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You searched for: Publication year rev 7981-2019 Remove constraint Publication year rev: 7981-2019 Subject process monitoring Remove constraint Subject: process monitoring Text Availability Citation in PubAg Remove constraint Text Availability: Citation in PubAg
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1. Adaptive Selection of Latent Variables for Process Monitoring

Author:
Lijia Luo; Shiyi Bao; Jianfeng Mao
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.21 pp. 9075-9086
ISSN:
1520-5045
Subject:
case studies; chemistry; process monitoring
Abstract:
... Latent-variable (LV) methods have been widely applied to multivariate statistical process monitoring. Using LV methods can simplify the process-monitoring problem through a projection of the high-dimensional process data into a low-dimensional LV space that retains most of the important information for fault detection. A key issue is the optimal selection of a subset of LVs to constitute such a lo ...
DOI:
10.1021/acs.iecr.8b05847

2. Automated Model Selection in Principal Component Analysis: A New Approach Based on the Cross-Validated Ignorance Score

Author:
Stefania Russo; Guangyu Li; Kris Villez
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.30 pp. 13448-13468
ISSN:
1520-5045
Subject:
artificial intelligence; automation; data collection; models; prediction; principal component analysis; process design; process monitoring
Abstract:
... Principal component analysis (PCA) is by far the most widespread tool for unsupervised learning with high-dimensional data sets. It is popularly studied for exploratory data analysis and online process monitoring. Unfortunately, fine-tuning PCA models and particularly the number of components remains a challenging task. Today, this selection is often based on a combination of guiding principles, e ...
DOI:
10.1021/acs.iecr.9b00642

3. Bayesian modeling and computation for analyte quantification in complex mixtures using Raman spectroscopy

Author:
Ningren Han; Rajeev J. Ram
Source:
Computational statistics & data analysis 2019 pp. -
ISSN:
0167-9473
Subject:
Bayesian theory; Markov chain; Raman spectroscopy; algorithms; biopharmaceuticals; data collection; glucose; models; process monitoring
Abstract:
... A two-stage algorithm based on Bayesian modeling and computation aiming at quantifying analyte concentrations or quantities in complex mixtures with Raman spectroscopy is proposed. A hierarchical Bayesian model is built for spectral signal analysis, and reversible-jump Markov chain Monte Carlo (RJMCMC) computation is carried out for model selection and spectral variable estimation. Processing is d ...
DOI:
10.1016/j.csda.2019.106846

4. Calorespirometric Investigation of Streptococcus zooepidemicus Metabolism: Thermodynamics of Anabolic Payload Contribution by Growth and Hyaluronic Acid Synthesis

Author:
Naresh Mohan; Satya Sai Pavan; Akshay Achar; Nivedhitha Swaminathan; Senthilkumar Sivaprakasam
Source:
Biochemical engineering journal 2019 pp. -
ISSN:
1369-703X
Subject:
Streptococcus equi subsp. zooepidemicus; biomass production; biopolymers; carbon; entropy; glucose; hyaluronic acid; metabolism; prediction; process monitoring
Abstract:
... Thermodynamic analysis of carbon flux competing for pathways of S. zooepidemicus in the production of catabolic (Lactic acid) and anabolic (Biomass and Hyaluronic Acid) products is investigated to assimilate the thermodynamic advantages of biopolymer production. Calorespirometry was employed to fingerprint the on-going HA production process and to predict reliable estimation of catabolic and anabo ...
DOI:
10.1016/j.bej.2019.107367

5. Data-Driven Dynamic Modeling and Online Monitoring for Multiphase and Multimode Batch Processes with Uneven Batch Durations

Author:
Kai Wang; Lee Rippon; Junghui Chen; Zhihuan Song; R. Bhushan Gopaluni
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.30 pp. 13628-13641
ISSN:
1520-5045
Subject:
algorithms; batch systems; data collection; dynamic models; fermentation; penicillins; process design; process monitoring
Abstract:
... Batch processes are often characterized by piecewise linear dynamics due to varying operating conditions. Multiphase and multimode modeling of batch processes is a common technique that offers insight into the process operation and improved online monitoring. However, existing monitoring methods have several drawbacks such as neglecting process dynamics, requiring separate treatment of transient b ...
DOI:
10.1021/acs.iecr.9b00290

6. A Debottlenecking Study of an Industrial Pharmaceutical Batch Plant

Author:
Franz D. Bähner; Jakob K. Huusom
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.43 pp. 20003-20013
ISSN:
1520-5045
Subject:
algorithms; business enterprises; case studies; equipment; information processing; models; process design; process monitoring; systems engineering
Abstract:
... Operational complexity in batch-operated plants is large, and companies struggle to produce at high equipment utilization. Owing to high fixed costs, capacity utilization is an important operational directive. Many current plants are not in a state that allows the application of rigorous process systems engineering tools due to modeling challenges. This work proposes identifying the right engineer ...
DOI:
10.1021/acs.iecr.9b03134

