Non-linear projection to latent structures

PhD ThesisThis Thesis focuses on the study of multivariate statistical regression techniques which have been used to produce non-linear empirical models of chemical processes, and on the development of a novel approach to non-linear Projection to Latent Structures regression. Empirical modelling relies on the availability of process data and sound empirical regression techniques which can handle variable collinearities, measurement noise, unknown variable and noise distributions and high data set dimensionality. Projection based techniques, such as Principal Component Analysis (PCA) and Projection to Latent Structures (PLS), have been shown to be appropriate for handling such data sets. The multivariate statistical projection based techniques of PCA and linear PLS are described in detail, highlighting the benefits which can be gained by using these approaches. However, many chemical processes exhibit severely nonlinear behaviour and non-linear regression techniques are required to develop empirical models. The derivation of an existing quadratic PLS algorithm is described in detail. The procedure for updating the model parameters which is required by the quadratic PLS algorithms is explored and modified. A new procedure for updating the model parameters is presented and is shown to perform better the existing algorithm. The two procedures have been evaluated on the basis of the performance of the corresponding quadratic PLS algorithms in modelling data generated with a strongly non-linear mathematical function and data generated with a mechanistic model of a benchmark pH neutralisation system. Finally a novel approach to non-linear PLS modelling is then presented combining the general approximation properties of sigmoid neural networks and radial basis function networks with the new weights updating procedure within the PLS framework. These algorithms are shown to outperform existing neural network PLS algorithms and the quadratic PLS approaches. The new neural network PLS algorithms have been evaluated on the basis of their performance in modelling the same data used to compare the quadratic PLS approaches.Strang Studentship European project ESPRIT PROJECT 22281 (PROGNOSIS) Centre for Process Analysis, Chemometrics and Control

Data integration for the monitoring of batch processes in the pharmaceutical industry

Advances in sensor technology has resulted in large amounts of data being available electronically. However, to utilise the potential of the data, there is a need to transform the data into knowledge to realise an enhanced understanding of the process. This thesis investigates a number of multivariate statistical projection techniques for the monitoring of batch fermentation and pharmaceutical processes. In the first part of the thesis, the traditional performance monitoring tools based on the approaches of Nomikos and MacGregor (1994) and Wold et al. (1998) are introduced. Additionally, the application of data scaling as a data pre-treatment step for batch processes is examined and it is observed that it has a significant impact on monitoring performance. Based on the advantages and limitations of these techniques, an alternative methodology is proposed and applied to a simulated penicillin fermentation process. The approach is compared with existing techniques using two metrics, false alarm rate and out-ofcontrol average run length. A further manufacturing challenge facing the pharmaceutical industry is to understand the differences in the performance of a product which is manufactured at two or more sites. A retrospective multi-site monitoring model is developed utilising a pooled sample variancecovariance methodology of the two sites. The results of this approach are compared with a number of techniques that have been previously reported in the literature for the integration of data from two or more sources. The latter part of the thesis focuses on data integration using multi-block analysis. Several blocks of data can be analysed simultaneously to allow the inter- and intra- block relationships to be extracted. The methodology of multi-block Principal Component Analysis (MBPCA) is initially reviewed. To enhance the sensitivity of the algorithm, wavelet analysis is incorporated within the MBPCA framework. The fundamental advantage of wavelet analysis is its ability to process a signal at different scales so that both the global features and the localised details of a signal can be studied simultaneously. Both existing and the modified approach are applied to data generated from an experiment conducted in a batch mini-plant and that was monitored by both physical sensors and on-line UV-Visible spectrometer. The performance of the integrated approaches is benchmarked against the individual process and spectral monitoring models as well as examining their fault detection ability on two additional batches with pre-designed process deviations.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC) : Overseas Research Students Award Scheme (ORSAS) : Centre for Process Analytics and Control Technology (CPACT)GBUnited Kingdo

Data integration for the monitoring of batch processes in the pharmeceutical industry

