Artificial intelligence and chemical kinetics enabled property-oriented fuel design for internal combustion engine

Fuel Genome Project aims at addressing the forward problem of fuel property prediction and the inverse problems of molecule design, retrosynthesis and reaction condition prediction. This work primarily addresses the forward problem by integrating feature engineering theory, artificial intelligence (AI) technologies, gas-phase chemical kinetics. Group contribution method (GCM) is utilized to establish the GCM-UOB (University of Birmingham) 1.0 system with 22 molecular descriptors and the surrogate formulation is to minimize the difference of functional group fragments between target fuel and surrogate. The improved QSPR (quantitative structure–activity relationship)-UOB 2.0 system with 32 molecular features couples with machine learning (ML) algorithms to establish the regression models for fuel ignition quality prediction. QSPR-UOB 3.0 scheme expands to 42 molecular descriptors to improve the molecular resolution of aromatics and specific fuel types. The obtained structural features combining with ML algorithms enable to predict 15 physicochemical properties with high fidelity and efficiency. In addition to the technical route of ML-QSPR models, another route of deep learning-convolution neural network (DL-CNN) is proposed for property prediction and yield sooting index (YSI) is taken as a case study. The predicted accuracy of DL-CNN is inferior to the ML-QSPR model at its current status, but its benefit of automated feature extraction and rapid advance in classification problem make it a promising solution for regression problem. A high-throughput fuel screening is performed to identify the molecules with desired properties for both spark ignition (SI) and compression ignition (CI) engines which contains the Tier 1 physicochemical properties screening (based on the ML-QSPR models) and Tier 2 chemical kinetic screening (based on the detailed chemical mechanisms). Polyoxymethylene dimethyl ether 3 (PODE3) and diethoxymethane (DEM) are promising carbon-neutral fuels for CI engines and they are recommended by the virtual screening results. Their ignition delay time, laminar flame speed and dominant reactions of PODE3 and DEM are examined by chemical kinetics and a new DEM mechanism including both low and high-temperature reactions is constructed. Concluding remarks and research prospects are summarized in the final section

Estudo da correlação quantitativa entre estrutura e propriedade (QSPR) usando descritores topológicos para compostos carbonílicos alifáticos

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas. Curso de Pós-Graduação em QuímicaNeste trabalho foi aplicada a relação quantitativa entre estrutura e atividade, empregando-se diferentes descritores moleculares para estimar o odor frutal de ésteres alifáticos. Os parâmetros estatísticos, obtidos nas equações para os ésteres, empregando-se o método de regressão linear múltipla, foram de boa qualidade. O modelo obtido teve uma alta capacidade de predição, como estabelecido pelo coeficiente de validação cruzada. O método semi-empírico topológico (IET) foi ampliado para estimar a retenção cromatográfica, em fases estacionárias de baixa polaridade, de ésteres, aldeídos e cetonas lineares e ramificados. Os parâmetros estatísticos das regressões lineares simples entre os índices de retenção de Kováts e o IET foram excelentes para todos os compostos. Os modelos de correlação quantitativa entre estrutura e retenção cromatográfica obtidos com um único descritor tiveram alta capacidade de predição, além de apresentarem uma melhora na ordem de precisão e exatidão que os métodos de regressão linear múltipla. Este IET foi aplicado para estimar o ponto de ebulição de aldeídos e cetonas e os valores de "threshold" de odor de cetonas com odor canforáceo e frutal. Os pontos de ebulição de 35 aldeídos e cetonas foram precisamente estimados através de uma regressão linear simples e os valores dos "thresholds" de odor de 27 cetonas foram estimados através de uma função polinomial quadrática. Assim, o método semi-empírico topológico, baseado no comportamento geral da retenção cromatográfica de ésteres, aldeídos e cetonas utilizando um único descritor, representa um grande avanço nos estudos de correlação quantitativa entre estrutura e propriedade (QSPR)

Índice semi-empírico topológico: desenvolvimento e aplicação de um novo descritor molecular em estudos de correlação quantitativa estrutura-propriedade (QSPR)