7. Distributed Statistical Process Monitoring Based on Multiblock Canonical Correlation Analysis

Author:
Xinchun Wan; Chudong Tong; Lijia Luo
Source:
Industrial & engineering chemistry process design and development 2019 v.59 no.3 pp. 1193-1201
ISSN:
1520-5045
Subject:
algorithms; models; process monitoring; regression analysis
Abstract:
... A novel multiblock canonical correlation analysis (MBCCA) algorithm is proposed for multiblock modeling and distributed process monitoring purposes. The MBCCA algorithm is formulated to seek for different projecting bases in accordance with different multiple blocks so that the squared sum of canonical correlation coefficients between the latent variables from different blocks could be maximized. ...
DOI:
10.1021/acs.iecr.9b04971

8. Dual-Wavelength Raman Fusion Spectroscopy

Author:
Johannes Kiefer
Source:
Analytical chemistry 2019 v.91 no.3 pp. 1764-1767
ISSN:
1520-6882
Subject:
Raman spectroscopy; analytical chemistry; lasers; multivariate analysis; process monitoring; quantitative analysis
Abstract:
... Spatially compressed dual-wavelength Raman spectroscopy allows recording the full Raman spectrum using a detection system with limited spectral range. The common approach is to record the spectra with the two excitation lasers consecutively and then concatenate the full spectrum. However, with this approach, quantitative analysis for process monitoring is not possible as the investigated object ma ...
DOI:
10.1021/acs.analchem.8b03909

9. Fault Detection and Root Cause Analysis of a Batch Process via Novel Nonlinear Dissimilarity and Comparative Granger Causality Analysis

Author:
He Fei; Wang Chaojun; Fan Shu-Kai S
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.47 pp. 21842-21854
ISSN:
1520-5045
Subject:
algorithms; batch systems; case studies; data collection; entropy; fermentation; penicillins; process monitoring
Abstract:
... Data-driven fault detection and root cause analysis methods become attractive in modern industrial production that can guarantee the safety and stability of process operation. If process monitoring technology is implemented for fault detection, and the root cause of faults is analyzed timely, it is beneficial to maintain and improve the quality of coming batches. In this paper, a framework of faul ...
DOI:
10.1021/acs.iecr.9b04471

10. Finding Significant Qualitative Trend Combinations for Multivariate Systems from Historical Data

Author:
Kuang Chen; Jiandong Wang
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.11 pp. 4518-4533
ISSN:
1520-5045
Subject:
algorithms; decision making; models; process design; process monitoring
Abstract:
... Data segments in similar patterns are often prerequisites for process monitoring, data mining, and decision making tasks. This paper proposes a probabilistic clustering method to automatically find significant qualitative trend combinations from historical time sequences of multiple process variables. The main idea is to obtain the histograms of decreasing, steady, and increasing qualitative trend ...
DOI:
10.1021/acs.iecr.8b05864

11. Functional Modeling View on Product and Process Engineering in Design and Operations

Author:
Sten Bay Jørgensen; Morten Lind; Niels Jensen
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.26 pp. 11129-11148
ISSN:
1520-5045
Subject:
chemical engineering; chemistry; models; process design; process monitoring; systems engineering
Abstract:
... Recent significant developments within process systems engineering demonstrate a need for more-flexible tools to handle the many diverse model-based tasks within product and process synthesis, design, innovation, and operation. Consequently, it is relevant to have a closer look at one of the modeling methodologies that have emerged. This paper addresses functional modeling for the purpose of the o ...
DOI:
10.1021/acs.iecr.8b06338

12. Glycopeptide Nanofiber Platform for Aβ-Sialic Acid Interaction Analysis and Highly Sensitive Detection of Aβ

Author:
Li Lei; Rui Geng; Zhiai Xu; Yijing Dang; Xianli Hu; Lingling Li; Ping Geng; Yang Tian; Wen Zhang
Source:
Analytical chemistry 2019 v.91 no.13 pp. 8129-8136
ISSN:
1520-6882
Subject:
Alzheimer disease; amyloid; cell membranes; early diagnosis; fluorescence; glycopeptides; nanofibers; neurons; pathogenesis; process monitoring; screening; sialic acids; titration
Abstract:
... The variation of amyloid β peptide (Aβ) concentration and Aβ aggregation are closely associated with the etiology of Alzheimer’s diseases (AD). The interaction of Aβ with the monosialoganglioside-rich neuronal cell membrane has been suggested to influence Aβ aggregation. Therefore, studies on the mechanism of Aβ and sialic acids (SA) interaction would greatly contribute to better understanding the ...
DOI:
10.1021/acs.analchem.9b00377

13. Hierarchically Distributed Monitoring for the Early Prediction of Gas Flare Events

Author:
Mengqi Fang; Fadi Ibrahim; Hariprasad Kodamana; Biao Huang; Noel Bell; Mark Nixon
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.26 pp. 11352-11363
ISSN:
1520-5045
Subject:
chemistry; data collection; environmental impact; prediction; principal component analysis; process design; process monitoring; wavelet
Abstract:
... Flare events in industrial processes are undesirable because of their negative economic and environmental impacts. Early warnings of flare events are needed in preventing occurrences of flaring, as corrective manual interventions can be performed by the plant operators. This article reports the successful application of a hierarchically distributed monitoring approach for flare event prediction. T ...
DOI:
10.1021/acs.iecr.8b06186

