Оптимізація параметрів процесу ферментативного гліцеролізу жирів комбінуванням методів генетичних алгоритмів і нейронних мереж

Use of artificial neural network-genetic algorithm technique made it possible to determine optimal process parameters of enzymatic glycerolizes of fats. The results of laboratory and experimental-industrial tests corroborated optimum modeling adequacy for four primary process parameters: substrates ratio, enzyme amount, temperature and time

Оптимізація параметрів процесу ферментативної етерифікації жирних кислот етанолом

Use of artificial neural network-genetic algorithm technique made it possible to determine optimal process parameters of enzymatic esterification fatty acids with ethanol. The results of laboratory and experimental-industrial tests corroborated optimum modelling adequacy for four primary process parameters: substrates ratio, enzyme amount, temperature and time

Оптимізація параметрів процесу ферментативної етерифікації жирних кислот етанолом

Use of artificial neural network-genetic algorithm technique made it possible to determine optimal process parameters of enzymatic esterification fatty acids with ethanol. The results of laboratory and experimental-industrial tests corroborated optimum modelling adequacy for four primary process parameters: substrates ratio, enzyme amount, temperature and time

RMN na caracterização de correntes processuais de resíduos pesados

Doutoramento em QuímicaThe main objective of this work was to monitor a set of physical-chemical properties of heavy oil procedural streams through nuclear magnetic resonance spectroscopy, in order to propose an analysis procedure and online data processing for process control. Different statistical methods which allow to relate the results obtained by nuclear magnetic resonance spectroscopy with the results obtained by the conventional standard methods during the characterization of the different streams, have been implemented in order to develop models for predicting these same properties. The real-time knowledge of these physical-chemical properties of petroleum fractions is very important for enhancing refinery operations, ensuring technically, economically and environmentally proper refinery operations. The first part of this work involved the determination of many physical-chemical properties, at Matosinhos refinery, by following some standard methods important to evaluate and characterize light vacuum gas oil, heavy vacuum gas oil and fuel oil fractions. Kinematic viscosity, density, sulfur content, flash point, carbon residue, P-value and atmospheric and vacuum distillations were the properties analysed. Besides the analysis by using the standard methods, the same samples were analysed by nuclear magnetic resonance spectroscopy. The second part of this work was related to the application of multivariate statistical methods, which correlate the physical-chemical properties with the quantitative information acquired by nuclear magnetic resonance spectroscopy. Several methods were applied, including principal component analysis, principal component regression, partial least squares and artificial neural networks. Principal component analysis was used to reduce the number of predictive variables and to transform them into new variables, the principal components. These principal components were used as inputs of the principal component regression and artificial neural networks models. For the partial least squares model, the original data was used as input. Taking into account the performance of the develop models, by analysing selected statistical performance indexes, it was possible to conclude that principal component regression lead to worse performances. When applying the partial least squares and artificial neural networks models better results were achieved. However, it was with the artificial neural networks model that better predictions were obtained for almost of the properties analysed. With reference to the results obtained, it was possible to conclude that nuclear magnetic resonance spectroscopy combined with multivariate statistical methods can be used to predict physical-chemical properties of petroleum fractions. It has been shown that this technique can be considered a potential alternative to the conventional standard methods having obtained very promising results.O principal objetivo deste trabalho foi monitorizar um conjunto de propriedades físico-químicas de correntes processuais pesadas através da espectroscopia de ressonância magnética nuclear, com o intuito de propor um procedimento de análise e processamento de dados em linha para o controlo processual. Vários métodos estatísticos que permitiram relacionar os resultados obtidos por espectroscopia de ressonância magnética nuclear com os resultados obtidos pelos métodos convencionais, aquando da caracterização das diferentes correntes, foram implementados a fim de desenvolver modelos de previsão dessas mesmas propriedades. O conhecimento em tempo real das propriedades físico-químicas dos derivados de petróleo é essencial para otimizar as operações de refinação, garantindo assim operações técnica, económica e ambientalmente adequadas. A primeira parte deste trabalho envolveu a realização de um elevado número de experiências, efetuadas na refinaria de Matosinhos, seguindo métodos convencionais normalizados, importantes para avaliar e caracterizar as correntes de gasóleo de vácuo leve, gasóleo de vácuo pesado e fuel óleo. As propriedades analisadas foram a massa volúmica, viscosidade cinemática, teor em enxofre, ponto de inflamação, resíduo carbonoso, valor P e a destilação atmosférica e de vácuo. Para além da determinação de todas estas propriedades físico-químicas, usando os métodos convencionais, as mesmas amostras foram analisadas por espectroscopia de ressonância magnética nuclear. A segunda parte deste trabalho esteve relacionada com a aplicação de métodos estatísticos multivariáveis que relacionam as propriedades físico-químicas com a informação quantitativa adquirida por espectroscopia de ressonância magnética nuclear. Vários métodos foram aplicados, incluindo a análise por componentes principais, a regressão múltipla por componentes principais, as regressões múltiplas parciais e as redes neuronais artificiais. A análise de componentes principais foi utilizada para reduzir o número de variáveis preditivas e transformá-las em novas variáveis, os componentes principais. Estes componentes principais foram utilizados como variáveis de entrada da regressão múltipla por componentes principais e das redes neuronais artificiais. Na regressão por mínimos quadrados parciais os dados originais foram usados como variáveis de entrada. Tomando em consideração o desempenho dos modelos desenvolvidos, com recurso a parâmetros estatísticos, foi possível concluir que a regressão múltipla por componentes principais contribuiu para piores desempenhos. Melhores desempenhos foram obtidos com a aplicação da regressão por mínimos quadrados parciais e das redes neuronais artificiais. No entanto, foi com os modelos de redes neuronais artificiais que melhores desempenhos foram obtidos em quase todas as propriedades analisadas. Tendo em conta os resultados obtidos, foi possível concluir que a espectroscopia de ressonância magnética nuclear combinada com métodos estatísticos multivariáveis pode ser usada para prever as propriedades físico-químicas de derivados de petróleo. Demonstrou-se que esta técnica pode ser considerada como uma potencial alternativa aos métodos convencionais tendo-se obtido resultados bastantes promissores

