Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Detection of discoloration in diesel fuel based on gas chromatographic fingerprints

In the countries of the European Community, diesel fuel samples are spiked with Solvent Yellow 124 and either Solvent Red 19 or Solvent Red 164. Their presence at a given concentration indicates the specific tax rate and determines the usage of fuel. The removal of these so-called excise duty components, which is known as fuel "laundering", is an illegal action that causes a substantial loss in a government's budget. The aim of our study was to prove that genuine diesel fuel samples and their counterfeit variants (obtained from a simulated sorption process) can be differentiated by using their gas chromatographic fingerprints that are registered with a flame ionization detector. To achieve this aim, a discriminant partial least squares analysis, PLS-DA, for the genuine and counterfeit oil fingerprints after a baseline correction and the alignment of peaks was constructed and validated. Uninformative variables elimination (UVE), variable importance in projection (VIP), and selectivity ratio (SR), which were coupled with a bootstrap procedure, were adapted in PLS-DA in order to limit the possibility of model overfitting. Several major chemical components within the regions that are relevant to the discriminant problem were suggested as being the most influential. We also found that the bootstrap variants of UVE-PLS-DA and SR-PLS-DA have excellent predictive abilities for a limited number of gas chromatographic features, 14 and 16, respectively. This conclusion was also supported by the unitary values that were obtained for the area under the receiver operating curve (AUC) independently for the model and test sets

CONDITION-BASED RISK ASSESSMENT STRATEGY AND ITS HEALTH INDICATOR WITH APPLICATION TO PUMPS AND COMPRESSORS

Large rotating machinery, such as centrifugal gas compressors and pumps, are widely applied as crucial components in the petrochemical industries. To enable in-time and effective maintenance of these machines, the concept of a health indicator is arousing great interest. A suitable health indicator indicates the overall health of the machinery and it is closely related to maintenance strategies and decision-making. It can be obtained either from near misses and incident data, or from real-time measured data. However, the existing health indicators have some limitations. On the one hand, the near misses and incident data may have been obtained from similar systems, reflecting population characteristics but not fully accounting for the individual features of the target system. On the other hand, the existing health indicators that use condition monitoring data, mainly focused on detecting incipient faults, and usually do not include financial cost factors when calculating the indicators. Therefore, there is the requirement to develop a single system "Health Indicator", that can show the health condition of a system in real-time, as well as the likely financial loss incurred when a fault is detected in the system, to assist operators on maintenance decision making. This project has developed such an integrated health indicator for rotating machinery. The integrated health indicator described in this thesis is extracted from a novel condition-based risk assessment strategy, which can be regarded as an integration of risk-based maintenance with improved conventional condition-based maintenance, with financial factors taken into account. The value of the health indicator is that it directly illustrates the risk to the system (or equipment), including likely financial loss, which makes it easier for operators to select the optimal time for maintenance or set alarm thresholds given the specific conditions in their companies or plants. This thesis provides a guide to set up an integrated maintenance model for large rotating machinery. It provides a useful reference for researchers working on condition-based fault detection and dynamic risk-based maintenance

Nonlinear data driven techniques for process monitoring

The goal of this research is to develop process monitoring technology capable of taking advantage of the large stores of data accumulating in modern chemical plants. There is demand for new techniques for the monitoring of non-linear topology and behavior, and this research presents a topological preservation method for process monitoring using Self Organizing Maps (SOM). The novel architecture presented adapts SOM to a full spectrum of process monitoring tasks including fault detection, fault identification, fault diagnosis, and soft sensing. The key innovation of the new technique is its use of multiple SOM (MSOM) in the data modeling process as well as the use of a Gaussian Mixture Model (GMM) to model the probability density function of classes of data. For comparison, a linear process monitoring technique based on Principal Component Analysis (PCA) is also used to demonstrate the improvements SOM offers. Data for the computational experiments was generated using a simulation of the Tennessee Eastman process (TEP) created in Simulink by (Ricker 1996). Previous studies focus on step changes from normal operations, but this work adds operating regimes with time dependent dynamics not previously considered with a SOM. Results show that MSOM improves upon both linear PCA as well as the standard SOM technique using one map for fault diagnosis, and also shows a superior ability to isolate which variables in the data are responsible for the faulty condition. With respect to soft sensing, SOM and MSOM modeled the compositions equally well, showing that no information was lost in dividing the map representation of process data. Future research will attempt to validate the technique on a real chemical process