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas. Programa de Pós-Graduação em Química.Neste estudo um novo descritor molecular - Índice Semi-Empírico Topológico (IET) - foi desenvolvido, a fim de estabelecer correlações quantitativas entre estrutura e propriedade (QSPR), para diferentes classes de compostos. Este Índice foi desenvolvido e otimizado para prever a retenção cromatográfica de alcenos ramificados, alcanos metil ramificados produzidos por insetos e álcoois saturados, em fases estacionárias de baixa polaridade. Foi avaliada, também, a habilidade de previsão do IET para a retenção cromatográfica de álcoois, aldeídos e cetonas em fases estacionárias mais polares. Os estudos preliminares aplicando o IET a diferentes propriedades/atividades apresentaram resultados promissores para a aplicação futura deste novo método. Para alcenos e álcoois foram obtidas correlações entre o IET e as propriedades (ponto de ebulição normal, refração molar, volume molar, calor de combustão, calor de vaporização molar e coeficiente de partição octanol/água), com valores de r > 0,94. As correlações quantitativas estrutura-atividade (QSAR) foram testadas para álcoois saturados, onde as atividades biológicas investigadas foram: atividade narcótica sobre larvas das cracas, toxicidade em aranhas e tomates e odor (r > 0,88). A qualidade dos resultados obtidos neste trabalho para a previsão de diferentes propriedades/atividades, empregando o IET como descritor molecular, pode ser considerada como uma importante etapa na direção de estudos futuros em QSAR/QSPR/QSRR

The prediction of chemosensory effects of volatile organic compounds in humans

An introduction to indoor air pollution is given, and the chemosensory effects in humans of volatile organic compounds (VOCs), singly and in binary mixtures, are described, together with the bioassays already developed to quantify the effects of VOCs. The need for predictive models that can take over the bioassays is emphasised. Attention is drawn to the establishment of mathematical models to predict the chemosensory effects of VOCs in humans. Nasal pungency threshold (NPT), eye irritation threshold (EIT) and odour detection threshold (ODT) values are available for a series of VOCs that cover a large range of solute properties. Each of these sets of biological data are regressed against the corresponding solute descriptors, E, S, A, B and L to obtain quantitative structure activity relationships (QSARs) for log(l/NPT), log(l/ODT) and log(l/EIT) taking on the form: LogSP = c + e.E + s.S + a.A + b.B + l.L The availability of solute descriptors is investigated. It is shown that solute descriptors, E an excess molar refraction, S the solute dipolarity/polarizability, A the solute overall hydrogen-bond acidity, B the solute overall hydrogen-bond basicity and L the logarithmic value of the solute Ostwald solubility coefficient in hexadecane at 298K, can be obtained through the use of various thermodynamic measurements. In this way descriptors for some 300 solutes have been obtained. A headspace gas chromatographic method is also devised to determine the 1:1 complexation constant, K, between hydrogen bond donors and hydrogen bond acceptors in octan-1-ol. The 30 complexation constants measured are then correlated with α2H*, β2H, a combination of the solute 1:1 hydrogen bond acidity and basicity, respectively, to give: Log K1:1 = 2.950. α2H*β2H - 0.74

Prediction of the physical properties of pure chemical compounds through different computational methods.

