditional projection to latent structures (PLS) based methods for quality-relevant fault detection has appeared in several industrial applications, while total PLS that performs more completely has been used as a better tool for monitoring associated with the product quality. However, the running/operating states for the process variables are often non-stationary, time-varying. Thus, the static PLS or TPLS for these processes will reduce the efficiency of the monitoring, unreliable monitoring results will affect the engineers׳ decision-making. Under this background, an adaptive modification on total PLS model named as recursive TPLS will be proposed to adapt the monitoring model on line. The new recursive version is achieved via a far more computation-efficient manner and the operating cost is significantly lowered. The simulation on TE process illustrates the effectiveness of the new adaptive fault monitoring approach based on RTPLS.

Online process operating performance assessment and nonoptimal cause identification for industrial processes

Although industrial processes are usually operated at the optimal point in the early stage of the production, the operating performance may deteriorate with time due to process disturbances. In order to pursue optimal comprehensive economic benefit (CEB), online process operating performance assessment on optimality has become a key issue. However, a little work has been published in this research area. In this paper, a new online operating performance assessment and nonoptimal cause identification method for industrial process are proposed. The contributions of this paper can be summarized as follows: a novel performance-similarity-based online operating performance assessment method is proposed; total projection to latent structures (T-PLS) is applied to the area of process performance assessment for the first time; the online assessment results include not only the deterministic performance grades, but also the performance grade conversions which were not covered in the existing assessment method; when the assessment result is nonoptimal, a novel automatic nonoptimal cause identification strategy is developed based on variable contributions, which is meaningful for guiding the further production adjustment. Finally, the feasibility and efficiency of the proposed method are illustrated with a case of gold hydrometallurgical process.

Output‐relevant fault detection and identification of chemical process based on hybrid kernel T‐PLS

The single kernel total projection to latent structures (T‐PLS) would lead to a higher missing alarm rate and false alarm rate because either global kernel function or local kernel function can be utilized. The hybrid kernel T‐PLS algorithm proposed in this paper combines global function with local function to solve non‐linear problems by projecting low‐dimensional input data to high‐dimensional feature space. The feature space is then divided into output directly correlated subspace, output orthogonal subspace, output uncorrelated subspace, and residual subspace by output variables. Based on fault detected by statistic D and Q in these subspaces, respectively, fault‐free data are reconstructed and the fault magnitude is estimated by generalized reconstruction‐based contribution (RBC). The simulation results of Tennessee Eastman process show the proposed algorithm can not only detect output‐relevant fault with higher detection rate, but also identify the type of fault.

Direct projection to latent variable space for fault detection

Partial least squares (PLSs) often require many latent variables (LVs) T to describe the variations in process variables X correlated with quality variables Y, which are obtained via the traditional nonlinear iterative PLS (NIPALS) optimal solution based on (X, Y). Total projection to latent structures (T-PLSs) performs further decomposition to extract LVs Ty directly related to Y from T, which are obtained by the PCA optimal solution based on the predicted value of Y. Inspired by T-PLS, combined with practical process characteristics, two fault detection approaches are proposed in this paper to solve problems encountered by T-PLS. Without the NIPALS, (X, Y) are projected into the latent variable space determined by main variations of Y directly. Furthermore, the structure and characteristics of several modified methods in statistical analysis are studied based on calculation procedures of solving PCA, PLS and T-PLS optimization problems, and the geometric significance of the T-PLS model is demonstrated in detail. Simulation analysis and case studies both indicate the effectiveness of the proposed approaches.

Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process

In this paper, a nonlinear fault diagnosis scheme is established for the hot strip mill process (HSMP). In HSMP, the faults affecting quality index are denoted as quality-related faults, which should be taken care as soon as possible. Projection to latent structures (PLS) is a basic model for quality-related fault detection in linear processes. In the presented work, a total kernel PLS (T-KPLS) model is utilized for modeling and monitoring HSMP, which is a typical nonlinear process. However, diagnosis tools have not been developed aiming at the nonlinear case based on T-KPLS model. Motivated by the successful use of contribution plot for the linear case, a contribution rate plot is proposed to extend contribution plots to the nonlinear case. In the end of this paper, the proposed method is applied to the hot strip mill process effectively.

Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application

Projection to latent structures (PLS) model has been widely used in quality-related process monitoring, as it can establish a mapping relationship between process variables and quality index variables. To enhance the adaptivity of PLS, kernel PLS (KPLS) as an advanced version has been proposed for nonlinear processes. In this paper, we discuss a new total kernel PLS (T-KPLS) for nonlinear quality-related process monitoring. The new model divides the input spaces into four parts instead of two parts in KPLS, where an individual subspace is responsible in predicting quality output, and two parts are utilized for monitoring the quality-related variations. In addition, fault detection policy is developed based on the T-KPLS model, which is more well suited for nonlinear quality-related process monitoring. In the case study, a nonlinear numerical case, the typical Tennessee Eastman Process (TEP) and a real industrial hot strip mill process (HSMP) are employed to access the utility of the present scheme.

Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach

In data-based monitoring field, the nonlinear iterative partial least squares procedure has been a useful tool for process data modeling, which is also the foundation of projection to latent structures (PLS) models. To describe the dynamic processes properly, a dynamic PLS algorithm is proposed in this paper for dynamic process modeling, which captures the dynamic correlation between the measurement block and quality data block. For the purpose of process monitoring, a dynamic total PLS (T-PLS) model is presented to decompose the measurement block into four subspaces. The new model is the dynamic extension of the T-PLS model, which is efficient for detecting quality-related abnormal situation. Several examples are given to show the effectiveness of dynamic T-PLS models and the corresponding fault detection methods.

Generalized Reconstruction-Based Contributions for Output-Relevant Fault Diagnosis With Application to the Tennessee Eastman Process

Multivariate statistical process monitoring technologies, including principal component analysis (PCA) and partial least squares (PLS), have been successfully applied in many industrial processes. However, in practice, many PCA alarms do not lead to quality deterioration due to process control and recycle loops in process flowsheets, which hinders the reliability of PCA-based monitoring methods. Therefore, one is more interested to monitor the variations related to quality data, and detect the faults which affect quality data. Recently, a total projection to latent structures (T-PLS) model has been reported to detect output-relevant faults. In this paper, a generalized reconstruction based contribution (RBC) method with T-PLS model is proposed to diagnose the fault type for output-relevant faults. Furthermore, the geometrical property of generalized RBC is analyzed. A detailed case study on the Tennessee Eastman process is presented to demonstrate the use of the proposed method without or with prior knowledge.

Output Relevant Fault Reconstruction and Fault Subspace Extraction in Total Projection to Latent Structures Models

Statistical data-driven process monitoring is critical for efficient operations of industrial processes. However, deviations from normal regions in the process data may or may not lead to poor quality of products. This paper proposes a new combined index for detecting output-relevant faults, which affect the output data, and studies the output-relevant fault detectability based on total projection to latent structures (T-PLS). Given actual fault direction, fault-free data can be reconstructed and output-relevant part of fault magnitude can be estimated. Two new methods are derived to extract output-relevant fault subspace from faulty data. A simulation example and a case study on the Tennessee Eastman process are used to show the effectiveness of the proposed methods.

Total projection to latent structures for process monitoring

AbstractPartial least squares or projection to latent structures (PLS) has been used in multivariate statistical process monitoring similar to principal component analysis. Standard PLS often requires many components or latent variables (LVs), which contain variations orthogonal to Y and useless for predicting Y. Further, the X‐residual of PLS usually has quite large variations, thus is not proper to monitor with the Q‐statistic. To reduce false alarm and missing alarm rates of faults related to Y, a total projection to latent structures (T‐PLS) algorithm is proposed in this article. The new structure divides the X‐space into four parts instead of two parts in standard PLS. The properties of T‐PLS are studied in detail, including its relationship to the orthogonal PLS. Further study shows the space decomposition on X‐space induced by T‐PLS. Fault detection policy is developed based on the T‐PLS. Case studies on two simulation examples show the effectiveness of the T‐PLS based fault detection methods. © 2009 American Institute of Chemical Engineers AIChE J, 2010

Total PLS Based Contribution Plots for Fault Diagnosis

摘要: 多变量统计过程监控对于复杂工业过程是一种有效的故障检测和诊断技术. 最小二乘(或称潜空间投影)模型是多变量统计过程监控中常用的一种投影模型, 能够同时对过程数据和质量数据进行建模. 讨论了一种新的基于全潜空间投影模型的故障诊断技术. 全潜空间投影模型中有4个检测统计量. 提出了一种新的T2贡献图计算方法, 对于所有检测统计量, 得到了相应的贡献图算法. 为了确定一个变量是否发生了故障, 计算所有变量贡献图的控制限. 该技术可以将辨识到的故障变量分为与Y有关和与Y无关的两类. 基于Tennessee Eastman过程的案例研究表明了该技术的有效性. 关键词: 数据驱动 / 全潜空间投影 / 贡献图 / 故障诊断

Total PLS Based Contribution Plots for Fault Diagnosis

Multivariate statistical process monitoring (MSPM) is an efficient data-driven fault detection and diagnosis approach for complex industrial processes. Partial least squares or projection to latent structures (PLS) is one of the latent projection structures used in MSPM, which uses process data X and quality data Y together. In this paper, we discuss a new fault diagnosis approach based on total projection to latent structures (T-PLS). Four kinds of monitoring statistics are used in T-PLS, and a new definition of variable contributions to T 2 of PLS is proposed. Then, definitions of variable contributions to all statistics are derived to identify the faults. Control limits for contribution plots are calculated to identify whether a variable is in abnormal situation or not. Further, the proposed method separates the identified variables into faulty variables related to Y and unrelated to Y more clearly than conventional method. A case study on Tennessee Eastman process (TEP) indicates the efficiency of the proposed approach.

N-acylated phospholipids and lysophospholipids of kenaf seeds

Three minor components have been isolated from the total PLs of kenaf seeds of variety Kuban'-333 by means of column and preparative thin-layer chromatography and they have been characterized by lysophosphatidylcholine and N-acylphosphatidylethanolamine and its lyso analog.

Minor phospholipids of cotton seeds

The minor components of the total PLs of seeds of the cotton plant of variety 108-F have been studied.