Ny forståelse av gasshydratfenomener og naturlige inhibitorer i råoljesystemer gjennom massespektrometri og maskinlæring

Gas hydrates represent one of the main flow assurance issues in the oil and gas industry as they can cause complete blockage of pipelines and process equipment, forcing shut downs. Previous studies have shown that some crude oils form hydrates that do not agglomerate or deposit, but remain as transportable dispersions. This is commonly believed to be due to naturally occurring components present in the crude oil, however, despite decades of research, their exact structures have not yet been determined. Some studies have suggested that these components are present in the acid fractions of the oils or are related to the asphaltene content of the oils. Crude oils are among the worlds most complex organic mixtures and can contain up to 100 000 different constituents, making them difficult to characterise using traditional mass spectrometers. The high mass accuracy of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) yields a resolution greater than traditional techniques, making FT-ICR MS able to characterise crude oils to a greater extent, and possibly identify hydrate active components. FT-ICR MS spectra usually contain tens of thousands of peaks, and data treatment methods able to find underlying relationships in big data sets are required. Machine learning and multivariate statistics include many methods suitable for big data. A literature review identified a number of promising methods, and the current status for the use of machine learning for analysis of gas hydrates and FT-ICR MS data was analysed. The literature study revealed that although many studies have used machine learning to predict thermodynamic properties of gas hydrates, very little work have been done in analysing gas hydrate related samples measured by FT-ICR MS. In order to aid their identification, a successive accumulation procedure for increasing the concentrations of hydrate active components was developed by SINTEF. Comparison of the mass spectra from spiked and unspiked samples revealed some peaks that increased in intensity over the spiking levels. Several classification methods were used in combination with variable selection, and peaks related to hydrate formation were identified. The corresponding molecular formulas were determined, and the peaks were assumed to be related to asphaltenes, naphthenes and polyethylene glycol. To aid the characterisation of the oils, infrared spectroscopy (both Fourier Transform infrared and near infrared) was combined with FT-ICR MS in a multiblock analysis to predict the density of crude oils. Two different strategies for data fusion were attempted, and sequential fusion of the blocks achieved the highest prediction accuracy both before and after reducing the dimensions of the data sets by variable selection. As crude oils have such complex matrixes, samples are often very different, and many methods are not able to handle high degrees of variations or non-linearities between the samples. Hierarchical cluster-based partial least squares regression (HC-PLSR) clusters the data and builds local models within each cluster. HC-PLSR can thus handle non-linearities between clusters, but as PLSR is a linear model the data is still required to be locally linear. HC-PLSR was therefore expanded into deep learning (HC-CNN and HC-RNN) and SVR (HC-SVR). The deep learning-based models outperformed HC-PLSR for a data set predicting average molecular weights from hydrolysed raw materials. The analysis of the FT-ICR MS spectra revealed that the large amounts of information contained in the data (due to the high resolution) can disturb the predictive models, but the use of variable selection counteracts this effect. Several methods from machine learning and multivariate statistics were proven valuable for prediction of various