Distillation anomaly and fault detection based on unsupervised dimensionality principal component analysis method

A multiple-input and multiple-output (MIMO) model, namely Recursive Orthogonal Least Square (ROLS) based radial basis function (RBF) is developed to estimate product compositions in a batch distillation process from temperature measurements. The process data is generated by simulating the differential equations of the batch distillation process, changing the initial feed composition and boiluprate from batch to batch. Moreover, the reflux ratio is also randomly varied within each batch to represent the exact dynamics of the batch distillation. Temperature and distillate composition is correlated by the RBF trained by ROLS algorithm. A Single RBF network estimate the quality of products in real-time. The results show that ROLS based estimator give correct composition estimations for a batch distillation process. The robustness of the ROLS algorithm and low computational requirement makes the estimator attractive for on-line use.

Chapter 6 - Modeling and optimizing dynamic networks: Applications in process engineering and energy supply

Chapter 11 - Distillation

BackgroundUnsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, however, become less robust or contradictory if multiple high-dimensional data sets per patient exist. Such a scenario is given when the focus is on 3-D data of multiple organs per patient, and a high-dimensional feature matrix per organ is extracted.MethodsWe use principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and multiple co-inertia analysis (MCIA) combined with bagplots to study the distribution of multi-organ 3-D data taken by computed tomography scans. After point-set registration of multiple organs from two public data sets, multiple hundred shape features are extracted per organ. While PCA and t-SNE can only be applied to each organ individually, MCIA can project the data of all organs into the same low-dimensional space.ResultsMCIA is the only approach, here, with which data of all organs can be projected into the same low-dimensional space. We studied how frequently (i.e., by how many organs) a patient was classified to belong to the inner or outer 50% of the population, or as an outlier. Outliers could only be detected with MCIA and PCA. MCIA and t-SNE were more robust in judging the distributional location of a patient in contrast to PCA.ConclusionsMCIA is more appropriate and robust in judging the distributional location of a patient in the case of multiple high-dimensional data sets per patient. It is still recommendable to apply PCA or t-SNE in parallel to MCIA to study the location of individual organs.

The rapid growth of Industry 4.0 and predictive methods fostered a great potential for state-of-the-art techniques in the industrial sector, especially in smart factories. The equipment failure or system breakdowns during run time of a factory creates a severe problems towards impoverishment of the production system and destitution of the business. Predictive Maintenance (PdM) is a cost-saving and data driven technique to predict the maintenance time of in-service equipment or systems to reduce breakdown time and increase productivity. Although PdM is pragmatically adopted in large-scale industries, there is a lack of studies that map the PdM adoption in small and medium-sized enterprises (SMEs). In this systematic mapping study (SMS), we focus on predictive maintenance from an SME perspective to explore the field for researchers, scientists, and developers to comprehend the potential of PdM systems, their challenges, distinctive characteristics, and best practices in SMEs. Our study is based on four research questions comprised of demographic data, key challenges, distinctive characteristics, and best practices of predictive maintenance in SMEs. We found that the current literature on PdM is deficient in the SME domain, especially the financial side is vague. There is a huge potential for PdM in SMEs to design cost models and focus on data availability impediments. Management and monitoring of PdM and skilled personnel are also inadequate. Thus, we present a study that extracts the knowledge from the existing literature about PdM in SMEs, finds the research gap, and can assist in identifying the barriers and challenges of PdM adoption in SMEs.

With the official launch of China’s national unified carbon trading system (ETS) in 2017, it has played an increasingly important role in controlling the growth of carbon dioxide emissions. One of the core issues in carbon trading is the allocation of initial carbon emissions permits. Since the industry emits the largest amount of carbon dioxide in China, a study on the allocation of carbon emission permits among China’s industrial sectors is necessary to promote industry carbon abatement efficiency. In this study, industrial carbon emissions permits are allocated to 37 sub-sectors of China to reach the emission reduction target of 2030 considering the carbon marginal abatement cost, carbon abatement responsibility, carbon abatement potential, and carbon abatement capacity. A hybrid approach that integrates data envelop analysis (DEA), the analytic hierarchy process (AHP), and principal component analysis (PCA) is proposed to allocate carbon emission permits. The results of this study are as follows: First, under the constraint of carbon intensity, the carbon emission permits of the total industry in 2030 will be 8792 Mt with an average growth rate of 3.27%, which is 1.57 times higher than that in 2016. Second, the results of the carbon marginal abatement costs show that light industrial sectors and high-tech industrial sectors have a higher abatement cost, while energy-intensive heavy chemical industries have a lower abatement cost. Third, based on the allocation results, there are six industrial sub-sectors that have obtained major carbon emission permits, including the smelting and pressing of ferrous metals (S24), manufacturing of raw chemical materials and chemical products (S18), manufacturing of non-metallic mineral products (S23), smelting and pressing of non-ferrous metals (S25), production and supply of electric power and heat power (S35), and the processing of petroleum, coking, and processing of nuclear fuel (S19), accounting for 69.23% of the total carbon emissions permits. Furthermore, the study also classifies 37 industrial sectors to explore the emission reduction paths, and proposes corresponding policy recommendations for different categories.