Integration of bioinformatics analysis and experimental biocatalysis for a comprehensive approach to the synthesis of renewable polyesters

La crescente domanda di poliesteri funzionalizzabili ha accresciuto l\u2019interesse nello sviluppo di nuove strade per la sintesi biocatalizzata di polimeri, dove gli enzimi sono in grado di rispondere alla sfida di combinare condizioni di reazione sostenibili dal punto di vista ambientale con l\u2019alta selettivit\ue0 ed efficienza della catalisi. Gli enzimi sono un\u2019attrattiva sostenibile ai catalizzatori tossici usati nelle policondensazione, come quelli metallici, stagno in particolare. L\u2019obiettivo di questa tesi \ue8 quello di integrare approcci sperimentali e bioinformatici per lo studio di nuovi biocatalizzatori per le policondensazioni. Una valida alternativa \ue8 infatti rappresentata dagli enzimi, i quali consentono riciclabilit\ue0, assenza di contaminazione del prodotto grazie all\u2019immobilizzazione della proteina. Attualmente, i supporti per l\u2019immobilizzazione sono di natura non rinnovabile (metacrilici e stirenici). In questa tesi (Capitolo 3) si \ue8 esplorata la possibilit\ue0 di utilizzare la lolla di riso \u2013 un economico materiale di scarto lignocellulosico - a questo proposito. Verr\ue0 proposto un confronto tra metodi chemoenzimatici per la funzionalizzazione della lolla, con l\u2019obiettivo di ottenere un supporto di immobilizzazione rinnovabile capace di rispondere alle sfide della green chemistry. Il metodo enzimatico utilizza un sistema laccasi-mediatore con l\u2019inserimento di un linker diamminico. Questo approccio consente di evitare l\u2019utilizzo del periodato di sodio, che \ue8 responsabile di importanti alterazioni nella struttura morfologica della lolla, come dimostrato da microscopia SEM. Candida antarctica Lipasi B e due asparaginasi sono state immobilizzate e testate. La lipasi immobilizzata \ue8 stata utilizzata per sintetizzare un poliestere con l\u2019acido itaconico. Mentre le lipasi sono la pi\uf9 comune scelta per le reazioni di policondensazione, il nostro gruppo si \ue8 concentrato sullo studio di nuove serin idrolasi da utilizzare in questo campo, nello specifico, le cutinasi. Questa classe di enzimi \ue8 gi\ue0 stata utilizzata per catalizzare la sintesi efficiente di poliesteri con monomeri biobased, lavorando in condizioni sostenibili dal punto di vista ambientale. Uno studio bioinformatico approfondito delle cutinasi verr\ue0 proposto nel Capitolo 4 utilizzando i descrittori GRID-based di BioGPS. Il software ha consentito di proiettare una selezione di cutinasi su un modello UPCA (Unsupervised Pattern Cognition Analysis) precedentemente studiato da questo gruppo di ricerca, confermando che l\u2019ambiente chimico-fisico pre-organizzato di Cutinasi 1 da Thermobifida cellulosilytica \ue8 molto simile a quello di Candida antarctica Lipasi B ed \ue8 in grado di offrire ulteriori vantaggi in termini di tipologie di substrato accettati grazie al suo sito attivo molto superficiale. BioGPS \ue8 stato usato anche per generare il \u201ccataloforo\u201d di differenti sottoclassi di serin idrolasi, permettendo di estrarre le caratteristiche minime proprie di ciascuna di esse. Utilizzando il \u201ccataloforo\u201d e studi di dinamica molecolare \ue8 stato possibile chiarire le ragioni alla base delle caratteristiche vantaggiose delle cutinasi nella sintesi di poliesteri.The rising demand for advanced polyesters, displaying new functional properties, has boosted the development of new biocatalysed routes for polymer synthesis, where enzymes concretely respond to the challenge of combining benign conditions with high selectivity and efficient catalysis. Enzymes are attractive sustainable alternatives to toxic catalysts used in polycondensation, such as metal catalysts and tin in particular. Moreover, they enable the synthesis of functional polyesters that are otherwise not easily accessible by using traditional chemical routes. The aim of the present thesis is to integrate experimental and bioinformatics approaches in order to study new biocatalysts to be used in polycondensations. A valid alternative to metal catalysts is represented by enzymes. Biocatalyst recyclability and avoidance of product contamination are usually obtained via enzyme immobilization on solid carriers. Nowadays, non-renewable