parameters from FT-ICR MS using both classification and regression methods.Gasshydrater er et av hovedproblemene for Flow assurance i olje- og gassnæringen ettersom at de kan forårsake blokkeringer i oljerørledninger og prosessutstyr som krever at systemet må stenges ned. Tidligere studier har vist at noen råoljer danner hydrater som ikke agglomererer eller avsetter, men som forblir som transporterbare dispersjoner. Dette antas å være på grunn av naturlig forekommende komponenter til stede i råoljen, men til tross for årevis med forskning er deres nøyaktige strukturer enda ikke bestemt i detalj. Noen studier har indikert at disse komponentene kan stamme fra syrefraksjonene i oljen eller være relatert til asfalteninnholdet i oljene. Råoljer er blant verdens mest komplekse organiske blandinger og kan inneholde opptil 100 000 forskjellige bestanddeler, som gjør dem vanskelig å karakterisere ved bruk av tradisjonelle massespektrometre. Den høye masseoppløsningen Fourier-transform ion syklotron resonans massespektrometri (FT-ICR MS) gir en høyere oppløsning enn tradisjonelle teknikker, som gjør FT-ICR MS i stand til å karakterisere råoljer i større grad og muligens identifisere hydrataktive komponenter. FT-ICR MS spektre inneholder vanligvis titusenvis av topper, og det er nødvendig å bruke databehandlingsmetoder i stand til å håndtere store datasett, med muligheter til å finne underliggende forhold for å analysere spektrene. Maskinlæring og multivariat statistikk har mange metoder som er passende for store datasett. En litteratur studie identifiserte flere metoder og den nåværende statusen for bruken av maskinlæring for analyse av gasshydrater og FT-ICR MS data. Litteraturstudien viste at selv om mange studier har brukt maskinlæring til å predikere termodynamiske egenskaper for gasshydrater, har lite arbeid blitt gjort med å analysere gasshydrat relaterte prøver målt med FT-ICR MS. For å bistå identifikasjonen ble en suksessiv akkumuleringsprosedyre for å øke konsentrasjonene av hydrataktive komponenter utviklet av SINTEF. Sammenligninger av massespektrene fra spikede og uspikede prøver viste at noen topper økte sammen med spikingnivåene. Flere klassifikasjonsmetoder ble brukt i kombinasjon med ariabelseleksjon for å identifisere topper relatert til hydratformasjon. Molekylformler ble bestemt og toppene ble antatt å være relatert til asfaltener, naftener og polyetylenglykol. For å bistå karakteriseringen av oljene ble infrarød spektroskopi inkludert med FT-ICR MS i en multiblokk analyse for å predikere tettheten til råoljene. To forskjellige strategier for datafusjonering ble testet og sekvensiell fusjonering av blokkene oppnådde den høyeste prediksjonsnøyaktigheten både før og etter reduksjon av datasettene med bruk av variabelseleksjon. Ettersom råoljer har så kompleks sammensetning, er prøvene ofte veldig forskjellige og mange metoder er ikke egnet for å håndtere store variasjoner eller ikke-lineariteter mellom prøvene. Hierarchical cluster-based partial least squares regression (HCPLSR) grupperer dataene og lager lokale modeller for hver gruppe. HC-PLSR kan dermed håndtere ikke-lineariteter mellom gruppene, men siden PLSR er en lokal modell må dataene fortsatt være lokalt lineære. HC-PLSR ble derfor utvidet til convolutional neural networks (HC-CNN) og recurrent neural networks (HC-RNN) og support vector regression (HC-SVR). Disse dyp læring metodene utkonkurrerte HC-PLSR for et datasett som predikerte gjennomsnittlig molekylvekt fra hydrolyserte råmaterialer. Analysen av FT-ICR MS spektre viste at spektrene inneholder veldig mye informasjon. Disse store mengdene med data kan forstyrre prediksjonsmodeller, men bruken av variabelseleksjon motvirket denne effekten. Flere metoder fra maskinlæring og multivariat statistikk har blitt vist å være nyttige for prediksjon av flere parametere from FT-ICR MS data ved bruk av både klassifisering og regresjon