Ph. D. University of KwaZulu-Natal, Durban 2014.Liquid thermal conductivities, viscosities, thermal decomposition temperatures, electrical conductivities, normal boiling point temperatures, sublimation and vaporization enthalpies, saturated liquid speeds of sound, standard molar chemical exergies, refractive indices, and freezing point temperatures of pure organic compounds and ionic liquids are important thermophysical properties needed for the design and optimization of products and chemical processes. Since sufficiently purification of pure compounds as well as experimentally measuring their thermophysical properties are costly and time consuming, predictive models are of great importance in engineering. The liquid thermal conductivity of pure organic compounds was the first investigated property, in this study, for which, a general model, a quantitative structure property relationship, and a group contribution method were developed. The novel gene expression programming mathematical strategy [1, 2], firstly introduced by our group, for development of non-linear models for thermophysical properties, was successfully implemented to develop an explicit model for determination of the thermal conductivity of approximately 1600 liquids at different temperatures but atmospheric pressure. The statistical parameters of the obtained correlation show about 9% absolute average relative deviation of the results from the corresponding DIPPR 801 data [3]. It should be mentioned that the gene expression programing technique is a complicated mathematical algorithm and needs a significant computer power and this is the largest databases of thermophysical property that has been successfully managed by this strategy. The quantitative structure property relationship was developed using the sequential search algorithm and the same database used in previous step. The model shows the average absolute relative deviation (AARD %), standard deviation error, and root mean square error of 7.4%, 0.01, and 0.01 over the training, validation and test sets, respectively. The database used in previous sections was used to develop a group contribution model for liquid thermal conductivity. The statistical analysis of the performance of the obtained model shows approximately a 7.1% absolute average relative deviation of the results from the corresponding DIPPR 801 [4] data. In the next stage, an extensive database of viscosities of 443 ionic liquids was initially compiled from literature (more than 200 articles). Then, it was employed to develop a group contribution model. Using this model, a training set composed of 1336 experimental data was correlated with a low AARD% of about 6.3. A test set consists of 336 data point was used to validate this model. It shows an AARD% of 6.8 for the test set. In the next part of this study, an extensive database of thermal decomposition temperature of 586 ionic liquids was compiled from literature. Then, it was used to develop a quantitative structure property relationship. The proposed quantitative structure property relationship produces an acceptable average absolute relative deviation (AARD) of less than 5.2 % taking into consideration all 586 experimental data values. The updated database of thermal decomposition temperature including 613 ionic liquids was subsequently used to develop a group contribution model. Using this model, a training set comprised of 489 data points was correlated with a low AARD of 4.5 %. A test set consisting of 124 data points was employed to test its capability. The model shows an AARD of 4.3 % for the test set. Electrical conductivity of ionic liquids was the next property investigated in this study. Initially, a database of electrical conductivities of 54 ionic liquids was collected from literature. Then, it was used to develop two models; a quantitative structure property relationship and a group contribution model. Since the electrical conductivities of ionic liquids has a complicated temperature- and chemical structure- dependency, the least square support vector machines strategy was used as a non-linear regression tool to correlate the electrical conductivity of ionic liquids. The deviation of the quantitative structure property relationship from the 783 experimental data used in its development (training set) is 1.8%. The validity of the model was then evaluated using another experimental data set comprising 97 experimental data (deviation: 2.5%). Finally, the reproducibility and reliability of the model was successfully assessed using the last experimental dataset of 97 experimental data (deviation: 2.7%). Using the group contribution model, a training set composed of 863 experimental data was correlated with a low AARD of about 3.1% from the corresponding experimental data. Then, the model was validated using a data set composed of 107 experimental data points with a low AARD of 3.6%. Finally, a test set consists of 107 data points was used for its validation. It shows an AARD of 4.9% for the test set. In the next stage, the most comprehensive database of normal boiling point temperatures of approximately 18000 pure organic compounds was provided and used to develop a quantitative structure property relationship. In order to develop the model, the sequential search algorithm was initially used to select the best subset of molecular descriptors. In the next step, a three-layer feed forward artificial neural network was used as a regression tool to develop the final model. It seems that this is the first time that the quantitative structure property relationship technique has successfully been used to handle a large database as large as the one used for normal boiling point temperatures of pure organic compounds. Generally, handling large databases of compounds has always been a challenge in quantitative structure property relationship world due to the handling large number of chemical structures (particularly, the optimization of the chemical structures), the high demand of computational power and very high percentage of failures of the software packages. As a result, this study is regarded as a long step forward in quantitative structure property relationship world. A comprehensive database of sublimation enthalpies of 1269 pure organic compounds at 298.15 K was successfully compiled from literature and used to develop an accurate group contribution. The model is capable of predicting the sublimation enthalpies of organic compounds at 298.15 K with an acceptable average absolute relative deviation between predicted and experimental values of 6.4%. Vaporization enthalpies of organic compounds at 298.15 K were also studied in this study. An extensive database of 2530 pure organic compounds was used to develop a comprehensive group contribution model. It demonstrates an acceptable %AARD of 3.7% from experimental data. Speeds of sound in saturated liquid phase was the next property investigated in this study. Initially, A collection of 1667 experimental data for 74 pure chemical compounds were extracted from the ThermoData Engine of National Institute of Standards and Technology [5]. Then, a least square support vector machines-group contribution model was developed. The model shows a low AARD% of 0.5% from the corresponding experimental data. In the next part of this study, a simple group contribution model was presented for the prediction of the standard molar chemical exergy of pure organic compounds. It is capable of predicting the standard chemical exergy of pure organic compounds with an acceptable average absolute relative deviation of 1.6% from the literature data of 133 organic compounds. The largest ever reported databank for refractive indices of approximately 12 000 pure organic compounds was initially provided. A novel computational scheme based on coupling the sequential search strategy with the genetic function approximation (GFA) strategy was used to develop a model for refractive indices of pure organic compounds. It was determined that the strategy can have both the capabilities of handling large databases (the advantage of sequential search algorithm over other subset variable selection methods) and choosing most accurate subset of variables (the advantages of genetic algorithm-based subset variable selection methods such as GFA). The model shows a promising average absolute relative deviation of 0.9 % from the corresponding literature values. Subsequently, a group contribution model was developed based on the same database. The model shows an average absolute relative deviation of 0.83% from corresponding literature values. Freezing Point temperature of organic compounds was the last property investigated. Initially, the largest ever reported databank in open literature for freezing points of more than 16 500 pure organic compounds was provided. Then, the sequential search algorithm was successfully applied to derive a model. The model shows an average absolute relative deviations of 12.6% from the corresponding literature values. The same database was used to develop a group contribution model. The model demonstrated an average absolute relative deviation of 10.76%, which is of adequate accuracy for many practical applications