14. ISO 11783-compatible industrial sensor and control systems and related research: A review

Author:
Dimitrios S. Paraforos; Galibjon M. Sharipov; Hans W. Griepentrog
Source:
Computers and electronics in agriculture 2019 v.163 pp. 104863
ISSN:
0168-1699
Subject:
agricultural machinery and equipment; data collection; decision making; fertilizers; industry; information management; process monitoring; seeds; tractors
Abstract:
... It has been more than 20 years that the ISO 11783 (commonly designated as ISOBUS) was formed and now it is the widely used standard in agricultural machinery communication. As over the last years the industry is actively promoting ISOBUS, a number of sensor and control system appeared that are ISOBUS-compatible. These systems are focused on two main areas: (i) decision-making, where the sensors ar ...
DOI:
10.1016/j.compag.2019.104863

15. Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning

Author:
Yang Zhou; Shaojun Li; Ning Xiong
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.9 pp. 3782-3796
ISSN:
1520-5045
Subject:
Bayesian theory; acetic acid; probabilistic models; probability; process design; process monitoring; vines
Abstract:
... This paper proposes a boosting vine copula-based dependence description (BVCDD) method for multivariate and multimode process monitoring. The BVCDD aims to improve the standard vine copula-based dependence description (VCDD) method by establishing an ensemble of submodels from sample directions based on a boosting strategy. The generalized Bayesian inference-based probability (GBIP) index is intro ...
DOI:
10.1021/acs.iecr.8b04081

16. In-situ measurement of tension and contact forces for weaving process monitoring: Application to 3D interlock

Author:
Carole Bessette; Mathieu Decrette; Michel Tourlonias; Jean-François Osselin; François Charleux; Dominique Coupé; Marie-Ange Bueno
Source:
Composites 2019 v.126 pp. 105604
ISSN:
1359-835X
Subject:
composite materials; process monitoring; sensors (equipment); spectral analysis; weaving
Abstract:
... This study is focused on the monitoring of 3D interlock weaving, which is a process used for structural composite reinforcements. As high-density weaving leads to significant damage on warp tows, the standard execution of the process is impacted. To increase the production rate, tow-to-tow interactions need to be clearly understood, which is the aim of in-situ measurements on a sequential weaving ...
DOI:
10.1016/j.compositesa.2019.105604

17. Joint Structure Preserving Embedding Model and Its Application for Process Monitoring

Author:
Yuanjian Fu; Chaomin Luo
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.45 pp. 20667-20679
ISSN:
1520-5045
Subject:
case studies; geometry; learning; models; principal geodesic analysis; process design; process monitoring
Abstract:
... Currently, dimensionality reduction methods based on manifold learning are widely applied to industrial process monitoring. However, considering that a number of manifold-based monitoring methods extract only the global geometric information of the original data or the local manifold structure of the neighboring data points, in this article, a novel dimensionality reduction method called joint str ...
DOI:
10.1021/acs.iecr.9b03077

18. Large-Scale Supervised Process Monitoring Based on Distributed Modified Principal Component Regression

Author:
Mengyu Rong; Hongbo Shi; Shuai Tan
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.39 pp. 18223-18240
ISSN:
1520-5045
Subject:
Bayesian theory; algorithms; models; probability; process design; process monitoring
Abstract:
... Traditional process monitoring methods construct a single monitoring model to detect if a fault happened. But the development of industrial technology has made industrial processes increasingly complex and huge. When considering local or quality-related faults, especially tiny faults in complex large-scale industrial processes, it is difficult to accurately detect these faults using a single monit ...
DOI:
10.1021/acs.iecr.9b02163

19. A New Nonlinear Process Monitoring Method Based on Linear and Nonlinear Partition

Author:
Fan Wang; Sen Zhang; Yixin Yin
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.37 pp. 17445-17454
ISSN:
1520-5045
Subject:
data collection; mathematical models; process design; process monitoring
Abstract:
... Traditional nonlinear process monitoring approaches deal with all process variables directly, without carefully investigating their complex relationships. In this Article, key performance indicators (KPIs) are monitored objects. Maximal information coefficient (MIC) is used to analyze correlation between process variables and KPIs. MIC is able to find complex associations and assess their signific ...
DOI:
10.1021/acs.iecr.9b03197

20. New insights into the CO2-steady and pH-steady cultivations of two microalgae based on continuous online parameter monitoring