\nThe availability of liquid waste of rise flour can be maintained in continuously and abundant, and is one of waste water that is underutilized, but liquid waste of rise flour has a starch content which can be used as one of the producers of ethanol. This study has the objective to produce bioethanol and a prototype bio-ethanol industry, and perform simulations before conducting experiments, as well as a reference in developing the bio-ethanol industry in Indonesia.\nBased on preliminary analysis obtained starch content of the rice is about 79% b/b, while the starch content of the liquid waste of rise flour is quite high about 30% v/v. The process of preliminary research that has been done is the hydrolysis with Bacillus then followed by bacterial fermentation of glucose by Saccharomyces cerevisiae. The quality of ethanol produced is influenced by the levels of starch, glucose, acidity (pH), the volume ratio of waste with Bacillus volume, the number of bacteria Saccharomyces cerevisiae, fermentation time that has been done in the preliminary study. The filtrate resulting from the hydrolysis and fermentation processes of contains ethanol about 16% v/v carried out a batch distillation process to obtain bioethanol, variables in a batch distillation process is ethanol composition and temperature. Before experimental batch distillation process simulation ternary system Ethanol-Water-Impurities (EWI) with a model of Differential-Algebraic-Equations (DAEs), using the programming language Mathlab version 7 in advance, so that in the determination of the variables can be more effective and more research costs cheap.\nThe results obtained experimentally study the composition of bioethanol a maximum of 95% to 96%, the results of experiments and simulations EWI ternary system form the temperature profile, the profile of the composition of liquid and vapor composition profile dimensionless time functions both at the bottom and in the distillate shows the results of the same approach. The simulation results before used reference in experiments performed the validation beforehand, so that the ternary system simulation EWI after validation of reference can be used in experiments.\n

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.

Guided wave-based structural health monitoring is extensively utilized in various industrial applications to ensure the integrity of components within industrial systems. Among these monitoring techniques, principal component analysis (PCA) reconstruction methods are widely used for anomaly detection due to their computational efficiency and interoperability. However, existing PCA reconstruction methods are semisupervised anomaly detection approaches that require training on historical normal data and fail to detect anomalous signals within the training set. To address this limitation, this work proposes a correntropy PCA (C-PCA), enabling fully unsupervised anomaly detection on raw training data without requiring label information, when the dataset contains a high proportion of abnormal signals. This method allows anomaly detection on real-time measurements without the need for precleaned historical normal data or can also be used to generate clean data for existing semisupervised anomaly detection methods. In correntropy PCA, principal components are extracted from the correntropy matrix rather than the correlation matrix. The correntropy, representing the statistical dependence between samples of guided waves, is estimated utilizing a Gaussian kernel with a specified kernel width. Through the optimization of the kernel width, the correntropy PCA reconstruction method demonstrates superior anomaly detection performance compared with the standard PCA reconstruction method, especially in scenarios where training data are contaminated by a significant proportion of abnormal signals. Guidelines for the optimization of the kernel width are provided. The effectiveness of the correntropy PCA reconstruction-based anomaly detection method is validated using data collected from ten regions over an 80-day period, encompassing guided waves induced by damage occurring over durations ranging from 2 to 20 days.

Synthesis, experiments and simulation of heterogeneous batch distillation processes

Job Creation by Company Size Class: Concentration and Persistence of Job Gains and Losses in Canadian Companies

Factors influencing the perception of exposure to climate risks: Evidence from the world’s largest carbon-intensive industries

Inferential model-based control of multicomponent batch distillation

Hyperspectral imagery (HSI) has high spectral and spatial resolutions, which are essential for anomaly detection (AD). Many anomaly detectors assume that the spectrum signature of HSI pixels can be modeled with a Gaussian distribution, which is actually not accurate and often leads to many false alarms. Therefore, a sparsity model without any distribution hypothesis is usually employed. Dimensionality reduction (DR) as a preprocessing step for HSI is important. Principal component analysis as a conventional DR method is a linear projection and cannot exploit the nonlinear properties in hyperspectral data, whereas locally linear embedding (LLE) as a local, nonlinear manifold learning algorithm works well for DR of HSI. A modified algorithm of sparsity divergence index based on locally linear embedding (SDI-LLE) is thus proposed. First, kernel collaborative representation detection is adopted to calculate the sparse dictionary matrix of local reconstruction weights in LLE. Then, SDI is obtained both in the spectral and spatial domains, where spatial SDI is computed after DR by LLE. Finally, joint SDI, combining spectral SDI and spatial SDI, is computed, and the optimal SDI is performed for AD. Experimental results demonstrate that the proposed algorithm significantly improves the performance, when compared with its counterparts.

Recently, Electronic Health Record (EHR) systems have gained a great attention in industry, research and public sectors in order to enhance the quality of healthcare. Security and privacy, however, are still major issues that require further investigations. The recognition of the significance of various security aspects in the adaptation of EHR solutions has been accompanied with an increasing research work that aims at tackling these issues. Security monitoring is one of the security issues that has attracted an increased attention in the last few years. Security monitoring is a security mechanism that aims at detecting security violations or anomalous behaviour during operation. Such violations can be identified at low technical layers or at the business layer (compliance, violation of legal requirements). In this tutorial, we tackle the issue security monitoring and discuss various analysis methods used to monitor and detect anomalies and security violations. We focus on the methods that have been used to detect anomalies in the heath care domain. Furthermore, we only consider security monitoring from a technical perspective, abstract regulations and legal aspects of malicious attacks and insider threats are out of scope of this tutorial. Thus, targeted audience are computer scientists with an interest on security monitoring and anomaly detection techniques applied in the healthcare domain. The tutorial provides, besides a brief theoretical background on anomaly and mis-use detection techniques, case studies in the healthcare domain. The case studies are based on a systematic literature review conducted by the author aiming at identifying the main characteristics of up-to-date security monitoring solutions targeting anomalous user behaviours in the healthcare domain. The survey showed that anomaly detection, both supervised as well as unsupervised methods, was the main technique used. For example, (1) applying supervised learning techniques to detect anomalous behaviour of users based on various features, e.g., Access time, role, and location, or (2) unsupervised learning method to detect anomalous insiders in the context of collaborative environments. Discussions about these case studies, the problems considered, the solutions proposed, and the results found will be presented.