petrochemical-based supports are used for this purpose, namely methacrylic and styrenic resins. In this thesis (Chapter 3), rice husk - a waste product of rice milling available worldwide at a negligible price - has been explored as an innovative and fully renewable lignocellulosic carrier endowed with morphological complexity and chemical versatility that makes it prone to multiple and benign chemo-enzymatic modifications. A comparison of chemical and enzymatic methods for the functionalization of rice husk has been carried out, enabling the development of a renewable immobilization carrier suitable for responding to the looming challenge of green chemistry. The enzymatic method relies on laccase oxidation using laccase from Trametes spec. and TEMPO-radical mediator, followed by the insertion of a diamine spacer. As compared to the classical cellulose oxidation performed via sodium periodate, the enzymatic method offers the advantage of preserving the morphology of rice husk, as demonstrated by SEM microscopy. Laccase oxidation also assures benign operative conditions. Candida antarctica Lipase B, and two commercially available formulations of asparaginase, were immobilized and tested. In the first case, the lipase was successfully applied in the polycondensation of the biobased monomer dimethyl itaconate whereas the immobilized asparaginases were applied in the hydrolysis of asparagine, a precursor of the toxic acrylamide in food. In addition, lignin removal via alkaline hydrogen peroxide bleaching has been tested as a method for increasing the specific activity of the immobilized formulation. While lipases being the most common alternative for polycondensation reactions, our research group focused on the study of a novel class of serine hydrolases to be used in these kind of reactions, namely the cutinases. The cutinase class proved to catalyse the efficient polycondensation of biobased monomers working in mild conditions in terms of pressure and temperature. A thorough bioinformatics study was carried out based on GRID-based BioGPS descriptors (Chapter 4). BioGPS allowed to project a selection of cutinases on a Unsupervised Pattern Cognition Analysis (UPCA) model previously published by this research group, confirming that the pre-organized physicochemical environment in the active site of Cutinase 1 from Thermobifida cellulosilytica is very similar to the one of Candida antarctica Lipase B, while offering increased capabilities in terms of the size of the substrate accepted, thanks to a superficial and wide active site. The said software was used also to generate the \u201ccatalophor\u201d of different serine hydrolase subfamilies, enabling to extract the structural features that distinguish the various sub-families of serine hydrolases. Exploiting the \u201ccatalophor\u201d tool and molecular dynamics studies it was possible to shed light on the particular behaviour that makes cutinases an advantageous biocatalyst to be used in polycondensation reactions

Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

Thermochemical conversion of waste biomass (cherry rejects) to renewable platform chemicals: FUR and HMF

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Strategic development in the petrochemical industry

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Comprehensive Two-Dimensional Gas Chromatography and Its Application to the Investigation of Pyrolytic Liquids

The chapter presents basic principles of one-dimensional gas chromatography (1D-GC) and comprehensive two-dimensional gas chromatography (GC × GC) related to the main advantages of the two-dimensional technique, as well as its application to the study of organic compounds in liquids derived from coal, mainly through pyrolysis and extraction. It also shows the investigation of compounds contained in bio-oils obtained from biomass through pyrolysis, using GC × GC. Advances in scientific knowledge related to the composition of these complex matrices are shown through different examples of GC × GC analyses, such as the identification of trace compounds that would not be perceived by 1D-GC, organized patterns of elution of structurally related compounds that help their identification, etc. Examples shown make it clear that GC × GC is the technique of choice to elucidate composition of these complex matrices