Author:
Xupeng Cao; Yimei Xi; Jiao Liu; Yadong Chu; Peichun Wu; Miao Yang; Zhanyou Chi; Song Xue
Source:
Algal research 2019 v.38 pp. 101370
ISSN:
2211-9264
Subject:
Isochrysis; Nannochloropsis; absorbance; biomass; carbon; carbon dioxide; chlorophyll; environmental factors; light intensity; microalgae; pH; process monitoring; research and development; temperature
Abstract:
... CO2-steady and pH-steady cultivations are two common strategies used in the cultivation of microalgae for the control of carbon supply. However, the differences between the two strategies have seldom been discussed due to the lack of an appropriate evaluation platform. Here, a precise comparison of CO2-steady and pH-steady modes was carried out using Nannochloropsis oceanica IMTE1 and Isochrysis z ...
DOI:
10.1016/j.algal.2018.11.021

21. Novel Distributed Alarm Visual Analysis Using Multicorrelation Block-Based PLS and Its Application to Online Root Cause Analysis

Author:
Qun-Xiong Zhu; Yi Luo; Yan-Lin He
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.45 pp. 20655-20666
ISSN:
1520-5045
Subject:
alarm systems; case studies; least squares; models; process design; process monitoring
Abstract:
... Process monitoring and alarm systems play an important role in production safety. Distributed alarm systems have been utilized to effectively reduce the burden of data calculations, contributing to fast-track root causes of alarms. However, the traditional distributed contribution analysis method cannot be well applied to online monitoring in time. In addition, the correlation-based variable recon ...
DOI:
10.1021/acs.iecr.9b02963

22. Optimal Feature Selection for Distributed Data-Driven Process Monitoring

Author:
Shaaz Khatib; Prodromos Daoutidis
Source:
Industrial & engineering chemistry process design and development 2019 v.59 no.6 pp. 2307-2317
ISSN:
1520-5045
Subject:
algorithms; case studies; computers; mathematical theory; process monitoring
Abstract:
... Two methods are proposed in this article to address distributed data-driven process monitoring. The first method selects sensors and allocates these sensors among subsystems with the objective of optimizing the diagnostic performance of a pattern recognition method when it is implemented in a distributed configuration subject to constraints that are imposed to meet operational requirements. The se ...
DOI:
10.1021/acs.iecr.9b03872

23. Parameters’ monitoring and in-situ instrumentation for resin transfer moulding: A review

Author:
Mauricio Torres
Source:
Composites 2019 v.124 pp. 105500
ISSN:
1359-835X
Subject:
autoclaves; composite materials; infrastructure; instrumentation; liquids; manufacturing; mathematical models; optical fibers; process monitoring; thermocouples
Abstract:
... This review summarizes the current state-of-art of real-time instrumentation of Resin Transfer Moulding (RTM). As RTM is gaining demand in the aerospace industry because of its higher properties-cost ratio compared with autoclave, better process monitoring and parameters tracking are required. RTM incorporates composite design flexibility and fast-medium volume production improving manufacturing e ...
DOI:
10.1016/j.compositesa.2019.105500

24. Pathogenetic process monitoring and early detection of pear black spot disease caused by Alternaria alternata using hyperspectral imaging

Author:
Ting-tiao Pan; Erkinbaev Chyngyz; Da-Wen Sun; Jitendra Paliwal; Hongbin Pu
Source:
Postharvest biology and technology 2019 v.154 pp. 96-104
ISSN:
0925-5214
Subject:
Alternaria alternata; discriminant analysis; fruits; hyperspectral imagery; least squares; pears; process monitoring; support vector machines
Abstract:
... Pathogenetic process monitoring and early detection of black spot disease caused by Alternaria alternata on pear fruit is still difficult, as it causes only minor changes in the appearance of the infected area during the early stages of infection. In this study, the potential of hyperspectral imaging (HSI) for monitoring the pathogenetic process and early detection of the disease on pear fruit was ...
DOI:
10.1016/j.postharvbio.2019.04.005

25. Process Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation

Author:
Jingxin Zhang; Maoyin Chen; Hao Chen; Xia Hong; Donghua Zhou
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.14 pp. 5579-5587
ISSN:
1520-5045
Subject:
algorithms; case studies; process design; process monitoring; statistical analysis
Abstract:
... By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP–MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, t ...
DOI:
10.1021/acs.iecr.8b05875

26. Process Monitoring Method Based on Double-Model and Multi-Subspace Vine Copula

Author:
Qiong Jia; Hongtao Deng; Xiang Ren; Shaojun Li
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.27 pp. 12137-12148
ISSN:
1520-5045
Subject:
chemistry; mathematical models; multivariate analysis; probability; process design; process monitoring; vines
Abstract:
... A process monitoring method based on double-model and multi-subspace vine copula (DMVC) is proposed in this paper. To improve the fault detection performance, process variables are divided into two sets according to their correlation, in which C-vine and D-vine are used to build models, respectively. The variables with stronger correlation are selected to build the C-vine model, and those with wea ...
DOI:
10.1021/acs.iecr.9b01781