PhD ThesisAdvances in sensor technology has resulted in large amounts of data being available electronically. However, to utilise the potential of the data, there is a need to transform the data into knowledge to realise an enhanced understanding of the process. This thesis investigates a number of multivariate statistical projection techniques for the monitoring of batch fermentation and pharmaceutical processes. In the first part of the thesis, the traditional performance monitoring tools based on the approaches of Nomikos and MacGregor (1994) and Wold et al. (1998) are introduced. Additionally, the application of data scaling as a data pre-treatment step for batch processes is examined and it is observed that it has a significant impact on monitoring performance. Based on the advantages and limitations of these techniques, an alternative methodology is proposed and applied to a simulated penicillin fermentation process. The approach is compared with existing techniques using two metrics, false alarm rate and out-ofcontrol average run length. A further manufacturing challenge facing the pharmaceutical industry is to understand the differences in the performance of a product which is manufactured at two or more sites. A retrospective multi-site monitoring model is developed utilising a pooled sample variancecovariance methodology of the two sites. The results of this approach are compared with a number of techniques that have been previously reported in the literature for the integration of data from two or more sources. The latter part of the thesis focuses on data integration using multi-block analysis. Several blocks of data can be analysed simultaneously to allow the inter- and intra- block relationships to be extracted. The methodology of multi-block Principal Component Analysis (MBPCA) is initially reviewed. To enhance the sensitivity of the algorithm, wavelet analysis is incorporated within the MBPCA framework. The fundamental advantage of wavelet analysis is its ability to process a signal at different scales so that both the global features and the localised details of a signal can be studied simultaneously. Both existing and the modified approach are applied to data generated from an experiment conducted in a batch mini-plant and that was monitored by both physical sensors and on-line UV-Visible spectrometer. The performance of the integrated approaches is benchmarked against the individual process and spectral monitoring models as well as examining their fault detection ability on two additional batches with pre-designed process deviations.Engineering and Physical Sciences Research Council (EPSRC: The Overseas Research Students Award Scheme (ORSAS): The Centre for Process Analytics and Control Technology (CPACT)

Data-driven, mechanistic and hybrid modelling for statistical fault detection and diagnosis in chemical processes

Research and applications of multivariate statistical process monitoring and fault diagnostic techniques for performance monitoring of continuous and batch processes continue to be a very active area of research. Investigations into new statistical and mathematical methods and there applicability to chemical process modelling and performance monitoring is ongoing. Successive researchers have proposed new techniques and models to address the identified limitations and shortcomings of previously applied linear statistical methods such as principal component analysis and partial least squares. This thesis contributes to this volume of research and investigation into alternative approaches and their suitability for continuous and batch process applications. In particular, the thesis proposes a modified canonical variate analysis state space model based monitoring scheme and compares the proposed scheme with several existing statistical process monitoring approaches using a common benchmark simulator – Tennessee Eastman benchmark process. A hybrid data driven and mechanistic model based process monitoring approach is also investigated. The proposed hybrid scheme gives more specific considerations to the implementation and application of the technique for dynamic systems with existing control structures. A nonmechanistic hybrid approach involving the combination of nonlinear and linear data based statistical models to create a pseudo time-variant model for monitoring of large complex plants is also proposed. The hybrid schemes are shown to provide distinct advantages in terms of improved fault detection and reliability. The demonstration of the hybrid schemes were carried out on two separate simulated processes: a CSTR with recycle through a heat exchanger and a CHEMCAD simulated distillation column. Finally, a batch process monitoring schemed based on a proposed implementation of interval partial least squares (IPLS) technique is demonstrated using a benchmark simulated fed-batch penicillin production process. The IPLS strategy employs data unfolding methods and a proposed algorithm for segmentation of the batch duration into optimal intervals to give a unique implementation of a Multiway-IPLS model. Application results show that the proposed method gives better model prediction and monitoring performance than the conventional IPLS approach.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Application of dynamic partial least squares to complex processes