Kinetic modelling simulation and optimal operation of fluid catalytic cracking of crude oil: Hydrodynamic investigation of riser gas phase compressibility factor, kinetic parameter estimation strategy and optimal yields of propylene, diesel and gasoline in fluid catalytic cracking unit

The Fluidized Catalytic Cracking (FCC) is known for its ability to convert refinery wastes into useful fuels such as gasoline, diesel and some lighter products such as ethylene and propylene, which are major building blocks for the polyethylene and polypropylene production. It is the most important unit of the refinery. However, changes in quality, nature of crude oil blends feedstock, environmental changes and the desire to obtain higher profitability, lead to many alternative operating conditions of the FCC riser. There are two major reactors in the FCC unit: the riser and the regenerator. The production objective of the riser is the maximisation of gasoline and diesel, but it can also be used to maximise products like propylene, butylene etc. For the regenerator, it is for regeneration of spent or deactivated catalyst. To realise these objectives, mathematical models of the riser, disengage-stripping section, cyclones and regenerator were adopted from the literature and modified, and then used on the gPROMS model builder platform to make a virtual form of the FCC unit. A new parameter estimation technique was developed in this research and used to estimate new kinetic parameters for a new six lumps kinetic model based on an industrial unit. Research outputs have resulted in the following major products’ yields: gasoline (plant; 47.31 wt% and simulation; 48.63 wt%) and diesel (plant; 18.57 wt% and simulation; 18.42 wt%) and this readily validates the new estimation methodology as well as the kinetic parameters estimated. The same methodology was used to estimate kinetic parameters for a new kinetic reaction scheme that considered propylene as a single lump. The yield of propylene was found to be 4.59 wt%, which is consistent with published data. For the first time, a Z-factor correlation analysis was used in the riser simulation to improve the hydrodynamics. It was found that different Z factor correlations predicted different riser operating pressures (90 – 279 kPa) and temperatures as well as the riser products. The Z factor correlation of Heidaryan et al. (2010a) was found to represent the condition of the riser, and depending on the catalyst-to-oil ratio, this ranges from 1.06 at the inlet of the riser to 0.92 at the exit. Optimisation was carried out to maximise gasoline, propylene in the riser and minimise CO2 in the regenerator. An increase of 4.51% gasoline, 8.93 wt.% increase in propylene as a single lump and 5.24 % reduction of carbon dioxide emission were achieved. Finally, varying the riser diameter was found to have very little effect on the yields of the riser products