27. Process analytical technologies to monitor the liquid phase of anaerobic cultures

Author:
Anika Bockisch; Erich Kielhorn; Peter Neubauer; Stefan Junne
Source:
Process biochemistry 2019 v.76 pp. 1-10
ISSN:
1359-5113
Subject:
acoustics; beverages; biochemical pathways; electrochemistry; fermentation; liquids; pharmaceutical industry; process monitoring; spectroscopy; viability
Abstract:
... Recent advances in sensor development and miniaturization offer new possibilities to monitor and control bioprocesses. Specific requirements for anaerobic processes in terms of low costs and high robustness against insoluble fractions and impurities in media led to a decelerated penetration of new technology in this field. Since no regulatory framework demands for process monitoring and documentat ...
DOI:
10.1016/j.procbio.2018.10.005

28. Randomized Kernel Principal Component Analysis for Modeling and Monitoring of Nonlinear Industrial Processes with Massive Data

Author:
Zhe Zhou; Ni Du; Jingyun Xu; Zuxin Li; Peiliang Wang; Jie Zhang
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.24 pp. 10410-10417
ISSN:
1520-5045
Subject:
chemistry; mathematical models; principal component analysis; process design; process monitoring
Abstract:
... Kernel principal component analysis (KPCA) has shown excellent performance in monitoring nonlinear industrial processes. However, model building, updating, and online monitoring using KPCA are generally time-consuming when massive data are obtained under the normal operation condition (NOC). The main reason is that the eigen-decomposition of a high-dimensional kernel matrix constructed from massiv ...
DOI:
10.1021/acs.iecr.9b00300

29. Recursive Gaussian Mixture Models for Adaptive Process Monitoring

Author:
Junhua Zheng; Qiaojun Wen; Zhihuan Song
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.16 pp. 6551-6561
ISSN:
1520-5045
Subject:
Bayesian theory; batch systems; case studies; models; probability; process design; process monitoring; variance covariance matrix
Abstract:
... Gaussian mixture models (GMM) have recently been introduced and widely used for process monitoring. This paper intends to develop a new recursive GMM model for adaptive monitoring of processes under time-varying conditions. Two model updating schemes with/without forgetting factors are both proposed. Bayesian inference probability index is used as the monitoring statistic in both of the continuous ...
DOI:
10.1021/acs.iecr.8b06101

30. Reducing the composting time of broiler agro-industrial wastes: The effect of process monitoring parameters and agronomic quality

Author:
M. Chiarelotto; F.M. Damaceno; H.E.F. Lorin; L.M.S. Tonial; L.A. de Mendonça Costa; M.A. Bustamante; R. Moral; F.C. Marhuenda-Egea; M.S.S.M. Costa
Source:
Waste management 2019 pp. -
ISSN:
0956-053X
Subject:
agricultural wastes; chicken meat; compost quality; composting; economic feasibility; fluorescence emission spectroscopy; maturity stage; meat processing; organic matter; process monitoring; processing waste; trees; water solubility
Abstract:
... The aim of this study was to evaluate the effect of considering different composting times on compost quality before the end of the bio-oxidative phase and after the maturation and storage phases. This study may provide useful information to optimize the composting process and increase the economic feasibility of the technology and its adoption without decreasing the quality of the end-product. In ...
DOI:
10.1016/j.wasman.2019.07.012

31. Robust Process Monitoring Methodology for Detection and Diagnosis of Unobservable Faults

Author:
Md. Tanjin Amin; Faisal Khan; Syed Imtiaz; Salim Ahmed
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.41 pp. 19149-19165
ISSN:
1520-5045
Subject:
Bayesian theory; algorithms; comparative study; models; prediction; principal component analysis; process design; process monitoring
Abstract:
... This paper presents a new integrated methodology for fault detection and diagnosis. The methodology is built using the multivariate exponentially weighted moving average principal component analysis (MEWMA-PCA) and the Bayesian network (BN) model. The fault detection is carried out using the MEWMA-PCA; diagnosis is completed utilizing the BN models. A novel supervisory learning-based methodology h ...
DOI:
10.1021/acs.iecr.9b03406

32. Short Ozonation of Lignocellulosic Waste as Energetically Favorable Pretreatment

Author:
Yan Rosen; Hadas Mamane; Yoram Gerchman
Source:
BioEnergy research 2019 v.12 no.2 pp. 292-301
ISSN:
1939-1234
Subject:
absorbance; byproducts; cellulose; delignification; energy balance; energy requirements; ethanol production; feedstocks; lignin; ozonation; ozone; process monitoring; saccharification; sugars; toxicity; transportation; wastes
Abstract:
... Lignocellulosic waste (here municipal trimmings) is a promising sustainable feedstock for ethanol production, but requires costly and polluting pretreatment, often resulting in toxic by-products. Ozonation, nonpolluting, effective pretreatment method, is not used commercially due to high energy requirements of ozone production at high ozone doses needed. Our results, however, demonstrated that low ...
DOI:
10.1007/s12155-019-9962-3