PhD ThesisMultivariate statistical modelling and monitoring is an active area of research and development in both academia and industry. This is due to the economic and safety benefits that can be attained from the implementation of process modelling and monitoring schemes. Most industrial processes in the chemistry-using sector exhibit complex characteristics including process dynamics, non-linearity and changes in operational behaviour which are compounded by the occurrence of non-conforming data points. To date, modelling and monitoring methodologies have focussed on processes exhibiting one of the aforementioned characteristics. This Thesis considers the development and application of multivariate statistical methods for the modelling and monitoring of the whole process as well as individual unit operations with a particular focus on the complex dynamic nonlinear behaviour of continuous processes. Following a review of Partial Least Squares (PLS), which is applicable for the analysis of problems that exhibit high dimensionality and correlated/collinear variables, it was observed that it is inappropriate for the analysis of data from complex dynamic processes. To address this issue, a multivariate statistical method Robust Adaptive PLS (RAPLS) was proposed, which has the ability to distinguish between non-conforming data, i.e. statistical outliers and a process fault. Through the analysis of data from a mathematical simulation of a time varying and non-stationary process, it is observed that RAPLS shows superior monitoring performance compared to conventional PLS. The model has the ability to adapt to changes in process operating conditions without losing its ability to detect process faults and statistical outliers. A dynamic extension, RADPLS, using an autoregressive with exogenous inputs (ARX) representation was developed to model and monitor the complex dynamic and nonlinear behaviour of an Ammonia Synthesis Fixed-bed Reactor. The resultant model, which is resistant to outliers, shows significant improvement over other dynamic PLS based representations. The proposed method shows some limitations in terms of the detection of the fault for its full duration but it significantly reduces the false alarm rate. The RAPLS algorithm is further extended to a dynamic multi-block algorithm, RAMBDPLS, through the conjunction of a finite impulse response (FIR) representation and multiblock PLS. It was applied to the benchmark Tennessee Eastman Process to illustrate its applicability for the monitoring of the whole process and individual unit operations and to demonstrate the concept of fault propagation in a dynamic and nonlinear continuous system. The resulting model detects the faults and reduces the false alarm rate compared to conventional PLS.Ministry of Higher Education and King Abdulaziz University, Saudi Arabi

OPC UA standardiin perustuva monimuuttaja-analysointi sekä tiedonkeruujärjestelmä kemometrisiin sovelluksiin