Aplicação da relação entre estrutura e retenção cromatográfica (QSRR) empregando diferentes descritores para cumarinas, acetofenonas e triterpenos: estudo de um novo índice para alcanos e alcenos /

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matematicas.Aplicação da relação entre estrutura e retenção cromatográfica (QSRR), empregando diferentes descritores moleculares para grupos de compostos que têm grande importância e aplicação farmacológica, tais como acetofenonas, cumarinas e triterpenos com o objetivo de identificar os fatores responsáveis pela retenção cromatográfica e também auxiliar na previsão da retenção de compostos similares. Foi desenvolvido um novo índice topológico (Índice semi-empírico topológico), IET, aplicado inicialmente para alcanos e posteriormente para alcenos com o objetivo de diferenciar os isômeros cis e trans

Integrated Chemical Processes in Liquid Multiphase Systems

The essential principles of green chemistry are the use of renewable raw materials, highly efficient catalysts and green solvents linked with energy efficiency and process optimization in real-time. Experts from different fields show, how to examine all levels from the molecular elementary steps up to the design and operation of an entire plant for developing novel and efficient production processes

Biomimetic Based Applications

The interaction between cells, tissues and biomaterial surfaces are the highlights of the book "Biomimetic Based Applications". In this regard the effect of nanostructures and nanotopographies and their effect on the development of a new generation of biomaterials including advanced multifunctional scaffolds for tissue engineering are discussed. The 2 volumes contain articles that cover a wide spectrum of subject matter such as different aspects of the development of scaffolds and coatings with enhanced performance and bioactivity, including investigations of material surface-cell interactions

Dynamic operation, efficient calibration, and advanced data analysis of gas sensors : from modelling to real-world operation

This thesis demonstrates the use of dynamic operation, efficient calibration and advanced data analysis using metal oxide semiconductor (MOS) gas sensors as an example – from modeling to real-world operation. The necessary steps for an applicationspecific, selective indoor volatile organic compound (VOC) measurement system are addressed, analyzed and improved. Factors such as sensors, operation, electronics and calibration are considered. The developed methods and tools are universally transferable to other gas sensors and applications. The basis for selective measurement is temperature cyclic operation (TCO). The model-based understanding of a semiconductor gas sensor in TCO for the optimized development of operating modes and data evaluation is addressed and, for example, the tailored and stable detection of short gas pulses is developed. Two successful interlaboratory tests for the measurement of VOCs in independent laboratories are described. Selective measurements of VOCs in the laboratory and in the field are successfully demonstrated. Calibrations using the proposed techniques of randomized design of experiment (DoE), model-based data evaluation and calibration with machine learning methods are employed. The calibrated models are compared with analytical measurements using release tests. The high agreement of the results is unique in current research.Diese Thesis zeigt den Einsatz von dynamischem Betrieb, effizienter Kalibrierung, und fortschrittlicher Datenanalyse am Beispiel von Metalloxid Halbleiter (MOS) Gassensoren – von der Modellierung bis zum realen Betrieb. Die notwendigen Schritte für ein anwendungsspezifisches, selektives Messystem für flüchtige organische Verbindungen (VOC) im Innenraum werden adressiert, analysiert und verbessert. Faktoren wie z.B. Sensoren, Funktionsweise, Elektronik und Kalibrierung werden berücksichtigt. Die entwickelten Methoden und Tools sind universell auf andere Gassensoren und Anwendungen übertragbar. Grundlage für die selektive Messung ist der temperaturzyklische Betrieb (TCO). Auf das modellbasierte Verständnis eines Halbleitergassensors im TCO für die optimierte Entwicklung von Betriebsmodi und Datenauswertung wird eingegangen und z.B. die maßgeschneiderte und stabile Detektion von kurzen Gaspulsen entwickelt. Zwei erfolgreiche Ringversuche zur Messung von VOCs in unabhängigen Laboren werden beschrieben. Selektive Messungen verschiedener VOCs im Labor und im Feld werden erfolgreich demonstriert. Dabei kommen Kalibrierungen mit den vorgeschlagenen Techniken des randomisierten Design of Experiment (DoE), der modellbasierten Datenauswertung und Kalibrierung mit Methoden des maschinellen Lernens zum Einsatz. Die kalibrierten Modelle werden anhand von Freisetzungstests mit analytischen Messungen verglichen. Die hohe Übereinstimmung der Ergebnisse ist einzigartig in der aktuellen Forschung