33. Sparse Robust Principal Component Analysis with Applications to Fault Detection and Diagnosis

Author:
Lijia Luo; Shiyi Bao; Chudong Tong
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.3 pp. 1300-1309
ISSN:
1520-5045
Subject:
case studies; models; principal component analysis; process design; process monitoring; variance covariance matrix
Abstract:
... Principal component analysis (PCA) is a popular method for modeling and analysis of high-dimensional data. In spite of its advantages, classical PCA also has two drawbacks. First, it is very sensitive to outliers in the data. Second, it cannot yield interpretable PCs because most of the loadings are nonzero. To overcome these drawbacks, we propose a new PCA method that has the properties of robust ...
DOI:
10.1021/acs.iecr.8b04655

34. Surface convexity of anammox granular sludge: Digital characterization, state indication and formation mechanism

Author:
Da Kang; Leiyan Guo; Qianyi Hu; Dongdong Xu; Tao Yu; Yiyu Li; Zhuo Zeng; Wenji Li; Xiaojing Shen; Ping Zheng
Source:
Environment international 2019 v.131 pp. 105017
ISSN:
0160-4120
Subject:
Zoogloea; anaerobic ammonium oxidation; biochemical pathways; bioreactors; extrusion; friction; granules; image analysis; microbubbles; microscopy; nitrogen; pollution load; process monitoring; processing technology; sludge; statistical analysis; wastewater
Abstract:
... Anammox granular sludge system is a promising biotechnology for nitrogen removal from wastewaters. The anammox granules possess the distinctive morphological features which can be developed as visible indicators for anammox process monitoring. In this study, the surface convexity of anammox granular sludge (AnGS) was systematically investigated for the first time. The AnGS was withdrawn from four ...
DOI:
10.1016/j.envint.2019.105017

35. Unsupervised Change Point Detection Using a Weight Graph Method for Process Monitoring

Author:
Ruqiao An; Chunjie Yang; Yijun Pan
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.4 pp. 1624-1634
ISSN:
1520-5045
Subject:
furnaces; process design; process monitoring
Abstract:
... Because industrial processes are complicated and time-varying in general, unsupervised, and nonparametric process monitoring methods are necessary. Recently, a graph-based change point detection method with a developed scan statistic, which is unsupervised and nonparametric, has been introduced. Industrial processes are primarily continuous with considerable important information contained in the ...
DOI:
10.1021/acs.iecr.8b02455

36. Using Labeled Autoencoder to Supervise Neural Network Combined with k-Nearest Neighbor for Visual Industrial Process Monitoring

Author:
Shifu Yan; Xuefeng Yan
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.23 pp. 9952-9958
ISSN:
1520-5045
Subject:
algorithms; neural networks; process design; process monitoring
Abstract:
... Data clustering and visualization are efficient methods for visual industrial process monitoring. Self-organizing map (SOM) and t-distributed stochastic neighbor embedding (t-SNE) are two of the most widely used algorithms. However, these methods do not make full use of label information. This paper presents a new strategy that uses labeled autoencoder (LAE) to supervise a neural network (NN) comb ...
DOI:
10.1021/acs.iecr.9b01325

37. The evolution of compost stability and maturity during the full-scale treatment of the organic fraction of municipal solid waste

Author:
Alessandra Cesaro; Anna Conte; Vincenzo Belgiorno; Antonietta Siciliano; Marco Guida
Source:
Journal of environmental management 2019 v.232 pp. 264-270
ISSN:
0301-4797
Subject:
compost stability; composting; humic substances; municipal solid waste; organic wastes; phytotoxicity; process monitoring; soil; Italy
Abstract:
... Composting is the method most commonly applied worldwide for the recovery of the source sorted organic waste. The process aims at stabilizing the organic matter, so as to produce a material with soil improver properties, referred to as compost. The effective recovery of the organic waste fraction via composting implies compost safe use on soil. In this view, the assessment of compost characteristi ...
DOI:
10.1016/j.jenvman.2018.10.121

38. Accelerated Kernel Canonical Correlation Analysis with Fault Relevance for Nonlinear Process Fault Isolation

Author:
Jiaxin Yu; Kai Wang; Lingjian Ye; Zhihuan Song
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.39 pp. 18280-18291
ISSN:
1520-5045
Subject:
isolation techniques; multivariate analysis; nonlinear models; process design; process monitoring; product quality
Abstract:
... In the field of multivariate statistical process monitoring (MSPM), fault isolation has attracted increasing attention, due to its importance in ensuring process reliability and product quality. However, the existing fault isolation methods are mostly limited to linear settings with single variable isolation. For nonlinear modeling, the kernel method is commonly used, but the time for solving a ke ...
DOI:
10.1021/acs.iecr.9b03069

39. Considerations for emission monitoring and liner analysis of thermally manufactured sewer cured-in-place-pipes (CIPP)