Chemical industries utilize a variety of different types of online analyzers, for example, in areas like quality monitoring and process control applications. Large production plants typically use several analyzer devices from multiple manufacturers which are employed to measure different target quantities. As manufacturers have their own proprietary protocols for accessing analyzer information it is usually only the target property estimates that are transferred to the higher level automation systems. Other analyzer data, for example spectra, are generally not in a suitable format for further action on the higher level systems. This thesis outlines a solution to the analyzer data acquisition problem by utilizing OPC UA standard and OPC UA analyzer devices companion specification (ADI) to create a data acquisition system for analyzer information. The created system consists of an OPC UA server, which is used as a single access point for all analyzer related information. In addition, the analyzer data is collected and archived into a SQL database, which is accessible through the OPC UA server. The data acquisition system makes it convenient for the end users to access plant analyzer information using the standardized protocols. Furthermore, the OPC UA based data acquisition system can be used to integrate analyzer data with other types of process measurements. This thesis presents an example application where this type of data integration is utilized to increase the accuracy of the target quantity estimates of an analyzer. The same system also has a wide range of other potential applications, some of which are briefly examined in this work. The literature part of the thesis mainly focuses on different aspects of chemometrics; pre-processing and multivariate modelling of the analyzer spectra. These techniques are used in the thesis' experimental part to process data obtained from Neste Oil Oy’s Porvoo refinery. The literature part also briefly examines different protocols used for the transfer of analyzer data through the automation networks. The experimental part the thesis consists of three main parts: The first part is a case study where the refinery measurement data and the analyzer spectra are utilized to demonstrate how product quality estimates accuracies can be improved through data integration. In the second part, the analyzer data acquisition system is developed, including the provision of a separate OPC UA ADI wrapper server for ABB online analyzers. This wrapper was created in order to obtain the data from the production unit analyzers used in the case study. In the final part, a chemometric calculation platform is designed in order to implement the data processing sequence used to process the refinery data. This platform also utilizes the newly created data acquisition system through the OPC UA protocol.Kemianteollisuudessa käytetään monia erityyppisiä online analysaattoreita, joita hyödynnetään esimerkiksi laadunvalvonnassa sekä prosessien ohjauksessa. Eri laitevalmistajilta olevia analysaattorilaitteita voi olla suuri määrä käytössä isoissa tuotantolaitoksissa, mitaten eri kohdesuureita. Usealla laitevalmistajalla on usein omat suljetut protokollat, joilla analysaattori-informaatiota luetaan sekä käsitellään. Tyypillisesti ainoastaan mitattu kohdesuure on helposti saatavilla tuotantolaitoksen ylemmistä informaatiojärjestelmistä. Muu analysaattori-informaatio, kuten mitatut spektrit ja diagnostiikkadata, eivät siten ole helposti saatavilla ylemmällä automaatiotasoilla. Tämä diplomityö esittää ratkaisun analysaattoritiedonkeruuongelmaan hyödyntämällä OPC UA standardia ja OPC UA analysaattorilaitespesifikaatiota (ADI), sekä luomalla näihin pohjautuvan analysaattoritiedonkeruujärjestelmän. Järjestelmä perustuu OPC UA palvelimeen, joka toimii yhteyspisteenä kaikelle laitoksella olevalle analysaattori-informaatiolle. Lisäksi järjestelmä tukee analysaattoridatan historiakeruuta SQL-tietokantaan, josta tietoa voidaan lukea OPC UA palvelimen kautta. Loppukäyttäjien näkökulmasta järjestelmä helpottaa analysaattori-informaation hallintaa. Diplomityö esittää myös käyttöesimerkin, jossa analysaattori- sekä prosessidataa hyödynnetään rinnakkain parantamaan tiettyjen laadunvalvontasuureiden estimointitarkkuutta. Luotu tiedonkeruujärjestelmä mahdollistaa dataintegraation, jossa yhdistetään mittausdataa monista erityyppisistä lähteistä. Työn tutkimusosa käsittää lähinnä kemometriaan liittyviä datan käsittely- ja mallinnusmenetelmiä. Näitä tekniikoita hyödynnetään kokeellisessa osassa, jossa käsitellään Neste Oil Oy:n Porvoon jalostamolta saatua prosessi- sekä analysaattoridataa. Tutkimusosa sisältää myös kuvauksen yleisimmistä analysaattoridatan tiedonsiirtomenetelmistä. Työn kokeellinen osa sisältää kolme pääosaa. Ensimmäisessä osassa havainnollistetaan, kuinka tiettyjen laadunvalvontasuureiden estimointitarkkuutta voidaan parantaa, kun analysaattori-informaatiota sekä prosessidataa hyödynnetään mallinnuksessa. Toisessa osassa esitellään analysaattoritiedonkeruujärjestelmä, joka esittää myös ADI sovittemen ABB online analysaattorille. Tämä OPC UA palvelimeen perustuva sovitin tarvittiin, sillä tutkittu jalostamoyksikkö käyttää ABB:n laitteita laadunvalvonnassa. Viimeisessä osiossa esitellään laskentapalvelu kemometrisiin laskentatarpeisiin. Tämä palvelu implementoi datakäsittelyketjun prosessi- sekä analysaattorimittauksille. Palvelu hyödyntää OPC UA protokollaa ja edellä luotua tiedonkeruujärjestelmää