Sensory quality control of alcoholic beverages using fast chemical sensors

Control de calidad sensorial de bebidas alcohólicas utilizando rápidos sensores químicosEn la presente tesis Doctoral, han sido aplicados dos sensores artificiales para el análisis debebidas alcohólicas: la nariz electrónica basada en la espectrometría de masas (MS) y la lenguaelectrónica basada en la espectroscopía infrarroja con transformada de Fourier (FTIR). Elpropósito fue desarrollar nuevas estrategias para analizar la autenticidad de estos productos,desde un punto de vista sensorial, por medio de técnicas las espectrales antes mencionadas.Adicionalmente, ha sido utilizado un espectrofotómetro UV-visible como ojo electrónico. Eltrabajo presentado pretende ser un avance significativo hacia el desarrollo de un catadorelectrónico mediante la fusión de los tres sensores químicos: nariz electrónica, lenguaelectrónica y ojo electrónico.Sensory quality control of alcoholic beverages using fast chemical sensorsIn the present Doctoral Thesis, two chemical artificial sensors are applied to the analysis ofalcoholic beverages: the Mass Spectrometry (MS)-based electronic-noses and Fouriertransform infrared (FTIR)-based electronic-tongue. The aim was developing new strategies totest the authenticity of these products, from a sensory point of view, by means of the spectraltechniques above mentioned. Additionally, has been used an UV-visible spectrophotometer aselectronic eye. The work presented wants to be a significant advance towards the developmentof an electronic taster through the fusion of three chemical sensors: electronic nose, electronictongue and electronic eye

Argonne National Laboratory annual report of laboratory directed research and development program activities for FY 1995.

The purposes of Argonne's Laboratory Directed Research and Development (LDRD) Program are to encourage the development of novel concepts, enhance the Laboratory's R&D capabilities, and further the development of its strategic initiatives

Multi–scale Modelling of Refinery Pre–heat Trains Undergoing Fouling for Improved Energy Efficiency

Fouling in pre–heat trains of refinery crude distillation units causes major energy inefficiencies, resulting in increased costs, greenhouse gas emissions, maintenance efforts and health and safety hazards. Although chemical and physical phenomena underlying fouling deposition are extremely complex and several details remain unknown, the understanding of the fouling process has progressed significantly in the past 40 years. However, this knowledge has so far not been exploited to effectively improve heat exchanger and heat exchanger network design and operation. As a result, old methodologies that neglect the local effects and dynamics of fouling, in favour of lumped, steady–state, heuristic models (e.g. using TEMA fouling factors) are still used. In this thesis a novel mathematical model for pre–heat trains undergoing crude oil fouling was developed, validated with plant data and used to propose mitigation strategies. The model is dynamic, distributed and considers simultaneously several scales of investigation. Key phenomena are captured at the tube level as a function of local conditions. These include the dependence of fouling rate on temperature and velocity, the variation of physical properties, the structural changes of the deposits over time (ageing) and the dynamics of surface roughness. The single tube model was then extended to describe a unit–scale heat exchanger geometry. This has been validated against plant data from four units in two refineries operated by major oil companies. The predicted outlet temperatures over extended periods (i.e. 4-16 months) are accurate within ±1% for the tube–side and ± 2% for the shell–side. Model simulations were then used to assist the retrofit of one particular unit for which it was possible to save ca. 22% of the energy losses (not including pumping power) produced by fouling over ca. a year of operation. Finally, the interconnection of single heat exchangers in a network allowed the simulation of the fouling behaviour of two existing pre–heat trains. To systematically assess the impact of fouling on refinery economics, a set of key performance indicators (KPIs) was proposed. Network–level simulations were used in conjunction with the KPIs to unveil complex interactions and propose network retrofit arrangements that improve energy recovery over time whilst reducing fouling. It is concluded that the model can be used with confidence to predict fouling and assist monitoring, design and retrofit of refinery heat exchangers and heat exchanger networks. The results shown indicate that the approach proposed can lead to substantial benefits