Author:
Kyungyeon Ra; Seyedeh Mahboobeh Teimouri Sendesi; Md. Nuruddin; Nadezhda N. Zyaykina; Emily N. Conkling; Brandon E. Boor; Chad T. Jafvert; John A. Howarter; Jeffrey P. Youngblood; Andrew J. Whelton
Source:
Journal of hazardous materials 2019 v.371 pp. 540-549
ISSN:
0304-3894
Subject:
air; air pollutants; air pollution; antioxidants; carcinogens; emissions; humans; methylene chloride; oxidation; phenol; pipes; plasticizers; process monitoring; sorbents; storms; styrene; volatile organic compounds; Africa; Asia; Europe; North America; Pacific Ocean Islands
Abstract:
... Cured-in-place-pipes (CIPP) are plastic liners chemically manufactured inside existing damaged sewer pipes. They are gaining popularity in North America, Africa, Asia, Europe, and Oceania. Volatile and semi-volatile organic compound (VOC/SVOC) emissions from storm sewer CIPP installations were investigated at a dedicated outdoor research site. Tedlar bag, sorbent tube, and photoionization detector ...
DOI:
10.1016/j.jhazmat.2019.02.097

40. Effect of tracer material properties on the residence time distribution (RTD) of continuous powder blending operations. Part I of II: Experimental evaluation

Author:
M. Sebastian Escotet-Espinoza; Sara Moghtadernejad; Sarang Oka; Yifan Wang; Andres Roman-Ospino; Elisabeth Schäfer; Philippe Cappuyns; Ivo Van Assche; Mauricio Futran; Marianthi Ierapetritou; Fernando Muzzio
Source:
Powder technology 2019 v.342 pp. 744-763
ISSN:
0032-5910
Subject:
ingredients; manufacturing; mechanical properties; mixing; models; pharmaceutical industry; powders; process monitoring; traceability; tracer techniques
Abstract:
... Residence time distribution (RTD) models are essential to understand process dynamics and support process monitoring and control in continuous manufacturing systems. RTD models can also be used to monitor material traceability and to isolate intermediate materials or finished products when specifications are not met. However, while pharmaceutical companies are currently making extensive use of RTD ...
DOI:
10.1016/j.powtec.2018.10.040

41. In-line monitoring of Ibuprofen during and after tablet compression using near-infrared spectroscopy

Author:
Himmat Dalvi; Alyssa Langlet; Marie-Josee Colbert; Antoine Cournoyer; Jean-Maxime Guay; Nicolas Abatzoglou; Ryan Gosselin
Source:
Talanta 2019 v.195 pp. 87-96
ISSN:
0039-9140
Subject:
active pharmaceutical ingredients; chemometrics; gels; geometry; ibuprofen; manufacturing; near-infrared spectroscopy; pellets; powders; prediction; process monitoring; product quality; quality control
Abstract:
... Near infrared spectroscopy (NIRS) used as process analytical technology tool to monitor Active Pharmaceutical Ingredient concentrations during tablet manufacturing has been reported to enhance overall product quality assurance. NIRS applications in different manufacturing stages are facilitated by their ability to handle different sample presentations – be it solids, liquids, gels or powders. The ...
DOI:
10.1016/j.talanta.2018.11.034

42. Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA

Author:
Xiaogang Deng; Jiawei Deng
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.6 pp. 2280-2295
ISSN:
1520-5045
Subject:
principal component analysis; process design; process monitoring
Abstract:
... Early detection of incipient faults is a challenging task in chemical process monitoring field. As an effective incipient fault detection tool, statistical local kernel principal component analysis (SLKPCA) has demonstrated its advantage over the traditional kernel principal component analysis (KPCA). However, how to improve its incipient fault detection performance is still a valuable problem. In ...
DOI:
10.1021/acs.iecr.8b04794

43. Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control

Author:
Melis Onel; Baris Burnak; Efstratios N. Pistikopoulos
Source:
Industrial & engineering chemistry process design and development 2019 v.59 no.6 pp. 2291-2306
ISSN:
1520-5045
Subject:
models; penicillins; process monitoring; regression analysis; support vector machines
Abstract:
... We propose a novel active fault-tolerant control strategy that combines machine learning based process monitoring and explicit/multiparametric model predictive control (mp-MPC). The strategy features (i) data-driven fault detection and diagnosis models by using the support vector machine (SVM) algorithm, (ii) ranking via a nonlinear, kernel-dependent, SVM-based feature selection algorithm, (iii) d ...
DOI:
10.1021/acs.iecr.9b04226

44. Intense Pulsed Light unprinting for reducing life-cycle stages in recycling of coated printing paper

Author:
Michael Dexter; Keri Rickman; Changqing Pan; Chih-hung Chang; Rajiv Malhotra
Source:
Journal of cleaner production 2019 v.232 pp. 274-284
ISSN:
0959-6526
Subject:
Fourier transform infrared spectroscopy; color; developmental stages; environmental impact; ethanol; light; optical properties; paper; paper recycling; process monitoring; ultraviolet-visible spectroscopy; waste paper; xenon
Abstract:
... Unprinting of paper can reduce multiple life-cycle stages in the recycling of paper to yield significant environmental impact. Laser-unprinting has been demonstrated for uncoated paper but causes significant damage to coated paper. This work explores a scalable optical (non-laser) process for unprinting coated paper. Printed coated paper is exposed to pulsed broad-spectrum Intense Pulsed Light (IP ...
DOI:
10.1016/j.jclepro.2019.05.387

45. Mutual Information–Dynamic Stacked Sparse Autoencoders for Fault Detection

Author:
Jie Yin; Xuefeng Yan
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.47 pp. 21614-21624
ISSN:
1520-5045
Subject:
autocorrelation; models; process monitoring; time series analysis
Abstract:
... Data-based process monitoring is gaining increasing attention, especially deep learning modeling methods. Given that process data are inherently dynamic, the dynamic relationship of process data needs to be incorporated into the monitoring model. Measured variables have diverse characteristics; some variables have strong autocorrelation, but some have no or weak autocorrelation. Hence, a new nonli ...
DOI:
10.1021/acs.iecr.9b04389

46. Novel Pattern-Matching Integrated KCVA with Adaptive Rank-Order Morphological Filter and Its Application to Fault Diagnosis

Author:
Yuan Xu; Cuihuan Fan; Qun-Xiong Zhu; Abbas Rajabifard; Nengcheng Chen; Yiqun Chen; Yan-Lin He
Source:
Industrial & engineering chemistry process design and development 2019 v.59 no.4 pp. 1619-1630
ISSN:
1520-5045
Subject:
case studies; models; multivariate analysis; process monitoring; seeds
Abstract:
... With the scale expansion of industrial processes, the relationship between process variables has become complex and highly nonlinear. As a result, the requirements for fault diagnosis and safety monitoring has become demanding. To address this problem, a novel and effective pattern-matching method using kernel canonical variate analysis (KCVA) integrated with an adaptive rank-order morphological f ...
DOI:
10.1021/acs.iecr.9b05403

47. Oxidation-reduction potential (ORP) as a tool for process monitoring of H2O2/LPMO assisted enzymatic hydrolysis of cellulose

Author:
Adnan Kadić; Piotr Chylenski; Mads Anders Tengstedt Hansen; Oskar Bengtsson; Vincent G.H. Eijsink; Gunnar Lidén
Source:
Process biochemistry 2019 v.86 pp. 89-97
ISSN:
1359-5113
Subject:
anaerobic conditions; cellulose; enzymatic hydrolysis; enzymes; glucose; hydrogen peroxide; hydrolysis; oxygen; process monitoring; redox potential; softwood
Abstract:
... Oxidation-reduction potential (ORP) is an environmental factor of importance in several biological conversion processes. Lytic polysaccharide monooxygenases (LPMOs) catalyze oxidative disruption of the cellulose chain in the presence of oxygen or hydrogen peroxide and increase enzymatic hydrolysis yields. However, the enzymes are also sensitive to oxidative damage and the level of oxidative agent ...
DOI:
10.1016/j.procbio.2019.08.015

48. Powder flow and mixing in a continuous mixer operating in either transitory or steady-state regimes: Mesoscopic Markov chain models

Author:
C. Ammarcha; C. Gatumel; J.L. Dirion; M. Cabassud; V. Mizonov; H. Berthiaux
Source:
Powder technology 2019 v.346 pp. 116-136
ISSN:
0032-5910
Subject:
Markov chain; mixing; models; powders; process monitoring
Abstract:
... Continuous powder mixing is gaining interest in the industrial community concerned with more and more functional powder products. The understanding of powder flow and mixing/segregation of particles as well as their translation into models that can be used in process monitoring and control is a major issue. In the present work, we describe the development of different mesoscopic Markov chain model ...
DOI:
10.1016/j.powtec.2019.01.085

49. Rapid volatiles fingerprinting by dopant-assisted positive photoionization ion mobility spectrometry for discrimination and characterization of Green Tea aromas

Author:
Jia Li; Haibo Yuan; Yuefeng Yao; Jinjie Hua; Yanqin Yang; Chunwang Dong; Yuliang Deng; Jinjin Wang; Haiyang Li; Yongwen Jiang; Qinghua Zhou
Source:
Talanta 2019 v.191 pp. 39-45
ISSN:
0039-9140
Subject:
acetone; chemometrics; flavor; green tea; manufacturing; odors; process monitoring; sensory evaluation; spectroscopy
Abstract:
... Aroma is one major concern of tea flavor and represents an essential criterion for quality evaluation. Herein, we presented a novel approach for rapid, non-destructive and real-time fingerprinting of green tea aromas using ion mobility spectrometry (IMS) without prior sample treatment. Positive photoionization with acetone as dopant was selected as suitable source for tea volatiles analysis in com ...
DOI:
10.1016/j.talanta.2018.08.039

50. Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

Author:
Qingchao Jiang; Xuefeng Yan; Biao Huang
Source:
Industrial & engineering chemistry process design and development 2019 v.58 no.29 pp. 12899-12912
ISSN:
1520-5045
Subject:
models; process design; process monitoring
Abstract:
... Process monitoring is crucial for maintaining favorable operating conditions and has received considerable attention in previous decades. Currently, a plant-wide process generally consists of multiple operational units and a large number of measured variables. The correlation among the variables and units is complex and results in the imperative but challenging monitoring of such plant-wide proces ...
DOI:
10.1021/acs.iecr.9b02391